fcm-based customer expectation-driven service dispatch system

METHODOLOGIES AND APPLICATION

FCM-based customer expectation-driven service dispatch system

Yen-Hao Hsieh • I-Hsuan Chen • Soe-Tsyr Yuan

� Springer-Verlag Berlin Heidelberg 2013

Abstract Maintaining long-term customer loyalty has been

an important issue in the service industry. Although customer

satisfaction can be enhanced with better service quality,

delivering appropriate services to customers poses challenges

to service providers, particularly in real-time and resource-

limited dynamic service contexts. However, customer

expectation management has been regarded as an effective

way for helping service providers achieve high customer

satisfaction in the real world that is nevertheless less real-time

and dynamic. This study designs a FCM-based customer

expectation-driven service dispatch system to empower pro-

viders with the capability to deal effectively with the problem

of delivering right services to right customers in right contexts.

Our evaluation results show that service providers can make

appropriate decisions on service dispatch for customers by

effectively managing customer expectations and arranging

their contextual limited resources and time via the proposed

service dispatch system. Meanwhile, customers can receive

suitable service and obtain high satisfaction when appropriate

services are provided. Accordingly, a high-performance eco-

system can be established by both service providers and cus-

tomers who co-create value in the dynamic service contexts.

Keywords Fuzzy cognitive map � Customer expectation

management � Service science � Service dispatch

1 Introduction

Services involve helping customers resolve problems

(Nicolaud 1989; Vargo and Lusch 2004; Spohrer et al.

2008). For instance, on-line markets usually provide a call-

center service in order to respond promptly to customer

inquiries. These call-centers aim to help customers solve

their problems and fulfill their needs. Service providers

must focus on delivering appropriate services to customers

to increase service quality and market competitiveness.

Quality services increase customer satisfaction and thus

long-term customer loyalty (Heskett et al. 1994). Hence,

maintaining high customer satisfaction remains important

for service providers. According to Parasuraman et al.

(1985, 1988), customer satisfaction comprises the differ-

ence between customer expectations and customer per-

ceptions. Customer satisfaction is high when the gap

between customer expectations and customer perceptions

is small. Consequently, customer expectation management

is an appropriate way for service providers to maximize

customer satisfaction with services provided (Parasuraman

et al. 1991; Pitt and Jeantrout 1994). In other words, service

providers should design services to meet customer expec-

tations in order to satisfy customers. Nevertheless, expec-

tation determinants often alter customer expectations in

dynamic environments (Parasuraman et al. 1991; Zeithaml

et al. 1993), making it difficult to understand customer

expectations during dynamic service delivery. Recognizing

the cause-and-effect relationships between customer

expectations and contextual factors is accordingly impor-

tant for service providers.

Communicated by G. Acampora.

Y.-H. Hsieh (&)

Department of Information Management,

Tamkang University, New Taipei, Taiwan

e-mail: [email protected]

I.-H. Chen � S.-T. Yuan

Department of Management Information Systems,

National Chengchi University, Taipei, Taiwan


S.-T. Yuan


123

Soft Comput

DOI 10.1007/s00500-013-1063-1

This study aims to investigate how service providers

consider the above issues in delivering appropriate services

to customers in dynamic service contexts (so-called service

dispatch). This study defines service dispatch as the action

of service providers in deciding on the suitable services to

be delivered to customers taking into consideration service

contextual resources, customer needs and delivery time.

Service providers should effectively allocate existing

resources and costs to dispatch proper services to target

customers in dynamic contexts. Consequently, the service

dispatch problem resembles the resource allocation prob-

lem (Rajkumar et al. 1997, 1998; Lai and Li 1999). Most

traditional dispatch decisions were driven by historical

transaction data and past experience (such as off-line pre-

dictions of workload or number of potential customers).

However, complexities must be considered in both effec-

tive service dispatch strategy and quality service perfor-

mance, particularly in dynamic contexts (Chen et al. 1993).

There exist several important issues related to service dis-

patch strategy. First, dynamic service context creates diffi-

culties in making service dispatch decisions. Numerous key

factors (e.g., customer needs, resource arrangement, and total

delivery time) influence service delivery performance in ser-

vice contexts, and most of these factors change over time and

between service encounters. Thus, service providers must

employ systematic approaches to achieve timely and accurate

delivery of appropriate and quality service. In particular,

service providers face numerous tough challenges (such as

settling on a service within a limited time) in gathering

information to establish suitable service dispatch strategies in

real-time service context. Second, customer expectation

management influences critically service dispatch strategies,

but is not systematically applied. Service providers design

services to meet customer expectations and maximize cus-

tomer satisfaction. Fathoming customer expectations as ser-

vice providers dispatch services is challenging. Consequently,

service providers must devise appropriate service dispatch

strategies by considering customer expectations.

In other words, it is important to establish an effective

service dispatch strategy considering customer expecta-

tions that can aid service providers in effectively planning

their resources and delivery times to boost service perfor-

mance and customer satisfaction. Service providers need to

devise suitable service dispatch strategies for better optimal

resolutions to serve customers during service delivery.

Quality service relies on a well-designed delivery process,

which is apt to change in dynamic service contexts.

According to the above contentions, this study examines

the following research questions.

1. What do service providers consider when building a

systematic service dispatch system in real-time

dynamic service contexts?

2. What impacts do service providers exert by managing

customer expectations during service delivery?

To investigate these research questions, this study uses

the Fuzzy Cognitive Map (FCM) approach to dispatch

suitable services to customers and manage customer ser-

vice expectations. Using FCMs is an inference-based

approach which has been employed in several fields (such

as economics, sociology, and simulation) to build AI-based

expert systems (Huerga 2002; Lee and Han 2000). Mean-

while, FCMs examine the relations and effects between

different factors by graphic analysis. Furthermore, since

customer expectations influence crucially service provid-

ers’ decisions on dispatching proper services, this study

also stresses the importance of customer mental status and

applies customer expectation management to the service

dispatch system. Hence, this study aims to establish a

FCM-based customer expectation-driven service dispatch

system.

The proposed system attempts to employ customer

mental information to make real-time service deployment

decisions that can achieve the business goals of service

providers and satisfy customers. Furthermore, Service-

Dominant logic (S-D logic) is essential for interpreting the

importance of services (Vargo and Lusch 2004; Lusch and

Vargo 2006). S-D logic views service as a process that

deals with something for another object. S-D logic gener-

ally utilizes intangible and dynamic operant resources

(namely, human skills, knowledge, and experiences) to

create innovative values. Not only do service providers

actively provide the operant resources to serve customers,

consumers are also involved as co-creators of value during

service delivery. That is, customers can be satisfied with

perceived services and contribute their values to service

delivery by joining interactions designed by service pro-

viders. Accordingly, this study examines how service

providers co-create value and design value co-creation

interactions with customers using the proposed system.

The remainder of this paper is organized as follows.

Section 2 illustrates the FCM method and expectation

theory. Meanwhile, Sect. 3 describes the FCM-based cus-

tomer expectation-driven service dispatch system. Sec-

tion 4 then details the evaluations and analyzes the results.

Finally, Sect. 5 presents conclusions.

2 Method and theory

This study attempts to establish an FCM-based customer

expectation-driven service dispatch system according to

the FCM method and expectation theory. The dispatching

logic of the proposed system is not only to consider cus-

tomer expectations but also service contexts, time,

Y.-H. Hsieh et al.

123

employees, service event and service preference. The

influence of these factors on service experiences is both

dynamic and uncertain; hence, FCM would be an apt

approach to representing their mutual interactions. This

section first discusses the key characteristics and applica-

bility of FCMs. Next, it describes the basic notions of

expectation theory and illustrates the importance of man-

aging customer expectations. Finally, this section details

the related works on service dispatch.

2.1 Fuzzy cognitive map

Fuzzy cognitive map (FCM) is a modeling approach with

advantageous features of flexible system design and model,

and abstract representation of complex systems (Acampora

and Loia 2011). FCMs are symbolic diagrams that com-

prise nodes (that is, concepts) and edges (that is, relation-

ships or causalities). The nodes exemplify different aspects

of system behaviors while the edges represent the influence

of concepts on one another. This study not only describes

the cause-and-effect relationships but also traces back the

model inferences and logic. For example, given a positive

(negative) relationship between two concepts, the value of

the edge is given by 1 (-1). Moreover, if two concepts are

unrelated, the value of 0 is assigned to the edges. Fur-

thermore, the weighted edges reflect the degree of influence

between different concepts and the interactions between

concepts illustrate dynamic situations (Kandasamy and

Smarandache 2003).

As shown in Fig. 1, the example situation involves three

concepts: C1, C2 and C3. C1 is assumed to have a value of

c1 and C3 is assumed to have a value of c3. c1 and c3

express the degree of their corresponding physical values.

The value of the edge between C1 and C2 is E12 while that

of the edge between C3 and C2 is E32. E12 and E32 are the

weights representing the strength of the causal link

between two concepts. Hence, the value of C2 can be

represented as C2 = c1 9 E12 ? c3 9 E32. Furthermore,

since FCM is developed from the idea of fuzziness, the

weights of the edges belong to the interval [1, -1]. Fuzzy

logic resembles the process of human reasoning, and the

concepts are also replaceable and extendable, thus per-

mitting thoughts and suggestions regarding graph

reconstruction. The running cycle of FCM is defined as the

time unit during which the values of the concepts are cal-

culated and changed. Consequently, human knowledge or

experience can be incorporated into the model through

learning from the data or model training.

To our knowledge, several previous investigations have

applied other simulation methods to service system design

(Oliva and Sterman 2001). For instance, Mascio (2007)

used the Monte Carlo method to assess the quality of the

service delivery process. Moreover, Hsieh and Yuan

(2010b) utilized system dynamics to model service expe-

rience design processes. However, FCM is a fuzzy-graph

structure displaying systematic causal propagation, partic-

ularly forward and backward chaining. Additionally, FCM

can help represent multi-factors and changeable service

contexts, as well as the relations between them (Acampora

and Loia 2011a, b). This study aims to clarify and analyze

the factors and influence potentially involved in a service

dispatch system. Accordingly, this study uses FCMs to

illustrate the key elements and causal relations within a

service dispatch system.

2.2 Expectation theory

Customer expectations are represented according to how

well a service fulfills customer needs (Westbrook and

Reilly 1983; Woodruff 1987). Moreover, customer expec-

tations are customer predictions concerning what they

believe will occur during service delivery (Clow et al.

1997). For example, when a company decides to buy an

information management system to boost its operating

performance and correct manual mistakes, that company

expects the system provider to offer a high-quality system

and service. Customer expectations comprise an index that

service providers should use to check if services match

customer needs. Consequently, service providers must

understand customer needs to fulfill their expectations and

achieve high customer satisfaction (Parasuraman et al.

1991; Coye 2004). Pitt and Jeantrout (1994) devised a

checklist for evaluating customer expectations from service

firms that can help service providers understand clearly

how to manage customer expectations. Clow and Beisel

(1995) proposed a model of critical antecedents (such as

tangible cues, distribution variable, word-of-mouth, firm

image, and customer satisfaction) of customer expectations

in low-margin, high-volume industries. Service providers

can utilize the above antecedents to manage customer

expectations and achieve high customer satisfaction.

Customer expectations comprise what customers expect

to gain from service providers. Services are intended to

fulfill customer needs and meet customer expectations of

deriving high satisfaction. To delineate customer expecta-

tions in details, Parasuraman et al. (1991) proposed that

C1

C2

C3

E12

E32

Fig. 1 Fuzzy cognitive map model

FCM-based customer expectation-driven service

123

customer expectations comprise two levels: desired and

adequate (as depicted in Fig. 2). Desired expectations

represent the high level of service that customers hope to

receive while adequate expectations represent the low level

of service they can accept. The area between adequate and

desired expectations is the zone of tolerance. When ser-

vices fail to meet customer expectations, service providers

suffer a competitive disadvantage. In contrast, service

providers can achieve a strong competitive advantage and

franchise when services exceed the zone of tolerance of a

customer.

Several complex factors affect the zone of tolerance of a

customer during service delivery. Zeithaml et al. (1993)

proposed a comprehensive framework of customer expec-

tations using 11 antecedent determinants (namely, enduring

service intensifiers, personal needs, transitory service

intensifiers, perceived service alternatives, self-perceived

service role, situational factors, predicted service, explicit

service promises, implicit service promises and word-of-

mouth communications, and past experience) that influence

desired and adequate expectations (as shown in Fig. 3).

Table 1 briefly describes these determinants. Understand-

ing these key determinants could help service providers

design suitable services and thus manage customer

expectations. For example, if a service provider wants to

narrow the zone of tolerance of a customer, he/she can

offer a price list of services (that is, implicit service

promises) and publicize recommendations made by previ-

ous customers (namely, word-of-mouth). By mapping the

existing services using these determinants, service pro-

viders can manage customer expectations. This study thus

attempts to apply customer expectation management to

service dispatch systems.

2.3 Service dispatch

This investigation attempts to build a service dispatch

system that focuses on making appropriate decisions

regarding suitable services to be delivered by considering

service resources, customer needs and delivery time. To

both service providers and researchers, dispatch problem

has been an important issue. The most common approaches

to dealing with allocation problems and dispatch problems

involve mathematical programming techniques and heu-

ristics (Elbrond and Soumis 1987; Lizotte et al. 1989). For

instance, Alshamsi et al. (2009) used the multi-agent self-

Competitive

Franchise

(Services exceed

desired level)

Adequate Desired

Competitive

Disadvantage

(Services fall below

adequate level)

Zone of tolerance Low High

E X P E C T A T I O N S

Competitive

Advantage

(Services fall in zone

of tolerance)

Fig. 2 Expected service level (Parasuraman et al. 1991)

Fig. 3 Nature and determinants

of customer expectations of

service (Zeithaml et al. 1993)

Y.-H. Hsieh et al.

123

organization technique to build a taxi service dispatch

system. The self-organization technique enables one agent

to add or eliminate other agents from their neighborhood to

obtain efficient and optimal routes for taxi dispatch.

D’Ariano and Pranzo (2009) proposed a train dispatch

system to realistically forecast and minimize train delay

propagation. The system was able to automatically manage

traffic and evaluate the detailed effects of train reordering

in real time.

Tan et al. (2000) developed a fuzzy dispatching system

for automated guided vehicles in a flexible manufacturing

context by adopting the genetic algorithm based method-

ology. Furthermore, an optimal dispatching controller for

elevator systems during up-peak traffic was designed using

dynamic programming techniques which could efficiently

minimize the average passenger waiting time (Pepyne and

Cassandras 1997). Ta et al. (2005) solved real-time truck

allocation problem by employing stochastic optimization

approach which could accommodate uncertain parameters

including the truckload and cycle time. Yang et al. (2002)

designed an optimal power-dispatching system using the

neural network approach to minimize the cost of power

consumption in the electrochemical process of zinc.

Table 2 shows the summary of the studies mentioned

above.

The proposed system thus resembles the resource allo-

cation problem, in which enterprises must allocate limited

resources among various activities to maximize returns

(Ibarraki and Katoh 1988; Lee et al. 2003). Enterprises

usually face constraints in both resources and time. How to

allocate existing resources and time among suitable

departments and optimize business profits is important

across different domains, particularly in operations

research. In other words, resource allocation aims to

identify optimal solutions given specific restrictions (Lits-

ios 1966; Whittaker and Cannings 1994).

Service dispatch is also related to resource allocation.

Service providers must also organize available resources to

dispatch suitable services to the right customers. To

achieve high value for both service providers and cus-

tomers (that is, for service providers to gain greater profits

from customers and for customers to be more satisfied with

the services received), service providers must effectively

manage their existing resources (such as operations,

employees, time and cost) to make appropriate decisions

regarding service dispatch (So 2000; Meyer and Schwager

2007). Meanwhile, as mentioned earlier, customers are

becoming more and more active in the market, thus

increasing the volatility of service contexts. Service pro-

viders must consider how to effectively allocate limited

resources and optimize value while delivering services

within real-time dynamic service contexts.

However, service dispatch and resource allocation

problems differ in terms of their focus. For resource allo-

cation problems, enterprises attempt to identify the opti-

mum resolution by effectively allocating limited resources

and costs from an operational perspective. In contrast, the

proposed service dispatch system not only deals with

resource allocation problems, but also, and more impor-

tantly, pays attention to value co-creation between cus-

tomers and service providers according to S-D logic (Vargo

and Lusch 2004; Lusch and Vargo 2006). For instance,

value-in-use is a foundational premise of S-D logic. Both

service providers and customers can create and acquire

value during service delivery. Service providers must

design suitable interactions for the service delivery process

that allow customers to be served and also participate in co-

creating value via the service dispatch system.

This study, therefore, compares the FCM-based cus-

tomer expectation-driven service dispatch system with

Table 1 The descriptions of the expectation determinants

Determinant Description

Enduring service

intensifiers

Enduring service intensifiers refer to stable and

individual factors that lead customers with a

high sensitivity to be served

Personal needs Personal needs are conditions or states

necessary to customers’ physical and

psychological well-beings

Transitory service

intensifiers

Transitory service intensifiers are individual,

provisional and short-range factors that lead

customers with a high sensitivity to be served

Perceived service

alternatives

Perceived service alternatives are the feelings

of customers which they can acquire services

from other provides

Self-perceived

service role

Customer self-perceived service role means

the customers’ perceptions of the degree to

which they themselves influence the level of

service they receive

Situational factors Situational factors refer to service-performance

contingencies in which customers are

perceived to be beyond the control of the

service provider

Predicted service Predicted service means that customers could

know what service they acquire, when their

levels of adequate service would be changed

by different qualities of services

Explicit service

promises

Explicit service promises, such as

advertisements, personal selling or contracts,

means the communications about services

which are made to customers by providers

Implicit service

promises

Implicit service promises indicate the

inferences about what the service should and

will be like

Word-of-mouth Word-of-mouth means personal and non-

personal statements which proffer customers

what the service encounter will be

Past experience Past experience refers to customers’ previous

exposure to service encounters


123

previous key studies from the perspective of S-D logic and

in terms of their respective performance. As aforemen-

tioned, the vital characteristics of S-D logic have to be

taken into account when constructing a high-value service

context. Hence, this study also employs two indicators of

S-D logic, namely customer involvement and value-in-use,

to evaluate the advantages of the different approaches.

Meanwhile, comparison of performance in solving the

dispatch and resource allocation problems should consider

the following indicators: long-term application, efficiency

of training time, and capability in dealing with problems in

real time (Alshamsi et al. 2009; D’Ariano and Pranzo

2009). As shown in Table 3, the previous dispatch systems

did not effectively fulfill all these critical indicators, while

our proposed system takes them fully into account when

solving the dispatch problem and resource allocation

problem.

Accordingly, innovative values and value co-creation

interactions generated by the service dispatch system

merit further investigation. Furthermore, the service dis-

patch problem exists in real-time service environments.

Hence, it must learn and be modified promptly to help

service providers offer appropriate services through inputs

of customer feedback and variations in the real-time ser-

vice context. Henceforth, this study attempts to develop a


system to deal with the above problems during service

delivery.

3 Framework of service dispatch system

As mentioned earlier, the FCM-based customer expec-

tation-driven service dispatch system is designed to make

real-time service deployment decisions to service specific

customers in a timely fashion with customer expectations

taken into consideration. To understand the logic

underlying an effective service dispatch system, this

study profiles four modules, including the FCM concept

set module, contextual factor module, determinant

assessment module, and service dispatch module (as

shown in Fig. 4). The FCM concept set module defines

the concepts and relations used in the FCM models

according to application domains. Furthermore, the con-

textual factor module analyzes the effects of contextual

factors. The determinant assessment module then selects

appropriate expectation determinants. Meanwhile, the

service dispatch module offers customers services

selected according to the expectation determinants.

The system framework starts from customer needs (such

as customer preferences) and outlines the customer

expectation-driven service dispatcher. The system

framework comprises several control steps, and the

detailed control techniques involved in each step are

elaborated below.

Table 2 Summary of key studies on dispatch problems

Author Research problem Method Limitations

Alshamsi et al.

(2009)

Taxi service dispatch Multi-agent self-organization

technique

Emphasis on service providers’ perspective

Non-real-time data

D’Ariano and Pranzo

(2009)

Train service delay Real-time optimization

approach

Applicable to short-term train schedule only

Gap between solution quality and computation times

Consecutive service dispatch not taken into consideration

Pepyne and

Cassandras (1997)

Elevator service

dispatch control

Dynamic programming A number of thresholds (e.g., passengers and available cars)

required to be pre-defined

Ta et al. (2005) Truck allocation Stochastic optimization

approach

Resource-based viewpoint

Tan et al. (2000) Vehicle dispatch Genetic algorithm-tuned fuzzy

rule approach

Focus on time factor only

Yang et al. (2002) Optimal power

dispatch

Neural networks Time-consuming training process

Table 3 Comparison of dispatch systems in different studies

Studies Indicators

Long-

term

Efficient

training

time

Real-

time

Customer

involvement

Value-

in-use

Alshamsi et al.

(2009)

V V

D’Ariano and

Pranzo (2009)

V V

Pepyne and

Cassandras (1997)

V V

Ta et al. (2005) V V

Tan et al. (2000) V V

Yang et al. (2002) V

This study V V V V V

Y.-H. Hsieh et al.

123

Step 1 Construct a FCM model as a two-part process,

including both core and application layers. Shape

the core layer according to customer expectations

and then build the application layer using the

association rule mining technique to

automatically create the second part of the FCM

model with the raw application data.

Step 2 Stratify the model into some strata according to

the meaning of the transaction to clarify what the

concepts in each stratum represent and to make

the associated information more meaningful.

Step 3 Import the data into the concepts as the periphery

of the model and thus enrich the value of every

model concept.

Step 4 Make decisions regarding individual stratum

according to the value of each concept.

Although decisions in each stratum can be

directly made according to the stratum results,

additional calculations are required.

3.1 FCM concept set module: automatic construction

of FCM

To maintain the flexibility of the FCM-based service dis-

patcher in dealing with various application domains, this

study divides the model into two layers: the core layer

(whose actions follow customer expectations and the

model structure of which never changes with the

application domains) and the application layer (the model

structure of which could be varied among application

domains when necessary). The first step in building the

two-layer model is to abstract the key concepts represent-

ing the real world.

3.1.1 Concept identification

The core layer follows strictly the expectation theory and

uses the antecedents of customer expectations to influence

adequate or desired customer expectations in the short run

(Zeithaml et al. 1993). The concepts include adequate

expectations, desired expectations, word-of-mouth com-

munications, explicit service promises, implicit service

promises, perceived service alternatives, and situational

factors. According to Zeithaml et al. (1993) and Parasur-

aman et al. (1991), these determinants could directly and

indirectly affect the levels of adequate and desired cus-

tomer expectations. The effects include both positive and

negative cause-and-effect. Positive cause-and-effect of the

edge value between two concepts is defined as ?1 while

negative cause-and-effect is defined as -1. Meanwhile, in

specific situations, there could be no cause-and-effect,

which is defined as 0. Hence, the values of weights in the

core layer of this study are represented by the set of {-1, 0,

1}. Figure 5 shows the structure of concepts identified in

the FCM model that includes the core layer concepts of

expectation determinants and other application-dependent

CustomerPattern

ExpectationImplements

Customer Preference

Determinant – Service Mapping

Service Economic

EncounterData

Determinant Assessment Module

Service Dispatching Module

FCM Concept Sets Module

Contextual Factors Module

FCM-based Next Destination

Contextual Concept Data

Assigning Mapping

Benefits

Determinant

Fine-Tuning

FCM-based Determinant Identification

FCM Concept Identification

FCM Model Adjusting

Fig. 4 The framework of the

service dispatcher system


123

concepts (e.g., four different contextual locations in addi-

tion to other contextual resources of concern to customers).

The application layer employs association rule mining to

extract an association rule (A ? B ? C ? D) from large

collections of historical data and thus discover the appli-

cation-related concepts, which are closely associated with

the concepts in the core layer. The association rule mining

algorithm adopted in this study is an APRIORI algorithm

(Agrawal et al. 1993). APRIORI algorithms use a ‘‘bottom

up’’ approach, where frequent subsets are extended one

item at a time (a step known as candidate generation) to

explore ‘‘A ? B’’ rules. The inference results obtained

using the association rules not only indicate how contextual

factors influence customer needs, but also help forecast

where customers will go to next. Tables 4 and 5 detail the

procedures of association rule mining and the results

achieved. Table 6 lists the concepts in the core and appli-

cation layers.

Table 4 shows the pseudo code of APRIORI algorithm

(Agrawal et al. 1993). First, we have to define the set of

candidate itemset of size k (Lk) and the set of frequent

itemset of size k (Ck). The next step is to generate length

(k ? 1) candidate itemsets from length k frequent itemsets.

According to the output, we need to prune candidate

itemsets containing subsets of length k that are infrequent.

Then the support of each candidate is counted by scanning

the database. The last step is to eliminate candidates that

are infrequent and leave only those that are frequent. In

summary, the process of association rule mining using

APRIORI algorithm involves two steps. The first is to

C1 C2

C8

C12 C13

C11 C9

C15

C10

C14

C1: Adequate C2: Desire C3: Word-of-Mouth Communications C4: Explicit service promises C5: Implicit Service Promises C6: Perceived Service Alternatives

C7: Situation Factors C8.C9.C10.C11: Destination 1~4 C12: Number of people in each destination C13: Time limited sale C14: Number of available employees C15: Service fitness for the preference

Co

re L

ay

er

Ap

plic

atio

n L

ay

er

C6 C7

-1

C3

11 C5C4

1 111

Fig. 5 The exemplar core

concepts involved in our search

dispatch system

Table 4 Pseudo code of

APRIORI algorithm

Y.-H. Hsieh et al.

123

generate all possible sets of attributes that have support

value greater than a predefined threshold. The second is to

generate association rules from the generated frequent

itemsets that have confidence greater than a predefined

threshold.

As shown in Table 5, association rule extraction indi-

cates how contextual factors influence customers to go to

Destination A. For instance, when Destination A is holding

a limited sale (i.e., a promotion sale for limited quantities),

its service fits well customers’ preferences, or there are

enough employees to serve customers, customers will

prefer to go to Destination A. On the other hand, crowd-

edness will also diminish customers’ willingness to go to

Destination A. (A destination represents a physical service

encounter that customers can be served and considered by

the FCM-based customer expectation-driven service

dispatch system). Without loss of generality and for sim-

plicity, four nearby destinations (north east, south and

west,) are considered in the concept map modeling.

In addition, the confidence values obtained from the

generation of association rules are adopted to represent the

causal relationship between concepts of the application

layer. That is, the values of the weights, which can represent

the uncertainties of the linkages among different concepts,

are dynamically derived from the confidence values

obtained using the APRIORI algorithm. This study adopts a

threshold of 0.95 for the confidence value of concepts and

weights of interconnections to be generated from the asso-

ciation rule mining results. For convenience of computing,

the interconnection weights (i.e., the confidence values that

are[0.95 or\-0.95) would be converted, respectively into

the value of 1 or -1, thus making the initial values of

weights in the application layer fall into the set of {-1, 0,

1}. However, this study also takes the determinants and

customer preferences into account to represent the uncer-

tainties of real-world situations. The interval of weights

should be further modified to increase their suitability for

the real world in the determinant assessment module. The

interconnections between the core and application layers

come from the Encounter Data Database and Expectation

Implement Database of the application, which contain

information describing the resources available at each

destination, as well as resources that are closely related to

the determinants that can be performed. The descriptions of

these two databases will be provided later.

3.1.2 Model adjustment

After the FCM model is created, the initial form of the

model is adjusted to customize the control process of the

FCM model according to the Customer Pattern Database.

The Customer Pattern Database comprises personal infor-

mation, historical preferred services, preferred service

providers and interaction experiences, which can refine the

modeling logic and weighted linkages. Hence, the FCM

model is made suitable for representing and explaining real

customer behaviors.

3.2 Contextual factor module: environment,

antecedents and stimuli

The system imports the data associated with the contextual

factors from the Encounter Data Database of the applica-

tion. The Encounter Data Database includes all information

of service encounters (such as provider information, cus-

tomer information, service duration, transaction data, and

service context). Owing to the multiform data structure, the

data were normalized before importation. The value of the

contextual factors influences the prediction regarding the

Table 5 The exemplar of the association rules extraction

Inference rule 1: Number of people in each destination

(T) ? Destination A(F) (46:30.6 %, 0.99)

Inference rule 2: Time limited sale(T) ? Destination

A(T) (148:98.6 %, 1.0)

Inference rule 3: Number of available employee(T) ? Destination

A(T) (35:23.3 %, 1.0)

Inference rule 4: Service fitness for the

preference(T) ? Destination A(T) (148:98.6 %, 1.0)

ł[Format]A ? B (times of occurrences: support value,

confidence value)

Table 6 The exemplar concepts of the core layer and application

layer

Concepts

(core)

Meanings Concepts

(application)

Meanings

C1 Adequate level of

customer

expectation

C8 Destination 1

C2 Desire level of

customer

expectation

C9 Destination 2

C3 Word-of-mouth

communications

C10 Destination 3

C4 Explicit service

promises

C11 Destination 4

C5 Implicit service

promises

C12 Number of people

in each

destination

C6 Perceived service

alternatives

C13 Time limited sale

C7 Situation factors C14 Number of

available

employees

C15 Service fitness for

customer

preference


123

next destination that customers wish to visit. Once the

destination is decided, the system checks which expecta-

tion determinants are available for implementation by

considering the destination contextual resources. The

details of the contextual factor module are described as

follows.

3.2.1 Contextual concept data mapping

We initiate the model form accepting the stimuli coming

from the environment and filling the value from outside the

model (that is, the contextual factors). The contextual

factors include various types of data. Computing the values

with these contextual factors in the original format could

generate biased experimental results. To avoid this, the

value of each contextual factor is transformed into per-

centages, meaning that the destination performance is

compared with that of other destinations, or the environ-

ment. In other words, in order to clarify and understand the

destination performance of contextual factors, this study

proposes several computable formulas for measuring the

values of contextual factors for potential destinations.

Without loss of generality, three types of transformation

are thus devised and the data suitable for each type of

transformation are described as follows.

3.2.1.1 Countable contextual data Data suitable for the

first type of transformation include the number of people at

each destination and the number of employees available at

a specific destination. This study compares the above local

values with global values such as total number of people at

the venue and the total number of employees at all desti-

nations. These ratios can measure the actual strength of

specific destinations.

Transformation formula I : The local value/the global value

The local value means the aforementioned numbers

manifested at each provider

The global value means the summation of the local

values

3.2.1.2 Event Data suitable for the second type of

transformation include events of limited scale. The occur-

rence of such events displays only two states: on and off,

the ‘‘on’’ state is represented by 100 % and the ‘‘off’’ state,

by 0 %.

Transformation formula II : value

¼100 %, if the state is ‘‘on’’

0 %, if the state is ‘‘off ’’

�

3.2.1.3 Service fit Data suitable for the third type of

transformation include service fitness to customer

preferences. To decide service fit, this study compares item

by item the specifications of the services offered by the

provider with the service preferences that customers have

specified.

Transformation formula III : the fit service items=

the total number of the service items a customer cares about

‘‘The fit service items’’ means the number of service

items specified that fall into the customer service

preference form.

‘‘The total number of service items a customer cares

about’’ means the total number of service items listed in the

customer service preference form.

3.2.2 Next destination assignment

According to the principles of contextual concept data

mapping, the results derived can be interpreted. The fol-

lowing provides an example C12 (i.e., number of people

at each destination), C13 (i.e., limited sale), C14 (i.e.,

number of available employees), and C15 (i.e., service

fitness for customer preference) represent individual des-

tinations (as shown in Table 5). After constructing the

FCM model, this study enriches the inference of the

system using the contextual values (0.3 for ‘C12’, for

‘C13’, 0.03 for ‘C14’, and 0.7 for ‘C15’), meaning that if

the venue contains a total of 100 people, 30 % of them

are located at Destination 1 and a limited sale is under-

way at Destination 1 (i.e., C8). If a total of 100

employees are available across all the destinations, only

3 % of them serve at Destination 1 and the service

offered by that destination provides a 70 % fit to cus-

tomer preferences).

As shown in Fig. 6, this example assumes that the val-

ues of 0.30, 1, 0.03, and 0.70 area assigned to contextual

factors C12, C13, C14, and C15, respectively. Moreover,

E8 12, E8 13, E8 14, and E8 15 are given the value of -1,

1, 1 and 1, respectively to represent the degree of influence

between the destination and the contextual factor.

According to the aforementioned definition of FCM

model, the score of Destination 1 is calculated as follows:

C8 ð1:43Þ ¼ C12 ð0:30Þ � E812ð�1Þ þ C13 ð1Þ � E813ð1ÞþC14 ð0:03Þ � E814ð1Þ þ C15 ð0:70Þ � E815 ð1Þ. With the

intention of focusing on the selected one and the remainder

subsequently becoming unimportant, the value of the

stand-out concept is retained, while other values are set to

be zero. According to the inference results of the model,

the willingness of the service receiver to go to Destination

1 (Destination 1 is scored higher than other destinations)

can be determined. Accordingly, the values of all concepts

can be calculated using a FCM model. Table 7 lists the

computed values of concepts shown in Fig. 6.

Y.-H. Hsieh et al.

123

3.3 Determinant assessment module: compatibility—

fitness of mental drivers

The objective of the third phase is to assess the efficacy of

the determinants selected in the former phase (which

means the destination guided has the resources to imple-

ment them) and select the most effective determinant. The

efficacy of each determinant varies with the key perfor-

mance indicators (KPIs) and the customer preferences.

KPIs represent the important factors that influence signif-

icantly service performances that stakeholders (including

service providers and customers) would pay attention to.

This study introduces the weight, which is defined in terms

of both KPIs and customer preferences of each determi-

nant, to the system, in an attempt to evaluate more realis-

tically the efficacy of each determinant. The output of this

phase is a determinant portfolio with the efficacy of

determinants taken into consideration.

3.3.1 Determinant fine-tuning

Determinants are selected by identifying those with high

efficacy in stimulating customer service expectations.

Besides considering the nature of the determinants them-

selves, to make the model more realistic, this study weights

the relation between determinants and expectations

according to the KPIs from the Encounter Data Database

and then fine-tunes the weighted determinants according to

the preferences of the service receiver.

For instance, the weights of determinants C3–C7

according to the KPIs are WeightKPI ½2; 1; 1; 2; 1� while

those of determinants C3–C7 according to the customer

preferences are WeightCP ½2; 3; 1; 1; 1�. The efficacy of each

determinant then becomes WeightEfficacy ¼WeightKPI þWeightCP ¼ ½4; 4; 2; 3; 2� (that is, WeightKPI denotes the

weights of determinants influenced by KPIs, WeightCP

represents the weights of determinants influenced by

C10.71

C20.95Expectation

Determinants 111

1

1

1

1

3

2 424

2 44Next Destinations

-1Contextual Factors

111

C7: Situation FactorsC8.C9.C10.C11: Destination 1~4 C12: Number of people in each destination C13: Time limited sale C14: Number of available employees C15: Service fitness for the preference


C15 0.70

C120.30

C131.00

C140.03

C8 1.43

C90.00 C10

0.00C11 0.00

C35.72

C40.00

C52.86

C60.00

C70.00

-1

Fig. 6 The calculation process

of the FCM-based system

Table 7 An exemplar of the results of the enriched inference with a stratified FCM

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 Contextual Factors

# # # # # # # # # # # 0.30 1.00 0.03 0.70

Next Destinations

0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.43 (-0.24)0.00

(0.76) 0.00

(0.62) 0.00

0.00 0.00 0.00 0.00

Determinants 0.00 0.00 5.72 0.00 2.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Expectation 0.71 0.95 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Final 0.71 0.95 5.72 0.00 2.86 0.00 0.00 1.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00


C7: Situation FactorsC8.C9.C10.C11: Destination 1~4 C12: Number of people in each destination C13: Time limited sale C14: Number of available employees C15: Service fitness for customer preference


123

customer preferences and WeightEfficacy represents the

efficacy of determinants, reflecting both influences). To

consider WeightEfficacy when choosing which determinant

to apply, this study fine-tunes the interconnection weights

between determinants and expectations by replacing the

original weights with those from WeightEfficacy.

3.3.2 FCM-based determinant identification

The results in Table 7 show that the ‘‘Word-of-mouth

communications’’ determinant (C3: 5.72) seems more

efficacious than the ‘‘Implicit service promises’’ determi-

nant (C5: 2.86), and so this study decides to exercise the

‘‘Word-of-mouth communications’’ determinant by draw-

ing support from an outside facilitator.

3.4 Service dispatch module: service dispatch

by considering compatibility and resources

3.4.1 Determinant-service mapping

The proposed system can output a bunch of service com-

ponents as the implements of the chosen determinants.

Additionally, both adequate and desired expectations could

be numerated by the Expectation measurement model

(Hsieh and Yuan 2010a), that incorporated the theories of

Fechner’s law (Thurstone 1929) and operation risk (Basel

2001) into a computable and quantitative customer

expectation measurement model to represent a customer’s

psychological status. This model can help facilitate the

integrity and effectiveness of the service dispatch process

managed by the proposed system.

The following example indicates the expectation state

obtained from the computation of the Expectation mea-

surement model:

Adequate; Desiredð Þ ¼ C1 : 0:71; C2 : 0:95ð Þ

Comparison with the preceding state can reveal the

movement of the expectation state. Let ES denote the

preceding expectation state and ES’ represent the latest

expectation state. The selection of service components

aims to fulfill the objective function of ES� ES0 � 0 (e.g.,

decreasing the adeq2uate expectations). Figure 6 illustrates

the calculation process of the FCM-based system.

3.4.2 Comparison of service economic benefits

Before delivering suitable services to customers, this

module examines the economic benefits of service com-

ponents selected via the Expectation Implement Database.

The Expectation Implement Database includes the cost,

benefit, and time information of implementing different

service components by considering customer expectations.

Hence, according to the data from the Expectation Imple-

ment Database of the application, the overall cost of each

service component can be calculated using their material

cost (MC), staff cost (SC) and equipment cost (EC) (see

Table 8). Consequently, components that offer economic

benefits and can be delivered using existing resources are

dispatched.

4 Evaluation and results

After designing the processing logics and the main

framework of the service dispatch system, we conduct

several simulation experiments to illustrate the proposed

system. As mentioned earlier, the service dispatch system

is developed from the idea of FCM. Hence, the simulation

model is also constructed using FCM. This study employs

Asp.Net development platform (C#) to simulate the real

scenarios and collect simulation results for evaluation. The

evaluation involves two parts. The first part of the evalu-

ation investigates both micro and macro perspectives of the

performance, effectiveness and feasibility of FCM-based

customer expectation-driven service dispatch system by

conducting several simulation experiments. The second

part of the evaluation collects some field test data using a

proof-of-service (POS) field test in TAITRONICS 2010,

and examines if the field test results could also support the

simulation results.

The micro perspective of simulation evaluation focuses

on training the FCM-based system to ensure its convergent

performance, using the AutoTronics 2009 exhibition data

as the training data. Owing to limited space, this paper

omits the descriptions of the micro evaluation results. The

macro perspective of simulation evaluation then examines

the effectiveness of the proposed approach in managing

customer expectations and creating service values from the

perspectives of different stakeholders and the overall per-

formance of the ecosystem (that is, service system, service

providers and customers). In addition, this study demon-

strates the key issues (namely, customer expectations,

service contexts, expectation determinants and service

Table 8 An exemplar list of services components cost

Service component Cost per service

(MC ? SC ? EC)

Consultant help 84.70

Advertisement player 69.78

Recommendation forum 57.34

Award report 47.26

Service specification 38.99

Y.-H. Hsieh et al.

123

resources) considered when building a systematic service

dispatch system via simulations.

Next, we will provide the macro simulation evaluation

results, followed by the field test results. Prior to describing

the evaluation results, the simulated application context

(i.e., AutoTronics 2009) will be illustrated first.

4.1 Experiment scenario

AutoTronics is an exhibition not only for providers to

announce new inventions, display services/products and

connect to potential customers, but also for the government

and businesses to probe the trend of industrial develop-

ment. Successful AutoTronics Exhibitions would attract

international buyers to visit and bring huge benefits to both

the prospect of businesses and the economy of the host

country. Over a hundred providers are present at the

exhibition and all of them have different characteristics and

come for different purposes; so do the visitors. The situa-

tion providers face is consequently real-time and dynamic

in terms of visitors’ preferences and providers’ real-time

available resources.

The stakeholders of the AutoTronics Exhibition include

the following.

• Organizer: The organizers including the Taiwan Exter-

nal Trade Development Council (TAITRA) and the

Taiwan Electrical and Electronic Manufacturers’ Asso-

ciation (TEEMA) host and plan the AutoTronics

Exhibition. They schedule the display date and venue

of the exhibition and are responsible for inviting

important international buyers for possible cooperation.

The most important objective of hosting AutoTronics

exhibition is to match buyers and providers seeking for

potential opportunities on business. The organizers

evaluate the performance of exhibitions with some

indicators such as total number of buyers and providers,

presence of important international and domestic

buyers and providers, and attendants’ appraisal of the

exhibition.

• Exhibitor: Providers participate in the exhibition to

look for potential customers (93.5 %), increase the

company exposure (91.8 %), connect to their old

customers (88.2 %), and display and promote new

products (83.5 %). These percentages indicate the

percentages of the various purposes for which providers

join the exhibition.

• Visitor: Buyers visit the exhibition for product purchase

(48.2 %), collecting market information (30.9 %), and

seeking international dealership (15.7 %). These per-

centages indicate the percentages of the various

purposes for which buyers visit the exhibition.

Figure 7 demonstrates an exhibition journey of a busi-

ness visitor dynamically provided with several services by

exhibitors through our service dispatch system. The top

diagrams in the figure show the services delivered at

AutoTronics, while the bottom diagrams depict the repre-

sentative expectation determinants associated with services

dispatched according to the expectation theory. Before

entering the exhibition, the visitor should input his/her

preference information (e.g., target services/products,

cooperative exhibitors, the duration of the exhibition and so

on). The proposed system would then understand the

Fig. 7 The journey example


123

visitor’s needs in order to provide him/her with suitable

services in different journey encounters according to his/

her preferences and customer expectation management. For

instance, arrangements would be made for exhibitors to

offer a product advertisement service (explicit services

promise) in the second encounter and the service/product

specification (implicit services promise) in the third

encounter to the visitor. Adequate and desired expectations

of this visitor would be decreased when he/she can get

sufficient information of his/her target services/products.

Hence, exhibitors dynamically manage the visitor’s

expectation to deliver suitable services (such as exhibitor

invitation services, visitor recommendation services, and

post-show reports) in the following encounters. In the end,

the visitor experiences a satisfactory exhibition journey.

4.2 Experiment for managing customer expectations

From a macro perspective, this investigation proposes a

solution for managing customer expectations in dynamic

contexts, and finally produces a well-performing service

ecosystem. One objective of the experiment is to demon-

strate that with the assistance of the FCM-based customer

expectation-driven service dispatch system, quality service

deployment decisions can be made through effective

management of customer expectations. The system could

manage customer expectations with the goal of reducing

customers’ adequate expectations in order to widen the

tolerance zone and thus gain customer satisfaction. That is,

when the system can manage customer expectations so as

to reduce adequate expectations, the effectiveness of the

system in dispatching services can be improved.

The specific simulated environment (i.e., an AutoTron-

ics Exhibition) we use to test and verify our research has

several characteristics, such as lots of types of customer

models involved, full of two-way interactions, as well as

dynamic and resource-limited contexts. In order to mirror

the customer characteristics into the simulation, the

experiments consider five indicators of customer variability

(Frei 2006): arrival (e.g., frequency of attending exhibi-

tions), request (e.g., nature of requirements like existence,

relation and growth), capability (e.g., knowledge and

skills), effort (e.g., efforts to engage) and subjective pref-

erence (e.g., expectation status). This study classified 265

data samples from the exhibition of AutoTronics 2009 into

three customer stereotypes. Table 9 lists the different

customer stereotypes, namely stereotypes A, B, and C, with

different degrees of variability in terms of the five indica-

tors. In general, stereotype A has low customer variability

while stereotype C has high customer variability, with

stereotype B somewhere in between the two. This experi-

ment examines the performance of customer expectation

management by simulating three customer stereotypes for

15 rounds. Each round represents experiencing one service

encounter. The experiment serves to imitate the interac-

tions between exhibitors and visitors. Visitors will select

their destinations where exhibitors can deliver suitable

services to them and also acquire their expectations in each

service encounter.

The system determines customer expectation status after

making and executing service dispatch decisions. The

variation of the expectation status is assessed using the

customer expectation measurement model. We observe that

the zone of tolerance of expectation becomes wider and

both adequate and desired expectations are decreased

regardless of the user stereotype. More specifically, we find

that the variations of both adequate and desired expecta-

tions of user stereotype A (Fig. 8) are more marked than

those of user stereotypes C and B (Figs. 9, 10) and so are

the variations of their respective zones of tolerance. In

other words, the variations of the zone of tolerance are as

follows: stereotype A [ stereotype C [ stereotype B.

These comparisons reveal that the system can indeed help

manage customer expectations. In an exhibition scenario,

visitors of stereotype A (low capabilities but high expec-

tations) may have faith in name brands or high-competence

exhibitors, but are unable to collaborate with them. Hence,

if other acceptable alternatives are introduced to these

visitors, they tend to be easily convinced, and service

providers are likely to lower their expectations.

0

2

4

6

8

10

the Value of the Level of

the Customer's Expectation

Service Dispatch

Adequate

Desire

Fig. 8 The progression trend of the expectation for user stereotype A

Table 9 The profile of each customer stereotype

Customers Stereotype indicators

Stereotype A Stereotype B Stereotype C

Arrival Seldom Seldom Often

Capability Low Medium High

Effort A little Medium Medium

Request Existence Relation Growth

Subjective

preference

High,

medium

Medium,

low

Medium,

low

Y.-H. Hsieh et al.

123

4.3 Experiments examining service match regarding

stakeholder considerations

When arranging services, service providers consider not

only contextual factors and customer expectations but also

the usage of company resources and individual stakeholder

objectives to obtain suitable service deployment decisions.

The quality service deployment decisions should ensure

that the services dispatched accomplish the 4Rs (allocate

the right resources to the right person at the right time in

the right place). Hence, this study compares the design

objectives of the dispatched service with the situation

under which the service is received, including the identities

of those served, when the customer receives the service and

the location where the service is provided, to see whether

the service fits its designed purposes. When the service is

used in situations extremely similar to the purposes for

which it was designed, the service dispatched should earn

its points and the related service deployment decision

should be viewed as a quality decision.

The degree of which the service matches the stake-

holders’ considerations (that is, customers, exhibitors and

organizers) is defined as below (one dispatched service has

each measurement). This study uses AV, AE, and AO to

represent the degree of match between the service and the

three stakeholders’ considerations.

Av: Degree of which the service matches a customer’s

considerations, if

there is no same item between ‘‘determinant preference’’

of a visitor and a service, then Av ¼ 0

there are same items between ‘‘determinant preference’’

of a visitor and a service, then Av ¼ 1

8>>><>>>:

AE: Degree of which the service matches an exhibitor’s

considerations, if

there is no same item between ‘‘resource’’

of an exhibitor and a service; then AE ¼ 0

there are same items between ‘‘resource’’

of an exhibitor and a service; then AE ¼ 1

8>>><>>>:

AO: Degree of which the service matches an organizer’s

considerations, if

there is no same item between ‘‘KPIs’’

of an organizer and a service; then AO ¼ 0

there are same items between ‘‘KPIs’’

of an organizer and a service; then AO ¼ 1

8>>><>>>:

SA: Degree of which the service matches all three

stakeholders’ considerations = ðAv þ AE þ AOÞ=3 %ð Þ

Table 10 illustrates the process for calculating the degree

of which a service matches the stakeholders’ consider-

ations. There are three stakeholders who have different

considerations (namely, personal preferences for determi-

nants, resources owned and KPIs). Each service should

match these considerations. The overall match depends on

the degree of which the service matches the considerations

of each stakeholder. In other words, if SA is high, the ser-

vice can fulfill all three stakeholders’ considerations.

As mentioned earlier, the simulated data are obtained

from the exhibition of AutoTronics 2009. The simulation

involves 25 customers (eight customers of stereotype A,

two customers of stereotype B and 15 of stereotype C)

experiencing their own journey by simulations, which

reveal the performance of service match for each stereo-

type. Five independent experiments are performed. As seen

in Fig. 11, there are no clear differences between the dis-

tributions of the average service match of the three

experiment groups. The average service match falls mostly

between 80 and 90 %. The service thus apparently fulfills

over half of the considerations of stakeholders. According

to the principle of low-cost service selection, this ‘more

than 50 %’ fulfillment is achieved with minimal service

costs. That is, the services dispatched are cost effective.

Dispatched services can thus fulfill to a large extent

stakeholder requirements, such as KPIs, limited resources

and customer preferences with low expenses incurred.

0123456789

10

Rou

nd 1

Rou

nd 2

Rou

nd 3

Rou

nd 4

Rou

nd 5

Rou

nd 6

Rou

nd 7

Rou

nd 8

Rou

nd 9

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nd 1

0

Rou

nd 1

1

Rou

nd 1

2

Rou

nd 1

3

Rou

nd 1

4

Rou

nd 1

5

the Value of the Level of the Customer's Expectation

Service Dispatch

Adequate

Desire

Fig. 10 The progression trend of the expectation for stereotype B

0123456789

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nd 1

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nd 2

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nd 3

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nd 4

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nd 5

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nd 6

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nd 9

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nd 1

0R

ound

11

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nd 1

2R

ound

13

Rou

nd 1

4R

ound

15

the Value of the Level of the Customer's Expectation

Service Dispatch

AdequateDesire

Fig. 9 The progression trend of the expectation for user stereotype C


123

4.4 Experiment for performance of service dispatch

ecosystem

This experiment aims to evaluate the performance of the

service dispatch ecosystem using the surplus value theory

(Marx 1952; Carlsson and Davidsson 2002). The equation

of surplus value is as follows:

S = P � C + Vð Þ;

where S denotes surplus value, and P represents the price of

the service and the product. That is, P is extended to

symbolize the total value of service providers or customers

within service contexts, which can be measured in terms of

market share and business profits. C is the total spending on

investments and materials, V denotes expenditure on labor,

and (C ? V) represents total cost of designing and

delivering services (service providers), or searching and

receiving services (customers). This study aims to

maximize surplus value for both service providers and

customers. The objective functions of surplus value for a

service dispatch ecosystem are as follows,

Maximum Sp ¼ Pp � C þ Vð ÞpMaximum Sc ¼ Pc � C þ Vð Þc

Pp represents the total value achieved by service

providers deploying the proposed system (i.e., a form of

control system to manage appropriately customer

expectations), the proportion of zone variation to the

original zone Pp. (C ? V)p indicates the effort service

providers are willing to expend on service encounters, and

can be represented as the degree of service component

utilization. Meanwhile, Pc represents the total value

customers achieve utilizing degree of customer

satisfaction. Customer satisfaction is maximized if more

service components are selected, and if those components

match more closely their preferences. (C ? V)c represents

the total cost and effort expended by customers on making

recommendations.

According to Baumgartner et al. (2004) and Krieger and

Green (1991), the Pareto optimal solution is one where

improvement in one objective does not lead to a simulta-

neous degradation of one or more of the remaining

objectives. The Pareto optimal solution thus illustrates the

surplus values of providers (Sp) and customers (Sc) that the

control system may or may not serve. Each pair of Sp and

Sc is viewed as the performance of expectation manage-

ment. Pareto optimal outcomes are those where no one can

be made better off without making someone else worse off,

thus achieving a high-performance ecosystem.

Figure 12 then shows the evaluation results. The

rhombuses represent the performance with the stakeholders

served by the customer expectation-driven FCM-based

service dispatcher, while the triangles express the perfor-

mance with the stakeholders not served by the control

system. As seen in the figure, all Sp (namely, the surplus

values of providers) and Sc (surplus values of customers) of

the served stakeholders (see the rhombus group) are posi-

tive, indicating that stakeholders served by the control

system are thus likely to receive value. Moreover, com-

pared with stakeholders not served by the control system,

those served by the system tend to have higher Sp, meaning

that managing customer expectations really adds value to

providers. The result also echoes the claims of the Pareto

Table 10 The example of the calculation of the degree of the service’s match based on the stakeholders’ considerations

Determinant preference/

determinant fulfillment

Resource KPIs

Situation where the service received Customer A Word-of-mouth

Implicit service promise

N/A N/A

Exhibitor A N/A Multimedia file N/A

Organizer A N/A N/A Number of people

visiting

Design purposes of a service Service A Word-of-mouth Multimedia file graphics Number of people

visiting

Measurement AV, AE, AO 1 1 1

SA (1 ? 1?1)/3 = 1 = 100 (%)

60%

80%

100%

1 2 3 4 5

Average ServiceMatch

experiments

Stereotype AStereotype BStereotype C

Fig. 11 The degree of the service match with the stakeholders’

considerations

Y.-H. Hsieh et al.

123

optimal solution, even though some points in the triangle

group have better performance in the Sc dimension than

those in the rhombus group, their performance in the Sp

dimension is worse than that of those in the rhombus group.

In other words, the points in the rhombus group are those

where no one can be made better off without making

someone else worse off. Since these two phenomena are

both observed in the experimental results, the related ser-

vice deployment decisions appear to contribute to

improving stakeholder productivity and benefit, thus

achieving a high-performance ecosystem.

5 Taitronics case study

To further validate our simulation results and the feasibility

of the proposed system, we present a case study of an

international exhibition, TAITRONICS 2010 (http://

www.taitronics.org/). TAITRONICS exemplified a

dynamic service context and the case study provided proof-

of-service (i.e., POS) for the proposed system. Focusing on

the B2B market, TAITRONICS has been held for 30 years.

TAITRONICS 2010 featured 739 exhibitors and 170

product/service categories (involving *1,724 items). The


was packaged as an innovative intelligent exhibition ser-

vice serving visitors via handheld devices. A total of 530

devices were provided to visitors. The customer service

interaction logs yielded facts that were both valuable and

inspiring.

As mentioned earlier, the exhibition involved three

kinds of stakeholders: organizers and exhibitors serving

different visitors with diverse requirements resulting from

personal factors (such as expectations, time, visit objec-

tives and purchase targets); and visitors attempting to

accomplish their missions within an information-over-

whelming and transient time frame. Stakeholders engaged

in lots of interactions and effort to get what they need

(namely, to transform short-term encounters into long-term

future relationships within a limited time period).

5.1 Feasibility evaluation

5.1.1 Increased customer acceptance through satisfactory

service

Among stakeholders involved in service encounters (i.e.,

visitors interacting with handheld devices), customers

accepted 60 % of the services delivered by the FCM-based

customer expectation-driven service dispatch system. That

is, 60 % of the services stimulated customers to engage in

further service interactions, indicating that satisfactory

acceptance of appropriate services dispatched to customers

via the proposed system indirectly helped service providers

(i.e., organizers and exhibitors) deploy effectively appro-

priate services to customers. Hence, even though service

providers had resource and cost constraints when offering

services, customers could perceive satisfactory services

through the assistance of the FCM-based customer expec-

tation-driven service dispatch system. The above analysis

from the case study then led to the third simulation (the

experiment examining service match regarding stakeholder

considerations).

5.1.2 Understanding customer preferences regarding

expectation determinants

The customer service interaction logs revealed that cus-

tomers served by the FCM-based customer expectation-

driven service dispatch system had over 90 % of their

interactions distributed among three services: recommen-

dation service (23 % of all interactions), query service

(49 % of all interactions) and advertising service (21 % of

all interactions). Those services are related to expectation

determinants of personal needs, explicit service promises

and predicted service, expressed as the most wanted ser-

vices by cross-referencing the after-exhibition user survey.

In other words, understanding customer preferences in

terms of expectation determinants enabled the proposed

system to work in the context of real-time service dispatch,

and empowered service providers to design the correct

services in advance, thus enabling effective service deliv-

ery to customers. Hence, this analysis of the case study can

support the simulation results of the experiments about

managing customer expectations.

5.1.3 Service acceptance increased with customer

involvement

Although the overall customer acceptance of the FCM-

based customer expectation-driven service dispatch system

Fig. 12 The performance of the FCM-based customer expectation

driven service dispatcher system


123

http://www.taitronics.org/

http://www.taitronics.org/

achieved 60 %, this case grouped customers according to

their degree of involvement, and found that customer

acceptance of services increased with their levels of

involvement. Specifically, visitors who interacted with the

handheld devices 0–5, 6–10, 11–15, 16–20, and C21 times

had 18, 37, 52, 50, and 87 % service acceptance rate,

respectively (as shown in Fig. 13). Thus, the performance

of the proposed system in dispatching appropriate services

to visitors increased with number of customer interactions.

As mentioned earlier, to evaluate the performance of the

service dispatch ecosystem, this study takes the perspective

of both provider and customer into account. From the

provider perspective, only 10 % of the promoted services

were implemented with the cooperation of the exhibitors at

TAITRONICS 2010, who were extremely concerned with

resource and cost constraints, but those 10 % of the ser-

vices accounted for 70 % of the interactions. Meanwhile,

from the customer perspective, 74.87 % of visitors using

the handheld devices were satisfied with these services and

80 % of visitors thought that services promoted through the

handheld devices had greater attraction. According to the

surplus value theory, the exhibitors avoided investing

heavily and thus acquired more surplus value. Service

providers can also obtain greater value without sacrificing

customer value (70 % of interactions involving exhibitors).

This win–win situation can be regarded as a high-quality

service ecosystem. Accordingly, the analyzable data from

the TAITRONICS case study also supports the simulation

results of high-performance service ecosystem.

5.2 Discussion

According to the above experimental simulations and the

TAITRONICS case study, the evaluation results are sum-

marized as follows.

5.2.1 Influences of customer expectation management

The evaluation results show that the FCM-based customer

expectation-driven service dispatch system can effectively

manage customer expectations (including both adequate

and desired expectations) to realize the objectives of ser-

vice providers (that is, reducing adequate and desired

expectations) by testing three customer stereotypes. The

simulation results indicate that the proposed system must

understand customer expectations and provide suitable

services to the right customers. Consequently, service

providers can accurately provide customers with proper

services via the service dispatch system, which considers

limited resources and costs carefully to manage customer

expectations in real-time service contexts. Good customer

expectation management is the key to both customer sat-

isfaction and good service dispatch system performance.

5.2.2 Service match regarding stakeholder considerations

The evaluation results show that the services dispatched

meet most stakeholder expectations. Hence, defining

appropriate service dispatch strategies enables service

providers to efficiently deliver suitable services to cus-

tomers given limited resources. The service dispatch sys-

tem demonstrates a good system performance in allocating

the right service resources to the right customers at the

right time and place.

5.2.3 Performance of service ecosystem

The analytical results demonstrate that the resolutions

served by the proposed system are better than those not

served. That is, the proposed service dispatch system can

help service providers manage customer expectations to

precisely understand customer needs and deliver useful

services accordingly. Customers can feel satisfied with

services obtained, and service providers can effectively

manage customer expectations to maximize customer sat-

isfaction and business profits. Accordingly, service pro-

viders or customers can achieve higher value in this service

context, which is represented as a high-performance ser-

vice eco-system.

6 Conclusion

This study designs the FCM-based customer expectation-

driven service dispatch system to solve the cost-effective

service deployment problem in real-time dynamic service

contexts (such as the exhibition or tourism service sectors).

The proposed system not only considers several context

factors but also examines the interdependent influences

among these factors. For this reason, the proposed system

helps service providers make service dispatch decisions

appropriately by analyzing current service contexts. Fur-

thermore, customer expectations constitute an important

consideration for delivering quality services and achieving

0.18

0.370.52 0.5

0.87

00.10.20.30.40.50.60.70.80.9

1

0-5 times 6-10 times 11-15 times 16-20 times over 21 times

Times of interacting with handheld

Ser

vice

acc

epta

nce

rate

Fig. 13 Service acceptance with diverse levels of customer

involvement

Y.-H. Hsieh et al.

123

customer satisfaction. Though difficult, service providers

must effectively understand customer expectations in

dynamic service contexts. Accordingly, the proposed sys-

tem developed from the expectation theory can detect

customer expectations and dispatch suitable services.

This study conducted several experiments and a field

case study to justify the feasibility and performance of the


system. Experiments conducted in a dynamic service

context simulating a real exhibition indicate that the system

can effectively manage customer expectations. Both levels

of expectation follow expected trends during service dis-

patch. Therefore, given a systematic control mechanism

and computation of the expectation status, it is possible for

service providers without high capabilities to manage sat-

isfactorily customer expectations even in a dynamic, highly

uncertain situation. High performance is more likely if

service providers deliver services via the proposed system

and thus maintain competitiveness. The analytical data of

the field case study also supports the simulation results.

To train the proposed system, this study used the

AutoTronics 2009 exhibition data as the historical training

data to continuously refine and model the necessary

parameters, stakeholders, and logics. To enhance the rigor

of our research, we employed the AutoTronics and Tai-

tronics exhibition data, respectively for the simulations and

field case study to derive the preliminary support and

evidences.

The proposed service dispatch system can be considered

as a high-level framework, which includes a series of

methods for solving the service dispatch problem. Given

that such problem exists in diverse service domains, this

study emphasizes how to apply innovative approaches

systematically and intelligently. To demonstrate the feasi-

bility and effectiveness of the proposed system, this study

used the exhibition service sector as an application. The

proposed system can be adjusted for application by service

providers according to their respective sectors with

dynamic service contexts.

This study have some limitations. First, several possible

factors, including customer emotions, service failure, ser-

vice recovery and so on, influence customer perceptions of

service quality. Emphasizing the influence of customer

expectations on the service dispatch system requires

focusing on customer expectations during service delivery.

Thus, other factors should be integrated into the proposed

system for further evaluation. Second, in practical situa-

tions, it is necessary to consider the thresholds of customer

training data used in the proposed system. Since the

learning mechanism of the proposed system is continuously

trained and modified through the input of real-world data

(namely, customers), the number of training sets and iter-

ations should be carefully considered. This investigation

conducted customer surveys at AutoTronics 2009 to

explore the suitable training sets and iterations through

experimental simulations. However, the given settings

might be imprecise and unsuitable for illustrating and

imitating real-world customer behavior, and thus the sys-

tem parameters must be further tuned by increasing the

inputted customer data. Although it is necessary to con-

tinuously learn and search suitable parameters in real-time

exhibition contexts, the tolerance of service providers

operating under resource and cost constraints is the most

important condition for terminating training iterations.

Nevertheless, findings of this study also contribute to the

literature of Service Science and Information Science.

Traditionally, service providers use questionnaires to

understand what customers expect and how they perceive

the services. With services already delivered, service pro-

viders cannot respond immediately to customer expecta-

tions during service delivery. In contrast, the proposed

system can dynamically offer suitable services to custom-

ers in real-time service contexts. Moreover, we have

developed a valuable method for gaining knowledge and

understanding of a design problem (in this study, dis-

patching services by managing customer expectations)

through the construction and application of the FCM-based

customer expectation-driven service dispatch system.

Hence, this study serves as a bridge between the manage-

ment issue and information technology.

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