CHAPTER 5 THE EVALUATION OF THE THEORETICAL CONCEPT
5.4 Conclusions
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However, the results also propose some research directions to improve and extend the functions and services of the U2EX service system given there are still 40% exhibitors who are not satisfied with the current service system. Owing to Orbi being the first exhibition service system implementation of its kind, there still existed exhibitors or visitors who felt doubtable of using this service system to attain the benefits and values.
Figure 5-4 The questionnaire survey results of exhibitors
5.4 Conclusions
In attempting to build a theoretical concept of combining the expectation theory with the emotion theory to customer satisfaction, we discerned three propositions (P1, P2, and P3) to test the relevance of this theoretical concept. In this study, we used a case (i.e. the U2EX service system) to validate proposed propositions. We conducted five interviews to gather the practical data that interviewees were selected randomly and willing to use the Orbi device and services in the TAISPO exhibition. Meanwhile, the simulations and questionnaire results of visitors were also collected at the same
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Our case analysis validated three propositions P1, P2, and P3. For testing P1, if service providers can provide customers with suitable services, customer expectations are affected to either increase or decrease for an appropriate zone of tolerance. When a customer has a suitable zone of tolerance, he can accept the delivered services and meet his needs easily. In order to understand the possible variation of customer expectations, we try to analyze what customer cognition changed. Consequently, the empirical data shows that customers could alter their cognitive thinking and expectative idea while receiving valuable services. For testing P2, we have known the importance of customer expectation management that really influences customer service experiences. However, customer expectations also could affect customer emotions. According to the interviewees‟ results, they had the high possibility to have the positive emotional status when staying the satisfactory service context.
P3 is to test if good customer expectation management can successfully achieve customer satisfaction given customers are in the positive emotional status. We concluded three classifications (willing to recommend, shortcoming improving, and actual/external behavior changing) to explore the obvious clues for evidences by analyzing the interviewees‟ experiences. Accordingly, the proposed propositions are supported via the multiple empirical data. The theoretical concept is also to delineate a clear viewpoint for service providers to take into account. That is, in order to reach high customer satisfaction, service providers must not only provide appropriate services to manage customer expectations but also establish an atmospheric service context to generate positive customer emotions. Furthermore, we also ask the exhibitors to understand if the U2EX service system can generate positive results via the questionnaire survey. The survey results show that exhibitors can also receive
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useful beneficial results through the U2EX service system.
However, some important issues were resulted from interviewees that are needed to further modify and take into account. First is the accuracy of delivered services of the U2EX service system, since some interviewees mentioned that they would receive some services which were not related to their expectative services and needs, and these services were considered as the non-useful information by the visitors. There is no doubt that wrong services would negatively influence customer expectations to result in failed service experiences and customer satisfaction. Hence, in order to offer appropriate services and successfully manage customer expectations, the U2EX service system should be adjusted further in the future to dispatch most relevant services that can fulfill customer needs and achieve high customer satisfaction.
Second, visitors who are from different countries around the world have different cultures, backgrounds and IT-familiar. For instance, some high IT-familiar visitors considered the product and exhibitor information services as the advertisement services given they had experiences of receiving promotion information over the Internet in the past. They could have diverse perceptions based on their existing cognitions while utilizing the delivered services from the U2EX service system.
Although we defined the research boundary that only expectation determinants can influence customer expectations based on the expectation theory, customer expectations still could be influenced by other factors (i.e. personal cognitions, IT-familiar, backgrounds and cultures). In the future, we need to deeply think how to combine our original idea with these factors and attain more positive support of the research results.
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CHAPTER 6
CUSTOMER EXPECTATION MEASUREMENT MODEL
Although customer expectation issue has been discussed for a long time, less research applied the determinants of expectations into practices. Service providers use the familiar empirical approaches (e.g. market survey, customer investigation or historical reports) to examine customer needs. Peppers et al. (1999) proposed that service providers can employ interactive and customized activities to realize what customers real want. However, these approaches do increase the cost and efforts of providers, yet the investigative results may not be good enough to represent actual customer needs during service delivery because previous operation design research was mainly investigated from the non real-time data (such as questionnaire surveys or case study).
Furthermore, there were no systematical mechanisms found in past research to effectively measure customer expectations (which are influenced by determinants within service delivery) to manage the service experience delivered.
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This chapter is to propose a quantitative and theoretical measurement model for measuring customer expectation in real time in order to comprehend actual customer expectations and behaviors, taking several important concepts, like customer expectation, operation design etc., into account to fulfill quality service experiences.
However, customer expectations, which are difficult to measure in terms of manipulation biases, can‟t be numerically represented and calculated by customers‟
external behaviors. Fechner‟s Law (Thurstone, 1929) is then adopted to serve as the psychological theory to underlie our expectation measurement approach to transforming customer expectations into numerical numbers during service experiences delivery. Besides, the concept of operation risk (which is built based on the probability concept) is used to portray the effect of the external stimuli (i.e.
expectation tactics and service operations) on customer expectations.
Hence, the contribution of this work is to integrate the theories of Fechner‟s Law and operation risk into a computable and quantitative customer expectation measurement model. Not only researchers can apply this theoretical expectation measurement model to investigate and recognize the customer‟s psychological status for the further research, but also service providers can deliver innovative service experience to their customers based on the results of the measurement model.
6.1 The Theory Groundings of Fechner’s Law
This study aims to propose a quantitative measurement model to measure customer expectations during services delivery. Therefore, customer expectations are also the mental status of humans. Fechner‟s Law is to represent the relationship between the external stimuli and the sensations of humans. According to Fechner‟s Law, if the magnitude and type of the external stimulus are known supposedly, the magnitude of sensations can be calculated through the mathematical formula. Hence,
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the service providers can utilize the determinants to influence their customer expectations. These determinants can be considered as the external stimuli and the customer expectations are likened to the sensations. This study tries to apply the concept of Fechner‟s Law into customer expectation management within services delivery for building an appropriately quantitative measurement model that can analyze and meet real customer needs in order to match the business goals.
6.1.1 Descriptions of Fechner’s Law
Gustav Theodor Fechner was not only a great physicist but also a specialized philosopher in Germany, who proposed “Psychophysics” discipline firstly to build the relationship between physics and psychology in 1860. However, how to evaluate the mental status of humans was a difficult and troublesome issue. Fechner believed that quantifying the mental responses of humans, which resulted from physical stimulus, was a feasible way to clearly represent the relationship between physics and psychology through the mathematical and quantifiable formulas.
In 1834 Ernst Heinrich Weber, a German physicist, presented a mathematical approach (Weber‟s Law) to measure the variation between two different stimuli (so-called Just Noticeable Difference, JND) which humans could be appropriately conscious of. The equation of the Weber‟s law shows as follows,
K = △ I ／ I (Constant),
where △I represents the difference threshold between two stimuli, I represents the initial stimulus intensity of a human and K represents the constant of the specific sense that is the so-called the Weber proportion. For example, if a human can raise 5 kg, he will notice that it takes some effort. If he adds 0.01kg and lift again, he may not notice any difference between 5kg and 5.01kg. Furthermore, if he continues to add weight, he can notice that the just noticeable difference is 0.5kg. When he lifts up from 10kg, the just noticeable difference is 1kg. Hence, the ratio of △I／I (0.5/5 =
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1/10 = 0.1) between these two examples is the same. However, Weber‟s Law does not explain that how a subjective consciousness of a human changes with the variation of external stimulus intensity.
Consequently, Fechner proposed that using the just noticeable difference as the basic sensation unit is feasible way for measuring the mental status of humans based on Web‟s Law. In other words, summarizing each just noticeable difference segment can be considered as the mental perception of humans. Fechner integrated the above equation to give another equation (so-called Fechner‟s Law or Weber-Fechner‟s Law) as follows (Thurstone, 1929),
S = K ＊ ㏒ R,
where S represents the intensity of mental perception of humans, R represents the intensity of the external stimulus and K is a constant. This equation of Fechner‟s Law shows that the relationship between mental perception and the external stimulus is logarithmic (as shown in Figure 6-1). The logarithmic relationship describes that if the variation of the stimulus is a geometric progression, the related mental perception of humans is changed in an arithmetic progression.
In summary, the concept of Fechner‟s Law is to find a way that can describe the mental state of a human through physical incentives externally. As mentioned earlier,
Figure 6 - 1 Fechner‟s law
R S
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customer expectations are difficult for service providers to realize which represent humans‟ psychological statuses. Thus, the point is that this study attempts to employ Fechner‟s Law to describe the customer expectations mathematically. This study uses the external stimuli (i.e. expectation tactics and service operations) to find the representative value of customer expectations based on Fechner‟s Law.
6.1.2 Applications of Fechner’s Law
Fechner‟s Law has been built for a long time that can represent all sensations of humans including vision, hearing, touch, taste or olfaction. There have been many previous studies applying Fechner‟s Law into different disciplines. McKone et al.
(2005) had an application of face recognition research with four experiments based on Fechner‟s Law. Li (2005) described that the human‟s perceptions of temperature, moisture and comfort under rain conditions would follow Fechner‟s Law. Furthermore, Babin (1995) proposed that product information (especially the price) that can be regarded as the external stimuli significantly influence customers‟ consumer behaviors. Consequently, Fechner‟s Law has been evaluated and applied by many previous studies in different discipline for a long time. This law can be regarded as a substantial and refined theory foundation for related research. Besides, we induce that Fechner‟s Law can apply to the research containing two main characteristics. One characteristic is that the research would focus on the influence of the external stimuli.
The other is that the research correlates with the sensational behavior. According to these critical features, this study is to use Fechner‟s Law as a theoretical foundation to establish the expectation measurement model.
6.2 The Capital Requirements of Operation Risk
In this study, we also apply the concept of operation risk on the expectation measurement model in order to fulfill customer expectations calculation
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systematically. According to Fechner‟s Law, the stimulus is the critical element to influence perceptions of a human. However, how to determine a representative value for the stimulus (i.e. the combinations of determinants) of managing customer expectations is extremely difficult in terms of limited past research. Thus, operation risk can be a theoretical support to transform determinants into a representative stimulus value.
Operation risk has been an important issue in traditional manufactories for a while (Beroggi and Wallace, 1994). However, service industries gradually realized that operation risk would conspicuously affect their performance and profit, the bank industry especially (Basel, 2001g; Embrechts et al., 2003). Banking businesses now become more and more complex and changeable in terms of high competitive environment, diverse customer needs and advanced technology, which would lead banks to face higher risks. Consequently, the Basel Committee defined the standard rules and norms for banks to reduce their operation risk. Furthermore, according to the New Basel Capital Accord (Basel, 2001g), the definition of operation risk is “the risk of direct or indirect loss resulting from inadequate or failed internal processes, people and systems or from external events.” The main concept of operation risk means that banks have to prepare additional capital beforehand to prevent operation risk. Accordingly, the capital charges of banks for risk measurement and management is an extremely essential matter.
According to the New Basel Capital Accord [1], the equation of Internal Models Approach is as follows,
Required Capital = Σi Σj [γ (i,j) * EI(i,j) * PE(i,j) * LGE(i,j) * RPI(i,j)],
where the γ represents each business line or loss type combination of banks, Exposure Indicator (EI) represents a parameter for the size of a particular business line‟s operational risk exposure, Probability of loss Event (PE) represents the possibility of
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occurrence of loss events, Loss Given Event (LGE) represents the ratio of transaction or exposure which would be disbursed as loss, given that event and Risk Profile Index (RPI) represents the bank specific risk profile which can be considered a capability of a bank for solving the risk problem. Besides, i is the business line and j represents the risk type.
In brief, operation risk is built by the concept of required capital (i.e. probability) which includes several indicators to calculate and represent the risk value. This study attempts to use the analogy of operational risk to compute the representative value of the determinants of influencing customer expectations. Hence, this approach can enhance the integrity and rationality with using Fechner‟s Law of customer expectation management.
6.3 The Expectation Measurement Model
As addressed in Chapter 4, the customer expectation management engine is composed of four theoretical methods that tightly cooperate with the expectation measurement module. The expectation measurement module is to measure the likely performance of selected determinants delivered by the aforementioned methods via calculating the computable indicators (e.g. the numbers of determinants, the average variation of customer expectation, provider capability, and so on). The expectation measurement module generates two outputs, which include the scores of customer expectation and feasible service tactics, to the aforementioned methods. The aforementioned methods, then, deliver the outputs from the expectation measurement module and information of the context in service encounters to the service component execution module in order for high-quality service experiences. Thus, the expectation measurement module is a critical function for realizing customer mental status, and ensures the integrity and effectiveness of service experience delivery of the customer
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6.3.1 Phase of Customer Expectation Measurement Model
The measure used for expectations is based on a mathematical model based on Fechner‟s Law (Thurstone, 1929) and operation risk (Basel Committee, 2001). Figure 6-2 represents the reasoning process of the expectation measurement model, which involves three separate stages, namely: expectation determinants, expectation measurement model and customer expectations. Furthermore, the measurement model also contains feedback which can continuously refresh a real-time database to measure customer expectations.
Expectation determinants stage
The input of the expectation measurement model comprises the combinations of determinants obtained from the methods of customer expectation management engine.
According to Zeithaml et al. (1993), these determinants include enduring service intensifiers, personal needs, transitory service intensifiers, perceived service alternatives, customer self-perceived service role, situational factors, expected service, explicit service promises, implicit service promise and word-of-mouth communications (Zeithaml et al., 1993).
Expectation measurement model stage
This step calculates the scores of the desired and adequate expectations, while
Figure 6 - 2 The process of expectation measurement model
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managing customer expectations are adequate expectation raise, adequate expectation abatement, desired expectation raise and desired expectation abatement. According to the management objectives and combinations of determinants, the stimuli can be computed using a stimulus intensity formula based on an analogical mapping between the factors considered by the operation risk and the stimulus intensity factors regarded in the dynamic service context. After obtaining the stimuli value, the expectation measurement model calculates the adequate or desired expectation scores based on Fechner‟s Law (i.e. the magnitude of the sensations can be calculated based on the magnitude of the external stimulus).
Customer expectations stage
Accordingly, the outputs of the expectations measurement model include adequate expectation score, desired expectation score and list of recommended expectation tactics. Once service providers understand actual customer expectations based on these outputs, they can propose suitable services to assist their customers in achieving their business goals (e.g. customer satisfaction, repeated customers, and business profit). Additionally, the expectation tactics list provides a reference. This list of appropriate expectation tactics can be mapped to specific service components to influence customer expectations via the aforementioned methods. After implementing the expectation tactics (namely, service components), the aforementioned methods should store the values of expectation variation and their capabilities indicators in the real time database. The expectation measurement model can then use the feedback control to reflect actual customer expectations.
6.3.2 Applying Fechner’s Law to Expectation Measurement Model
According to Zeithaml et al. (1993), there are two customer expectation levels (that is, desired expectation and adequate expectation) which can be influenced by determinants. This study describes the details of two expectation measurement models
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6.3.2.1 Desired Expectation Measurement Model
Desired services are the high level expectation of customers. It means that the service customers hope to receive (Zeithaml et al., 1993). Besides, the desired expectation is incredibly stable and changeless. For example, some customers always concern about the high-quality of services or the lower prices, so, in other words, their basic needs can‟t change. Consequently, the desired expectation measurement model can be approximately applied by Fechner‟s Law, yet the difference between them is that the mental perceptions of customer would increase slowly in the desired expectation measurement model in terms of the stability of desired expectations.
Hence, the equation of the desired expectation measurement model can be modified in the form,
E
D= K
＊㏒
αI
,in which
E
D is the desired expectation value of the customer affected by the external stimuli, andI
is the stimulus magnitude of the expectation determinants that would be computed through the approach of operation risk. In addition,K
is the constant which can represent the type of customers. According to the dissimilar type of customers, their mental perceptions must be quite different when they touch on the expectation determinants. α is to represent the desired expectation of customers.‧
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Figure 6-3 clearly shows the curve of the desired expectation measurement model. When the intensity of stimuli enlarges with time, the desired expectation value
Figure 6-3 clearly shows the curve of the desired expectation measurement model. When the intensity of stimuli enlarges with time, the desired expectation value