Self-service technology

Determining Consumer Satisfaction and Commitment through Self-Service Technology and Personal Service Usage

Abstract

Self-service technology (SST) is very popular in any Hong Kong industry nowadays. Automatic Teller Machine (ATM) is one of SST which provides bank service. However, there is still a certain unknown about SST and personal service usage on its consumer satisfaction and consumer commitment.

The purpose of this report was to design a survey through bank service (i.e. ATM and personal service usage) in order to determine the consumer satisfaction and multidimensional measure of consumer commitment including: Affective Commitment, Continuance Commitment and Calculative Commitment. First of all, literature review will be set up in this report so as to define the SST, personal service usage, consumer satisfaction and commitment in bank service. Then the hypothesis model and conceptual framework will be predicted. The research methodology will then followed to indicate the data collection, sample design and data analysis. Furthermore, a survey questionnaire will be designed for ATM user to obtain respondent data. Then the survey results in sample size of 110 and data will be analyzed in SPSS. The data will then perform in descriptive statistic, reliability analysis, factor analysis and t-test. Discussion will be provided to study and explain the data that how to reach the aim of the report. Lastly, conclusion will be involved with further potential research.

Keywords:

Self-service technology, personal service, consumer satisfaction, consumer commitment, affective commitment, continuance commitment and calculative commitment

List of Abbreviations

SST = Self-Service Technology

PSU = Personal Service Usage

ATM = Automatic Teller Machine

SPSS = Statistical Package for the Social Sciences

PCA = Principal Component Analysis

Chapter 1

Introduction

In this chapter, background of the study will be introduced. The study aim and research objectives will be discussed next. Follow with a report outline and scope of study will be presented. The limitation of research will be shown in the last part.

1.1 Background of the study

Services are very important between a company and consumers. Personal service usage is the most common and traditional style between front-line staff and consumers. Personal service usage is a simple but practical service where the company can provide direct assistance from front-line staff to customer. However, the market is being revolutionized by technological innovations in every Hong Kong industry. Nowadays, this traditional service was replaced by a modern type of service: Self-service technology (SST). Many organizations are increasing the use of technology to provide competitive services in order to attract more customers and stay ahead in their market, especially in bank service. Self-service technology (SST) defines as a technology interface that enables customer to produce and consume services without direct assistance from firm employees (Meuter et al. 2000). SST can certainly reduce labor cost to the service provider where personal service can provide direct service to client. This trend raises some important point about the impact of technology on service quality and customer satisfaction levels. For example: can the company maintain or improve the levels of customer satisfaction in removing employee/customer interface from its front line? There are limited empirical research studies on self service technologies impact on customer’s perceived value on service delivery and its impact on their satisfaction level (Dabholkar 1994). Therefore, further study is required to have better insights on customers’ satisfaction and commitment on self-service technology, especially on the account of increases in technological developments, and constant drive to use newer technologies to gain competitive advantage in the market place.

1.2 Study Aim and Research objectives

The major aim of this report is to determine the consumer satisfaction and commitment through Self-service technology (SST) and personal service usage. A survey will be performed in this report and develop a test model of the affect of SST on consumers’ satisfaction of the service and their commitment to the service provider. This report will also focus on the literature review which relevant to the study and the development of the hypotheses which drive the study. On the other hand, the conceptual framework will then describe and the findings are presented by a deep investigation. After analyze by SPSS, a discussion of the results will then follows in this research. Eventually, further research will concludes the report. This report will contribute the understanding of the consumer satisfaction and commitment of the SST and personal service usage.

1.3 Report Outline

In this Chapter, rationale and background of the study will be stated. Moreover, aims and objectives will be mentioned. The final part will be the outline of the study.

Chapter 2 states a review of relevant literature on SST, personal service usage, consumer satisfaction and commitment. A hypothesis model and the conceptual framework will then be set up.

Chapter 3 will focus on the Methodology of this research project. Research setting will be discussed in the first part. Then data collection will then follow to define how to gather data information for the survey method. Besides, a brief introduction of the questionnaire will also show in this chapter including pre-test and the structure of questionnaire. Lastly, data analysis will be presented how to analyze the obtained data.

Chapter 4 is the important part of the report that will present the result and discussion from the SPSS. Further discussion on the result of some skill: profile of interviewees, Descriptive Statistic, Reliability Analysis, Factor Analysis, Correlations Matrix, Factor Loadings and T-Test.

Chapter 5 will show the conclusion of the report and future studies will also suggested for further researcher.

1.4 Scope of Study

This study aims to determine the consumer satisfaction and commitment through Self-service technology (SST) and personal service usage. Focus is placed on the bank service field which ATM will be an example of Self-service technology (SST) and teller will be an example of personal service usage.

1.5 Limitation of Research

As atmosphere is a silent language, there is no definite answer on which one is better and which one is more attractive. The answers change among different people as different kind of people have different perceptions, like some people love pink color while some do not. Therefore, the figure and data is for reference only.

This study is going to be conducted by survey questionnaire, which may cause some disadvantages as interviewees may be too subjective, the researcher may fail to recognize the richness of the data collected and glean the insights on offer due to the lack of experience.

Chapter 2

Literature Review

This chapter will focus on the literature review which relevant to the study and the development of the hypotheses which drive the study. On the other hand, the conceptual framework will then describe.

2.1 Self-Service Technology

In order to respond the rapid change of the external environment, service provider of most industries has increase the use of technology so as to improve their productivity and competitiveness. Kelley (1989) discuss that the role of technology in service organizations is to reduce costs and eliminate uncertainties apparently. In the service sector, technology has been used to standardize services by reducing the interaction between employee and customer (Quinn, 1996). Some prior studies suggest that the traditional marketplace interaction is being replaced by a market space transaction (Rayport and Sviokla 1994, 1995). That means technology service weed out personal service through competition. Also, there are numbers of customers will to interact with technology to create service outcomes instead of interacting with a service firm employee.

SST is an example of market space transactions in which no interpersonal contact is required between buyer and seller (Matthew L. Meuter, Amy L. Ostrom, Robert I. Roundtree, & Mary Jo Bitner ,2000). Previous studies have demonstrated that consumer benefits of using SST include convenience (Meuter et al., 2000; Reichheld and schefter, 2000; szymanski and hise, 2000), save time and money (Meuter et al 2000), avoiding interpersonal interaction (Dabholkar, 1996; Meuter et al., 2000) and being in control (Dabholkar, 1996; Zeithaml et al., 2000).

SST is a technological interface that enables customers to produce a service independent of direct service employee involvement. There are many kinds of examples about SST which are increasing across a range of service, from traditional high contact service to low contact service, they are: booking tickets to watch a film through internet rather than going into the ticket counter; checking out of a hotel via the automated facility on the TV set in your hotel room rather than going down to the reception desk and queue a long time to interact with hotel staff; fuelling the car in the station by VISA card rather than asking staff to inject fuel.

Although there are many examples of SST listed above, the most classic example of SST is Automatic Teller Machine (ATM). ATM is an electronic computerized telecommunications device which allows bank’s customers to make cash withdrawals and check their account balances at any time without the need for a human teller. It provides the clients of a financial institution with access to financial transactions in a public space without the need for a cashier, human clerk or bank teller. The idea of this kind self-service in retail banking developed through independent and simultaneous efforts in Japan, Sweden, the United States and the United Kingdom. Many ATMs also allow people to deposit cash or cheques, transfer money between their bank accounts or even buy postage stamps. The general features and further information of Automatic Teller Machine will be posted in the Appendix.

2.2 Personal Service Usage (e.g. Teller)

In recent years, people rely on gradually matured SST. Our daily life has become more and more convenient. But back to basic, SST was converted from the original and traditional kind of service: personal service usage. Personal service usage is a kind of service that has direct interaction between the front-staff of organization service provider and the consumer. Consumer can receive direct service from the staff without any technology. Moreover, consumer can raise any question at that moment and can have prompt respond/answer from the organization. There are many example of personal service usage: ticketing counter, people can reach the ticketing counter to buy the appropriate ticket and watch film by asking the front-desk staff; reception desk, people can go to the reception desk to apply check-out service in a hotel; however, bank teller is the most appropriate example of personal service usage in this report.

A bank teller is an employee of a bank who deals directly with most customers. These employees are known as a cashier in some places. Tellers are considered as a “front line” in the banking business. This is because they are the first people that customers reach at the bank and are also the people most likely to detect and stop cheat transactions in order to prevent losses at a bank (imitative currency and checks, identity theft, confidence tricks, etc.). The position also requires tellers to be friendly and interact with the customers, providing them with information about customers’ accounts and bank services.

2.3 Consumer Satisfaction

Consumer satisfaction can be defined as high quality. As Fournier and Mick (1999) state, consumer satisfaction is a fundamental marketing concept. High consumer satisfaction ratings are widely believed to be the best indicator of a company’s future profits (Kotler 1991). Consumer satisfaction can also defined as an evaluation based on the consumer’s experiences with a service provider over the period of time (Garbarino and Johnson1999). Consumer satisfaction usually use as a criterion for interpret product or service performance and it has been linked to overall firm performance. It is well established that consumer satisfaction can affect customer retention and profitability (Anderson and Fornell 1994; Mano and Oliver 1993;Oliver 1993, 1997; Price, Arnould, and Tierney 1995; Reichheld and Sasser 1990). Consumer satisfaction is a main concept in modem marketing thought and practice. It is always as a primary objective to the managers. It also serves as a very important feedback mechanism for each organization. Moreover, consumer satisfaction may be affected by the interaction with technology or with the companies’ staff. The marketing concept emphasizes delivering satisfaction (not just products) to consumers and obtaining profits in return. As a result, overall quality of life is expected to be better.

In this report, consumer satisfaction is conceptualized as overall satisfaction and is defined as an affective state or overall emotional reaction to a service experience (Amanda 2006). Assessments of overall satisfaction will be updated after interaction between consumer and the staff from the organization. It is important to identify the key drivers of this satisfaction assessment as they enable the managers to find out the relative importance of different components of the service (Garbarino and Johnson 1999). Thus, the organization can focus on those which are the most importance to consumers by identifying these components with the objective to improve overall satisfaction.

2.4 Consumer Commitment

Consumer commitment plays a central role in relationships. As Scanzoni (1979) stated “commitment is the most advanced phase of partners’ interdependence”. Consumer commitment can be defined as an essential ingredient for successful long-term relationships which is similar to trust. Commitment is also recognized as “an euduring desire to maintain a valued relationship” (Moorman, Zaltman, and Deshpande 1992). In previous study, it is agreed that mutual commitment among partners in business relationships produces fateful benefits for companies. Organization can improved product developments, increase margins and market shares, and gain profit. Distributors gain deeper market penetration and higher customer satisfaction. Commitment is a central concept in the relationship marketing paradigm (Dwyer et al., 1987). The conceptualization of commitment stems from industrial/organizational psychology and has been viewed as an intention to continue a course of action or activity (Fehr, 1988). Commitment in the buyer-seller relationship literature is defined as “an implicit or explicit pledge of relational continuity between exchange partners” (Dwyer et al., 1987). It can also define as psychological attachment to an organization (Gruen et al. 2000). Commitment is seen as a sentiment that is critically important in the development of long-term channel relationships or as a favorable affective reaction (Kumar et al., 1995). Therefore, commitment is a psychological sentiment of the mind through which an attitude concerning continuation of a relationship with a business partner is formed (Martin Wetzels 1998). Commitment is just like a force that binds an individual to a course of action of relevance to one or more targets (Meyer and Herscovitch 2001). And this force refers to different psychological state that reflect the nature of the individual’s relationship with the target of interest and that have implications for the decision to continue that relationship (Meyer and Allen 1997). This psychological state can be classified as three components: Affective commitment, Continuance commitment and Calculative commitment.

2.41 Affective Commitment

According to Martin Wetzels (1998), commitment is an affective state of mind an individual or partner has toward a relationship with another individual or partner. This kind of commitment is called affective commitment. Affective commitment is brought about by a person sharing, identifying with, or internalizing the values of the organization (Morgan and Hunt, 1994). Affective commitment is based on a sense of liking and emotional attachment to the partnership. (i.e. employees stay with the organization just because they want to). Affective commitment is a desire-based attachment to the organization. Moreover, it is the most effective for developing and maintaining mutually beneficial relationships between partners (Kumar et al., 1994). Affective commitment has strong positive influences on: performance; desire to stay in a relationship; intention to stay in a relationship; and willingness to invest in a relationship. Besides that, it was found that it has strong negative influences on: opportunistic behavior; and development of alternatives for a relationship. It just like a psychological force that is affective and binding the consumer to the service provider out of desire in nature.

2.42 Continuance Commitment

Continuance commitment defines as a constraint-based force binding the consumer to the organization out of need. It means that consumer stay with a service provider because they feel they have to. It reflects a sense of being “locked in” to the service provider (Meyer and Herscovitch 2001). Continuance commitment is also similar to “constraint-based” relationship in which consumer believe they cannot end a relationship because of economics, social or psychological costs. On the other hand, continuance commitment is a cost-based attachment where an employee feels he or she has to stay with the organization (i.e. employees remain with the organization because they need to). It just like a psychological force that is normative and binding the consumer to the service provider out of perceived obligation in nature. Continuance commitment is associated with the perceiver cost associated with discontinuing a course of action (Meyer and Herscovitch 2001). It is also associated with the perception that there is a lack of alternatives available (Meyer and Allen 1997). For example, if the cost of switching are high or if the consumer perceives that there are few alternative providers available, then this continuance force (constraint-based) occurs and binding the consumer to the service provider out of need. As the switching cost like” time, effort or money increase, consumers are more likely to perceive that they are “locker in” to their service providers, which in turn results in them being less likely to switch service providers. Alternately, to the extent that other service providers are perceived to be attractive, consumers are less likely to feel “locker in” with their existing service provider, which increase the possibility of switching.

2.43 Calculative commitment

Calculative commitment was negatively influenced by trust (Geyskens and Steenkamp, 1995). Calculative Commitment is conceptualized as the amount of effort put in the process of calculating costs and benefits of a relationship. It seems that if partners in a relationship trust each other more they are more emotionally involved and less consciously weighing the benefits against the costs of that relationship. The other view sees commitment as being more behavioral than affective. This form is referred to as calculative commitment and stems from a cognitive evaluation of the instrumental worth of a continued relationship with the organization. All gains and losses, plusses and minuses or rewards and punishments are added up (Morgan and Hunt, 1994). Geyskens et al. (1996, p. 304) define commitment as the perceived need to “maintain a relationship given the significant anticipated termination or switching costs associated with leaving”. Calculative commitment is an obligation-based attachment to the organization (i.e. employees stay with the organization just because they ought to – the “right” thing to do). It just likes a psychological force that is continuance and binding the consumer to the service provider out of need in nature.

2.5 Conceptual Framework

It is necessary to develop a conceptual framework in order to study relational commitment and its relationship with various antecedents and consequences. Consumer satisfaction is a fundamental marketing concept (Fournier and Mick 1999). As mentioned in previous chapter, Consumer satisfaction is conceptualized in this study as overall satisfaction. As well as assessing consumer satisfaction, it is essential to identify the key drivers of this satisfaction assessment as they enable managers to ascertain the relative importance of different components of the service (Garbarino and Johnson 1999). These components are being identified and managers are able to focus on those primary importances to consumers, so as to improve consumer satisfaction. Furthermore, some specific service problems could be able to pinpoint by focusing on those specific components. In an example, consumer may satisfy with three or four attributes but dissatisfied with the performance of one or two attributes. An assessment of consumer satisfaction cannot detect in this situation. It has important effect as a diagnostic tool for the organization.

The essential of service attributed has been point out in previous study by a numbers of authors. Moreover, the relationship of these attributes between consumer satisfactions has been found in previous studies (Voss, G., Parasuraman, A. and Grewal, D. 1998). The service organization (service provider) could understand more on the affective and cognitive assessment of the service encounter by investigating the consumer satisfaction and service attributes.

“Trust may be perceived as the most important attribute for a mechanic” or “empathy may be a key attribute for a doctor to exhibit” by Amanda Beatson 2006. Different industries may have different attributes which are more important to that industry. In this study, attributes may also differ between two service-delivery mode (SST and personal service usage). That can explain different elements become more important when presented with different service-delivery modes. The outcome of self-service technology may be more important (e.g. the speed or convenience of the SST) whereas the manner in which a service is delivered from the staff may be more important when using personal service (e.g. the friendliness of staff). As a result, the service-delivery modes are described as separate constructs in this conceptual framework. In current report, both attributes for SST and personal service usage are very important. There are many examples of these attributes: attributes for SST include self-service save time, self-service convenience, self-service fast respond, etc; attributes for personal service include: professional service, prompt service, reliable service, etc.

2.6 Hypotheses

Same to previous study by (Meuter 2000), this report is hypothesized that direct relationships exist from the attributes of the two service-delivery modes (self-service technology and personal service usage) to the overall consumer satisfaction. It can be assumed those consumers are more likely to be satisfied overall with the complete service experience if they rate the performance of the various components of the service positively. Therefore, it shows that an overall evaluative judgment is made based on the individual elements that contribute toward this overall judgment. For example: based on the important components of the SST which the consumer feel, if the consumer is satisfied with the performance of the SST, they are presumable to be satisfied with the overall service as well. Hence, when performance on SST attributes have high rates, consumers are more likely to be satisfied with the total service. As a result, the first hypothesis is suggested to be:

H1: Self-Service Technology (ATM) attribute will have positive impact on consumer satisfaction

Similarly, based on the important components of the personal service which the consumer feel, if the consumer is satisfied with the performance of the personal service, they are presumable to be satisfied with the overall service as well. Hence, when performance on personal service attributes have high rates, consumers are more likely to be satisfied with the total service. Therefore, the second hypothesis is suggested to be:

H2: Personal Service Usage (Teller) attribute will have positive impact on consumer satisfaction

As Garbarino and Johnson (1999) mentioned that consumer satisfaction can be linked with consumer commitment. In the previous chapter of literature review, it is proposed that consumer commitment in this study is conceptualized as a multidimensional construct with three dimensions; affective commitment, continuance commitment and calculative commitment. Affective commitment is conceptualized as a consumer desire to continue the relationship with an organization because of a positive attitude toward the organization. Continuance commitment can be defined as the longevity of consumer’s commitment to the organization, or expectations of continuity (Garbarino and Johnson 1999). Calculative commitment is that the consumer stays with or leave the organization depends on the existence of perceived costs which can be economic or psychological in nature (Morgan and Hunt 1994).

It is reasonable to assume that there are important relationship in between satisfaction and all three dimensions of commitment. For example, if consumers satisfy with the overall service, it is perceived that they are likely to feel a positive attitude toward the organization and want to continue to return to the organization because they like to. This suggests that consumer satisfaction leads to affective commitment.

H3: Consumer satisfaction will have positive impact on Affective Commitment

Similarly, if consumers satisfy with the overall service, it is perceived that they will want to go back to that same service provider when they want the service again. This suggests that consumer satisfaction leads to continuance commitment.

H4: Consumer satisfaction will have positive impact on Continuance Commitment

Likewise, if consumers satisfy with the overall service, it is perceived that they will not leave that service provider as the costs to join new organization or elsewhere maybe too high and need to spend time and effort to find another one. This suggests that consumer satisfaction leads to calculative commitment.

H5: Consumer satisfaction will have positive impact on Calculative Commitment

As explored in this study, it is hypothesized that a direct relationship exists between service attributes and those three commitment (affective, continuance and calculative). In the same way that SST attribute and personal service attribute were proved to relate to consumer satisfaction, and so it can also be assumed that they will relate to the commitment dimensions. For example, if consumers have a positive feedback with the SST attributes or the personal service attributes that are important to them, consumers will have more positive feedback toward the organization and wish to return. Thus SST attributes and personal service attributes are suggested and contributed to affective commitment. Likewise, if consumers have a positive feedback with the self-service technology attributes or the personal service attributes that are important to them, consumers are likely to return to the service provider in future. This shows that self-service technology attributes and personal service attributes are suggested and contributed to continuance commitment. Equally, if consumers have a positive feedback with the SST attributes or the personal service attributes that are important to them, consumers may not wish to leave the relationship with the organization as the cost is too high and they may feel they have invested in that relationship. Therefore, it shows that SST attributes and personal service attributes are suggested and contributed to calculative commitment. Finally, it is hypothesized that:

H6: Self-Service Technology (ATM) attribute will have positive impact on Affective Commitment

H7: Self-Service Technology (ATM) attribute will have positive impact on Continuance Commitment

H8: Self-Service Technology (ATM) attribute will have positive impact on Calculative Commitment

H9: Personal Service Usage (Teller) attribute will have positive impact on Affective Commitment

H10: Personal Service Usage (Teller) attribute will have positive impact on Continuance Commitment

H11: Personal Service Usage (Teller) attribute will have positive impact on Calculative Commitment

Chapter 3

Research Methodology

In this chapter, it will focus on the Methodology of this research project. Research setting will be discussed in the first part. Then data collection will then follow to define how to gather data information for the survey method. Besides, a brief introduction of the questionnaire will also show in this chapter including pre-test and the structure of questionnaire. Lastly, data analysis will be presented how to analyze the obtained data.

3.1 Introduction

The research method of this paper will be introduced in this chapter. The background of the research method and advantages will be discussed. The analyzing method will also be explained in the last part of this chapter.

Same to the title of this report, the target of this study is to determine consumer satisfaction and commitment through SST and personal service usage. In order to fulfill the target of this study, ATM has been selected as the subject of this case study.

In this report, the first step that is to determine the research method and survey has been chosen in this study. On the other hand, SPSS is chosen to help analysis the survey result and take further study on the data. Secondly, it is essential to set up the Conceptual Framework and Hypotheses which related to the literature review in the previous chapter. Thirdly, research setting and data collection will be study and then follow up by develop the questionnaire with the response scale and data analysis.

3.2 Research Setting

With the literature in the previous chapter, the aim of this report is to determine consumer satisfaction and commitment through SST and personal service usage. There is a trend especially in bank service that, front-line staff of traditional teller is being replaced by SST i.e. ATM. However, some service in some organization have found that is difficult to introduce SST to their service because of lack of human interaction, while still keeping personal service to maintain service quality.

The research setting for this report was all people in Hong Kong. This report is looking for a wide range of respondents in the survey such as bankers, students, workers, etc… Moreover, this report will focus on the consumer satisfaction and commitment of ATM and teller. There are many reasons that ATM and teller was selected in this report. Firstly, ATM is the most classic example of SST as mentioned in previous chapter. While ATM is the classic example of SST, and it is belong to the bank service, so teller is the appropriate example of personal service in this report. Secondly, ATM was traditionally classified as a high contact services with a high degree of personalization. Beside that, teller is particularly important in bank service for determining consumer satisfaction and consumer commitment.

Examples of SST used in bank service include: automatic cash withdrawal, automatic transfer, automatic credit card repayment, automatic bill payment, automatic close account, automatic deposit etc. With the above reasons, ATM and teller are an appropriate setting for this report study. It must be acknowledged. However, the focus in this report is SST used in bank service stay versus personal service in traditional service with front-line staff.

3.3 Data Collection

As mentioned, the research setting for this report was all people in Hong Kong. This report is looking for a wide range of respondents in the survey such as bankers, students, workers, etc…It is because SST is still in a beginning stage that is not accepted by all people, so a simple random sample of the population would likely result in this report. Questionnaire is widely accepted as a method using in a survey report. A questionnaire will be set up and distribute to friends, class student and over the internet by using “my3q” which is an online survey system. Moreover, it will also be distributed in some bank with Express Banking Center which contain ATM and teller counter. Certainly, a higher population of the survey would be better for analyzing this report. However this report will only expect around one hundred in population sample size due to the limited time and resources. Although the sample may not be representative of the best result, it is enough to show the needs of this study. In this research, emails and online message were executed to the target in order to achieve the statistical data.

3.4 Questionnaire

A questionnaire is a research instrument consisting of a series of questions and other prompts for the purpose of gathering information from respondents. Although they are often designed for statistical analysis of the responses, this is not always the case. The questionnaire was invented by Sir Francis Galton (From Wikipedia).

Cheap is one of the advantages of questionnaires over some other types of surveys. It does not require as much effort from the questioner as verbal or telephone surveys, and often have standardized answers that make it simple to compile data. However, such standardized answers may frustrate a user which is the disadvantage of questionnaire. Questionnaires are also sharply limited by the fact that respondents must be able to read the questions and respond to them. Thus, for some demographic groups conducting a survey by questionnaire may not be practical.

A survey using a questionnaire is widely accepted as a method for a research study. To verify the hypothesis model, a field study technique was applied in the questionnaires. The questionnaire contains the question regarding the trust and subjective norm, which were refer to prior studies and relevant theories. Respondents were asked to give their preference on a five points scale which is similar to Likert scale. This scale type has always been used in questionnaires and survey researches, since it can be used to measure the positive or negative response to the statement. The scale is ranging from strongly disagree (1); disagree (2); neutral (3); agree (4); strongly agree (5) in order to evaluate the subject’s agreement with each item. The questionnaire was distributed to users and potential users of the SST-enabled equipments in convenience stores.

3.41 Pre-Test

In order to enhance the competitiveness of the questionnaire, pre-test had been done. The pre-test was conducted on several classmates to get the feedback about the clarity of items. The final questionnaire contained 35 items and it was decided to give the questions in English as well as the local language Chinese. It is recommended that to split up to four income range (<$10000, $10001-$20000, $20001-$50000, $50000+) in question 34. Lastly, some minor mistakes like spellings and wordings had also been amended after the pre-tests.

3.42 Structure of the questionnaire

The structure of the questionnaire is based on some prior studies and research.

After considering each item in detail, and necessary changes were made by simplifying, removing and replacing some of them, there are 35 items has been set up in the questionnaire. These questions were reworded and rephrased to suit local working practices and culture. The questionnaire have separate into 5 major sections: SST (ATM), Customer Service (Teller), Consumer Commitment, Consumer Satisfaction and Statistical Information.

Section A – Self-Service Technology (ATM)

In the first section, it is designed to take research on the level of performance in using SST (here ATMs) of a bank including 7 question as below. (Assume respondent existing bank)

– In using bank service by ATM, I found it is easy to use.

– In using bank service by ATM, I found it is easy to control.

– In using bank service by ATM, I found the navigation is clear

– In using bank service by ATM, I found the interface understandable.

– In using bank service by ATM, it provides a prompt respond.

– In using bank service by ATM, it provides a prompt transaction.

– I can find out ATM easily as it always located nearby

Section B – Customer Service (Teller)

In the second section, it is designed to take research on the level of performance in Customer Service (Teller) of a bank including 7 questions as below. (Assume respondent existing bank)

– In using bank service by teller, it provides professional service.

– In using bank service by teller, it provides effective service.

– In using bank service by teller, I can get accurate transaction without any mistakes.

– In using bank service by teller, I feel save and confident to use.

– In using bank service by teller, I can get responsive interaction.

– In using bank service by teller, it does not require a lot of my mental effort.

– In using bank service by teller, I feel warm.

Section C – Consumer Commitment

In the third section, it is designed to study in why customer use the bank service of the existing bank by several consumer commitment ‘Affective Commitment’, ‘Continuance Commitment’, and ‘Calculative Commitment’ including 11 questions as below. (Assume respondent existing bank)

– I feel this bank is reliable.

– I feel a strong sense of “belonging” to this bank.

– I feel this bank is trustworthy.

– I feel helpful when you have doubt or difficulties inside or outside a bank.

– The bank employees are polite and with positive attitude to the customer.

– I have the intention to use the service of this bank in general again in future.

– I have the intention to keep a long-term relationship with this bank.

– I feel that would be very hard for me to leave this bank right now, even if I wanted t

– I feel that is too inconvenience if I decide to leave this bank.

– I feel that it’s not worth in effort if I change the bank because of many concernments.

– I feel that I have too few options to consider leaving this bank.

Section D – Consumer Satisfaction

In the fourth section, it is designed to take research on the level of overall consumer satisfaction of the bank service including 6 questions. (Assume respondent existing bank)

– I am satisfied with this bank service.

– I am pleased with this bank service.

– I am contented with this bank service.

– I am delighted with this bank service.

– I am happy in using this bank service.

– I feel comfortable in using this bank service.

Section E – Statistical Information

In the last section, it is designed to obtain respondent demographic information for statistical analysis.

– Age

– Gender

– Income range

– Education level

3.5 Data Analysis

After collecting all the results from the questionnaire, SPSS (originally, Statistical Package for the Social Sciences) will be employed to analysis the data. SPSS was first released in 1968 by Norman H. Nie and C. Hadlai Hullis. It is the most widely used programs for statistical analysis in social science in the world and is used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations and others.

After screening the data, only 110 data were sorted out as valid data. In order to investigate the hypothesis in chapter 2, Mean, Standard Deviation, Pie Chart, Bar Chart, Reliablity Analysis and Factor Analysis were being used. The result and the discussion will be shown in the next Chapter.

Chapter 4

Result and Discussion

This chapter is the important part of the report that will present the result and discussion from the SPSS. Further discussion on the result of some skill: profile of interviewees, Descriptive Statistic, Reliability Analysis, Factor Analysis, Correlations Matrix, Factor Loadings and T-Test.

4.1 Profile of interviewees

The demographic questions had been asked in the last part of the questionnaire. It aims to give out some background information about the respondents to the readers. There are 110 respondents in the research questionnaire. First of all, over 51% of the respondents are female and over 48% are male. Details are shown in Fig X. Secondly, more than 74% of the interviewees are aged within 18-30 which is the largest scale. 18.31% of them are in the age group of 31-50 and there are only few percentages of the interviewees are in the age group of <18 and 51-65, Details are shown in Fig X. Besides, nearly 50% of them are in undergraduate level in the educational background. Follow by nearly 40% of them are in the group of Diploma/Higher Diploma. Less than 10% of them are master or above and secondary level. Only one of the interviewee are belong to below secondary level. Details are shown in Fig X. On the other hand, about 60% of them are earning $10001-$20000 per month while over 20% are earning below $10000. Less than 20% of them are earning $20001-$50000 income. Lastly, only 1% of them earn over $50000 per month. Details are shown in Fig X.

4.2 Descriptive Statistic

Table 1: Descriptive Statistic for the research

Descriptive statistics are used to describe the main features of a collection of data in quantitative terms where it aims to quantitatively summarize a data set. The mean is the sum of the observations divided by the number of observations. It is often quoted along with the standard deviation: the mean describes the central location of the data, and the standard deviation describes the spread. The sample size of the data is 111 which including 6 variable of SST Attribute, Personal Service usage Attribute, Affective Commitment, Continuance Commitment, Calculative Commitment and Consumer Satisfaction.

Based on the Table X, The mean and standard deviation for SST Attribute is 26.38 and 4.34 respectively. Since there are 7 items in SST Attribute, the estimate mean of each item is around 3.76. Compare with the five-point scale of the questionnaire, it shows that respondent are feel nearly “Agree” with the questions in SST Attribute. Moreover, the standard deviation is quite in a low standard that indicates the data points tend to be very close to the mean.

For Personal Service usage Attribute, the mean and standard deviation is 24.89 and 4.34 respectively. Since there are 7 items in Personal Service usage Attribute, the estimate mean of each item is around 3.55. Compare with the five-point scale of the questionnaire, it shows that the respondent are feel between “Neutral” and “Agree” with the questions in Personal Service usage Attribute. Moreover, the standard deviation is quite in a low standard that indicates the data points tend to be very close to the mean.

Regarding to Affective Commitment, the mean and standard deviation is 21.91 and 3.94 respectively. Since there are 5 items in Affective Commitment, the estimate mean of each item is around 4.382. Compare with the five-point scale of the questionnaire, it shows that the respondent are feel nearly “Strongly Agree” with the questions in Affective Commitment. Moreover, the standard deviation is quite in a low standard that indicates the data points tend to be very close to the mean.

Over Continuance Commitment, the mean and standard deviation is 7.75 and 1.24 respectively. Since there are only 2 items in Continuance Commitment, the estimate mean of each item is around 3.875. Compare with the five-point scale of the questionnaire, it shows that the respondent are feel nearly “Agree” with the questions in Continuance Commitment. Moreover, the standard deviation is a very low standard that indicates the data points tend to be very close to the mean.

Concerning Calculative Commitment, the mean and standard deviation is 13.14 and 3.10 respectively. Since there are 4 items in Calculative Commitment, the estimate mean of each item is around 3.285. Compare with the five-point scale of the questionnaire, it shows that the respondent are feel more than “Neutral” with the questions in Calculative Commitment. Moreover, the standard deviation is quite in a low standard that indicates the data points tend to be very close to the mean.

For Consumer Satisfaction, the mean and standard deviation is 21.77 and 3.27 respectively. Since there are 6 items in Consumer Satisfaction, the estimate mean of each item is around 3.62. Compare with the five-point scale of the questionnaire, it shows that the respondent are feel nearly “Agree” with the questions in Consumer Satisfaction. Moreover, the standard deviation is quite in a low standard that indicates the data points tend to be very close to the mean.

4.3 Reliability Analysis

A questionnaire must not only be valid, but also reliable. Reliability is basically the ability of the questionnaire to produce the same results under the same conditions. To be reliable the questionnaire must first be valid.

Reliability of an instrument is the extent to which it yields the same result on repeated trials. Out of various methods used for measuring reliability, the internal consistency method is considered to be the most effective method, especially in field studies. The internal-scale reliability (Cronbach Alpha) of the scale was estimated as 0.95 which is above the acceptable limit of 0.6 (Hair et al., 1995).

One of the important things for questionnaire reliability is: “Scale if item deleted”. This option provides a value of Cronback’s alpha for each item on the scale. It indicate what value of alpha would be if that item were deleted. If the questionnaire is reliable then it is not expect any item to greatly affect the overall reliability. In other words, no item should cause a substantial decrease in alpha. If it does then it has serious cause for concern and that item should consider to drop from the questionnaire. As per previous research, 0.6 is a good value of alpha, therefore, all value of alpha “if item deleted” should be around 0.6 or higher.

Table 2: Reliability Analysis for SST Attribute

In Table X, it shows the results of the basic reliability analysis for SST Attribute. The construct reliability of these questions in SST Attribute is 0.8445 which is above the acceptable limit of 0.6 by Hair et al., 1995, therefore these question is reliable to the research.

The values in the column labeled “Corrected Item-Total Correlation” are the correlations between each item and the total score from the questionnaire. In a reliable scale, all items should correlate with the total. So, the analysis is looking for items that don’t correlate with the overall score from the scale: if any of these values are less than about 0.3 then it means that a particular item does not correlate very well with the scale overall. Items with low correlations may have to be dropped. In Table X, Q1-Q6 are having acceptable value that greater than 0.3. However, the value of “Corrected Item-Total Correlation” between Q7 and Q1 to Q6 is lower than 0.3 which is Q7-Q1: 0.1648, Q7-Q2: 0.2758, Q7-Q3: 0.1639, Q7-Q4: 0.2918, Q7-Q5: 0.2039, Q7-Q6: 0.2899). That means Q7 is having low correlations with Q1-Q6, and therefore it might have to be dropped.

The values in the column labeled “Alpha if Item is Deleted” are the values of the overall alpha if that item isn’t include in the calculation. As such, it reflects the change in Cronbach’s alpha that would be seen if a particular item were deleted. The overall alpha is 0.8445, and so all values in this column should be around that same value. If the values of alpha greater that the overall alpha, that means the deletion of this item increase Cronbach’s alpha and the deletion of this item improves reliability. In Table X, the worst offender is Q7: deleting this question would increase the alpha from 0.8445 to 0.8744.

Table 3: Reliability Analysis for Personal Service Usage Attribute

In Table X, it shows the results of the basic reliability analysis for Personal Service Usage Attribute. The construct reliability of these questions in Personal Service Usage Attribute is 0.8018 which is above the acceptable limit of 0.6 by Hair et al., 1995, therefore these question is reliable to the research. On the other side, Q9-Q12 & Q14 are having acceptable value of “Corrected Item-Total Correlation” that greater than 0.3. However, the value between Q13-Q10, Q13-Q11, Q13-Q12 is lower than 0.3 which is 0.1873, 0.2387, 0.1753 respectively. That means Q13 is having low correlations with Q10, Q11 and Q12. Lastly, the worst offender is Q13 which having 0.8397 values of “Alpha if Item is Deleted” where the overall alpha is 0.8018. That means Q13 are recommended to be deleted and that would increase the alpha from 0.8018 to 0.8397.

Table 4: Reliability Analysis for Affective Commitment

In Table X, it shows the results of the basic reliability analysis for Affective Commitment. The construct reliability of these questions in Affective Commitment is 0.8991 which is above the acceptable limit of 0.6 by Hair et al., 1995, therefore these question is reliable to the research. Besides that, all value of “Corrected Item-Total Correlation” are greater than the acceptable range of 0.3. Therefore, it shows that all questions (Q15-Q19) for Affective Commitment in this research is inter-correlated. Also, since the overall alpha is 0.8991, the worst offender is Q16 which having 0.8965 values of “Alpha if Item is Deleted” where it is still lower than the overall alpha. That means none of the items in Affective Commitment could be deleted to obtain a higher reliability.

Table 5: Reliability Analysis for Continuance Commitment

In Table X, it shows the results of the basic reliability analysis for Continuance Commitment. The construct reliability of these questions in Continuance Commitment is 0.7855 which is above the acceptable limit of 0.6 by Hair et al., 1995, therefore these question is reliable to the research. The “Corrected Item-Total Correlation” value for Q20 and Q21 is 0.656 where is greater than the acceptable range of 0.3. Therefore, it shows that Q21 and Q21 for Continuance Commitment in this research are inter-correlated. However there are too less items for Continuance Commitment to generate the values of “Alpha if Item is Deleted”.

Table 6: Reliability Analysis for Calculative Commitment

In Table X, it shows the results of the basic reliability analysis for Calculative Commitment. The construct reliability of these questions in Calculative Commitment is 0.8221 which is above the acceptable limit of 0.6 by Hair et al., 1995, therefore these question is reliable to the research. On the other hand, all value of “Corrected Item-Total Correlation” for Calculative Commitment is greater than the acceptable range of 0.3. Therefore, it shows that all questions (Q15-Q19) in this research is inter-correlated. Lastly, since the overall alpha is 0.8221, the worst offender is Q25 which having 0.8630 values of “Alpha if Item is Deleted”. That means Q25 are recommended to be deleted and that would increase the alpha from 0.8221 to 0.8630.

Table 7: Reliability Analysis for Consumer Satisfaction

In Table X, it shows the results of the basic reliability analysis for Consumer Satisfaction. The construct reliability of these questions in Consumer Satisfaction obtain a high value of 0.9434 which is also above the acceptable limit of 0.6 by Hair et al., 1995, therefore these question is very reliable to the research. Besides, all value of “Corrected Item-Total Correlation” for Consumer Satisfaction is greater than the acceptable range of 0.3. Therefore, it shows that all questions (Q26-Q31) for Consumer Satisfaction in this research is inter-correlated. Since the overall alpha is 0.9434, the worst offender is Q26 which having 0.9410 values of “Alpha if Item is Deleted” where it is still lower than the overall alpha. That means none of the items in Consumer Satisfaction could be deleted to obtain a higher reliability.

4.4 Factor Analysis

Construct

No. of items

Factor Loadings

Inter-item Correlations

Lowest

Highest

Lowest

Highest

Self-Service Technology Attributes

7

0.559

0.857

0.073

0.532

Personal Service Usage Attributes

7

0.490

0.829

0.010

0.688

Affective Commitment

5

0.604

0.804

0.075

0.694

Continuance Commitment

2

0.828

0.828

0.205

0.667

Calculative Commitment

4

0.377

0.819

0.010

0.205

Consumer Satisfaction

6

0.691

0.851

0.027

0.694

Table 8: Result table of Factor Analysis

Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. Factor analysis is often used in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables. Factor analysis can also be used to generate hypotheses regarding causal mechanisms or to screen variables for subsequent analysis. It is related to principal component analysis (PCA) but not identical because PCA performs a variance-maximizing rotation of the variable space, it takes into account all variability in the variables. In contrast, factor analysis estimates how much of the variability is due to common factors (“communality”).

First of all, calculating a correlation matrix of all variables of interest is the starting point for factor analysis. This starting point provides some initial clues as to how factor analysis works. It is clear that factor analysis is derived from some combinations of inter-correlation. If the correlation coefficient between two items is positively and greater than 0.3 with correlation significant at the 0.01 level (2-tailed), this shows that two items are highly inter-correlated with positive impact. In other words, if there are no significant correlations between the items, then this means that they are unrelated and that it would not be worthwhile to go on to conduct further factor analysis.

Secondly, the portion of a variable’s variance that is associated with variance on the common factors is an important question in a factor analysis (i.e., the proportion of the variable’s variance that is explained by the common factors). This amount is called communality or the common variance and is calculated by

(h2i is the communality of variable i)

Consequently, it is equal to the sum of the squared factor loadings for all factors for a given variable (row) is the variance in that variable accounted for by all the factors

The communality is usually a number less than 1. In the “Initial Statistics” matrix, all the communalities are 1. This is because all factors are included in this solution. When all factors are included in the solution, all of the variance of each variable is accounted for, and there is no need for a unique factor in the model. The proportion of variance accounted for by the common factors, or the communality of a variable, is therefore 1 for all the variables. If the communality exceeds 1.0, there is a spurious solution, which may reflect too small a sample or the researcher has too many or too few factors.

4.41 Correlations Matrix

Table 9: Inter-correlations of the variable

It is shown that the above correlation matrix that the largest correlation coefficient occurs between Consumer Satisfaction and Affective Commitment (i.e. 0.694) and correlation is significant at the 0.01 level (2-tailed). This shows that Consumer Satisfaction and Affective Commitment are highly inter-correlated with positive impact. Therefore, it highly reflects the hypothesis “H3: Consumer satisfaction will have positive impact on Affective Commitment” which have been predicted in Chapter 2. The second largest correlation coefficient is 0.688 and correlation is significant at the 0.01 level (2-tailed), which occurs between Personal Service Usage Attribute and Affective Commitment. This shows that Personal Service Usage Attribute and Affective Commitment are also highly inter-correlated with positive impact. It highly reflects the hypothesis “H9: Personal Service Usage (Teller) attribute will have positive impact on Affective Commitment”. For the Consumer Satisfaction, the correlation coefficient of both SST Attribute and Personal Service Usage Attribute is around 0.53 which is both acceptable and correlation is significant at the 0.01 level (2-tailed). This result can prove the hypothesis “H1: Self-Service Technology (ATM) attribute will have positive impact on consumer satisfaction” and “H2: Personal Service Usage (Teller) attribute will have positive impact on consumer satisfaction” where SST Attribute and Personal Service Usage Attribute is also correlated with Consumer Satisfaction. Similarly, Continuance Commitment has a good value of correlation coefficient with Consumer Satisfaction (i.e. 0.614) and correlation is significant at the 0.01 level (2-tailed). Therefore, it verify that hypothesis “H4: Consumer satisfaction will have positive impact on Continuance Commitment” where Continuance Commitment is inter-correlate with Consumer Satisfaction. Besides, SST Attribute obtain an acceptable value of correlation coefficient with both Affective Commitment and Continuance Commitment (i.e. 0.386 and 0.426 respectively) and correlation is significant at the 0.01 level (2-tailed). So this justifies the hypothesis “H6: Self-Service Technology (ATM) attribute will have positive impact on Affective Commitment” and “H7: Self-Service Technology (ATM) attribute will have positive impact on Continuance Commitment” where SST Attribute is inter-correlated with both Affective Commitment and Continuance Commitment respectively. On the other hand, Personal Service Usage Attribute occur a good value of correlation coefficient with Continuance Commitment at 0.618 and correlation is significant at the 0.01 level (2-tailed). This can support that Personal Service Usage Attribute is correlate with Continuance Commitment and prove the hypothesis “H10: Personal Service Usage (Teller) attribute will have positive impact on Continuance Commitment”.

However, not all correlation coefficient are acceptable. The result for Calculative Commitment has obtain some low correlation coefficient value which is below r=0.1 and unacceptable in between SST Attribute (r=0.073), Personal Service Usage Attribute (0.01) and Consumer Satisfaction (0.027). This result cannot prove the hypothesis “H5: Consumer satisfaction will have positive impact on Calculative Commitment”, “H8: Self-Service Technology (ATM) attribute will have positive impact on Calculative” and “H11: Personal Service Usage (Teller) attribute will have positive impact on Calculative Commitment”. The result shows that respondent might still maintain a relationship with the organization even the significant anticipated termination or switching costs associated with leaving. Since the previous data were obtained in foreign countries such as US, UK etc, and so the result obtained in this report might not conform to the previous one. It is because the banking environment in HK is different from other countries. For example in Q25, “I feel that I have too few options to consider leaving this bank”, respondent from other countries may really feel too few options to consider leaving the bank. However, this situation is not suit to HK because HK has many options of different banks for consumers since the distribution of the branch is close while there are really few options of bank and branch in some foreign countries.

4.42 Factor Loadings

The Factor Loadings is also called component loadings in principal component analysis PCA. It is the correlation coefficients between the variables (rows) and factors (columns). Analogous to Pearson’s r, the squared factor loading is the percent of variance in that indicator variable explained by the factor and that is the Communality. To get the percent of variance in all the variables accounted for by each factor, add the sum of the squared factor loadings for that factor (column) and divide by the number of variables. By one rule of thumb in confirmatory factor analysis, loadings should be 0.7 or higher to confirm that independent variables identified the predicted hypothesis are represented by a particular factor, on the rationale that the 0.7 level corresponds to about half of the variance in the indicator being explained by the factor. However, the 0.7 standard is a high one and real-life data may well not meet this criterion. By some previous researchers, particularly for exploratory purposes, a lower level (i.e. 0.3) will be used for the central factor in this report. Consequently, if the factor loading is greater than 0.3, this shows the variables identified the predicted hypothesis are represented by a particular factor.

In TableX, it shows the highest and lowest factor loading of each variable. All of the factor loadings of all variables is greater than 0.3. These values shows the variables identified the predicted hypothesis model: “H1: Self-Service Technology (ATM) attribute will have positive impact on consumer satisfaction”, “H2: Personal Service Usage (Teller) attribute will have positive impact on consumer satisfaction”, “H3: Consumer satisfaction will has positive impact on Affective Commitment”, “H4: Consumer satisfaction will has positive impact on Continuance Commitment”, “H5: Consumer satisfaction will has positive impact on Calculative Commitment”, “H6: Self-Service Technology (ATM) attribute will have positive impact on Affective Commitment”, “H7: Self-Service Technology (ATM) attribute will have positive impact on Continuance Commitment”, “H8: Self-Service Technology (ATM) attribute will have positive impact on Calculative Commitment”, “H9: Personal Service Usage (Teller) attribute will have positive impact on Affective Commitment”, “H10: Personal Service Usage (Teller) attribute will have positive impact on Continuance Commitment”, and “H11: Perso