In this chapter the methodological framework upon which the thesis is based, is discussed and presented. It evaluates and describes the research purpose, approach, design ,data collection methods, analysis methods and credibility that are applied to the work in this research.
There are many ways of classifying research, depending on the purpose of the research, how the data that is collected, and how such data are analyzed (Gratton & Jones, 2010). According to Saunders et al. (2011), the classification of research purpose most often used in the re-search methods literature is the threefold one of exploratory, descriptive and explanatory research (Cooper & Schindler, 2003).
Explanatory research can be defined as a causality type of research. It attempts to uncover the relationships between the reasons for something and its chain effects (Hedrick et al., 1993). Explanatory research is done when there is already a hypothesis as to why something is happening. Questions and tests are designed to support that hypothesis, and prove whether it is correct or not. Explanatory research is usually performed in relation to marketing or when studying social phenomena, the third-party online payment is one part of social phe-nomena, so we decided to use explanatory for our research purpose (Robson, 1997).
This research is an explanatory research. Not only is third-party online payment one part of social phenomena, but it is also a cause and effect type of research. Therefore, the research questions are aiming to measure how people perceive and accept third-party online payment in China’s B2C context. The Unified theory of acceptance and use of technology (UTAUT) has been used by many researchers in many research contexts. This research examines the model within a differently specified context. The research model attempts to explain college students and young workers’ attitudes and behavioral intentions towards third-party online payment. Hence, this study comprises an explanatory purpose.
Research approaches are the particular strategies that researchers use to collect the evidence necessary for developing and testing theories (Frey et al., 2000). Specifically, there are two ways of constructing a research: induction and deduction (see figure 3.1). Induction is built on empirical evidence, while deduction is based on logic (Ghauri & Grønhaug, 2005). The deductive approach is used to test the hypothesis through developing a theory and hypothesis and designing a research strategy (Saunders et al., 2011). Through the inductive approach, researchers draw general conclusions via the process of empirical observations, findings, and theory building, as findings are incorporated back into existing knowledge to improve theo-ries (Ghauri & Grønhaug, 2005).
Researchers not only have to deduce hypotheses from existing literature but also have to show them in operationalization, to present how data can be collected to examine these hy-potheses and the theories being used (Merton, 1967). Normally, the process of deductive research is divided into five steps: deducing a hypothesis, expressing the hypothesis in oper-ational terms, testing this operational hypothesis, examining the specific outcome of the in-quiry, if necessary, modifying the theory in the light of the findings (Robson, 1997) While, in inductive research, theory is the outcome of research (Bryman & Bell, 2011), it is related to the qualitative type of research (Ghauri and Gronhaug, 2005). Researchers in inductive re-search favours qualitative data and to adopt a variety of methods to collect these data in order to conduct different views of phenomena (Easterby-Smith et al., 2012).
In this research, the thesis has been conducted in a deductive manner. There are extensive documentations of third-party online payment and technology acceptance. The authors pro-posed six hypotheses are concerned with research questions and UTAUT on causal relation-ships between variables in the conceptual model. Quantitative data was collected through survey, then analyzed statistically in order to test the hypotheses. Consequently, users’ per-ceptions and behavioral intentions measured and to test UTAUT model, the research aims at providing an answer for the future of user adoption of third-party online payment in a China’s B2C market and is being conducted from users’ perspective.
According to Yin (2009), research design is defined as “a logical plan for getting from here to there, where here may be defined as the initial set of questions to be answered, and there is some set of conclusions (answers) about these questions ” (p. 64). On the other hand, (Ghauri & Grønhaug, 2005) states that the research design builds a plan or a framework for data collection and its analysis. The reason why it is important to identify a study’s research design is important is it conveys information about key features of the study, which can differ for qualitative, quantitative, and mixed methods (Harwell, 2011).
Trochim and Land (1982) defined quantitative research design as the “glue that holds the research project together. A design is used to structure the research, to show how all of the major parts of the research project—the samples or groups, measures, treatments or programs, and methods of assignment—work together to try to address the central research questions.” (p. 1). Harwell (2011) stated that quantitative meth-ods are often described as deductive in nature, in the sense that inferences from tests of statistical hypotheses lead to general inferences about features of a population. Regarding the deductive approach adopted in this study, the research design is quantitative, collecting and analyzing quantitative data in order to test hypotheses. As a result, the relationship between the factors of the UTAUT model and users’ acceptance of Alipay will be clarified.
Below, the research design for this study is formulated according to the following perspec-tives:
• Data collection methods;
• Data collection instruments;
• Data source;
• Quantitative vs. Qualitative nature of data;
• Data analysis methods;
Table 3.1 depicts a summary of the research design for this research. Each of the perspectives represented in table is discussed in following sections.
When it comes to the data collection, there are three different kinds of data sources should be considered. Among them, primary and secondary data are widely used in research. Primary data are always unknown before the research being undertaken and obtained directly for a specific research project (Currie, 2005). In this study the authors decided to use only primary data collection and secondary collection. Primary data is designed for collecting data for re-search projects. Secondary data refers to data used for a research project that was originally collected for some other purpose. With a combination of these two data sources, the re-searchers were able to generate a complementary and investigation. Tertiary data refer to international data compiled from international sources which are not used in this study (Saunders et al., 2011).
Primary data collection
Interviews could be explained as a purposeful discussion between two or more people (Kahn & Cannell, 1957). It can help you to gather valid and reliable data which are relevant to your research objectives and questions, another aspect should be considered is the level of for-mality of the interview. The interview could be conducted in three ways: structured, semi-structured and unstructured interviews. An unstructured interview is developed as an infor-mal conversation between the interviewer and the respondent to explore a general area on the subject of interest in depth. Semi-structured interviews are based on a list of themes and questions but these can vary from interview to interview. The structured interview is used with an emphasis on identical set of questions is exiting (Saunders et al., 2011).
In this study, only one telephone interview was performed. The advantages of telephone interviews are that they are time and cost-effective. The authors could interview the Chinese company from Sweden to ask for background data about Alipay (see Appendix 1). The back-ground data obtained from the telephone interview formed the basis for the sampling of the respondents of the questionnaire.
In this study, the target population is customer who shop on the B2C sites in China. Cur-rently, there are 300 million B2C customers in China. Because of time and resource con-straints, the authors draw a sample from the target population to investigate user adoption of Alipay in China’s B2C context.
Sampling is defined as observing a part in order to gain information about the total (Corbetta, 2003). In order to gain the information about the whole in this case, all sampling techniques were checked and the appropriate one was being chosen. Kumar (1999) demonstrated the different sampling techniques (see Figure 3.3). According to Figure 3.2, sampling is divided into three categories, random sampling, non-random sampling, and mixed sampling.
In this study, the authors decided to use quota sampling as the sampling techniques. According to Saunders et al. (2011), quota sampling is probably the most widely used sample design, especially in market research and in opinion polls. To implement the procedure, the population must first be subdivided into a certain number of strata defined by a few variables of which the distribution is known.
According to Comrey and Lee (1992) a sample size of is 100 is poor, 200 is fair, 300 is good, 500 is very good, 1,000 or more is excellent. They urge researchers to obtain samples of 500 or more observations whenever possible. Hence, the number of 300 was selected as the sample size in this research.
The sample includes both B2C customer who uses Alipay on B2C sites (user sample) and B2C customer who did not use Alipay (non-user sample). Here, the non-user sample is viewed as potential user of Alipay on B2C sites. Although they currently do not use Alipay on B2C sites, they have a certain level of understanding and perception about it. These per-ceptions will affect the potential user use and accept Alipay on B2C sites in the future. As a result, the authors think that both the user sample and the non-user sample are applicable for the proposed research hypothesis model in this research.
According to China’s B2C Online Shopping User Behavior Report, the population of Chi-nese B2C customers is mainly composed of company employees, college student, and em-ployees from the party and government organs and institutions. Here authors set quotas, which were based on the user distribution presented in the pie chart (Figure 3.3). Company employees, students and employees from the party and government organs and institutions account for 81.9% of B2C customer. The remaining category of 18.1% is labeled “other”. We think the three job categories can represent B2C customers in China, so the authors set quotas according to the ratio between these three job categories. More specifically, the sam-ple is composed of 149 company employees, 95 students and 56 employees from the party and government organs and institutions. The authors contacted friends, colleagues and rela-tives who are company employees, college students, employees from the party and govern-ment organs and institutions; those helped the authors to spread the questionnaires in China.
In this study, the questionnaire technique is used to collect primary data in order to explain how users perceive third-party online payment in China’s B2C context. Considering that the respondents are all Chinese who prefer the mother tongue, Chinese, the questionnaire was translated into Chinese. Usually, questionnaires are divided into two categories; self-admin-istered questionnaires and interviewer-administered questionnaires. Respondents complete self-administered questionnaires, as the name implies. Such questionnaires include Internet-mediated questionnaires, postal questionnaires, delivery and collection questionnaires (Saun-ders et al., 2011). Here, the user survey was conducted as an Internet-mediated questionnaire, inviting respondents to access the questionnaire through a hyperlink (web link) and fill it in online. Moreover, compared to other survey modes, online surveys are faster, simpler, and cheaper.
The Chinese online survey tool “Sojump” (问卷星)2, a web based service for conducting online surveys, was used to deliver the questionnaires and the collect responses. A Prize Draw for Questionnaire, one “Amazon” gift card worth 500 CNY (about 527 SEK) was offered as a reward to give respondents incentives to complete the questionnaire, which in turns increased the response rates.
The survey included three parts described below (See Appendix 4):
1. The first part (from question 1 to question 5) collected background information, including respondent’s basic information, such as gender, age, occupation, education level and monthly income.
2. The second part (from question 6 to question 9) collected information about use of Alipay on B2C sites. Question 6 is dichotomous and was used to screen out invalid responses. Then, the payment methods used on B2C sites were categorized, a questions was asked if the re-spondent used or did not use Alipay and the reason for that.
3. The third part of the questionnaire is referred to as “Perceptions and attitudes about Alipay” and was used to collect data on user acceptance from the respondents. A seven point Likert-style rating scale was used in which the respondent was asked how strongly she or he agreed or disagreed with a statement or series of statements (Saunders et al., 2007), ranging from 1 (completely disagree) to 7 (completely agree). The six constructs included in the hypotheses were all investigated:
• Service quality (SQ) from question 10 to 12.
• Perceived Risk（PR）from question 13 to 15.
• Performance Expectancy (PE) from question 16 to 18.
• Effort Expectancy (EE) from question 19 to 21;
• Social Influence (SI) from question 22 to 24.
• Facilitation Conditions (FC) from question 25 to 27;
• Behavioral Intention (BI) from question 28 to 30.
An open-ended question was designed to provide additional opinions about Alipay. This type of questions are usually answered through a continuous text varying in length and con-tent. Open-ended questions can yield useful information, especially when researchers need to explore complex issues that do not have a finite or predetermined set of responses (Carey et al., 1996).
After formulating the first draft of questionnaire, the questionnaire was pilot tested to ensure that respondents had no problems understanding or answering the questions and were able to follow the instructions correctly (Fink, 2003).
In the pilot test, the questionnaires were answered by respondents among Chinese students at Jonkoping University. The number of respondents was 20. There are 16 questionnaires were returned, making the effective return ratio into 80%. Most questionnaires took about three to five minutes to complete. However, there were some problems during the pilot test. Some respondents complained that several questions were unclear or ambiguous. Some sen-tences were not clearly or accurately translated from the original English version into the Chinese version. To solve these problems, the authors did a new translation for those ques-tions.
Exploring Secondary Data
Secondary data can provide a useful source from which to answer, or partially to answer our research questions (Saunders et al, 2011). In this thesis, the authors have used Google Scholar and Jönköping University Library databases as the primary search engines to gather literature. DIVA is also an effective tool for secondary data searching. The main purpose of collecting secondary data was to find out which factors other authors have already pointed out and how other researchers have investigated user acceptance of third-party online payment. After this, the authors listed the keywords, (see Table 3.2).
In order to clarify the knowledge gap about third-party online payment research, the authors use “third party online payment” as key to do keywords retrieval on Google scholar, there is 163 results. Among them, 27 articles are about third-party online payment. much of the re-search of third party online payment focus has been on finance, security, supervision, risk, the relationship between commercial banks and third party payment provider, business strat-egy, trust problem, etc.
In this research, SPSS version 22.0, statistical software was used to perform the statistical analysis, and to achieve the desired objectives of the study. In this section, the statistical analysis performed in the study will be described.
In this study, the questionnaire collected information about the number of respondents in the sample, the number and percentage of males and females in the sample, the range and mean of ages, education level, and any other relevant background information (Pallant, 2010). Moreover, before testing hypothesis, every constructs’ descriptive statistics was obtained, which included the mean, and standard deviation of each constructs in the questionnaire.
The reliability of a scale is defined as the degree to which the instrument is free from random error. Two frequently used indicators of a scale’s reliability are test-retest reliability and in-ternal consistency (Pallant, 2010). Internal consistency includes correlating the reponses to each question in the questionnaire with those to other questions in the questionnaire (Saun-ders et al., 2011). Internal consistency tends to be a frequently used type of reliability in the IS domain (Sekaran, 2006). In this study, internal consistency was used as a measure to assess the construct reliability.
The validity of a scale is defined as the degree to which it measures what it is supposed to measure (Pallant, 2011). As a rule of thumb, the higher the validity, the more accurate the instrument used. In order to test the construct validity, researchers usually utilize exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to examine factors of the em-pirical data. Exploratory factor analysis is employed in the early process of research to gather information about the interrelationships among variables. Confirmatory factor analysis is a more complex and sophisticated set of techniques used later in the research stage to examine specific hypotheses or theories concerning the structure underlying a set of variables (Pallant, 2010).
In this study, exploratory factor analysis was carried out in order to figure out the common factors that affact the dependent variable (behavioral intention).
In order to test the proposed UTAUT model, relations between variables should be meas-ured. Pearson Correlation aims at exploring the strength of the relationships between con-tinuous variables (Pallant, 2010). A correlation analysis was carried out based on each of these constructs in the proposed model.
In this study, the standard multiple regression was performed. All independent variables were entered into the equation simultaneously. Each independent variable was evaluated in terms of its predictive effects, over and above that offered by all the other independent variables (Pallant, 2010).
Analysis of open-ended questions
Open-ended question responses are often elicited in organizational research to gather new information about an experience or topic, to explain or clarify quantitative findings, and to explore different dimensions of respondents’ experiences, open question usually used in or-ganizational research to explore, explain, and/or reconfirm existing ideas with the text data in the form of a brief (Jackson, 2002).
In this study, the answers to the open-ended questions were analysed by directed content analysis (Hsieh & Shannon, 2005). The responses to the open-ended questions were read through and grouped into categories. The coding categories for the responses were “ad-vantages” and “disadvantages” with Alipay.
There are several types of criteria for evaluating the quality of conducting research. (Collis et al., 2003) claim that historically, reliability, replication and validity have been very common measurements of the quality of the scientific work.
Easterby-Smith (2008) defines reliability as the extent “to which your data collection techniques or analysis procedures will yield consistent findings ” (Easterby-Smith et al., 2008, p. 109). The authors used internal consistency to test the reliability of the questionnaire. Each construct was examined by calculating the value of Cronbach’s Alpha in order to ensure the internal consistency reliability.
According to Saunders et al. (2011), validity is concerned with whether the research findings are really about what they appear to be about. In this study, the authors will mainly discuss the content about the validity of the questionnaire in our survey as content validity concerns the degree to which the measurement questions in the questionnaire delivers sufficient coverage of the investigative question (Saunders et al., 2011). In order to provide “ sufficient coverage ”in our questionnaire, the authors have read about lot of the literature review and prior discussions before authors defined this research purpose. Furthermore, the questions in the survey are based on the standard scale of the UTAUT model which has been proposed and tested by other researchers before(See Appendix 2 and 3). The questionnaire items for the new factors “service quality” and “perceived risk” were created from the literature review and the expert interview to ensure the content validity.
Empirical findings and analysis
This chapter presents the empirical findings and the statistical analysis performed on empirical data. The descriptive findings are presented and the hypotheses are tested.
In order to get a more convenient understanding of t B2C user’s acceptance from employees, students and employees from party and government organs and institutions, a descriptive analysis was applied for the demographic information collected in the survey. Frequency statistics for the general questions in the survey were calculated. In Table 4.1, we can see that 130 of the respondents were male, the rest of the respondents were female.
68 respondents of the B2C customers responding to the questionnaire were between 18 and 24 years old (36 males and 32 females) . 143 respondents were between 25 and 30 years old are (71 males and 72 females). The age group between 31 and 35 years included 32 respond-ents (18 males and 14 females), and the age above 36 were 17 respondents (6 males and 11 females). As shown in Figure 4.1, young people constituted the main part of B2C users in the study.
As shown in Figure 4.2, Alipay is the largest share in the figure (113 responses), next, cash on delivery had 82 responses. The rest of the respondents, 65 individuals, used internet bank. There are 43% of the B2C consumers choose Alipay to pay their purchases. Hence, to un-derstand the behavior B2C consumers can help Alipay to acquire more market share.
As shown in Table 4.2, from the occupational perspective, we found that 74 of the respondents were students, 135 were company employees, and the rest of the respondents were employees from party and government organs and institutions.
As shown in Table 4.3, regarding respondents’ education levels, 168 of the respondents had a bachelor degree, and 82 respondents had the master degree or a higher education
As shown in Figure 4.3, we found that 101 of the respondent’s monthly incomes was less than 3000 CNY, because the most of the respondents are students. 90 respondents had a monthly income between 3000 and 5000 CNY, and 42 people was between 5000-8000 CNY. Only 28 respondents had a monthly income over 8000 CNY.
As shown in Table 4.4, the products that the respondents bought from online shops were clothes (101 respondents), books (79 respondents), electronic products (53 respondents, ap-pliances (27 respondents), and daily necessities (92 respondents). The rest of the respondents chose the product category “another”.
The reasons for respondents not using Alipay on B2C sites are presented in Table 4.5. 42 respondents think that they do not need Alipay to buy products from B2C websites, 45 re-spondents think that Alipay has security concerns, 43 respondents think that registration and operation of Alipay are too complicated, and 25 respondents give other reasons for not using Alipay on B2C websites.
An arithmetic mean is a term for what most people call an “average.” As shown in Table 4.6, the authors used mean values to estimate values of the constructs in the survey. The authors uses SPSS for calculating the arithmetic mean for constructing items.
A standard deviation shows how much variation or dispersion of the average exists. As shown in Table 4.6. The authors use std. deviation to simply substitute it. The authors used SPSS to calculate standard deviation for construct items in the questionnaire. The mean val-ues for all constructs of the research model were between 4 and 6. The largest mean occured in Performance Expectancy, which was 5.59. The smallest mean occurred in Perceived Risk, which was 3.81. This can be interpreted as the respondents finds Alipay useful and do not perceive a high degree of risk when using Alipay(See Appendix 5).
Table of Contents
1.4 Research questions
2 Frame of references
2.1 Third-party online payment related theoretical review
2.2 Online Shopping and Online Stores
2.3 The current condition of Alipay
2.4 Similar third-party payment systems in China
2.5 Service Quality
2.6 Perceived Risk
2.7 Review on Technology Acceptance
2.8 Research model and hypotheses
3.1 Research purpose
3.2 Research approach
3.3 Research Design
3.4 Data collection
3.5 Data analysis
3.6 Analysis of open-ended questions
4 Empirical findings and analysis
4.1 Descriptive analysis
4.2 Analysis of Open-ended question
4.3 Analysis of the proposed UTAUT model
5.1 Discussion of results
5.2 Discussion of the proposed UTAUT model
5.3 Discussion of method
5.4 Research implications
5.5 Suggestions for further research
7 List of references
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