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N9265503 YANG SHAN (TAK) AMB201 Marketing and Audience Research Quantitative Research Report Determinants of Online Retail shopping Student Name: YANG SHAN (TAK) Student Number: N9265503 Tutor’s Name: Jennifer Doig Tutorial Time: Thursday, 1:00pm-2:00pm Words: 1759 0 N9265503 YANG SHAN (TAK) Table of Contents Participation Reflection………………………………………………………….......2 Executive Summary.....................................................................................................3 1.0 Introduction and Background………………………………………………... 1.1 Importance of the research……………………………………………….…...4 1.2 Scope of the report……………………………………………………………4 1.3 Research question………………………………………………….................5 1.4 Aims and Objectives………………………………………………………..5-6 2.0 Method……………………………………………………………………….....6-8 2.1 Methodological considerations and assumptions………………………..……6 2.2 Sample considerations……………………………………………...…………7 2.3 Data collection and framework, and analytical considerations……………..7-8 3.0 Ethical considerations……………………………………………………………8 4.0 Analysis………………………………………………………………………..8-17 4.1 Cleaning and coding………………………………………………………….8-9 4.2 Descriptives………………………………………………………………….9-11 4.3 Exploring the data (t-tests)……………………………………………........11-13 4.4 Correlation…………………………………………………………………13-14 4.5 Multiple regressions………………………………………………………..14-16 4.6 Social desirability…………………………………………………………..16-17 5.0 Discussion and Recommendations…………………………………….……17-19 5.1 To examine how self-concept dimensions relate to online shopping attitudes……………....17-18 5.2 To determine the impact of individual characteristics on online retail shopping attitudes……...18 5.3 To evaluate how social desirability may influence the results of the research………………18-19 6.0 Limitations…………………………………………………………………........19 7.0 Reference List………………………………………………………………..20-21 8.0 Appendix: Two completed surveys 1 N9265503 YANG SHAN (TAK) Participation Reflection The course name of AMB201 is marketing and audience research so the research is strongly of importance to this course. Before writing this report, I handed two surveys to two females who live in Australia. It is strongly important to mention that asking two female to complete the surveys is significant for me to increase my confidence in talking with others in English. This means that the research helps me to improve my communication skills with others. Before handing the survey to the first female, I forgot to remind her that the survey may cost about half an hour. The female had an important appointment at that time so she finished about 1/3 of the survey in a short time. Although the female participant finally completed the survey, it still wasted the participant’s time. Besides, she did not answer the questions in survey carefully and this can exert a negative impact on the result of the research. Thus, if I will ask others to do the survey for the research in the future, I will remember to remind participants of the length of the survey. This is because it is important for the researcher to clarify some essential matters, such as the length of the survey. This is also because participants have their right to know about the length of the survey. Besides, during the process of completing the surveys, the respondent asked me some questions about the survey because they could not understand what some questions mean, but I did not answer respondents’ questions well because I could not understand some question in the survey as well. This will also negatively influence the result of the research because respondents cannot answer some questions accurately. Thus, it is necessary for me to gain a deep understanding of all questions in the survey. This is because the in-depth understanding of survey questions can help the researcher to draw some useful conclusions and think out more feasible recommendations. It is worth mentioning that it is strongly valuable for researchers to also take part in the research as a participant. This can be explained by the fact that researchers can consider some questions from participants’ point of view if researchers can take part in the research as participants. It is also important to mention that researchers can correct some mistakes when they find out some faults or problems as a participant. Therefore, it is necessary and valuable for researchers to also take part in the research as a participant. 2 N9265503 YANG SHAN (TAK) Executive Summary In today’s society, online market has become the main market. The purpose of this report is to quantitatively examine the determinants of Australian consumers’ attitudes towards online retail shopping. Descriptive and quantitative research approaches will be applied to this report. Surveys were used to collect Australian Internet users’ opinion about online retail shopping. The answers to research questions are strongly important and beneficial to develop the online market well. The results of this report will provide some ideas and practical suggestions. This can be helpful to implement some useful strategies of online shopping to improve its continuance as effective means of market share and overall revenue of online businesses. According to the findings and the three objectives, this report provides some recommendations: marketers can also apply the advertising which contains conventional and innovative elements to attract consumers’ attention and drive them to purchase items online; online shopping marketers can strive to cooperate with some sectors or companies which dedicate to maintain online security; it is necessary for marketers to do further research or observational studies to determine what the actual performance do respondents have associated with online purchasing behaviour. However, there are some limitations in this report, such as the age group of the respondents. 3 N9265503 YANG SHAN (TAK) 1.0 Introduction and Background 1.1 Importance of the research According to MENA (2013), online shopping has become increasingly popular in Australia in recent years. It is worth firstly mentioning that the majority of Australian Internet users are online shoppers (MENA, 2013). This means Australian online shoppers can be helpful to promote the ongoing development of online transaction economy. Besides, it is significant for marketers to use some information from the marketing research to drive the business growth. Many mistakes may occur without the marketing research and this can exert an adverse impact on a firm’s business profits. It is useful to use the marketing research to learn more about targeting audience, a product or the service’s market viability or a firm’s image or reputation (Sontakki, 2010). Thus, it is necessary to examine the predictors of Australian shoppers’ attitudes toward online retail shopping. In this report, the descriptive research can be applied to gain a good understanding of the determinants of Australian consumers’ attitude toward online retail shopping. 1.2 Scope of the report This report is based on three provided surveys from QUT Blackboard and conducted using two English-speaking adults from two different age cohorts which are younger people (18-40 years) and older people (41+ years) who consistently use Internet in Australia. In addition, the report is based on self-report study method. There are many different types of methods of the self-repot study method, including questionnaires, inventories, interviews and focus groups (Basch, DeCicco and Malfetti, 1989). However, some potential problems may associate with applying the self-report method. This can be explained by Curtis’s (2004) state that the data from self-report are personal and it may not reality because people are not always truthful. It is important to mention that there is no classification of specific consumer dataset in this report. 4 N9265503 YANG SHAN (TAK) 1.3 Research question What are the determinants of Australian consumers’ attitudes towards online retail shopping? In recent years, online retail has experienced significant development in Australia with an increasing number of Internet users purchasing products online. This means it is useful for online retailers to understand what leads Australian consumers to shop online because of the considerable growth of the online retail. If marketers intend to expand their businesses and achieve their business outcomes, it is necessary for them to gain a good understanding of the determinants of Australian consumers’ attitudes toward online retail shopping. This is because online retailers can carry out effective marketing strategies, sell products which consumers are likely to buy online, and improve some aspects of online retail to attract more online consumers to purchase products online according to the determinants of Australian consumers’ attitudes toward online retail shopping. Thus, it is significant for online retailers to deeply understand the determinants of Australian consumers’ attitudes toward online retail shopping. 1.4 Aims and Objectives The aim of this report is to quantitatively examine the determinants of Australian consumers’ attitudes toward online retail shopping. Specific objectives include: i) To examine how self-concept dimensions relate to online retail shopping attitudes; Self-concept refers to a person’s self-image. It can be conceptualised as having a variety of dimensions, some of which may relate to the dependent variable. Correlation will be used to examine the relationship between self-concept dimensions and attitude toward online retail shopping. 5 N9265503 YANG SHAN (TAK) ii) To determine the impact of individual characteristics on online retail shopping attitudes; Multiple regression will be used to examine the impact of five potential predictors of attitude toward online retail shopping. iii) To evaluate how social desirability may influence the results of the research. Social desirability is a potential bias in data because people tend to present themselves favourably. An analysis of the distribution of social desirability scores will allow for an evaluation of how this might contribute to results. 2.0 Method 2.1 Methodological considerations and assumptions This report will apply the descriptive research method and quantitative methodologies rather than using the casual research method. According to Barker, Pistrang and Elliott (2005), descriptive research is used to describe features of a population or phenomenon instead of answering the reasons of specific questions. In addition, the casual research focuses on the cause-effect relationships (Zikmund, Lowe, Babin, D’Alessandro & Winzar, 2014). In this case, as this report is to examine the determinants of online shopping behaviour instead of the cause-effect, the descriptive research method can be more appropriate. It is also important to use an appropriate method for investigation. Quantitative research is applied in this report and this kind of research is to explain specific phenomenon by collecting numerical data (Watson, 2015). Additionally, quantitative data are collected by mathematical method such as statistics and this type of data is more objective than qualitative research (Aliaga & Gunderson, 2000). However, there are some limitations in quantitative research. This is because the quality of the research is dependent on the researchers’ skill, can be easily influenced by investigators’ personal biases, and is time-consuming (Claire, 2010). In this report, the researcher assumes all respondents can accurately answer the questions which can stand for respondents’ real insight. 6 N9265503 YANG SHAN (TAK) 2.2 Sample considerations The targeted population of the research is English-speaking adults in Australia who are used to surf the Internet. Target cohorts are divided into two age groups, which are younger people (18-40 years) and older people (41+ years). According to Bownas (1985), the objective of the quantitative sampling approach is to draw a representative sample from the population so that results can easily be generalised from the population. Regarding the sampling technique, non-probability sampling, such as quota sampling, is applied in this research. This can be explained by Moser’s (1952) state that it is suitable to choose the quota sampling technique when researchers know about the distribution of population for certain characteristic. 2.3 Data collection and framework, and analytical considerations The two surveys were handed to two English-speaking adults who are respectively from the age groups of 18-40 years and 41+ years in Australia. These surveys were pre-tested by the administer DR. Weeks. This is because the pre-test can provide information on how the issues of reliability and validity were solved (Anonymous, 1992). Researchers firstly downloaded the provided surveys from QUT Blackboard and then presented hard copies to the targeted population. According to the requirement from QUT, researchers with family names beginning A-L should collect two male respondents, with one from 18-40 years and 41+ years age cohort. For researchers with family names beginning M-Z should collect two female respondents, with one from 18-40 years and 41+ years age cohort. It is also important to mention that researchers are required to ask their target respondents to sign the consent form. The next step is to upload the completed surveys on QUT Blackboard from researchers. This can provide the SPSS System with the opportunity to sort out descriptive and bivariate statistics, and predict outcomes (Hinton, 2004, p.2). In terms of analysis of the collecting data, Descriptive, t-tests, Correlations and Multiple Regressions will be 7 N9265503 YANG SHAN (TAK) used. 3.0 Ethical Considerations Before handing out the surveys to the respondents, it is necessary for researchers to explain the survey consent form and require respondents to sign the consent form. This can be explained by Kolb’s (2008) state that during the process of marketing research, it is responsible for researchers to minimise the risks of harm to participants and clarify both the potential benefits and risks of the survey. This means researchers have the responsibility to treat participants in a fair way and to gain the accurate information from the respondents (Kolb, 2008). It is worth mentioning that in the quantitative non-experimental research, it is important for researchers to explain the purpose of the research, respondent demographics and how the results of the research will be used (Roe & Webb, 1998). 4.0 Analysis 4.1 Cleaning and Editing In the analysis, it is necessary to clean some inappropriate data by data cleaning. The processes of data cleaning include deleting respondents with uninterpretable responses with non-existent postcode; changing suburb listing to postcode or within range; converting birth year to age in years; altering mixed number/text responses to number only; converting approximations to conservative estimate. In this report, reverse coding, which can help to reverse any negatively phrased survey items, is used by SPSS. For example, RA5 was reversed such that responses of 1 were recoded as 7, responses of 2 were recoded as 6, etc. Reversed items in this report are: ATTA3, ATTC3 RA5, IMP3, IMP4, VS6, VS7 SocDes1, SocDes2, SocDes5, SocDes8 8 N9265503 YANG SHAN (TAK) In terms of construct calculations, the average of the relevant items is rearded as construct value: ATTA = (ATTA1 + ATTA2 + ATTA3 + ATTA4)/4 ATTC = (ATTC1 + ATTC2 + ATTC3 + ATTC4 + ATTC5)/5 ATTBI = (ATTBI1 + ATTBI2 + ATTBI3)/3 RA = (RA1 + RA2 + RA3 +RA4 + RA5 + RA6)/6 IMP = (IMP1 + IMP2 + IMP3 + IMP4)/4 VS = (VS1 + VS2 + VS3 + VS4 + VS5 + VS6 + VS7)/7 CS = (CS1 + CS2 + CS3 + CS4 + CS5 + CS6 + CS7)/7 PC = (PC1 + PC2 + PC3 + PC4)/4 SocDes = (SocDes1 + SocDes2 + …+ SocDes8 + SocDes9 + SocDes10)/10 According to the survey and analysis, ATTA, ATTC and ATTBI were measured, but only ATTBI will be analysed. 4.2 Descriptives Figure 1: Descriptive Statistics ATTA ATTC ATTBI Risk Aversion Impulsiveness Variety Seeking Convenience Seeking Price Consciousness Social Desirability N Mean Std. Deviation 659 659 659 659 659 659 659 659 659 4.4256 5.0847 4.9727 4.6171 3.7075 4.4917 4.7657 4.9522 1.4980 1.38845 1.25722 1.49702 .94226 1.15526 .78855 .79547 .99876 .21652 Figure 2 & 3: Gender Distribution Frequency Male Valid Female Total Percent 325 334 659 49.3 50.7 100.0 Valid Cumulative Percent Percent 49.3 49.3 50.7 50.7 100.0 Age Cohort* What is your gender? 9 N9265503 YANG SHAN (TAK) Younger Age Cohort Older Total What is your gender? Male Female 166 167 159 167 325 334 Total 333 326 659 Figure 2 & 3 shows that the final sample population in the research is 659. Female respondents (334) are few higher than male respondents (325). Specifically, there are respectively 166 and 167 younger people in the male and female group, while there are respectively 159 and 167 older people in the male and female group in the research. Figure 4-5: Relationship Status Distribution Frequency Single Valid Partnered Total 216 443 659 Percent 32.8 67.2 100.0 Valid Cumulative Percent Percent 32.8 32.8 67.2 100.0 100.0 Age Cohort * What is your relationship status? Age Cohort Younger Older Total What is your relationship status? Single Partnered 177 156 39 287 216 443 Total 333 326 659 Figure 4 & 5 illustrates most of participants are partnered (67.2%), and the larger part of partnered status is belonging to older age group which includes 287 respondents. The larger part of single respondents is younger age group, including 177 participants. Figure 6 & 7: Age Distribution Valid Younger Older Frequenc Percent y 333 50.5 326 49.5 Valid Cumulative Percent Percent 50.5 50.5 49.5 100.0 10 N9265503 YANG SHAN (TAK) Total 659 100.0 100.0 Figure 6 & 7 shows that two age groups are involved in the report, which include younger age group and older age group. The population of younger age group is 333 (50.5%), which is slightly larger than the population of older age group (326; 49.5%). The average age of all respondents in this research is 36.54; however; the majority of respondents are 18-25 years old (Figure 7). 4.3 Exploring the data (t-tests) Q: Does attitude toward online retail shopping (ATTBI) differ between younger and older people? Figure 8 & 9: Group Statistics ATTBI Age cohort based on age Younger Older N 333 326 Mean 5.5335 4.3998 Std. Deviation 1.22035 1.53768 Std. Error Mean .06687 .08516 Independent Samples Test 11 N9265503 YANG SHAN (TAK) Levene's Test t-test for Equality of Means for Equality of Variances F Sig. t df Sig. Mean (2-tailed) Difference Std. Error 95% Confidence Difference Interval of the Difference Lower Equal variances 27.071 .000 10.495 657 .000 1.13374 .10802 .000 1.13374 .10828 Upper .92163 1.34585 assumed ATTBI Equal variances 10.470 618.983 .92109 1.34683 not assumed Figure 8 & 9 shows that the mean of younger age group (5.5335) is a little higher than that of older age group (4.3998), and the Sig. (2-tailed) value is less than 0.05 (0). Thus, there is statistically significant difference between younger people and older people. Q: Does attitude toward online retail shopping differ between males and females? Figure 10 & 11: Group Statistics What is gender? Male ATTBI Female your N Mean 325 334 4.9005 5.0429 Std. Deviation Std. Error Mean 1.49302 .08282 1.49980 .08207 Independent Samples Test Levene's Test for Equality of Variances F Sig. t Equal variances .270 assumed ATTBI Equal variances not assumed .603 t-test for Equality of Means df -1.221 657 Sig. Mean Std. Error 95% Confidence (2-tailed Difference Difference Interval of the ) Difference Lower Upper .222 -.14240 .11660 -.37135 .08655 -1.221 656.658 .222 -.14240 .11659 -.37134 .08654 12 N9265503 YANG SHAN (TAK) Figure 10 & 11 displays that female have more positive attitudes toward online retail shopping because the mean of female (5.0429) is higher than that of male (4.9005). The Sig. (2-tailed) value of them are both 0.222, which are higher than 0.05. Therefore, there is not statistically significant difference between these two groups. Q: Does attitude toward online retail shopping differ between those who more frequently use email compared to online chat? Figure 12 & 13: Group Statistics ATTBI Which method of online N Mean Std. Deviation Std. communication do you more Mean frequently use? Email 332 4.5402 1.52347 .08361 Online Chat 327 5.4118 1.33523 .07384 Error Independent Samples Test Levene's Test for Equality of Variances F Sig. t Equal 8.585 variances assumed ATTBI Equal variances not assumed .004 t-test for Equality of Means df -7.806 657 Sig. Mean Std. Error 95% Confidence (2-tailed) Difference Difference Interval of the Difference Lower Upper .000 -.87166 .11166 -1.09092 -.65241 -7.814 648.268 .000 -.87166 .11155 -1.09070 -.65262 Figure 12 & 13 indicates that the mean of online chat (5.4118) is higher than that of email (4.5402), and the Sig. (2-tailed) value is less than 0.05 (0). Thus, there is a statistically significant difference between two groups. 4.4 Correlation Figure 14: Correlations of Self Concept Dimensions with ATTBI 13 N9265503 YANG SHAN (TAK) Conventional|||Unconventional Innovative|||Routine Uncomfortable|||Comfortable Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N ATTBI -.150** .000 659 -.238** .000 659 .022 .565 659 As can be seen in Figure 14, three results from top to bottom are for negative significant correlation, positive significant correlation and non-significant correlation because their values of Pearson Correlation respectively less than, more than, and equal to 0. Additionally, it can be concluded that people who are more conventional and innovative are more likely to purchase items online than the unconventional and routine people. However, there is invalid assumption about attitudes toward online retail shopping between people who are uncomfortable and comfortable because its person correlation value is 0.022, and the Sig. (2-tailed) value is more than 0.05 (0.565). Therefore, this group (uncomfortable and comfortable) is regarded as not significant correlation. 4.5 Multiple regressions In this section, Multiple Regression will be used to test overall model, which involves testing multiple predictors of a dependent variable and is an extension of Bivariate Regression. It aims to generate a linear equation fitted to the observed data, then use it to create predictions associated with the dependent variable through plugging in values for each independent variable. All constructs entering to the analysis are indicated in Figure 15: Figure 15: Variables Entered/Removed* Variables Entered Variables Removed Method Model 14 N9265503 YANG SHAN (TAK) Risk Aversion, Impulsiveness, Variety Seeking, Convenience Seeking, Price Consciousness, . Enter a. Dependent Variable: ATTBI b. All requested variables entered. Figure 16: Model Summary Model R .623a 1 R Square Adjusted R Square .388 Std. Error of the Estimate .383 1.17564 a. Predictors: (Constant), SOC, PC, INF, RA, CS, VS, WOR, IMP, ENT Figure 16 shows that “R” indicated that the strength of correlation between the observed and predicted values of the dependent variable is 0.623, and the relationship is positive. “R Square” shows the proportion of variance in the dependent variable explained by the regression model is 0.388. “Adjusted R Square” indicates the model occupies 38.3% of variance in the dependent variable, which can more closely reflect the goodness of fit than “R Square”. Figure 17: ANOVAa Model Regression 1 Residual Total Sum of Squares 572.090 902.530 1474.619 df Mean Square 5 114.418 653 658 1.382 F 82.784 Sig. .000b a. Dependent Variable: ATTBI b. Predictors: (Constant), Price Consciousness, Risk Aversion, Convenience Seeking, Variety Seeking, Impulsiveness As can be seen in Figure 17, the significance value of F statistics is .000 (less than 0.5) so the predictors do a good job explaining the variation in the dependent variable. Figure 18: Coefficientsa 15 N9265503 YANG SHAN (TAK) Model 1 (Constant) Risk Aversion Impulsiveness Variety Seeking Convenience Seeking Price Consciousness Unstandardised Coefficients B Std. Error 2.202 .509 -.581 .055 -.007 .046 .551 .062 .537 .061 .090 .049 Standardised t Coefficients Beta 4.382 -.366 -10.489 -.005 -.152 .290 8.888 .285 8.831 .060 1.842 Sig. .000 .000 .879 .000 .000 .066 a. Dependent Variable: ATTBI Figure 18 shows that impulsiveness and price consciousness have no significant impact on attitudes toward online retail shopping because the significant levels of them are more than 0.05 (0.879 and 0.066 respectively). In contrast, other models, such as risk aversion, significantly affect the attitudes of online shopping. Importantly, risk aversion has the most degree influence on the purchasing attitudes because it got Beta -0.366, which is the biggest numeric in Standardised Coefficients. Besides, variety seeking (0.29) has the strongest positive influence on online shopping attitudes. 4.6 Social Desirability Considering how the relationships differ for low vs high rating individuals can assist to evaluate social desirability effects on the results. The results were scored from 1.00 to 2.00, and they should be separated to two groups, including low and high social desirability groups. Based on the median (1.50), scores of and under 1.50 are considered as low group, and scores of over 1.50 are considered as high group (Figure 19 below). Figure 19: Social Desirability 16 N9265503 YANG SHAN (TAK) 5.0 Discussion and Recommendations 5.1 To examine how self-concept dimensions relate to online retail shopping attitudes According to the analysis of correlations of self-concept dimension with ATTBI (Figure 14), people who are more conventional and innovative have strongly positive attitudes toward online retail shopping. Conversely unconventional and routine people have weak desire to purchase items online. However, there is no impact on online retail shopping no matter consumers who are comfortable or uncomfortable. These findings can be helpful for marketers to position target market. Marketers can target the audiences who consider themselves are more conventional and innovative. Additionally, marketers can also apply the advertising which contains conventional and innovative elements to attract consumers’ attention and drive them to purchase items online. This can be explained by Rodgers and Thorson’s (2012) state that advertising is an effective marketing strategy, which can be beneficial for marketers to target consumers and attract consumers’ attention. Thus, it is an effective way for 17 N9265503 YANG SHAN (TAK) marketers to implement the advertising strategy which contains conventional and innovative elements. 5.2 To determine the impact of individual characteristics on online retail shopping attitudes As Figure 18 shows, apart from impulsiveness and price consciousness, all constructs of individual characteristics have a significant impact on online retail shopping. The figure also indicates that risk aversion has the strongest negative impact on consumers’ attitude toward online retail shopping, and variety seeking has the most positive impact on online retail shopping. This means people think variety seeking information of products is the most appealing factor for online retail shopping but they are worried about the risks at purchasing items online. Thus, marketers should pay more attention to designing briefer website, and try to show different kinds of information about the products to attract more consumers’ attention. Moreover, online shopping marketers can strive to cooperate with some sectors or companies which dedicate to maintain online security. This can help to improve the security system of online retail shopping and be beneficial for consumers to reduce the risks at buying items online (Miyazaki & Fernandez, 2001). This can finally be helpful for marketers to raise consumers’ confidence in the security of online shopping, thereby improving the sales volume of products sold online. 5.3 To evaluate how social desirability might influence the results of the research According to Zikmund et al (2014), gauging the effects of social desirability from respondents’ responses can contribute to gain an accurate understanding of results. Considering the relationships differ from low and high rating individuals can assist to evaluate the social desirability effects on the results. The impact of social desirability has been exposed. As Figure 19 shows more respondents belong to the low social desirability group but there are inadequate resources about the social desirability in this research. However, it can be concluded that it probably has social desirability bias, which will influence the result of the research. For marketers, they need to examine and get rid of the influence of social desirability bias, and try to find out the causes 18 N9265503 YANG SHAN (TAK) and effects of the social desirability. To prevent inaccuracy of the research and report, it is necessary for marketers to do further research or observational studies to determine what the actual performance do respondents have associated with online purchasing behaviour. 6.0 Limitations The form of questions in the survey is scale items which respondents indicate a rating or extent of attribute. For scale items, respondents can just circle the number to indicate their agreements. Thus, the respondents’ responses cannot accurately express their thinking. Besides, too many scale items can cause the respondents losing their patience about answering questions. This can lead to the fact that the responses probably come out by skipping many information of the questions. To relieve these problems, researchers can use many kinds of questions to generate a survey. Unevenly distributing is the second main limitation. According to Figure 7, the age of most respondents is between 18 and 25; besides, the average age of respondents is 36.54. This means the age group of young people occupies a large proportion of all respondents and this can lead to the overbalance of the results. Thus, researchers should avoid this situation by distributing appropriate respondents. The error of sampling method will be unavoidably occurred by statistical fluctuation because of the chance variation (Zikmund, et al, 2014). It is necessary for researchers to use a better sampling frame to solve this issue. 19 N9265503 YANG SHAN (TAK) 7.0 Reference List Aliaga M. & Gunderson B. (2000). Interactive Statistics. Saddle River, NJ: Prentice Hall. Anonymous (1992). Pre-test. Pediatric INCORPORATED. 06/1992, 21 (6). Annals. Published by SLACK Barker C., Pistrang N., & Elliott R. (2005). Research Methods in Clinical Psychology: An Introduction for Students and Practitioners. Published by J. Wiley. Doi: 10.1002/0470013435 Basch C. E., DeCicco I. M., & Malfetti J. L. (1989). A Focus Group Study on Decision Processes of Young Drivers: Reasons that May Support a Decision to Drink and Drive. Health Education Quarterly, 16 (3), 389-396 Bownas D. A. (1985). A Quantitative Approach to Evaluating Training Curriculum Content Sampling Adequacy. Personnel Psychology. Published by Blackwell Publishing Ltd. 1985, 38 (1) Claire A. (2010). Presenting and evaluation qualitative research. American Journal of Pharmaceutical Education. 74 (8). Curtis S. R. (2004). Attachment self-report questionnaires: Refining the method. Loma Linda University. Published by Polskie Towarzystwo Psychiatryczne. PP: 93 Hinton P. R. (2004). SPSS Explained. Doi: 10.4324/9780203642597 Kolb B. (2008). Marketing research. Published by SAGE Publications Ltd. MENA Report (2013). Australia : Online Shopping in Australia to account for nearly 10% of total retail sales by 2017. Published by Albawaba (London) Ltd. Miyazaki, A. D., & Fernandez, A. (2001). Consumer perceptions of privacy and security risks for online shopping. The Journal of Consumer Affairs, 35(1), 27-44. doi:10.1111/j.1745-6606.2001.tb00101.x Moster C. A. (1952). Quota Sampling. Journal of the Royal Statistical Society. Published by Blackwell. 1952, 115 (3). Roe B. & Webb C. (1998). Quantitative research design. Research and Development in Clinical Nursing Practice. Published by Whurr Publishers Ltd. PP: 112-134 Rodgers, S., & Thorson, E. (2012). Advertising theory (1st ed.). GB: Routledge Ltd. 20 N9265503 YANG SHAN (TAK) doi:10.4324/9780203149546 Sontakki, C.N. (2010). Marketing Research. Published by Global Media. Watson R. (2015). Quantitative Research. Nursing standard (Royal College of Nursing (Great Britain) : 1987), 04/2015, Volume 29, Issue 31. Publisher: RCNi Zikmund W., Lowe B., Babin B, D’Alessandro S., & Winzar H. (2014). Marketing Research: an Asia-Pacific perspective. South Melbourne: Cengage Learning Australia 21