Introduction and Background



AMB201 – Market Audience and ResearchQuantitative ReportStudent Name: Claire GordonStudent Number: n9476393Due Date: 22nd October, 2015Tutor Name: Jennifer DoigWord Count: 1912Table of Contents TOC \o "1-3" 1.0Introduction and Background PAGEREF _Toc307065602 \h 51.1Importance of the Research PAGEREF _Toc307065603 \h 51.2Scope of the Report PAGEREF _Toc307065604 \h 52.0Method PAGEREF _Toc307065605 \h 62.1Methodological Considerations and Assumptions PAGEREF _Toc307065606 \h 64.0Analysis PAGEREF _Toc307065607 \h 94.1Description of Data Undertaken PAGEREF _Toc307065608 \h 94.3T-Test Analysis PAGEREF _Toc307065609 \h 124.4Correlation PAGEREF _Toc307065610 \h 144.5Multiple Regression PAGEREF _Toc307065611 \h 154.6Social Desirability PAGEREF _Toc307065612 \h 165.0Discussion and Recommendations PAGEREF _Toc307065613 \h 175.1Objective 1: To Explore the Perceptions of Online Retail Shopping PAGEREF _Toc307065614 \h 175.2Objective 2: To Examine Motivations for Engaging in Online Retail Shopping PAGEREF _Toc307065615 \h 175.3Objective 3: To Understand the Types of People who Shop Online PAGEREF _Toc307065616 \h 186.0Limitations PAGEREF _Toc307065617 \h 197.0References PAGEREF _Toc307065618 \h 20Participation Reflection In AMB201, I have had the opportunity to take part in real research problems current being run within the business school. To be a good researcher, it includes the ability to take part as a participant and gain another perspective in researching. Part of my contribution in AMB201 included taking part in 2 quantitative studies. My chosen research included two studies – one about employee gratitude, and another about consumer attitudes towards television commercials. Each of these studies took approximately 30 minutes to complete, and were available to be completed online. When deciding which studies to complete, my main priority was to chose the surveys which I could complete online. With current work and university commitments, I believed it would be much more convenient for me to access the studies online. As I needed to complete two studies rather than one, this meant that I would not need to organise two times to travel to uni to complete the surveys, and instead could be easily accessed from my laptop with flexible times. When choosing the online research that I would take part in, it was crucial that I decided on topics that were relevant to me, and that I could contribute a lot to in order to assist researchers. Firstly, I completed a survey, which detailed employee gratitude. As I am currently employed, I met the requirements for this study and therefore believed I could contribute a lot to this research topic in detailing my work experiences, and how I felt towards my workplace. The other chosen survey topic included examining consumer attitudes towards television advertisements. This research involved watching several TV advertisements, and then selecting answers that represented my attitude towards the ad. There were no eligibility requirements for this study, and therefore I could complete this survey effectively. On completing these surveys, I was able to gain insight into quantitative research, and the types of closed-ended question that are generally asked in surveys. I was able to understand that in surveys, it is very important to always provide honest and accurate answers in order to assist researchers to gain as much information as possible. I found that being able to take the role as a participant allowed me to gain an understanding from the perspective of a participant, so I can take this into consideration when asking two people to complete my survey.I believe that it is extremely important for researchers to also take part in research as a participant. By participating in research as well as conducting research, it allows for a greater understanding in both perspectives the further enhance the quality of research undertaken. Being a participant allows for researchers to understand the type of questions that are asked in quantitative research, and the importance of providing accurate answers. Executive SummaryThe following report investigated the attitudes of Australian consumers who purchase items online. Surveys were conducted on males and females, between the ages 18-40 and 40+. These surveys revealed data that allowed researchers to gain insight into the perceptions and motivations of online shopping, as well as the types of people who are likely to shop online. Data was clean, coded and analysed in order to identify key points, which related to the objectives in understanding perceptions, motivations and likely participants of online shopping. It was found that people with innovative and unconventional characteristics were more likely to have behavioural intentions to shop online and therefore perceive online shopping favourably. It was then determined that the key motivations behind online shopping were variety and convenience seeking, as well as price. It was revealed that people who have some social desirability are likely to shop online also. Introduction and BackgroundImportance of the ResearchIn Australia, the prevalence of online shopping in growing considerably. Research from the Australian Bureau of Statistics (2014) showed that out of 4 Australians shopping online during the period of 1 year. Market research is critical in order to understand consumer patterns and habits. In particular, applying market research to online shopping allows researchers to gain understanding from a theoretical and practical viewpoint and as a result make conclusions about online shopping in Australia. Market research can achieve a greater understanding about consumers, both others and ourselves, in order to make future recommendations to assist stakeholders. As qualitative research has been applied to online shopping and patterns were determined, quantitative research will further enrich an understanding of online shopping in Australia in order to determine distinct patterns through data collection. Scope of the ReportThe following report will investigate online shopping habits and attitude within Australia. This report will reveal the detriments of online shopping behaviour using quantitative methods including surveys. The data be gathered from male and females over a range of ages, however all respondents are over the age of 18. Specific objectives will assist in aiming to data in order to best understand and evaluate the quantitative research. This report will not include data from the whole Australian population, rather a selected sample available. Research Problem/Question The research question identified for this investigation was: What are the determinants of Australian consumers’ attitudes toward online retail shopping? This research problem is important in order to generate objectives and surveys, which gain insight to make conclusions about online shopping in Australia. Aims and ObjectivesThe aim of this research activity was to use quantitative research in order to analyse the determinants of Australian consumer’s attitudes toward online retail shopping. These were done through three key objectives:1. To examine how self-concept dimensions relate to online retail shopping attitudes.2. To determine the impact of individual characteristics on online retail shopping attitudes.3. To evaluate how social desirability might influence the results of the research.Method Methodological Considerations and AssumptionsThe nature of this research activity used quantitative data, using surveys to examine to drivers of online retail shopping behaviour. Quantitative research is described as explaining a phenomenon by collecting numerical data that are analysed using mathematically based methods (Norman Blaikie). As qualitative data has already been applied to the research problem, using a quantitative approach allows for researchers to gain another perspective and further understanding into Australian consumers and online shopping. Cross-sectional research was used as opposed to longitude; in order to gain accurate answers in a short period of time. A survey was used to conduct research, in order to represent an honest and reliable response.Sample ConsiderationsWhen considering the samples for this research activity, non-probability sampling was used. Due to limitations of time and money, the whole population could not be randomly sampled (Hair, Joseph F). Therefore, convenience sampled was used to conduct these surveys. Each researcher was able to choose a respondent that was easily accessible to them, such as a family member or friend. The targeted respondents were aged into two categories: 18-40 year olds, and people aged 40+. There was a total of 659 respondents to the surveys, with 333 respondents aged 18-40 (50.5%), and 326 respondents aged 40+ (49.5%). Out of the 659 respondents, there were 583 Australians, and 76 not Australians. The sample consisted of 325 males (49.3%) and 334 females (50.7).Data Collection and Framework, and Analytical ConsiderationThere were a series of steps undertaken to collect the data required for the activity. To begin, each researcher was required to select 2 respondents to complete the online survey questionnaire. These respondents were required to be male or female, depending on the letter of their last name. Students with the last name beginning A-L selected two males, and students with last names beginning M-Z selected two females. Before completing the survey, respondents were required to complete an ethical approval form.Once ethical consideration forms were complete, surveys took place. Surveys took approximately 10-15 minutes to complete. As surveys were a short period of time, respondents were more likely to successfully complete the survey as opposed to a survey, which may have taken 30 minutes to complete. Surveys consisted of a range of questions, such as agreeing or disagreeing to a statement, or indicated yes or no to a question. Survey questions were close-ended, however some questions including: ‘What is your nationality?’ allowed respondents to supply their own answer. Surveys were completed on a hard copy, however were later uploaded onto a secure database by researchers. Once all of the surveys had been completed and uploaded online, Clinton Weeks then sorted and analysed the data accordingly. From this, students used this data to further analyse and make conclusions about online shopping in Australia. Ethical ConsiderationsEthical consideration is extremely important in order to protect the participant’s personal information, maintain professional standards, safeguarding the rights and wellbeing of participants, and risk management (Deakin University, 2015). As market research involves a large number of stakeholders, it is vital the ethical standards are implemented. In this research study, it was critical that ethical considerations were applied. Part of the research activity required participants to respond to closed ended questions based on their personal opinions, and also provide person details including name, age, relationship status and postcode. In order to act in an ethical manner, respondents received an ethical approval form to read and sign before commencing the study. The ethical approval form outlined the participation that was required for the interview, the expected benefits, risks, privacy and confidentiality. All surveys were posted on a safe database, which could only be accessed by students to upload survey data during a certain time period. After this, the survey data was made available to students again in a range of Excel documents, however all respondents were left unidentifiable. All data provided in the research activity is protected by QUT, and is able accessible to the research team involved in the project as outlined in the ethics conduct form. AnalysisDescription of Data UndertakenAfter surveys were conducted and uploaded online, the AMB201 data was cleaned and coded. The cleaning process was a vital stage in the analysis of results, and issues were identified where respondents could freely enter data. Coding data ensures reliability and consistency among each respondent to ensure the most accurate data possible. Data cleaning occurred in the following stages:Deleting respondents from the research when they provided uninterpretable responses.Deleting respondents when providing a non-existent postcode.Changing suburbs into the appropriate postcodeChanging a respondent’s birth year to years.Altering mixed text into numbers only.Approximations changed into appropriate estimations.Throughout the coding process, reverse coding was applied to questions where the intended response changed to negative. Reverse coding is applied to order to create an accurate average of the data. If a respondent answered 7,7,1,1 – the average would be 4, however if applied reverse coding, the average would be 7. The process of reverse coding was applied by reversing negatively scaled items such as 1-7, 26.Descriptive DataFigure 1 – Construct MeanNumberMeanATTBI6594.9727Risk Aversion6594.6171Impulsiveness6593.7075Variety Seeking6594.4917Convenience Seeking6594.7657Price Consciousness 6594.9522Social Desirability6591.4980Figure 1 demonstrates the mean of each construct relevant to the analyses. It was determined that the dependent variable demonstrates the highest mean of 4.9727. Meanwhile social desirability calculated mean was the lowest at 1.4980. Figure 2 – Age Frequency Frequency PercentYounger33350.5Older32649.5Total659100Figure 2 demonstrates that the highest age frequency was in the younger age bracket. It was found that there 333 people in the 18-40 year old bracket, making 50.5% of the sample, and 326 people in the 40+ year old bracket, making the other 49.5% bracket. Figure 3 – Nationality Frequency FrequencyPercentAustralian 58388.5Not Australian7611.5Total659100Figure 3 demonstrates that there were 583 Australians (88.5%) who completed the survey, and 76 (11.5%) Non-Australians. One of the requirements for this research was for respondents to be Australians who online shop. Figure 4 – Gender FrequencyFrequencyPercentMale32549.3Female33450.7Total659100Figure 4 clearly indicates that the highest frequency of respondents were females, with 334 respondents making up 50.7% of the data. 325 males made up 49.3% of data, indicating an unequal sample. This may be a result of fewer students with the last names beginning A-L.Figure 5 – Age Distribution Figure 5 shows the distribution of ages among respondents. It is evident from this that the younger age bracket had a high proportion of respondents aged 20 years old. In the older age bracket, the majority of respondents were aged 40-60, and there were few respondents aged 60+. Figure 6 – Age Cross TabulationYoungerOlderTotalMale166159325Female167167334Total333326659Figure 6 demonstrates that there was an even split in the females, consisting on 167 females in the younger age bracket and 167 females in the older age bracket. It was also evident that males had a relatively even split however there were more male respondents in the younger bracket than the older age bracket.T-Test AnalysisFigure 7 – Group StatisticsQ: Does attitude toward online retail shopping (AATBI) differ between younger and older people?Age CohortNumberMeanStd. DeviationStd. Error MeanATTBIYounger3335.53351.220350.06687Older3264.39981.537680.08516Figure 7 shows that the mean for online shopping behaviour for the younger age bracket (5.5335) was higher than the older age bracket (4.3998). This indicates that younger generations have a more positive attitude towards online retail shopping than older generations. Figure 8 – Independent Samples TestLevene’s Test for Equality of VariancesT-Test for Equality of MeansFSig.tdfSig. (2 tailed test)Mean DifferenceStd. Error DifferenceATTBIEqual Variances Assumed27.071.00010.495657.0001.133740.10802Figure 8 shows the statistical measurement between the two means. The t-test measured a sig.(2-tailed test) of .000, which is lower than 0.005. It can be concluded that there was a significant difference between older and younger generation and their behavioural intentions towards online shopping. Figure 9 – Group StatisticsQ: Does attitude toward online retail shopping differ between males and females?GenderNumberMeanStd. DeviationStd. Error MeanATTBIMale3254.90051.493020.08282Female3345.04291.499800.08207Figure 7 shows that the mean for online shopping behaviour for males (4.9005) was lower than females (5.0429). This indicates that males are likely to have a less positive attitude to online shopping than females. Figure 8 – Independent Samples TestLevene’s Test for Equality of VariancesT-Test for Equality of MeansFSig.tdfSig. (2 tailed test)Mean DifferenceStd. Error DifferenceATTBIEqual Variances Assumed0.270603-1.221657.222-142400.11660Figure 8 shows the statistical measurement between the two means of males and females. The t-test measured a sig.(2-tailed test) of .222, which is higher than 0.005, indicating that there is no statistical difference between the two means, and no difference between the male and female online shopping attitudes. CorrelationFigure 9 – Correlations of Self Concept Dimensions with ATTBIDimensionATTBIConventional – Unconventional Pearson CorrelationSig. (2 tailed)N.150**.000659Innovative – RoutinePearson CorrelationSig. (2 tailed)N -.238**.000659Uncomfortable – Comfortable Pearson CorrelationSig. (2 tailed)N.022.565659Figure 9 examines three self-concepts. The first dimension of conventional/unconventional found that there was a significant relationship with this behaviour as .000 is less than 0.05. A correlation of 0.150 indicated a weak positive strength, indicating that as respondents rated more unconventional the behavioural intentions increased. The next concept evaluated innovative/routine. It was determined there was a significant relationship as .000 is less than 0.05. A correlation of -.238 indicated a moderate negative relationship. Therefore, behavioural intentions increased as respondents selected innovative. The last dimension identified was uncomfortable/comfortable. Figure 9 determines that there was a non-significant relationship as .565 is more than 0.05. The strength of correlation was weak positive at 0.22. Therefore, there was little relationship between responses and behavioural intentions. Multiple RegressionFigure 10 – Model SummaryModelRAdjusted R Square1.623.383R indicates the strength of correlation between the predicted and observed values of the dependent variable, which recorded .623. The adjusted R square was .383 which mean that 38.3% of variation is accounted for by the model. Figure 11 – ANOVASum of SquaresdfMean SquareFSig.RegressionResidualTotal572.090902.5301474.6195653658114.4181.38282.784.000Dependent Variable: ATTBIPredictors: (Constant), Price Conciousness, Risk Aversion, Convenience Seeking, Variety Seeking, ImpulsivenessFigure 11 determines that the F statistic is less than 0.05 (0.00), therefore the predictors do a good job explaining the variation in the dependent variable. Figure 12 – CoefficientsBetaSig.ConstantRisk AversionImpulsivenessVariety SeekingConvenience SeekingPrice Consciousness-.366-.005.290.285.060000.000.879.000.000.066Figure 12 demonstrates that all models were significant, except for Impulsiveness and Price Consciousness, which had significance levels above 0.05. Variety Seeking had the strongest impact on Behaviour intentions and online shopping.Social DesirabilityFigure 13 – Social Desirability FrequencyFigure 13 shows the distribution of results, where yes was scored 1 and no was scored 2. Therefore the average mean for social desirability was scored as 1.5 indicating no relationship between social desirability and online shopping. Discussion and RecommendationsObjective 1: To Explore the Perceptions of Online Retail ShoppingAfter analysing the data obtained from the surveys, there were evident findings relating to the objectives of exploring online perceptions. It was found that when analysing the correlations of self-concept dimensions, unconventional and innovative had impacts on online shopping behavioural intentions. It was found that when consumers were unconventional and innovative, they were more likely to present behavioural intentions to shop online. It can be concluded from this that people with more outgoing and curious personality may be more likely to shop online. By understanding the self-concept dimensions relating to online shoppers, it will benefit future marketers. From this they will be able to evaluate how to advertise online shopping specifically to target people who display innovative and unconventional lifestyles. This pattern was found to be predictable, as it would be expected that routine and conventional people are more likely to stick to traditional shopping methods. Objective 2: To Examine Motivations for Engaging in Online Retail ShoppingThe next objective was to examine motivation for engaging in online retail shopping. This was determined through analysis of coefficients. It was found that Variety Seeking had the strongest impact on behavioural intentions to shop online. Followed was Convenience Seeking, and the other motivation with a positive impact on behavioural intentions was Price Consciousness. These observations are critical to future market researchers, in being able to determine the main motivations behind online shopping. Therefore, marketers can emphasise convenience and variety as the key motivations, and use this to advertise and promote online shopping accordingly. Objective 3: To Understand the Types of People who Shop OnlineThe final observations from this research study related to understanding the types of people who shop online. Social desirability was the key determinant into the type person to show behavioural intentions to online shop. It was found that the majority of respondents were situated around the average. This therefore indicated that people who online shop are likely to have some desirability to present themselves well. By gaining understanding into the types of people who are likely to shop online, it will assist marketers in being able to identify a target market and effectively reach them. Analysis undertaken throughout this report was able to successfully reach a conclusion to the research topic, in being able to identify the determinants of Australian consumers attitudes toward online shopping. Statistical analysis specifically was able to isolate the key points that related to the objectives. LimitationsWith any type of research project, there is susceptibility to issues, which may limit or alter the quality of the research. The main limitation identified with this research was the inability to determine if surveys conducted were credible. As students uploaded surveys, there was susceptibility for data to be altered/created and decrease the accuracy of results. The main factors influencing this are inconvenience and students being time poor. Another limitation identified was the sample size of the data. The total number of surveys completed was 659. Whilst this was suitable for the purpose of the report, this is not an accurate representation of the Australian online shopping population. One occurring issue with the sample size was the age distribution. As convenience sampling was used, students chose participants who were friends/family. Therefore, was a large amount of respondent’s aged 20, and was therefore not an accurate representation of the age bracket 18-40. In future, AMB201 can create smaller age brackets such as 18-30, 30-40, 40-60, 60-80. This would create a greater distribution of ages, and overall increase the accuracy of results. Whilst data will not always be completely accurate, these methods can be implemented in order to increase the credibility and reliability of data. ReferencesAustralian Bureau of Statistics (2013). Household use of Information Technology 2012-13 [Online]. Retrieved from: University (2015). The Importance of Research Ethics & Why is it Important? [Online]. Retreived from: , Joseph F. Marketing Research (4th Edition). New South Wales, NSW: McGraw-Hill EducationNorman Blaikie (2003). Analysing Quantitative Data. Sage Publications. Page 1. Retrieved from: ................
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