Respondents' Attitude toward Factors of Internet Shopping - Assignment Example

?Dissertation: Chapter 4 Contents Chapter 4: Data presentation, analysis and results 3 4 Introduction 3 4.2 Descriptive analysis 3 4.3 Demographic profile 4 4.4 Respondents' attitude toward relevant factors of Internet shopping 7 The results:- 8 4.5 Conclusion 25 Reference 26 Chapter 4: Data presentation, analysis and results 4.1 Introduction The present dissertation deals with the theme, “The factors influencing the Thai consumer’s online shopping decision”. We have already discussed the potential factors in the previous chapters. We came to know that factors like trust, consumer rights and security, attitude, intentions, culture, perception, personal values and environmental effect, word of mouth and personal experience plays a significant role in directing the decision making process of the consumer. In this chapter of the dissertation, we will showcase the data gathered, the analysis done and the results drawn from those analyses. 4.2 Descriptive analysis Throughout the study we have mainly done the analysis part with the help of one-way ANOVA and T test. ANOVA is an assortment of statistical tools that helps the reader to arrive at a certain inference. Inference is the process of obtaining rational conclusions from some assumed or known assertions. ANOVA analyzes the deviation between the means of various groups as well as the deviation that happens to take place within the groups (physics.csbsju.edu, n.d.). Moreover in an ANOVA background, the detected variance existing in a definite variable is classified into particular components that feature a range of variation sources. ANOVA can be used for multiple factors as well as for a single factor. In the present dissertation we have divided the main questions into seven groups and for each group we have conducted one way ANOVA i.e. ANOVA for single factor (csse.monash.edu.au, n.d.). The single factor ANOVA is used to differentiate between the mean values of two or more samples with the application of F test (people.richland.edu, n.d.). This procedure is only suitable for analyzing numerical data. The technique involves testing the null hypothesis of the samples belonging to two or more groups within a population having equal mean values. The ANOVA generates an F statistic, where if the mean values of the groups are obtained from population having similar mean values, then the group means variance should be lesser than that of the variance contained by the samples (Leitzel, 2009). On the contrary a greater ratio consequently signifies that the samples were gathered from the populations having different value for their means. After the vivid description of the ANOVA technique, we would now discuss the T test. It is conducted for testing statistical hypothesis in which the statistics of the test pursue a Student’s t distribution in the case when the null hypothesis is supported. We are considering the T test because the standard deviation of the population here is unknown. The “Levene’s Test for Equality of Variances” have been used in the analysis of the data as this test provides us with the information stating whether a hypothesis of the t-test has been achieved (Engineering Statistics Handbook, n.d.). T-test presumes that the inconsistency of each group remains almost same. When the assumptions of the t test are not met, then a specific kind of the t-test is applied which we will discuss in the next sections of the chapter. 4.3 Demographic profile The demographic profiles of the consumers both belonging to the UK and Thailand will be discussed in this section. Here we have used the descriptive statistics technique for analyzing the data gathered relating to the demographic profiles of the respondents. This technique is basically used for summarizing a sample and it is different from inferential statistics (Khan Academy, 2013), where the data is used for learning about a population which the sample has been assumed to represent. Moreover descriptive statistics is not based on probability theory unlike the inferential statistics. We will start with the descriptive statistical analysis of the respondents in Thailand. For the rest part of the analysis we must keep in mind that the total number of respondents in Thailand is 88 and in UK the number is 75. The given table showcases the data and its analysis- Here the approximate male, female ratio is 32:56 which means the majority of the respondents consisted of females. Among those respondents almost 84% people belonged to the age-group 20-29. Also the respondents exhibit that around 37 out of 88 belong to the undergraduate level. Nearly 93% of the respondents responded positively when asked whether they have any experience regarding the online shopping procedure and around 33% people said that they opt for online shopping once or twice a month. Thus through these details we can assume that mostly the female shoppers of Thailand are habituated to the online shopping technique and majority of these females are young consisting of college going girls, job holding ladies and married women. As the respondents have mentioned that they go for online shopping once or twice a month, therefore it can be assumed that the country’s economy is turning slower which in turn is hindering the frequent online shopping activities. We can relate this situation with the prevailing recession in the country that started in the 2nd quarter of 2013. The 0.3% reduction in GDP from April to June trailed an earlier contraction of 1.7% throughout the 1st quarter of the current year. According to the analysts, the main factors responsible for the recession are growing prices and rising household arrears which had caused a dropped rate of interest during the month of May (BBC News, 2013). Next we will discuss the analysis of the feedbacks obtained from the respondents staying at UK. The above table exhibits almost the similar traits as those of the respondents of Thailand. In this case also we find that the majority (73%) of the respondents comprised of females belonging to the age group 20-29 years (63%) and most of them having an online shopping experience (nearly 92%). However, even after staying at UK, which is not going through any kind of economic downturn at the recent times (BBC News, 2013), the Thai respondents showcases alike traits as that of the shoppers staying at Thailand. This implies that even though the people are staying at different places, their perception and habits related to online shopping activities demonstrates similar outcomes. 4.4 Respondents' attitude toward relevant factors of Internet shopping For the analysis of this part of the findings we have used the ANOVA, T test, F test and Levene’s Test for Equality of Variances. We have proceeded by dividing the entire questionnaire into seven categories. For each category we have analyzed the gathered data by the application of the above mentioned inferential statistical tools. For interpreting the result of the analysis we should keep in mind- 1. We assume that the null hypothesis (Ho) is “Two target groups demonstrate different results as an impact of living in separate countries in spite of belonging to the same country.” 2. The null hypothesis is considered as true. 3. The alternative hypothesis (H1) automatically becomes “Two target groups are living in different countries but still they are demonstrating similar results due to the reason that they belong to the same country.” 4. The level of significance implies the estimation of the reliability of results obtained from a survey on a randomly chosen sample which can be applied in a generalized term on the population to which the sample belongs. Here the level of significance is .05 (numberwatch.co.uk, n.d.). It is generally accepted that if the p-value is equal to or lower than the level of significance, then the null hypothesis gets rejected. The results:- Trust in Online Shopping In the ANOVA table, we can see that the parameters trust and security, Reputation of website and Playful features of website encourage the consumers to use online shopping, presents significance level values of .013, .834 and .010 respectively, which means for the 1st and 3rd parameter the null hypothesis gets rejected (academic.udayton.edu, n.d.). This portion of the analysis provides the descriptive statistics for both the groups. For instance we can consider the 1st parameter that denotes, 88 respondents belonging to Thailand, and they have, 3.22 people on average from Thailand who thinks that trust and security is a major concern for making purchase over the internet, with a standard deviation of .651. There are 75 respondents from the UK, and they have, 3.47 respondents on average from the UK who thinks that trust and security is a major concern for making purchase over the internet, with a standard deviation of .072. Thus it can be concluded from this information as the mean and standard deviations do not form a huge difference, therefore it can be assumed that the consumers of both the countries have similar views about online shopping. The column named "Sig” denotes the significance value and for the 1st parameter the value of significance is .484 (people.vcu.edu, n.d.). When this value is equal to or lesser than .05, then we can reject the null hypothesis by considering the row named "Equal variances not assumed." However if the value is greater than .05 then we will consider the "Equal variances assumed” row. For the 1st parameter, as the significance value is greater than .05 therefore we consider the “equal variances assumed” row, according which the value of T is 2.50. The column "Sig. (2-tailed)" presents the two-tailed p value related to the test. In this case, it is .013. Hence the decision criterion is stated by: If p ? ?, reject H0. Thus in this case, .013 is less than or equal to .05, so we can reject H0 (academic.udayton.edu, n.d.). Consumer rights and security The ANOVA table shows that the parameters like feeling uncomfortable about paying money without the assurance of the product quality, worrying about the theft of credit card number and worrying about the non-delivery of the products scare the consumers to use online shopping, and these data presents significance level values of .274, .934 and .427 respectively, which means all the values exceed .05 and in this case the null hypothesis doesn’t get rejected. The group statistics portion of the analysis provides the descriptive statistics for both the groups. We can consider the 1st parameter that denotes, out of 88 respondents belonging to Thailand, 3.17 people on average feels uncomfortable to pay money before seeing the product. In UK, this number is 3.05. For the 2nd and 3rd parameters we find that 3.33, 3.32 and 3.13, 3.21 are the average people stating the parameters to be true, belonging to Thailand and UK respectively. The column named "Sig” under the independent samples test table, denotes the significance value and for the 1st, 2nd and 3rd parameters the value of significance are .495, .682 and .886. Just like the earlier analysis here we will consider the “equal variances assumed” row for all the three values since the values are more than the p value. Thus the value of T comes becomes 1.098, 0.083 and 0.796. In this case the null hypothesis doesn’t get rejected and for this parameter it is considered true that two target groups demonstrate different results as an impact of living in separate countries in spite of belonging to the same country. The reason for this variation might be the difference in consumer rights and security policies between UK and Thailand. Attitude and online shopping The ANOVA table shows that the parameters like Clarity of information and having positive attitude towards online shopping encourages shoppers towards online shopping. This data presents significance level values of .922 and .853 respectively, which means all the values exceed .05 and in this case the null hypothesis doesn’t get rejected. The group statistics portion of the analysis states that, out of 88 respondents belonging to Thailand, 3.25 people on average feels that getting detailed information about the product influences their shopping decision. In UK, this number is 3.24. Similarly the next parameter shows average number of 2.81 and 2.83 people belonging to Thailand and UK respectively that has got positive attitude for online shopping. The "Sig” column under the independent samples test table, presents 0.198 and 0.089 significance values for the two parameters respectively and it means that the values exceed the p value. In this case the null hypothesis is considered to be true. The reason for this variation might be due to the difference in product manufacturers, product packaging or product dealers between UK and Thailand. Intentions in online shopping Here the ANOVA table shows that the parameters like friend’s influence, family influence, price, promotion and convenience factor encourages shoppers towards online shopping. This data presents significance level values of .003, .593, .918, .942 and .307 respectively, which means all the values exceed .05 except the friend’s influence and in this case the null hypothesis doesn’t get rejected for the other factors except the friend’s influence parameter. Hence it can be stated that all other factors might not have equal impacts on the consumer’s online shopping behavior, but when they get influenced by their friends then the country where they stay doesn’t play any significant role in affecting their decision. The group statistics portion of the analysis states that, almost all the mean values between Thailand and UK are similar and this means on average, more or less both the countries showcase similar behaviors regarding this parameter. The independent samples test table, shows that all other values are greater than the p value except family influence. Thus only for the family influence parameter, the null hypothesis get rejected. The analysis shows that friends and family has great influence on the shopper’s intention towards making online purchase and this impact is equally applicable for both the countries. Personal perceptions on online shopping Here the ANOVA table shows that the parameters like quality of service, personal recommendation and preference for online shopping in the near future provides a positive inclination of the consumers towards online shopping. This data presents significance level values of .051 and .673 for the parameters like quality of service and preference for online shopping in the near future respectively, which means that in the case of quality the null hypothesis, gets rejected. Hence we can conclude quality plays a major role in the decision making process of the consumers, both in UK and Thailand. The group statistics portion of the analysis states that, both the mean values between Thailand and UK are almost similar. Where Thailand exhibits 3.07 average people stating quality as a major factor, UK demonstrates 2.84 average people with similar consideration. It means on average, more or less both the country’s shoppers behave alike. The independent samples test table, shows that the quality aspect is lower than the p value which means quality is considered to be a crucial factor by the residents of both UK and Thailand when they opt for online shopping. Personal altruistic values and the environmental effect Here the ANOVA table shows that the parameters like local facilities for online shopping, having internet and being in love with online shopping are the factors that display a positive trend for online shopping both in UK and Thailand. According to this data, the local area facilitating online shopping shows significance value of .048 which is lower than the p value. Hence only in this case the hypothesis gets rejected, therefore it can be concluded that encouragement by the locality is an essential factor for online shopping and it is applicable for both the countries. The group statistics portion of demonstrates an almost similar mean value which again proves that these parameters are true for both the inhabitants of UK and Thailand with minimal deviations. The independent samples test table, shows that all the aspects are greater than the p value hence the null hypothesis gets established. These variations might be caused due to differences in the shopping behavior of the local residents of UK and Thailand. Another reason could be the swift service of the internet which might cause great difference among the pattern of online shopping in UK and Thailand. Culture and online shopping Here the ANOVA table shows that the parameters like cultural factors and positive predictions about the online shopping trend influences the consumer behavior to a great extent. According to this data, the significance values for both the factors are greater than the p value. However in the The independent samples test table, the significance level for the cultural factor is shown as .011 which is lower than the p value and thus the null hypothesis gets rejected in this case. For the future prediction parameter the significance value is greater than the p value and therefore the null hypothesis gets established in this case. The group statistics portion however displays almost similar mean values. This conflicting response might have been caused due to the presence of both similar and dissimilar cultural characteristics between the countries of UK and Thailand. On the contrary the average responses states that people residing in both the countries exhibit some almost similar behavioral traits regarding online shopping. 4.5 Conclusion It can be concluded from the entire analysis that factors like quality, family influence, friend’s influence, trust, security of consumers, attractiveness of the website, and culture plays a significant role in influencing the decision making process for the online shoppers of UK and Thailand. However the other factors like price, promotion, local facilities, reputation of the website etc. seems to be dominant in one country and insignificant in the other. Thus it can be stated that there are certain factors which influences the consumer’s purchase decision irrespective of the place where they reside. It’s true that the country where the consumer resides has got some impact on his/her buying preference but certain factors like suggestion from a close friend or considering the quality of a product plays dominating role in influencing the choice of the consumer as these are the basic criteria and the most trusted factors common to consumers of the entire world. Reference 1. academic.udayton.edu. n.d. Using SPSS for One Way Analysis of Variance. [Online] Available at < http://academic.udayton.edu/gregelvers/psy216/spss/1wayanova.htm> [Accessed 13th September, 2013] 2. academic.udayton.edu. n.d. Using SPSS for t Tests. [Online] Available at < http://academic.udayton.edu/gregelvers/psy216/spss/ttests.htm > [Accessed 13th September, 2013] 3. BBC News, 2013. British Chambers of Commerce: Recovery gaining momentum. 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