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Table of Contents

CHAPTER 4: DATA ANALYSIS AND DISCUSSION

Introduction

This chapter is designed for analysing the data collected for the purpose of achieving objectives of the present study. This chapter includes the demographic analysis which highlights the general information about the participants. In addition to this, this chapter also includes a descriptive analysis which is based on the analysing the questionnaire results as filled by the participants of the research. Moreover, this chapter also correlation analysis in which the researcher has analysed the correlation between the variables on the basis of Pearson’s Correlation. Furthermore, this chapter includes regression analysis in which the researcher has analysed regression which is also presented with a regression equation. Finally, the chapter concludes with hypotheses assessment and overall discussion of the objectives.

Demographic Analysis

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Table 1: Gender

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Figure 1: Gender

The table and pie chart presented above portrays that out of 100 participants 58 were male while 42 were female who participated in the research.

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Table 2: Age

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From the table and graph presented above, it can be analysed that out of 100 participants of the research, 27 falls under the category of 20-24 years, 41 falls under the age bracket 25-29 years, 23 participants were from the age bracket of 30-34 years, 8 participants were from the age group of 35-39 years where only participant was from the age above 40 years. Most of the participants of the survey were from the age bracket of 25-29 years and apparently all of the participants were the customers of Jing Dong and Taobao.

Descriptive Analysis

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Table 3: Income of the person influences his buying patterns

From the table presented above, it can be analysed that out of 100 participants of the survey, 34 participants strongly agree with the question statement that income of the person influences his patterns of purchasing with respect to online retail websites. The results further represent that around 36 participants of the survey agree to the question statement. Moreover, 29 remained neutral to the statement where only 1 participant strongly disagrees with the statement. The results confirm the study of Powers and Jack (2013) which stated that income has a significant impact on the purchasing behaviour.

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Table 4: People purchases the products which advocate their role in society

From the table illustrated above, it reflects that out of 100 participants of the research survey 28 participants strongly agree with the question statement that people purchases those online products which advocated their role in the society. On the contrary side, there were 52 participants who agree with the statement where only 18 participants remained neutral to the statement while only two participants disagree with the statement. The results, however, reflects the study of Hollensen (2015) in which it was stated that online purchases of the customers are reflected by their social role in the society.

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Table 5: Purchases from online websites reflects a consumer lifestyle.

From the table presented above, it reveals that out of 100 participants of the research survey 33 participants strongly agree with the question statement that implies that purchases from online retail websites reflect a consumer lifestyle. On the contrary side, 44 of the total participants agrees with the question statement where only 20 participants’ remained neutral while only 1 participant disagrees with the statement. The results were significant with the study of Imam (2013) in which it was mentioned that customers purchases products from the online retailing websites as it is reflected from their lifestyle.

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Table 6: Social Class can influence behaviour of the customers

The table presented above explains that out of 100 participants there were 45 participants who strongly agree with the statement where 38 only agree with the question statement that social class can influence the behaviour of the customers on online retail websites. However, only 12 participants remained neutral with the statement while in total of 5 participants disagrees with the statement. The results match with the outcomes of the study carried out by Jie (2010) which highlighted that social class of the people encourages them to purchase the product online.

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Table 7: Friends and Family influences the online purchase decision

The results presented above in the table is illustrating that out of 100 research participants of the survey conducted around 90 of the accumulated participants strongly agree with the statement that their friends and family influences their online purchase decisions from the online retail websites. On the contrary side, only 9 participants remained neutral to the statement where 1 participant disagrees with the question statement. The results can be related to the study of Verhagen and van Dolen (2011), which highlighted that reference groups are one of the most influential factors which influences the buying decision of the customer from the websites.

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Table 8: Online products purchases can meet social needs of the customers

The results presented above in the table is illustrating that out of 100 research participants of the survey conducted around 88 of the accumulated participants strongly agree with the statement that online retail stores motivates the customer to buy the products as it meets their social needs. On the contrary side, only 8 participants remained neutral to the statement where 4 participants disagree with the question statement. The results can be related to the study of Solomon (2014), which highlighted online products available on the websites enables the customer to purchase as it meets their expectations.

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Table 9: Consumer Perception towards Online products influences their buying decision

The table presented above highlights that out of 100 customers there were 49 participants who strongly agree with the statement while 38 participants agree with the question that the consumer perception towards online products along with the brand influences their buying decision. Furthermore, only 10 of the participants remained neutral with the statement while 3 disagrees in this nature of the question. The results are significant and compliant with the study of Shobeiri, Mazaheri and Laroche (2015) which highlighted that brand name influences the perception of the customers and further encourages purchasing the products online.

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Table 10: Level of motivation towards online products influences the buying behaviour

From the table presented above, it can be analysed that out of 100 participants of the survey, 42 participants strongly agree with the question statement that level of motivation towards online products influences the buying behaviour of the customer. The results further represent that around 37 participants of the survey agree to the question statement. Moreover, 18 remained neutral to the statement where only 3 participants strongly disagree with the statement. The results confirm the study of Oliver (2014) that online products available on different online retail websites motivates the customers to buy them online.

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Table 11: Online Products create a positive attitude of customers to buy products

From the table presented above, it can be analysed that out of 100 participants of the survey, 39 participants strongly agree with the question statement that online products create a positive attitude of the customers to buy the products from online retail websites. The results further represent that around 37 participants of the survey agree to the question statement. Moreover, 21 remained neutral to the statement where only 3 participants strongly disagree with the statement. The results are related to the study of Joung (2014) in which it was stated that positive attitude and perception towards the products which are offered by online retail store has an impact and can shape the buying process of the consumers.

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Table 12: Low risk in the online retail websites influences the customer behaviour

The table mentioned above highlights that out of 100 participants of the survey, 34 participants strongly agree with the question statement that low risk in the online retail websites can influence the behaviour of the customer as they can feel secure towards that website. The results further represent that around 44 participants of the survey agree to the question statement. In addition to the above statement, 18 remained neutral to the statement where only 4 participants strongly disagree with the statement. The results analysed reflects the study of Antoniou, Doukas, and Subrahmanyam (2013) which stated that attitude and perception towards the offering of the retails store changes when the risks are associated with the products and services offered on the online websites.

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Table 13: Consumers prefer to buy from those websites which have secure websites

The table presented above mentions that out of 100 participants of the survey, 36 participants strongly agree with the question statement that consumers prefers to buy products from those online websites which have a secured payment system. The result further signifies that around 34 participants of the survey agree to the question statement. In addition to the above statement, 28 participants remained neutral to the statement where only 2 participants strongly disagree with the statement. The results analysed reflects the study of Wilson (2012) which stated that the customers should have access to the internet and a valid payment method which can complete their transactions for instance credit card, debit card or should be using services such as PayPal which increases their trust towards the website with which they want to purchase products.

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Table 14: Consumers prefer to buy products from websites having low product risk

The results presented in the table above reflects that out of 100 participants of the survey 43 participants agrees with the statement that consumers prefer to buy products from those online websites which have less product risk. Furthermore, 31 participants agree with the statement where 26 respondents remained neutral to the statement. This statement was not denied by a single participant who reflects that product risk is important for the customers as they have invested their finances in the product for which they want a perfect product.

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Table 15: Online purchases are influenced by customer attitude

The results presented in the table above reflects that out of 100 participants of the survey 53 participants strongly agrees with the statement that online purchases are largely influenced by consumer attitude towards online retailing store. However, 29 out of 100 participants agree with the question statement. Furthermore, 18 respondents remained neutral to the statement. Only one participant strongly disagrees with the question statement. The results are significant with the study of Antoniou, Doukas, and Subrahmanyam (2013) which stated that consumer’s attitude has a significant impact on the behaviour of the customers.

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Table 17: Importance of performance of products

From the table presented above, it can be analysed that out of 100 participants of the research, there were 35 participants who strongly agree with the statement that the performance of the product should meet the expectations of the customers to influence their online purchases. In addition, 46 participants agree with the statement while only 14 respondents remained neutral to the statement. There were a total of 5 participants which strongly disagree with the statement. This reflects that performance of the products is important for the customers as it should be focused towards meeting their expectation in order to influence their online purchases.

Correlation Analysis

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Table 18: Correlation Table

The table above presents the correlation of the independent and dependent variables of the study. For the purpose of analysing the correlation between the variables for identifying the relationship between the variables, it is viewed from the Pearson Correlation. Furthermore, the sig value of the variables is also important in this scenario as it reflects the confirmation or rejection of hypotheses.  It can be asserted from the table mentioned above that there is a strong association between the variables for which the null hypotheses of the study is rejected. The value of the Pearson Correlation also represents strength and direction of the variables of the research. It can be observed from the values of Pearson correlation that all sig-value of the variables are greater than 0.6 and possess a positive sign which signifies positive and strong relationship existing between the variables.

 

Consumer Buying Behaviour

The regression analysis is used by the researcher in order to examine whether independent variables have a significant relationship with the dependent variable. The results of the regression analysis are explained from the tables provided below:

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Table 19: Model Summary

From the table of model summary presented above, it can be analysed that the value of R Square is 0.979 which implies that the predictors selected for the present research are signifying 97.9% of the variation with respect to the dependent variable. Therefore, the dependent variable can be significantly explained by the independent variable.

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Table 20: ANOVA Table

The ANOVA table explains the variance between the variables along with the reliability level which can be shown on the data along with the regression test. The sig value in the ANOVA table represents that the sig value is 0.000 which implies that dependent variable can be significantly explained by the predictors highlighted in the study.

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Table 21: Correlation Table

The coefficient table explains the results of the regression analysis whether there is a significant influence of predictors on the dependent variable. In this case, the sig values of the mediating and independent variables should be less than 0.005 for the purpose of rejecting null hypotheses. Therefore, from the table above it can be analysed that the sig value of all independent variables are below 0.005 which explains that the null hypotheses are rejected in this study. So it can be said that,

  • Personal factors have a significant impact on customer behaviour in online purchases.
  • Social Factors have a significant impact on customer behaviour in online purchases.
  • Psychological factors have a significant impact on customer behaviour in online purchases.

Risk factors have a significant impact on customer behaviour in online purchases.

Discussion of Objectives

Objective 1: To identify the factors affecting customer behaviour

The first objective proposed by the research was theoretical in nature which is inclined towards identification of the factors which are affecting customer behaviour. The first objective was proposed by the researcher in order to get clear understanding about the research topic which was successfully achieved by the researcher. There were certain factors identified which were affecting customer behaviour in this study. The researcher identified that cultural, personal, social, psychological and risk factors are those which are highly influential for the customer behaviour. Each factor has its own influence on the buying behaviour of the customers which changes in case of online purchases. According to Rahbar and Abdul Wahid (2011) retailers are formulating different strategies in order to facilitate the customers with the online purchasing from their online retail websites. Moreover, the online retailers should also take into account the factors which are associated with their online purchases so that the customers are attracted towards their products and services.

Objective 2: To study the importance of customer behaviour and factors affecting online purchase decision of a customer

The second objective proposed by the researcher was also theoretical in nature as it is focused towards studying the importance of customer behaviour and factors affecting online purchase decision of a customer. Hence, the objective was successfully achieved by the researcher where a thorough analysis was conducted based on past theories and concepts in order to study the importance of customer behaviour with respect to online purchases. According to Chu and Kim (2011), it is significant for the online retailer to study the behaviour of the customer in order to influence their decisions regarding online purchases. Moreover, the study on the customer behaviour helps the company to stay competitive with respect to their competitors because a loyal customer would help the company in bringing profitability and increase their revenues.

Objective 3: To critically analyse the impact of factors affecting customer behaviour on online in the context of online retail stores (Jing Dong and Taobao)

The third objective proposed by the researcher was statistical in nature which was tested on the SPSS software as the objective was focused towards analysing the impact of factors affecting customer behaviour on online purchases with the retail stores Jing Dong and Taobao. The objective was successfully achieved by the researcher by the methods of survey analysis in which around 100 customers were selected that were the customers of Jing Dong and Taobao and were surveyed online. The results of the questionnaire analysed that all factors of the customer behaviour including personal, social, and psychological and risk factors were affecting online purchases in case of the online retail websites Jing Dong and Taobao.

Reference

Andrew, S., & Halcomb, E. J. (2007). Mixed methods research is an effective method of enquiry for community health research. Contemporary nurse, 23(2), 145-153.

Antoniou, C., Doukas, J. A., and Subrahmanyam, A. (2013). Cognitive dissonance, sentiment, and momentum. .Journal of Financial and Quantitative Analysis., 245-275.

Battaglia, M. P. (2008) Non-Probability Sampling.Encyclopedia of Survey Research Methods.Sage Publications. Available from: http://www.sagepub.com/chambliss4e/study/chapter/encyc_pdfs/5.2_Nonprobability%20Sampling.pdf

Bernard, H.R. and Bernard, H.R., 2012. Social research methods: Qualitative and quantitative approaches. Sage Harwell, M.R., 2011. Research design in qualitative/quantitative/mixed methods.CONRAD, Clifton F.; SERLIN, Ronald C. The SAGE Handbook for Research in Education: Pursuing ideas as the keystone

Bruwer, J., Saliba, A. and Miller, B., 2011. Consumer behaviour and sensory preference differences: implications for wine product marketing. Journal of Consumer Marketing28(1), pp.5-18.

Cheah, I., Phau, I., and Kea, G. (2016). Modelling effects of consumer animosity: Consumers’ willingness to buy foreign and hybrid products. Journal of Retailing and Consumer Services., 184-192.

Chu, S.C. and Kim, Y., 2011. Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International journal of Advertising30(1), pp.47-75.

Chu, S.C. and Kim, Y., 2011. Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International journal of Advertising30(1), pp.47-75.

Cooper, D.R. and Schindler, P.S., 2006. Marketing research. New York: McGraw-Hill/Irwin.

Creswell, J. W. (2012). Qualitative inquiry and research design: Choosing among five approaches. Sage.Pp. 11.

Daniel, J., 2011. Sampling essentials: Practical guidelines for making sampling choices.Sage.

George, B.P. and Yaoyuneyong, G., 2010. Impulse buying and cognitive dissonance: a study conducted among the spring break student shoppers. Young Consumers11(4), pp.291-306.

Goddard, W., & Melville, S. (2004). Research methodology: An introduction. Juta and Company Ltd. [Online] Available from <http://www.newagepublishers.com/samplechapter/000896.pdf> [Data retrieved on 23/09/2016].

Harwell, M.R., 2011. Research design in qualitative/quantitative/mixed methods.CONRAD, Clifton F.; SERLIN, Ronald C. The SAGE Handbook for Research in Education: Pursuing ideas as the keystone

Hasan, U. and Nasreen, R., 2012. Cognitive dissonance and its impact on consumer buying behaviour. IOSR Journal of Business and Management1(4), pp.07-12.

Hollensen, S., 2015. Marketing management: A relationship approach. Pearson Education.

Horner, S. and Swarbrooke, J., 2016. Consumer behaviour in tourism. Routledge.

Hsin Chang, H. and Wang, H.W., 2011. The moderating effect of customer perceived value on online shopping behaviour. Online Information Review35(3), pp.333-359.

Imam, F. (2013). Gender Differences in Impulsive Buying Behavior and Post-Purchasing Dissonance Under Incentive Conditions. Journal of business strategies. , 23.

Izuma, K., Matsumoto, M., Murayama, K., Samejima, K., Sadato, N. and Matsumoto, K., 2010. Neural correlates of cognitive dissonance and choice-induced preference change. Proceedings of the National Academy of Sciences107(51), pp.22014-22019 Wicklund, R.A. and Brehm, J.W., (2013). Perspectives on cognitive dissonance. Psychology Press.

Javadi, M.H.M., Dolatabadi, H.R., Nourbakhsh, M., Poursaeedi, A. and Asadollahi, A.R., 2012. An analysis of factors affecting on online shopping behavior of consumers. International Journal of Marketing Studies4(5), p.81.

Jie, Z., 2010. Research onthe Integrated Evaluation of Competitiveness for C2C E-commerce Websites——Taking www. taobao. com as an Example [J]. Journal of Intelligence3, p.014.

Joung, H. M. (2014). Fast-fashion consumers’ post-purchase behaviours.  International Journal of Retail and Distribution Management., 688-697.

Kim, C., Galliers, R.D., Shin, N., Ryoo, J.H. and Kim, J., 2012. Factors influencing Internet shopping value and customer repurchase intention. Electronic Commerce Research and Applications11(4), pp.374-387.

Kuhl, J., and Beckmann, J. (2012). Action control: From cognition to behavior. . Springer Science and Business Media.

Kumar, R. (2010) ‘Research Methodology: A Step-by-Step Guide for Beginners’, SAGE Publications Ltd; Third Edition

Macdonald, S. &Headlam, N. (2011) Research Methods Handbook by Centre for Local Economic Strategies (CLES) Available from: http://www.cles.org.uk/wp-content/uploads/2011/01/Research-Methods-Handbook.pdf.

Marciniak, R., and Gad Mohsen, M. (2016). Post-Purchase Consumer Behaviour, Sustainability and its Influence on Fashion Identity.

Martin, L.L. and Clore, G.L., 2013. Theories of mood and cognition: A user’s guidebook. Psychology Press.

Maxwell, J.A., 2012. Qualitative research design: An interactive approach: An interactive approach. Sage.

McCole, P., Ramsey, E. and Williams, J., 2010. Trust considerations on attitudes towards online purchasing: The moderating effect of privacy and security concerns. Journal of Business Research63(9), pp.1018-1024.

Merriam, S. B. (2009) Qualitative Research: A Guide to Design and Implementation, Jossey-Bass.

Oliver, R. L. (2014). Satisfaction: A behavioral perspective on the consumer.Routledge.

Pickard, A., (2012). Research methods in information.Facet publishing.

Powers, T. L., and Jack, E. P. (2013). The influence of cognitive dissonance on retail product returns. . Psychology and marketing., 724-735.

Rahbar, E. and Abdul Wahid, N., 2011. Investigation of green marketing tools’ effect on consumers’ purchase behavior. Business strategy series12(2), pp.73-83.

Saunders, M., Lewis, P. &Thornhill, A. (2012) Research Methods for Business Students (6th Ed.) Pearson.

Shobeiri, S., Mazaheri, E., and Laroche, M. (2015). Shopping online for goods vs. services: where do experiential features help more?.  International Journal of Consumer Studies., 172-179.

Solomon, M. R. (2014). Consumer behavior: Buying, having, and being.

Solomon, M., Russell-Bennett, R. and Previte, J., (2012). Consumer behaviour. Pearson Higher Education AU.

Solomon, M.R., 2014. Consumer behavior: Buying, having, and being. Engelwood Cliffs, NJ: prentice Hall.

Verhagen, T. and van Dolen, W., 2011. The influence of online store beliefs on consumer online impulse buying: A model and empirical application. Information & Management48(8), pp.320-327.

Wilcox, A.B., Gallagher, K.D., Boden-Albala, B. and Bakken, S.R., (2012). Research data collection methods: from paper to tablet computers. Medical care, 50, pp.68-73

Wilson, A., Zeithaml, V.A., Bitner, M.J. and Gremler, D.D., 2012. Services marketing: Integrating customer focus across the firm. McGraw Hill.

Yin, R. K. (2013) Case study research: Design and methods. Sage publications.,  pp. 15-34

Zhou, T., 2011. Understanding online community user participation: a social influence perspective. Internet Research21(1), pp.67-81.

Questionnaire

The main aim of this study is to critically analyse the factors affecting customer behaviour on online purchases considering the case of Chinese online retailers Jing Dong and Taobao.

Demographics

  1. Gender
  • Male
  • Female
  1. Age
  • 20– 24 years
  • 25 – 29 years
  • 30 – 34 years
  • 35 – 39 years
  • Above 40 years

Independent Variable: Factors affecting Customer Behaviour

Personal factors

  1. The income of the person influences his buying patterns.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree
  1. People prefer to purchase those products online which advocate their role in society.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree
  1. Purchases from online retail websites reflect a consumer lifestyle.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree

Social Factors

  1. Social class can influence behaviour of the customers on online retail websites.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree
  1. Friends and family members can influence an online purchase decision of the customers.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree
  1. Online retail stores motivate the customer to buy the products as it meets their social needs.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree

Psychological factors

  1. The consumer perception towards online products and the brand also influences his buying decision.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree
  1. Level of motivation towards online products influences the buying behaviour of the consumers.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree
  1. Online products create a positive attitude of the customers to buy the products from online retail websites.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree

Risk factors

  1. Low risk in the online retail websites can influence the customer behaviour.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree
  1. Consumer prefers to buy from those online websites which have secured payment system.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree
  1. Consumers prefer to buy from those online websites which have less product risk.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree

Dependent Variable: Online Purchases

  1. Online purchases are largely influenced by consumer attitude towards online retailing store.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree
  1. The online purchases are influence by cultural and social norms.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree
  1. The performance of the products should meet the expectation of the customers to influence online purchases.
  • Strongly Agree
  • Agree
  • Neutral
  • Disagree
  • Strongly Disagree
Strongly Agree Agree Neutral Disagree Strongly Disagree
Brand love (Independent variable)
I am passionate about brand
I love this brand
I am very attached to this brand
This brand is pure delight
This brand is totally awesome
This brand makes me feel good
This is a wonderful brand
This brand makes me feel very happy
Consumer buying behaviour (dependent variable)
I purchase a brand that reflects the type of person I see myself to be.
I purchase a brand that facilitates me to communicate with my self-identity.
I purchase a brand that helps me to express myself.
I like brands that depicts symbol of social status.
I prefer a brand that helps me to fit important social situations.
I like to be seen associated with this specific brand.
I like to plan my purchases rather than relying on impulse.
There is impact of brand love on consumer buying