A Study on Psychographic Determinants of Online Consumer Behaviour

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1 A Study on Psychographic Determinants of Online Consumer Behaviour Mahima Sodadasi University of hyderabad India Dr. Jyothi Polepeddi University of hyderabad India ABSTRACT: Electronic commerce in India has been developing tremendously and revolutionized the shopping patterns from brick and mortar to online. Online consumer behaviour, an emerging component relates to the behaviour of online consumers, is being influenced by many factors and is always everchanging. This study focused on the relationship between possible psychographic determinants of online consumer behaviour namely motivation, beliefs and attitudes of consumers with that of their purchasing behaviour. These factors were identified from earlier research and were increasingly being considered more important, as retailers can better understand their consumers to meet their expectations and can improve their marketing strategy. Data consisted of responses collected from consumers who did online shopping in the last six months. Regression analysis enabled to understand the extent of the relationship between the dependent and independent variables considered in the study. Keywords: Online consumer behaviour Motivation Beliefs Attitude Online Purchase Intention. INTRODUCTION: In the era of globalization, rapid growth of World Wide Web and internet has changed the scenario of marketing. In the last decade, electronic commerce is a great transformation, and most of the business organizations are adapting the technological changes. E-commerce is now the backbone of business growth and Business to Consumer (B2C) e-commerce has paved the way for online shopping in the late 1990s. Online shopping is a process of selling and buying of goods and services on World Wide Web. Online shopping is an efficient method for online retailers and their consumers to perform online transactions through commercial websites (Ranganathan & Ganapathy, 2002). It was invented and pioneered by Michael Aldrich in the UK in With its wide range of potential benefits, online shopping has become an attractive alternative, worldwide. Easy access to the internet has driven consumers to shop online and according to the University of California, Los Angeles (UCLA) communication policy (2001), online shopping is the third most popular activity on the internet followed by ing and web browsing. It was reported that despite the economic downturn, the volume of electronic commerce is expanding rapidly (Hu et al., 2010). Online sales have attracted an increasing share of overall sales revenues (Van der Meer, Dutta and Datta, 2012), and a number of consumers are engaged in online retailing interactions. In India, recently this sector has seen tremendous growth with 66 million users, generating the revenue of Rs.81, 525 crores in 2014 (Source: IMRBI-Cube 2014, All India Estimates, December 2014). If this remarkable growth continues, the country s e-commerce market has been forecasted to grow at a CAGR of over 36% during (Source: TechSci Research, October 2015).Thus online shopping industry in India is growing rapidly and will continue to see exponential growth. While electronics is an attractive segment, the growth areas are also books, air, railway, bus ticket reservations, movie tickets, home furnishing, cosmetics, accessories, baby care items, groceries, apparels and jewelry. In this scenario, it is essential for online retailers to understand the behaviour of the consumers who use online mode of purchasing. Consumer Behaviour is multi-disciplinary and is defined by Schiffman et al. (2007) as the study of the processes involved when individuals or groups select, purchase, use or dispose of products, services, ideas or experiences to satisfy their needs and desires. Adelaar et al. > RJSSM: Volume: 05, Number: 12, April 2016 Page 186

2 (2003) suggested that consumer behavior resulted from an emotional response and that the consumers can make on the spot purchase of a product or service. The primary aim of analyzing consumer behavior is to explain why consumers act in certain ways under certain circumstances. There are several factors which determine online consumer behaviour. The possible determinants were identified from previous research works, which falls under various disciplines that contribute to it. This study focuses on the psychographic factors which include: Motivation, Perception, Personality, Beliefs, Attitudes, Activities, Interests, Opinions, Values and lifestyles. Among all those, a relationship between motivation, beliefs, attitude and purchase behavior have been focused in this study. Psychographics: The term psychographics as it is used in the field of market segmentation was coined by Demby in Later, Wells (1975) defined psychographics in a broad view as personality traits and lifestyles, activities, interests and opinions as well as attitudes, beliefs, motivations, needs and values. It is a tool for researchers to measure psychological dimensions of consumers and segmenting target market with common traits of consumers. Psychographic segmentation is a precise technique to produce products according to consumers needs and wants. It helps the marketers to know the internal characteristics of a person or his/her lifestyle. Two psychographic factors namely: Motivation, beliefs are considered for this study. Motivation - Motivation refers to a person s need that must be satisfied (Childers et al, 2001). Utilitarian motivation includes convenience-seeking, variety seeking, searching for quality of merchandise, and reasonable price rate, etc. Whereas emotional needs of individuals with enjoyable and entertaining shopping experiences constitute hedonic motivation. (Bhatnagar & Ghosh, 2004). Beliefs - Belief is explained by Kotler and Armstrong (2007) as a descriptive thought about something and is based on real knowledge, opinions or faith. A person can have a belief through learning and experience. Normative belief is an individual's perception of normative social pressures that he or she should or should not perform a behavior. Control belief is an individual's beliefs about the presence of factors that may facilitate or impede the performance of the behaviour. These variables have an impact on a consumer's attitude to make an online purchase. Attitude: Consumer s attitude towards online shopping refers to their psychological state regarding making purchases over the Internet. Attitude towards buying on the Internet is defined as, "a consumer's positive or negative feelings about performing purchasing behaviors on the internet" (Chi et al., 2005). It is considered as a personal or emotional factor that can affect intention in a positive or negative manner (Icek Ajzen 1985, 1991). Online purchase Behaviour: Online buying behavior refers to the totality of a consumer's attitudes, preferences, intentions and decisions while purchasing a product online. Thus, a relationship between the determinants and their influence on the purchase attitude and actual purchase would provide us with a new insight into the determinants of online consumer behaviour, which is a major component for the success of online retail. REVIEW OF LITERATURE: The following prior research works have been considered for the framework of the study: Kim & Lee, (2001) conducted the study on online shopping and in his research it has become quite apparent that to survive and be profitable, online businesses must pursue the fundamentals of good retailing. One of these principles is knowledge about existing, and potential consumers and their preferences Retailers must pay attention to the target market's perceptions, attitudes, and behavior rather than the technological characteristics of their websites. Childers, Carr, Peck and Carson, (2001) in their study titled - Hedonic and Utilitarian motivations for online retail shopping behavior- considered utilitarian and hedonic motives in e-retail context and developed an attitudinal through integrating constructs from technology acceptance research and web behavior models. > RJSSM: Volume: 05, Number: 12, April 2016 Page 187

3 Wu (2003) conducted the study on - 'The relationship between consumer characteristics and attitude toward online shopping' and the study identified the consumer characteristics using four areas: consumer demographics, consumer purchasing preference, and consumer benefits perception and consumer lifestyle. Consumers characteristics have a significant relationship with the attitude toward online shopping; the attitude toward online shopping has a significant relationship with the online shopping rate. Pavlou & Fygenson (2006) in their study Understanding and predicting electronic commerce adoption: An extension of the theory of planned Behavior extended Ajzen s (1991) theory of planned behavior (TPB) to explain and predict the process of e-commerce adoption by consumers. They proposed an e-commerce adoption model through validating the TPB by the conceptualization of normative and control beliefs. Rahim et al. (2014) in the study entitled Psychographic characteristics influencing customer behavior on online purchase intention, identified four psychographic factors which influence online purchase intention. This study has proved that there exist differences regarding psychographic characteristic on online purchase intention and opened the way for further research After having reviewed the previous research works which deal with online purchase behaviour, the present study was undertaken to analyze the consumer behaviour in the light of psychographic factors. THEORETICAL FRAMEWORK: The following theoretical framework has been framed to examine the relationships between the independent and dependent variables. OBJECTIVE OF THE STUDY: The broad objective of the study is to identify the extent of the relationship between the psychographic variables considered in the study and online purchase behaviour. HYPOTHESES FORMULATED: The following hypotheses have been formed based on the objectives: 1. There is a significant relationship between utilitarian shopping motivation and online purchase attitude. 2. There is a significant relationship between hedonic shopping motivation and online purchase attitude. > RJSSM: Volume: 05, Number: 12, April 2016 Page 188

4 3. There is a significant relationship between normative beliefs and online purchase attitude. 4. There is a significant relationship between control beliefs and online purchase attitude. 5. There is a significant relationship between attitude and online purchase behaviour. MEASUREMENT ITEMS: Variable Cronbach's Alpha Hedonic shopping motive Utilitarian shopping motive Normative beliefs Control beliefs Attitude Online purchase behaviour SAMPLE SIZE: A convenient sample was drawn among the online consumers of Hyderabad city. The sample size of 150 respondents was considered for the analysis. The criteria for choosing the sample was consumers who have indulged in online shopping in the last six months. DATA SOURCES: Data was collected from two sources: a) Primary data was collected with the help of structured questionnaire designed for the study. b) Secondary source included scholarly journals, research reports, databases, books related to e- commerce, e-magazines and World Wide Web. STATISTICAL TOOLS EMPLOYED: Appropriate descriptive and inferential statistical tools were used using SPSS 20.0, and the analysis is presented below. DATA ANALYSIS AND FINDINGS: Data analysis includes Factor Analysis and Regression. The reliability of the constructs has been examined using Cronbach s alpha. Principal component factor analysis with varimax rotation has been done the seven constructs. Multiple regression analysis has been done to examine the hypothesized relationships between the constructs. For regression analysis mean score of each construct has been calculated by dividing the summated score of all items in a construct with the number of items. Three regression models have been estimated. First regression model: H1: There is a significant relationship between utilitarian shopping motivation and online purchase attitude. H2: There is a significant relationship between hedonic shopping motivation and online purchase attitude. In this model, online purchase attitude was regressed on utilitarian and hedonic motivation variables, and the results are mentioned below: Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate a a. Predictors: (Constant), AVG_UTILI, AVG_HEDONIC b. Dependent Variable: AVG_ATTITUDE > RJSSM: Volume: 05, Number: 12, April 2016 Page 189

5 ANOVA a Model Sum of Squares df Mean Square F Sig. Regression b 1 Residual Total a. Dependent Variable: AVG_ATTITUDE b. Predictors: (Constant), AVG_UTILI, AVG_HEDONIC a Model Unstandardized Standardized t Sig. B Std. Error Beta (Constant) AVG_HEDONIC AVG_UTILI a Model Correlations Collinearity Statistics Zeroorder Partial Part Tolerance VIF 1 (Constant) AVG_HEDONIC AVG_UTILI Analysis and Interpretation: A standard multiple regression was performed between online purchase attitude as the dependent variable and hedonic, utilitarian shopping motives as independent variables. The adjusted squared multiple correlation was significantly different from zero (F = , p <.001) and 36.1% of the variation in the dependent variable was explained by the set of independent variables. All the independent variables were found to significantly contribute to the prediction of online purchase attitude, namely, hedonic motivation (sr 2 = 0.132, t = 5.562, p =.000), utilitarian motivation (sr 2 = 0.096, t = 4.745, p =.000). The overall regression equation can be framed as: Online purchase attitude = (Hedonic motivation) (Utilitarian motivation). The data satisfied the assumptions of multicollinearity, normality of residuals and homoscedasticity while no outliers were identified. Therefore, hypotheses H1 and H2 are accepted, signifying that there exists a relationship between the dependent and independent variables. Second regression model: H3: There is a significant relationship between normative beliefs and online purchase attitude. H4: There is a significant relationship between control beliefs and online purchase attitude. In this model, online purchase attitude was regressed on normative and control beliefs, and the results are represented below: Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate a > RJSSM: Volume: 05, Number: 12, April 2016 Page 190

6 1 ANOVA a Model Sum of Squares Df Mean Square F Sig. Regression b Residual Total a Model Unstandardized Standardized t Sig. Correlations B Std. Error Beta Zero-order (Constant) AVG_NORMB AVG_CONTB a Model Correlations Collinearity Statistics Partial Part Tolerance VIF (Constant) AVG_NORMB AVG_CONTB Analysis and Interpretation: A standard multiple regression was performed between online purchase attitude as the dependent variable and normative, control beliefs as independent variables. The adjusted squared multiple correlation was significantly different from zero (F = , p <.001) and 26.6% of the variation in the dependent variable was explained by the set of independent variables. All the independent variables were found to significantly contribute to the prediction of online purchase attitude, namely, normative beliefs (sr 2 = 0.049, t = 3.167, p =.000), control beliefs (sr 2 = 0.140, t = 5.336, p =.000). The overall regression equation can be framed as : Online purchase attitude = (normative beliefs) (control beliefs). The data satisfied the assumptions of multicollinearity, normality of residuals and homoscedasticity while no outliers were identified. Therefore, hypotheses H3 and H4, are accepted and there exists a significant relationship between the dependent and independent variables. Third regression model: H5: There is a significant relationship between attitude and online purchase behaviour. In this model, the extent of relationship between online purchase attitude and online purchase behaviour have been examined, and the results are represented below: Model Summary b Model R R Square Adjusted R Std. Error of the Estimate Square a > RJSSM: Volume: 05, Number: 12, April 2016 Page 191

7 ANOVA a Model Sum of df Mean Square F Sig. Squares Regression b 1 Residual Total a Model Unstandardized Standardized t Sig. B Std. Error Beta 1 (Constant) AVG_ATTITUDE Analysis and Interpretation: A linear regression analysis was conducted to evaluate the prediction of online purchase behavior from online purchase attitude. A relationship between the two variables was reflected in an R of and adjusted R2 of % of the variance of online purchase behavior was accounted for by its linear relationship with online purchase attitude. The overall regression was significant with F = , p < The regression equation for predicting online purchase behavior is: Online purchase behaviour = (online purchase attitude). Therefore, hypothesis H5 is accepted, and there exists a significant relationship between the dependent and independent variables. Implications of the findings: The analysis showed that hedonic and utilitarian motives have a positive association with online purchase attitude. In order to retain consumers, it is necessary for online retailers to have to increase the hedonic value of their online stores. The consumers having utilitarian shopping motive perceive greater benefits in online shopping. This can be accounted for convenience factor to a large extent. Normative and control beliefs too create positive online purchase attitude in consumers. Finally, positive online purchase attitude leads to actual purchase in online stores. Conclusion: In today s world, virtual shopping has been increasing at a steady pace and has been attracting many consumers around the globe. It is crucial for an e-tailer to retain their consumers in this cut throat competition. Therefore, a better understanding of their consumers is of vital importance so as to cater the ever changing demands and interests of consumers. As psychographics deals with internal traits of a consumer, this study made an attempt to throw light on the underlying motives, beliefs and attitude of consumers towards online shopping. REFERENCES: Ajzen, I. and M. Fishbein, Understanding attitude and predicting social behaviour. Eaglewood Cliffs, NJ Prentice Hall. Ajzen, I., & Madden, T.J. (1985). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. University of Massachusetts at Amherst. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior And Human Decision Processes, vol. 50, pp > RJSSM: Volume: 05, Number: 12, April 2016 Page 192

8 Adelaar, T., Chang, S., Lanchndorfer, K. M., Lee B. and Morimoto M., (2003). Effects of media formats on emotions & impulse buying behaviour. Journal of Information Technology, 18, Agarwal, R. and Prasad, J. A., (1998). Conceptual and operational definition of personal innovativeness. Bhatnagar, A., & Ghosh, S. (2004). A latent class segmentation analysis of E-Shoppers. Journal of Business Research, 57(7), Chi, Y., Lin, C, & Tang, L. (2005). Gender differs: Assessing a model of online purchase intentions in e-tail service Jnternational Journal of Service Industry Management, 16(5), Childers, T.L., C.L. Carr, J. Peck, and S. Carson, (2001), 'Hedonic and utilitarian motivations for online retail shopping behaviour', Journal of Retailing, 77(4), pp Demby, E.H. (1965), Psychographics: The birth of a technique, Marketing News, 23(1), pp. 21. IMRB and IAMAI report, 2014 Kim, S., Williams, R., & Lee, Y. (2001). Attitude toward online shopping and retail website quality: A comparison of US and Korean consumers. Journal of International Consumer Marketing, vol 16 (1). Kotler, P. and Armstrong, G. (2007) principles of Marketing, (12 th edn), Upper Saddle River, Prentice Hall. Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned Behavior. MIS Quarterly, 30(1), Rahim, H. L., Abidin, Z. Z., & Khairuddin, N. N. (2014). Psychographic Characteristics Influencing Customer Behaviour on Online Purchase Intention. Australian Journal of Basic & Applied Sciences, 8(5). Ranganathan, C. and Ganapathy S., (2002). Key dimensions of business-to-consumer web sites, Information and Management 39 (6), Schiffman, L. G., et al., (2007). Consumer Behavior, 9th. ed. New Jersey: Prentice Hall. Van der Meer, D., Dutta, K., and Datta, A. "A Cost-Based Database Request Distribution Technique for Online e-commerce Applications," Management Information Systems Quarterly (36:2) 2012, pp Wells, W., Tigert, D., Activities, interest and opinions. Journal of Advertising Research 11 (4), Wu, S. (2003), The relationship between consumer characteristics and attitude toward online shoppingǁ Marketing Intelligence and Planning, 21 (1), > RJSSM: Volume: 05, Number: 12, April 2016 Page 193