Mukherjee, Shreya

Mulky, Avinash

Sarkar, Arimit

English

DigitalCommons@Kennesaw State University

 146  Key Drivers of Impulse Purchase Online: An Exploration in an Emerging Market   Avinash Mulky, avinash.mulky@iimb.ac.in ABSTRACT The aim of this paper is to explore the drivers of impulse purchase online in an emerging market where internet penetration is growing rapidly. We used in-depth interviews to understand consumers’ impulsive buying behavior online. We also carried out a survey to identify factors that influence consumers’ impulse buying online. The findings from interviews indicate consumers who have been using the internet for 3 to 5 years and those who enjoyed shopping in general are more likely to make impulse purchases online. Products valued at less than 2000 Indian Rupees (INR) and certain trusted websites that offer a large product variety are favored during impulsive online purchase. Results from a factor analysis and subsequent regression analysis of survey data indicate that a customer-product factor and a price factor were the two major drivers of impulse purchasing online. The influence of risk was not as significant as expected.    Emerging markets are important because they represent huge potential for future growth-for example, India has a population of over 1.2 billion people and the country’s economy that has been growing at over 5 percent per annum for almost two decades. A recent report by Morgan Stanley (qz.com, 2018) predicts that online retail in India will grow from $15 billion in 2016 to $200 billion in 2026. The rapid growth is expected to happen because of increased internet penetration, falling data costs, shift to smartphones and proliferation of digital payment systems. In line with growth of online retail, we can expect also a growth in impulsive purchasing online in the Indian market.    The rapid growth of online retail all over the world has led to growing academic interest in online buying behavior and there are many articles on this topic in the information systems, management and marketing journals. In our literature review for this paper, we focused on papers that proposed new conceptualizations and papers which reported research carried out in emerging markets. In one of the early papers on the topic, Madhavram and Laverie (2004) have conceptualized impulsive buying online in terms of a change in purchaser’s intention regarding purchase when exposed to hedonically charged stimuli. Chan, Cheung and Lee (2017) carried out a systematic review of the literature on online impulse buying research and concluded that the “Stimulus-Organism-Response (SOR)” and the “Technology Acceptance Model (TAM)” frameworks were the most commonly used theoretical foundations in empirical studies. In their  147  analysis Chang, Cheung and Lee (2017) have identified a number of elements in the stimuli and organism related factors that drive online impulse buying. At a broad level, these elements can be classified into website stimuli, marketing stimuli and internal consumer stimuli at the stimuli level and cognitive and affective reactions at the organism level.    Sun and Wu (2011) have found that a consumer trait model can provide an explanation for online buying impulsiveness. The trait drivers include internet addiction, task orientation, self-efficacy, emotional instability, conscientiousness, materialism and need for arousal. Lo, Lin and Hsu (2016) found that Herzberg’s two factor theory could be used as a framework to explore online impulse buying. They found that motivators such as promotional offers, gifts, discounts; and hygiene factors such as easy to use websites, security, nice fonts, member centers and product categorization were important factors in stimulating online purchase. In a study carried out in India, Badgaiyan and Verma (2014) explored the impact of personality traits on online impulsive buying behavior. They found that extraversion, collectiveness, shopping enjoyment, materialism and impulsive buying tendency had a positive influence on online impulsive buying while the trait of conscientiousness had a negative influence. We did not find any empirical study that has explored the influence of various types of risks on online impulse buying. In our study we aim to explore the impact of customer related factors, product related factors, website related factors and risk related factors on impulse buying online.    We carried out in-depth interviews with 20 consumers who have made at least one online purchase during the last one year. The interviewees were selected based on convenience sampling of people who had online shopping experience of more than 1 year. The interviews were carried out using a structured set of questions. Among the interviewees, 60% were from age 20-25 years, 20% were from 26-30 years, 5% were from 31-35 years and 15% were from age 36-40 years. Also, we had 55% females and 45% males as our interviewees. Among the interviewees, 55% were students, 15% work in IT services, 15% are managers, 10% are teachers and 5% were freelance performer. Also, we had 75% graduates and 25% postgraduates as our interviewees. 70% respondents indicated that they carried out online impulsive purchases mainly in the apparel and electronics categories for products priced at less than INR 2000 (less than US$ 30). When the price was more than INR 2000, 75% respondents mentioned that they did not make an impulsive purchase and preferred to gather more information. 75% of the respondents identified the reason for Impulse purchase as “sale on products” and “push notifications from mobile applications”. 90% interviewees indicated that they tend to impulse purchase only from sites they trust and have used before. In the case of new websites, they reported making impulse purchases only when the website had a good replacement policy. Preferred websites for online impulse purchase were those that had had an appealing design, were easy to use and those that had a large variety of products. 65% of the respondents who were buying on impulse online reported that they enjoyed shopping in general, and felt a sense of satisfaction after the impulse purchase. 30% of the respondents also indicated that they felt a sense of guilt after the online purchase.     148  Based on the literature review, we developed a model of drivers of online impulse purchase. We conceptualized the dependent variable as online impulse buying behavior. The dependent variable was calculated as the average score for a set of 3 Likert scale items representing online impulse buying behavior. We conceptualized the key drivers of online impulse purchase as a set of four independent variables-customer related factors; product related factors; website related factors; and risk related factors. We developed a set of 15 items to represent the four drivers, the items were measured using a 5 point Likert scale. An online questionnaire was employed to obtain responses from a sample of  consumers whose profile matched that used in the in-depth interviews. Out of the participating respondents we selected only 106 surveys which were completely filled. The gender distribution of the respondents was - 58% Males and 42% Females. The age-wise distribution of the respondents was - 78% respondents between 21-30 years, 10% between 31-40 years, 9% between 41-50 years, and 3% above 50 years. Of the 106 respondents 18% were graduates, 79% were postgraduates, 3% were doctorates.      We measured the internal consistency of the items that measured the dependent variables (3 items) and independent variables (15 items). The Cronbach alpha was .903 for the dependent variables and 0.712 for the independent variables which indicates high internal consistency. We carried out an exploratory factor analysis using varimax rotation for the 15 items representing the independent variables. The KMO statistic was 0.75, and the Bartlett’s test of sphericity was significant, indicating that factor analysis was appropriate for the data. Factor analysis resulted in four factors that cumulatively explain 59.8 percent of variance. We flagged the items that had a loading of more than 0.5 on the corresponding factor in the rotated factor matrix. The first factor had high loadings for the items shopping enjoyment, materialism, instant gratification, hedonic products, sale on products and ready to use products. We have named the first factor as “Enjoyment.” The second factor had high loadings for the items financial risk, performance risk and psychological risk. We have named the second factor as “Risk.”. The third factor had high loadings for the items visual appeal, product availability and website trust, so we have named the factor as “website characteristics.” The fourth factor had only one item that loaded highly and that was price, hence we have named the fourth factor as “Price.” During factor analysis, we had saved the factor scores and we were able to carry out a regression with online impulse purchase behavior as the dependent variable and the four factors-enjoyment, risk, website characteristics and price as the independents. The regression model had an adjusted R squared of 0.330 and two independent variables-enjoyment factor and the price factor were significant at p < .05.     Conclusion  Four factors influencing online impulse purchase were explored and evaluated using qualitative and quantitative methods. The factors that were significant were-enjoyment related and price related factors. The website related factors were not as significant as observed in previous literature. Excellence in design, ease of use and visual appeal have proved to be a big advantage for e-commerce websites in the past but most established ecommerce websites have reached basic level of ease of usage and visual appeal. These factors no longer act as stimuli for impulse  149  purchase. E-commerce firms now need to focus on user enjoyment and pricing to stimulate impulse purchasing. Our research has a few limitations-our respondents were primary young adults in the age group 25-40 and our sample size was small. Future research must include a wider sample of respondents across various demographic groups. We used a simple model of factors that influence online buying. Future research can be based on a model that explicitly considers mediators and moderators.  References:  Badgaiyan, A. J .and Verma, A. (2014) Intrinsic factors affecting impulse buying behavior: Evidence from India, Journal of Retailing and Consumer Services, Vol 21(4) pp 537-549.  Chan, T.K.H., Cheung, C.M.K., and Lee, Z.W.Y. (2017). The state of online impulse buying research: A literature analysis, Information and Management, Vol. 54 (2), pp 204-217.  https://qz.com/1089559/morgan-stanley-explains-why-indias-e-commerce-market-is-a-hot-investment-opportunity/ Accessed on April 10, 2018  Lo, L.Y., Lin, S. and Hsu, L. (2016) Motivation for online impulse buying: A two factor theory perspective. International Journal of Information Management, Vol 36 (5), pp759-772.  Madhavaram, S. R. and Laverie, D. A. (2004) "Exploring Impulse Purchasing on the Internet", NA - Advances in Consumer Research, Volume 31, pp 59-66.  Sun T., and Wu, G. (2011) Trait predictors of online impulse buying tendency: A hierarchical approach, Journal of Marketing Theory and Practice, Vol 19(3), pp 337-346.   Track: Consumer Behavior/Retailing  ID#:  1285      

Key Drivers of Impulse Purchase Online: An Exploration in an Emerging Market

https://digitalcommons.kennesaw.edu/cgi/viewcontent.cgi?article=1285&amp;context=ama_proceedings

Key Drivers of Impulse Purchase Online: An Exploration in an Emerging Market

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