A review of models on buying decision-making process in online shopping

A previous article in this study explored a few key models of the consumer decision-making process. The article explained its application in the present shopping environment where online shopping has overtaken traditional in-store shopping in terms of growth. Over the years, many more such models of consumer behaviour have been proposed, particularly in the context of the online shopping environment. A few of these models are; Engel-Kollat Blackwell Model, Kim, Ferrin and Rao’s (2008) model, Yan and Dai’s (2009) model, and Fang’s (2012) model.

Engel-Kollat-Blackwell model (1968)

Engel Kollat Blackwell model was first developed in 1968 describing the expansion and flourished knowledge concerning consumer behaviour, consisting of four stages (Prasad & Jha, 2014). It is today one of the most popular consumer behaviour models and is also popularly known as the learning model of consumer behaviour. It encompasses the components of decision making and describes the relationship between them, demonstrating consumer behaviour.


Figure 1: Engel-Kollat-Blackwell Model (1968)

The authors identified six critical components in the consumer behaviour model. These are as follows.

  • Information input- stimuli (mass/ personal).
  • Information processing- exposure, attention and perception.
  • Decision making- problem recognition, search, alternative evaluation and choice.
  • Product brand evaluations- beliefs, attitudes and intentions.
  • Internal influences- personality and lifestyle.
  • External influences- societal norms and culture.

Out of the above six components, the third component, i.e. the decision-making process is most crucial. It helps to identify the stages of a consumer’s decision-making process (Jisana, 2014). This component consists of four stages. The first stage is the problem recognition stage, involving recognising the problem, foraging alternatives, evaluating, purchasing and outcomes. The second stage is the information search, where the consumer gathers the information from both market and non-market sources before making a purchase decision. If a consumer fails in making a decision, then a search of external information is required in order to arrive at a choice. The third stage is the alternative evaluation stage where the consumer evaluates and compares different alternatives available to fulfil their need in the market (Blackwell & Miniard, 2006). Next is the choice stage, where the consumer decides which product to purchase. This is based on their intentions and attitude. The last stage is the outcome, which can either be positive or negative.

Ha¨ubl (2000), emphasised on the adaptability of the internet by young generation and its influence on the consumer’s decision-making process. The advent of the internet helped customers in exploring new products and services to fulfil their urges. According to Patwardhan (2005), online marketing has gained popularity as customers can explore the marketplace and new products world-wide without stepping out. It is considered as an evolution of traditional shopping. Many researchers like Tan (2008), Zhang (2014), emphasised on the decision-making behaviour towards online shopping and concluded various merits of online shopping over traditional shopping.

Nelson (2009), stated that the availability of varieties of product and ease in connecting to the sellers has motivated the consumers to repeat orders. Customers relying more on online shopping take more risk in a transaction than the traditional method. They are more conservative in exploring the market and trying new non-branded products (Kumar, 2015).

Kim, Ferrin and Rao’s (2008) model of online decision making

In the context of the online shopping environment, additional models have been proposed in recent years. Kim, Ferrin and Rao’s (2008) model of online decision making is considered as one of the first models in this regard. In this model, the emphasis is on factors like, risk, trust and benefit which are typical to the online shopping environment. They have an inevitable impact on the intention of the consumer in purchases (Gross, 2014). The figure below demonstrates this model.

Kim, Ferrin and Rao’s (2008) model of consumer behaviour in an online shopping environment.

Figure 2: Kim, Ferrin and Rao’s (2008) model of consumer behaviour in an online shopping environment.

According to this model, there are three basic components that affect the consumer’s intention to purchase a product. These are:

  • Perceived risk.
  • Consumer trust.
  • Perceived benefit.

Perceived risk consists of various types of risks such as financial risk, product risk and information risk. These risks are specific to e-commerce (Masoud, 2013). Customer trust in online shopping is based on the transactional process. It can be maintained by making fair transactions. The risk related to the quality of product and financial risks is unavoidable. Some issues which may hinder consumer intention to an online purchase include defective products, technical error and credit card fraud. The model concludes that trust somehow inversely or directly influences the purchasing decision.

Fang’s (2012) interactivity variable research model

Interactivity variable research model proposed by Fang (2012), focused on the interaction level and effect on online shoppers. The proposed interactivity model illustrated the idea of information flow and control to the consumer. The information availability provides interaction between shopper and supplier to discern information regarding a product. However, the intention of making a transaction is based on the perceived description and perceived diagnosis of the product available for sale (Gems, 2015). The figure below demonstrates this model.

Interactivity variable research model

Figure 3: Interactivity variable research model (Fang, 2012).

The model above shows, the process of flow of information from the e-tailer to the consumer. Information pertaining to the product helps shape the consumer’s perception of a product, and their decision to purchase. In addition to that, Esteban-Millat, et al (2014), assert that the consumer’s experience in the e-tail store influences their opinion about a product. It is therefore important to design websites to enable a seamless flow of information with an interactive navigational process to lead a prospect to make a purchase.

Yan and Dai’s (2009) model of online shopping decision-making

A more comprehensive model of consumer’s online decision-making process was offered by Yan & Dai (2009). According to the authors, two major elements that make up the consumer’s decision to purchase a product online are; perceived risk and perceived benefit. This is similar to Kim, Ferrin and Rao’s (2008) model. However, Yan and Dai’s model includes many sub-elements. The figure below depicts the model.

Yan and Dai (2009) Model of online shopping decision-making

Figure 4: Yan and Dai (2009) Model of online shopping decision-making.

As the model shows, perceived benefit and perceived risk have several sub-elements and the interactivity between them determines a consumer’s decision to make a purchase online. Consumer’s perceived benefit consists of four major components:

  1. Convenience of purchasing
  2. Availability of diverse information
  3. Product personality
  4. Low cost

On the other hand, the perceived risks of online shopping are made up of the following.

  • Economic risk: refers to the risk of monetary loss while engaging in online shopping.
  • Product functional risk: refers to the risk of the product/ service not meeting the consumer’s expectations.
  • Time-loss risk: refers to the risk of time lost during the online shopping process.
  • Service risk: refers to the unavailability of certain additional services offered by offline stores such as personal consultation, rights to exchange goods, etc.
  • Information risk: refers to the risk brought arising from the lack of authentic information online.
  • Social contact risk: refers to the risk of consumer’s normal social interactions undergoing changes due to online shopping.
  • Health risk: refers to the physical or psychological risks of online shopping.

Furthermore, there are three online-shopping related elements in this model.

  • Consumer: is the main participant in the online shopping process. This involves information such as personal characteristics, demographic profile, involvement in the internet, etc.
  • Product: is the object of online shopping.
  • Website: is the business intermediary platform on which the online shopping activity takes place.

Review of the consumer decision-making models

The four models of online consumer decision-making illustrate a different point of view. Kim, Ferrin and Rao’s (2008) decision-making model stated that risk is the inevitable factor and influences the intention of the consumer in making a purchase decision. Since consumers prefer less risk, it hinders the adoption of online shopping. On the other hand, Fang’s (2012) Interactivity variable research model proposed that manufacturers adopt interactive strategies by providing information to consumers. Manufacturers may manipulate information in order to accomplish more customers, but the transaction intention is based on perceived diagnosis and perceived description of the product. Lastly, Yan and Dai’s (2009) model of online shopping decision-making details the elements of the decision-making process and explores how each individual element affects the ultimate shopping decision of consumers. E-marketing is popular due to various merits it has but these factors affecting the intention of consumers in making a decision is unavoidable.

References

  • Blackwell, R. D., & Miniard, P. W. (2006). Consumer Behavior. Thomson: CRC Press.
  • Esteban-Millat, I., Martínez-López, F. J., Luna, D., & Rodríguez-Ardura, I. (2014). The concept of flow in online consumer behavior. In Handbook of Strategic e-Business Management. Berliin: Springer Berlin Heidelberg.
  • Fang, Y. (2012). Does online interactivity matter? Exploring the role of interactivity strategies in consumer decision making. Computers in Human Behavior, 28(5), 1790–1804.
  • Gems, E. (2015). A Focus on Consumer Behaviours and Experiences in an Online Shopping Environment. WA: British Library.
  • Gross, R. (2014). A Theoretical Consumer Decision Making Model: The Influence of Interactivity and Information Overload on Consumers Intent to Purchase Online. International Journal of Business Management and Economic Research, 5(4), 64–70.
  • Ha¨ubl, G. (2000). Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids. Marketing Science, 19(1), 4–21.
  • Jisana, T. K. (2014). CONSUMER BEHAVIOUR MODELS: AN OVERVIEW. Sai Om Journal of Commerce & Managament, 1(5), 34–43.
  • Kumar, D. (2015). Consumer Behaviour. Oxford university press.
  • Masoud, E. Y. (2013). The Effect of Perceived Risk on Online Shopping in Jordan. European Journal of Business and Management, 5(6), 76–87.
  • Nelson, M. L. (2009). Value Creation in E-Business Management. San Francisco: AMCIS.
  • Patwardhan, P. (2005). RATIONAL INTEGRATIVE MODEL OF ONLINE CONSUMER DECISION MAKING. Journal of Interactive Advertising, 6(1), 2–13.
  • Prasad, R. K., & Jha, M. K. (2014). Consumer buying decisions models: A descriptive study. International Journal of Innovation and Applied Studies, 6(3), 335–351.
  • Tan, A. (2008). A decision making model to maximise the value of reverse logistics in the computer industry. Int. J. Logistics Systems and Management, 4(3), 297.
  • Yan, X., & Dai, S. (2009). Value Creation in e-Business Management. In LNBIP. Berlin: Springer-Verlag Berlin Heidelberg.
  • Zhang, K. Z. K. (2014). Examining the influence of online reviews on consumers’ decision-making: A heuristic–systematic model. Elsevier, 78–89.

Priya Chetty

Partner at Project Guru
Priya is a master in business administration with majors in marketing and finance. She is fluent with data modelling, time series analysis, various regression models, forecasting and interpretation of the data. She has assisted data scientists, corporates, scholars in the field of finance, banking, economics and marketing.

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