Understanding Online Consumer Decision Making

From the way we communicate and connect with each other, to the way we receive information about the world around us, we are steadily moving our everyday life online. Shopping could not be an exception with an estimation of almost 2 billion people worldwide purchasing goods and services via online platforms (Statista, 2020). Given the rise of ecommerce on a global level, it is no wonder that a great amount of literature has been dedicated to the way individuals make decisions online in order to better predict and outline consumer behaviour.

The notion of online decision making depicts the cognitive process which takes place in the mind of a consumer prior to reaching to a conclusion regarding a decision online. Academics have identified several factors that can influence this behavior. Kuhlmeier and Knight (2005), argue that long before considering an online purchase, individuals evaluate the associated risks as well as their level of internet literacy. In a similar way, the willingness of a consumer to buy goods online, is positively affected by the perceived usefulness of the webstore, the trust it evokes as well as by its social presence (Dash and Saji, 2007). According to Lai and Wang 2012, the usefulness of online shopping equals the amount of perceived added value they experience when engaging to online purchases.

Trust is essential when making decisions on the web since it builds a relationship between the business and the buyer, further reinforced by a high quality interface which leads to an pleasant customer experience (Corbitt et al., 2003). The same academic research has drawn attention to the way prominent business partnerships, flexible payment methods and “word of mouth” tactics can also assist in tackling any feelings of mistrust towards online purchases. Cheung and Lee (2006), also showcase that the perceived level of trust that the web vendor evokes (quality of security screenings, perceived efficiency), along with factors that provide external certification of the website, can tremendously impact the perception that consumers hold for online shopping.

One fundamental characteristic of online shopping is that individuals are not able to physically evaluate the alternative options before completing the purchase, with the payment process also not involving an in-person interaction of the buyer and the business. Therefore, it is no wonder that during an online purchase, consumers have to evaluate the risk associated with the delivery and return policy (Martin, 2005), the risk of being exposed to malicious software and the fear of their personal data being exploited (Constantinides, 2004, Ernst and Young, 2000). In order to overcome these concerns, online users usually prefer websites that they consider as trustworthy or brands they have prior experience with (Chen and He, 2003).

A noteworthy effort to explore the way in which consumers behave online is the work of Dange and Kumar in 2012. Their academic paper presents a model describing that online buying behavior is equally affected by internal and external to the individual factors. The external components include among others, demographic characteristics, the socioeconomic and cultural status as well as the associated marketing activities. In respect of the internal factors, they include the attitudes of the consumer towards e-commerce, self image beliefs and the motivation to engage in online purchases. Finally both the external and internal factors, constitute the buying motives of the online consumers which are filtered by the perceived level of trust, security and privacy.

The context in which a decision is presented, has been showcased to have an equally strong effect on its perceived complexity. Therefore, interaction decision aids and recommender systems have been fully incorporated in the online decision making process of individuals today. With the utilization of these tools, online consumers can better assess the available choices by either removing from their consideration set, options that do not meet their requirements or by choosing certain product characteristics which are essential to them (Pfeiffer, 2012). Interestingly enough, online recommendations have a stronger positive influence in the assessment of the alternatives presented on a webstore, even though individuals perceive them as less knowledgeable compared to humans who have an expertise in the given choice set (Senecal and Nantel, 2004). It is also possible that consumers ignore the use of recommender systems even when they are present in a website, opting to any prior acquired experience or knowledge (Olshavsky, 1985).  

References:

1. Statista. (2020). Number of digital buyers worldwide from 2014 to 2024. https://www.statista.com/statistics/251666/number-of-digital-buyers-worldwide/
2. Kuhlmeier, D., & Knight, D. (2005). The Role of Online Trust in the Decision to Continue to Transact with an e-Service Provider. In System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on (pp. 57b-57b). IEEE.
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