Online Shopping: A Guide for Managers
How Daniel Kahneman’s Theory of Risk helps us understand Online Shopping: A Guide for Managers
An analysis of Prospect Theory: The Impact of negative word of mouth on purchase Intention in online shopping.
Digital media has fundamentally transformed consumer behavior. The advent of online shopping, accelerated by the necessities of the COVID pandemic, shifted business attention to creating seamless omnichannel strategies. As many thought leaders in both business and tech have declared over the past 15 years: Omnichannel is the future. Yet, in many ways, online shopping environments remain ambiguous in nature, leaving many opportunities for improvement of omnichannel strategies. As one writer in Forbes declares in a 2019 article, “Omnichannel is Dead. The Future is Harmonized Retail,” being ever present is not enough today, retailers must work to provide stellar consumer experiences that consider various touchpoints in the consumer journey and cater to “the right consumers where it really matters, in remarkable ways” (Dennis 2019, Forbes). These touchpoints include various electronic streams of word of mouth — with Nielsen’s 2021 “Trust in Advertising” report suggesting:
word of mouth from people consumers know is the most trusted channel for consumers at 89%, followed by brand websites at 84% and TV at 78% (Bowler 2021).
Additionally, millennials hold the highest trust in opinions shared online (Nielsen 2015). Similarly, Prasad et al. (2019) suggest social media and electronic word of mouth (eWOM) play a large role in Generation Y’s purchasing behavior. Overall, NielsenIQ reported the distruption in online shopping increasing by 325% in 2020 with 67% of consumers in 2021 planning to continue to continue purchasing online (Llamas 2021). Furthermore, Nielsen warns that those failing to improve will be left behind in the next phase of global ecommerce maturation (Llamas 2021).
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WOM & eWOM: Word of Mouth & Electronic Word of Mouth
Embracing this reality, a focus has been turned to better understanding online communication, particularly in the form of electronic Word of Mouth (eWOM). A meta-analysis provided a revised definition of eWOM as “consumer-generated, consumption related communication that employs digital tools and is directed primarily at other consumers” (Rosario et al. 2019).
Consumers look at online reviews as a form of risk mitigation. A meta-analysis of eWOM including a review of 1,050 studies published between 1996 and 2019 suggests that consumers seek eWOM to reduce pre-purchase anxiety and perception of risk (Rosario et al. 2020, Moe and Trusov 2011). In addition, consumers seek eWOM to reduce perceptions of risk, particularly for “products with attributes that are difficult to observe, predict, verify, or control associated with higher levels of risk” (Lee and Bell 2013). Motivations to seek reviews can include functional, financial, and social risk or reduction of cognitive dissonance or even serve as a leisure activity or include accidental exposure (Rosario et al. 2020).
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Leveraging Electronic Word of Mouth
Leveraging online word of mouth purposefully is largely still an under-developed marketing strategy and businesses have yet to extrapolate its full value (Berger 2014, Liousas 2018, Ruvio et al. 2020).
WOM and eWOM is a rich topic for research with many intricacies that may help understand consumer behavior in this new digital era defined by multiple streams of communication and consumption. Further, a recent study on Consumer Arrogance and WOM suggests that fundamental shifts may be occurring in perceptions of WOM communication, the study specifically refers to how arrogance once viewed as a vice, in today’s culture is seen as acceptable and gives rise to word-of-mouth behaviors (Ruvio et al. 2020). In digital environments, the way WOM is perceived may influence purchase intention and its adaptation in marketing strategies. As this recent research on the drivers of eWOM suggests, “WOM communication has become increasingly critical to companies success” particularly due to the growing numbers of generations growing up with digital communication as a mainstay (Ruvio et al. 2020).
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Electronic WOM & Opportunities for Improvement
One key opportunity in the insights field is reducing consumer’s perceptions of risk in online environments and providing ample product/service information aligning with consumers’ needs to make informed decisions and allow for repeat purchases online. However, consumer decision making at its central core, is often subject to irrational behavior (Xinhui and Han 2016, Ariely 2020). As McKinsey group explains,
“A shopper’s mind is not a clean slate. Information and experience are refracted through the lens of belief. Information that’s inconsistent with those beliefs is likely to be rejected. Even experience is malleable. . . “(Cummings et al., McKinsey 2015).
Fortunately, theories of behavioral economics, becoming central to the Market Research industry, can help us make sense of consumers’ propensities and ultimate decision-making processes. One such theory is Daniel Kahneman’s Prospect Theory to model behavior considering risk.
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In Thinking, Fast and Slow, Daniel Kahneman explains,
“Confidence is a feeling, which reflects the coherence of the information and the cognitive ease of processing it. It is wise to take admissions of uncertainty seriously, but declarations of high confidence mainly tell you that an individual has constructed a coherent story in his mind, not necessarily that the story is true.” ―Daniel Kahneman, Thinking, Fast and Slow
Kaheman argues that consumer behavior is dominated by “fast” system 1 thinking which relies heavily on “automatic and often unconscious processes” rather than the opposing system “slow” 2 thinking which relies on “agency, choice and concentration” (Kaheman 2011).
Kaheman explains that the “anomalies of consumer behavior” are judged by weighing negative outcomes as a cost or an uncompensated loss, with losses being viewed as worse (Kahneman & Tversky 1984). As Figure 1 from Lin 2018 explains the application of prospect theory on people’s perceptions of losses and gains. We can see in Figure 1 that the slope is much greater on the negative side of the utility graph, explaining risk aversion behavior.
Figure 1: Application of Prospect Theory (Lin 2018).
In other words, people will generally avoid risk due to perceived loss, even if it is irrational. One reference point can be considered consumer expectations. In Kahneman’s and Tversky’s original theory the reference point is not specified, but consumer expectations may serve a valid reference point to explain consumer behavior. Studies such as Wang 2018 suggest price expectations are a valid reference point to consider.
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The Association for Consumer Research suggests that more study is needed considering the impact of reference points on consumer outcomes as they are dynamic and may change as consumers move through purchase experience, acquire more knowledge or other factors (Klein et al. 2022). As ACR suggests, “understanding what the reference point is, and how it affects the evaluation of your offering, may be the highest priority for a marketer” (Klein et al. 2022). We can assume one reference point to be consumer expectations based on the disconfirmation-of-expectations model, which poses satisfaction as a function of expectations and performance (Oliver 1997 cited in Spreng and Thomas 2001).
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Further, research by Spreng and Thomas suggests that “high confidence subjects use both disconfirmation and perceived performance to form feelings of satisfaction” (2001). The disconfirmation-of-expectations model has been used in prior eWOM studies as illustrated in Figure 2 below. Minnema et al 2016 considered positive reviews and returns and used pre-purchase expectations as a reference point. Their study suggests that overly positive review valences induce more purchases, but also cause more returns due to increasing level of expectation which causes negative expectation disconfirmation (Minnema et al 2016).
Figure 2 Online Reviews with Consumer Expectations as Reference Point from Minnema et al 2016.
Regarding the possibility of risk, “people are often risk seeing in dealing with improbable gains and risk averse in dealing with unlikely losses” (Kahneman & Tversky 1984).
A 2019 meta-analysis of eWOM (Rosario et al) suggests that one goal of seeking reviews is to reduce uncertainty and perception of risk before purchase (Moe and Trusov 2011).
Moreover, products are associated with high levels of risk due to the inability to perceive, predict or control or validate key attributes (Lee and Bell 2013) and online environments presumably exacerbate these problems (Rosario et al 2019). Different types of products pose myriad forms of risk mitigated by searching for eWOM, such as
“high functional risk (e.g. new products whose performance is unknown; Ho-Dac et al. 2013),
high financial risk(e.g. long term investments; Grewal et al. 2004), and/or
high social risk (e.g. publicly consumed products (You et al. 2015) and in addition to these,
personal or privacy risk” (Kim et al. 2004).
In addition, consumers may seek eWOM after purchase [as a way] to reduce cognitive dissonance” (Rosario et al. 2019).
With online shopping environments being more uncertain than brick and mortar shopping, research has shown that trust plays a role in promoting online purchases (Campbell & Walker 2010).
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Similarly, economist Daniel McFadden explains that consumer choices are riddled with complexities surrounding the choice such as time constraints, preference for the status quo, and other mechanisms at play (Thompson 2014, The Atlantic). Regarding ambiguous environments McFadden explains in an interview with The Atlantic.
“A lot of behavior is consistent with pursuit of self-interest, but in novel or ambiguous decision-making environments there is a good chance that our habits will fail us and inconsistencies in the way we process information will undo us.” -Daniel McFadden, 2014
In his paper the New Science of Pleasure, McFadden explores new frontiers to re-evaluate consumer behavior, challenging tenets of prior economic theories and proposing that choice in free markets is overwhelming for consumers, and he refers to the Dutch proverb,
“He who has choice has trouble.”
Further, McFadden explains that consumers largely “limit choice through procrastination, rules, pre-commitment, habit, and imitation. . . Ambiguity and risk offer the prospect of dissonance and regret.”
In regard to WOM, McFadden’s theory elucidates that
“our primary sources of information of new objects come from others through observation, advice and association . . .learning by imitation rather than learning by doing. The more painful a potential mistake, the more valuable information that may help avoid mistakes” (McFadden 2005).
When describing social networks as determinants of choice, McFadden points out that information can improve choices if information is accurate or may make consumers lazy decision makers as they may encourage herd behavior (Thompson 2014).
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NEGATIVE REVIEWS & eWOM
Electronic word of mouth is one such source of social information which consumer influences choices such as purchase intention, with negative eWOM proving an important consideration for ecommerce. Negative eWOM is complex in nature (Lis & Fischer 2020).
A meta-analysis study of 26 empirical studies of online reviews, suggests that negative reviews are more impactful than positive reviews on sales elasticities (Floyd et al. 2015).
In other words, companies and brands want to be really careful about how they treat negative reviews, and plan how they will respond to these.
Negative reviews consist of both performance-based (functional) and values-based (ethical) criticism (Liu et al., Lis & Fischer 2020).
A recent study evaluating different types of eWOM, suggests functional criticisms provoke greater declines in consumer sentiment, yet ethical criticisms are more difficult to reverse, even with subsequent positive reviews (Lis & Fischer 2020), and further, functional criticism can be reversed with renewed positive eWOM.
In terms of weighing risk, “compared to positive content, negative reviews are perceived as more helpful regarding the localization and assessment of risks, increasing perceived usefulness” (Yin et al. 2–12).
In addition, evaluation of rating scales for online reviews suggest that consumers claim higher propensity to create eWOM when the rating is on a 5-point scale versus a 100-point scale due to capturing the underlying utility more fully (Chen and Godes 2012).
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TAKE AWAYS FOR MANAGERS
In other words, people really attention more to 5 point scales, like Amazon uses, and people also see negative reviews as more helpful to assess risk.
A distinction can also be made between constructive reviews (attribute-specific) and destructive reviews (driven by anger & frustration) which foster vindictive eWOM (Wetzer et al. 2007, Lis & Fischer 2020). Constructive, functional criticism has a greater influence on consumer attitude and renewed positive reviews can improve attitudes towards a brand or product (Fischer 2020).
For these reasons, functional product reviews employing a 5 point review scale for online reviews, respond best to managerial interventions and can be categorized more fully on social media. Managers should focus on addressing these most.
Lopez-Lopez & Parra (2015) suggest that in terms of positive reviews,
“ . . . positive information that is not narrowly related to the consumer’s goals is notdirectly incorporated in the attitude formation process.”
In other words, consumers only consider positive reviews that clearly serve their aims and distrust others. This result relates to literature suggesting that consumers tend to be reluctant to accept positive, over-the-top reviews as they suspect that a commercial source may be behind it, and consequently, the opinion may be fake” (Mayzlin et al, 2012, quoted in Perez-Perez & Parra 2015).
Prior studies have documented the relationship between eWOM in the form of consumer reviews and Daniel Kahneman’s Prospect theory to explain consumer behavior and perception of risk in online shopping in response to negative reviews (Lee et al. 2007, Li and Shimizu 2018).
Furthermore, studies analyzing this relationship suggest sales outcomes are more influenced by negative reviews than positive ones (Li and Shimizu 2018).
In addition, prior researchers have used reviews from websites such as Amazon.com to evaluate or gather data (Mishra and Satish 2016). It is important to note as Karabas et al 2021 clarifies about star ratings presented that Amazon updated their algorithm for computing average star ratings to consider, “the age of a review, helpfulness of votes by customers, and whether the reviews are verified purchases” and further explained that this information was found by hovering over the average star rating on Amazon.com average star ratings. The authors of the study explain that Amazon’s algorithm is not publicly disclosed. Thus, the most recent explanation of the algorithm is provided below in Figure 3, and this may change over time.
Figure 3: 2022 Explanation of Amazon Star Rating Algorithm on Amazon.com
This is an important consideration as certain metrics like trustworthiness may be “baked in” on their platform. It also included as a consideration of future research investigation on platforms where this information is presented differently, or where different algorithms are used. Algorithms can change considerably over time, and this may change consumer perceptions significantly. However, many consumers use Amazon as a baseline to compare reviews before shopping on other sites with a recent study suggesting 66% if consumers begin purchase journeys on Amazon and 89% of consumers saying they are more likely to buy on Amazon than other e-commerce sites (Feedadvisor study cited in Forbes 2019), thus it is a widely used consumer platform for evaluating reviews (Masters 2019).
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FUTURE CONSIDERATIONS
Future research should consider the growing need of fully understanding the complex aspects of eWOM and myriad consumer interactions. For example, do consumers perceive particular shopping platforms such as Facebook, Instagram, DTC websites, or brand websites as more or less risky than more traditional ecommerce platforms such as Amazon/Walmart? Another question is what, if any generational factors play a role in the perception of risk.
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