How Amazon Uses Behavioral Economic Biases to Drive Sales
Amazon has proliferated over the last several years to become the most popular online store worldwide. Using principles of behavioral economics is central to the company’s approach to increasing revenue. Amazon relies heavily on behavioral economics, which examines how customers’ emotions, thoughts, and intuitions affect their purchasing choices (Iyengar & Lepper, 2000).
Amazon has effectively encouraged users to purchase more things, spend more time on the site, and post favorable reviews by capitalizing on several cognitive biases. This article will examine how Amazon takes advantage of customers’ inherent biases at each phase of the sales funnel (Fritze et al., 2018). We hope our research will shed light on the success of Amazon’s tactics and show how they might be used to increase revenue in other sectors.
Stage 1: Awareness or Problem Recognition
Determining if there is a need or interest in the product or service is the first step in the consumer buying process. In order to increase profits, Amazon is now using cognitive biases discovered in behavioral economics (Fritze et al., 2018). These slants aim to sway consumers’ preferences toward the Amazon brand and drive up sales (Lo et al., 2022). This section introduces the cognitive biases Amazon employs during the awareness stage and includes examples of how these biases are applied on Amazon’s website. Fifteen distinct forms of cognitive bias are used by Amazon, all of which will be investigated here. Evidence of their usage will be shown via screenshots of the Amazon.es website. Insight into Amazon’s use of cognitive biases to boost sales will be provided, as will consumer education on e-commerce sites’ strategies during the buying process’s research phase.
Scarcity Bias
The scarcity bias originates from the widespread belief that a commodity is scarce or will soon be out of stock. By emphasizing “limited offers” and “last units in stock” alerts, Amazon makes use of this behavioral economics phenomenon (Jesse et al., 2021). The term “scarcity bias” was coined by Robert Cialdini, who first discussed it in his seminal work “Influence: The Psychology of Persuasion.” This bias increases sales by making prospective customers feel they must quickly acquire a product before supplies run out (Fenko et al., 2017). Figure 1.1 in the supplemental materials illustrates this bias on the Amazon.es website.
Amazon’s goal in appealing to the scarcity bias is to increase sales by encouraging buyers to act quickly before product supplies are depleted. This bias occurs in “Stage 1 because it encourages the buyer to decide fast before the product is out of stock. Consequently, this raises the issue of the potential loss of a desired good or service for the first time.
Anchoring Bias
The anchoring bias occurs when a person weighs too much on the information they first hear. Amazon employs this bias by prominently displaying a product’s reduced and original pricing, lulling customers into thinking the lower price is a better deal. For more, see (Rezaei, 2021). Amos Tversky and Daniel Kahneman discovered this prejudice in their seminal paper “Judgment under Uncertainty: Heuristics and Biases.” To get consumers to commit to a specific price and see a discount as a good bargain, businesses use the anchoring bias during the awareness phase of the customer purchasing process (Rezaei, 2021).
Figure 1.2 in the supplemental materials demonstrates this bias on the Amazon.es website. Amazon applies the anchoring bias to influence purchasing decisions by presenting the pricing in a manner that makes it seem like a bargain. This bias is included in “Stage 1 because it might affect a consumer’s first impression of an item’s worth and serve as a benchmark for future choices.
Framing Bias
The term “framing bias” refers to the impact of presentation choices on how an audience interprets and remembers information. According to Zhao et al. (2020), Amazon takes advantage of this propensity by providing favorably biased product descriptions and reviews. Amos Tversky and Daniel Kahneman introduced this bias in their book “The Framing of Decisions and the Psychology of Choice.” Buyers’ perceptions of the product and its advantages may be influenced by the bias as early as the Awareness stage (Florence et al., 2022).
A phenomenon indicative of this trend is shown by a product description on Amazon.es, which emphasizes the device’s mobility and processing speed more than its meager storage capacity. Figure 1.3, which may be found in the Appendix, is an example of this phenomenon. Since the consumer’s perspective and motivation to take action may be influenced by how a problem or opportunity is presented, this bias is especially prevalent in Stage 1.
Confirmation Bias
Confirmation Bias refers to the inclination of individuals to actively seek out and construe information in a manner that aligns with their preconceived notions and convictions. The firm takes advantage of customers’ propensity to shop on Amazon by recommending goods similar to those they have already browsed. (Curmi, 2021). Peter Wason initially presented the bias in his research “On the Failure to Eliminate Hypotheses in a Conceptual Task.”
The bias above is exhibited during the stage of customer awareness, as it enables Amazon to strengthen customers’ inclinations toward specific categories of merchandise. If a consumer has previously engaged with a specific shoe brand, Amazon’s algorithm will suggest similar styles and brands to the consumer on their homepage. An instance of this is illustrated in the Appendix through the depiction presented in Figure 1.4. This bias occurs in Stage 1 because consumers may seek information that validates their opinions or perceptions, affecting their initial awareness and problem recognition.
Endowment Effect
The term “Endowment Effect” describes how property is often overvalued by its owners. By prominently showcasing customer evaluations and ratings on product pages, Amazon capitalizes on this cognitive Bias (Achtypi et al., 2021). This bias is included in Stage 1 due to its potential to affect an individual’s initial valuation of a thing and to foster an emotional investment in it. This may influence the consumer’s propensity to part with their money and encourage them to take action to get the product.
“Endowment bias” was coined by Richard Thaler, who first discussed it in his book “Misbehaving: Consumers’ Endowment Effect is fueled during the awareness phase, increasing their emotional investment in the product and their likelihood to purchase it (Renaud, 2021). Users may see user reviews and ratings right beside Amazon’s product description. Figure 1.5 in the Appendices shows this instance on the amazon.es website. This strategy has as its ultimate goal the increased purchase of the company’s products by the general public.
Social Proof
Social proof is a groupthink capitalizing on people’s tendency to agree with the majority. Prominently showing the number of purchasers, customer reviews, and ratings alongside the product description on the product page. To influence consumer decisions, Amazon plays on this predisposition (Jain et al., 2019). Robert Cialdini initially described this prejudice in his seminal book, “Influence: The Psychology of Persuasion.” Throughout the awareness phase, Amazon presents this bias to potential buyers to gain their trust and, hopefully, their business (Fenko et al., 2017).
Potential consumers are given social evidence in the form of ratings and reviews, as seen on the Amazon.es product page, right alongside the description of the product. Figure 1.6 from the supplemental materials serves as an example. This bias is included in Stage 1 because it might impact first awareness of a problem or opportunity by demonstrating that others have faced similar problems. As a result, the perceived significance and relevance of the issue or opportunity increases.
Loss Aversion
The psychological phenomenon referred to as loss aversion pertains to a pronounced inclination towards evading losses instead of attaining gains. The authors of Ivaschenko et al. (2020) assert that Amazon exploits this cognitive bias by providing a hassle-free return policy, thereby mitigating the perceived purchasing risk for customers. The identification of bias was initially conducted by scholars Amos Tversky and Daniel Kahneman in their research paper titled “Prospect Theory: An Analysis of Decision under Risk.”
The bias described above is demonstrated throughout the procurement stage to allay the concerns of potential customers over potential risks involved with purchasing a particular product (Guo et al., 2021). This partiality in operation is observable on the Amazon.es webpage, as depicted in Figure 1.7 in the Appendix. This bias is classified as Stage 1 because it may instill dread or urgency about losing up on a desired thing. As a result, the customer becomes aware of the possible loss and is motivated to take action.
Primary Effect
The primary effect describes a cognitive bias toward remembering information presented earlier over later. Amazon takes advantage of shoppers’ natural tendency to select one of the top-ranked products displayed immediately following a search due to the primacy effect (Cheng et al., 2021). Hermann Ebbinghaus initially noted this prejudice in his book “Über das Gedächtnis” (On Memory).
This bias is revealed at this juncture because it shows how Amazon’s search algorithm, which decides the order in which goods are shown, is geared toward capitalizing on the significant impact to boost sales (Moerth-Teo et al., 2021). This bias is evident on Amazon.es when searching for a popular product like “a smartphone,” where only established companies with good user ratings appear towards the top of the results. Figure 1.8 illustrates and may be found in the Appendix. This bias occurs in Stage 1 because the initial information provided to the customer may disproportionately influence their later decision-making process, impacting their initial awareness and problem identification.
Reciprocity Bias
The “reciprocity bias” refers to the tendency to feel obligated to return a favor or act of kindness. Amazon capitalizes on this propensity by giving its most loyal customers exclusive discounts and access to member-only sales. Robert Cialdini initially presented reciprocity bias in his landmark book “Influence: The Psychology of Persuasion.” Amazon capitalizes on this bias by focusing on consumers who have identified a problem and are actively researching possible solutions (Moerth-Teo et al., 2021).
Amazon favors returning customers by offering exclusive sales and discounts on previously purchased items. Figure 1.9 is a supporting visual that may be found in the Appendix. This bias is under Stage 1 because it might generate a feeling of responsibility or debt to a seller who has supplied value or help. As the consumer considers how to reciprocate the perceived benefit, the consumer’s initial awareness and problem recognition are influenced.
Authority Bias
Authority bias refers to the tendency for individuals to provide further weight to the opinions of those in authoritative positions. As a cognitive bias, the “Authority Bias” causes individuals to be more receptive to the direction of those in authoritative positions. Amazon uses authorities like experts, influencers, and celebrity endorsements to sell its items, creating the impression that they are high-quality and trustworthy. Customers who hold the authority person in high esteem are more likely to buy after hearing this. The “Amazon’s Choice” section of the Amazon.es website illustrates this principle since it features items carefully selected and evaluated by the company’s specialists (Kumari, 2023). These items are featured or displayed prominently to encourage shoppers to choose them over alternatives. In the 1960s, psychologist Stanley Milgram initially postulated the Authority Bias (Kumari, 2023).
This bias is introduced since it is a standard marketing strategy influencing customer decisions. Figure 1.10 from the supplementary materials illustrates. This bias occurs in Stage 1 because the perceived competence or credibility of the source providing a problem or opportunity might influence the consumer’s first awareness and problem identification. As a result, their following decision-making process is influenced.
Stage 2: Information Search
In the research phase, consumers look into various offerings to find one that best fits their requirements. Amazon makes money during this time by swaying customers’ decisions by providing them with incomplete or incorrect information (Pham, 2020). Amazon uses the heuristic availability bias to its advantage by presenting shoppers with information that has been well-chosen to make its items more enticing (Sarwar et al., 2019). Additionally, they provide initial pricing that sets the tone for the rest of the customer’s decision-making process using anchoring bias. Positive evaluations and social proof are highlighted to support the customer’s choice, tapping into the confirmation bias in the process.
Confirmation Bias
Customers who use Amazon’s search engine are presented with relevant product suggestions, which may drive them to buy goods they might not have considered otherwise. This bias was initially postulated by Peter Wason in his study “On the Failure to Eliminate Hypotheses in a Conceptual Task.”
Amazon’s search algorithm might influence the purchasing decisions of customers who have previously indicated an interest in a product (Mrkva et al., 2020). Figure 2.1, given in the Appendix, depicts an example of this phenomenon. Consequently, Amazon’s search algorithm can potentially significantly impact consumer purchase choices, reinforcing the confirmation bias. This bias occurs in Stage 2, when consumers actively seek information supporting their previous views or preferences, impacting their appraisal and selection of prospective choices.
Framing Effect Bias
Amazon takes advantage of this prejudice by highlighting a product’s strengths while downplaying its weaknesses to boost its popularity and sales. When a customer searches for a laptop on Amazon, for instance, that laptop’s specifications—such as its battery life, processing speed, and storage capacity—are shown in a manner that is most likely to pique the customer’s interest. This bias gets more significant during the deliberation phase as consumers compare and assess their alternatives (Sarwar et al., 2019).
Figure 2.2 in the supplemental materials depicts this framing effect. Psychologists Amos Tversky and Daniel Kahneman initially recognized this cognitive bias in their seminal work, “The Framing of Decisions and the Psychology of Choice.” This bias is present in Stage 2 because how information is presented may affect how it is viewed and assessed, influencing the consumer’s search for relevant information.
Anchoring Bias
In order to increase the perceived value of the reduction, Amazon uses this tendency by consistently presenting the list price alongside the reduced price. This slant is shown to entice buyers by making them think they would save money if they bought now. If a product’s original price was €17.39 but has now been reduced to €15.82, Amazon will show both prices, emphasizing the reduction. Amazon could encourage more people to purchase by emphasizing the value of the reduction by highlighting the higher initial price (Dowling et al., 2019).
This prejudice is evident on the whole show on Amazon.es, where sale prices are posted alongside the total retail price. Figure 2.3 in the supplemental materials illustrates this concept. This bias occurs in Stage 2 because the first information supplied to the customer might act as a reference point for further assessment, affecting the consumer’s search and consideration of other possibilities.
Social Proof Bias
Amazon employs the social proof phenomenon by presenting customer evaluations and feedback on its platform to sway prospective purchasers’ perceptions and enhance revenue. The display of past customers’ evaluations and scores on Amazon’s platform during product searches is aimed at exerting an impact on the decision-making process of potential customers, as noted by Fenko et al. (2017). According to Jain et al. (2019), Muzafer Sherif and Solomon Asch, renowned psychology and social influence figures, were among the early proponents of this particular bias.
According to Jain et al. (2019), the social proof bias exhibits maximum efficacy when introduced at the outset of the purchase process while establishing trust and credibility. This partiality can be observed in Figure 2.4, as depicted in the supplementary materials. This bias exists in “Stage 2 because the views and conduct of others might influence the consumer’s appraisal and selection of choices.
Authority bias
Amazon takes advantage of this fallacy by highlighting endorsements from authorities such as celebrities, professionals, agencies, well-known brands, and other important people. In order to increase sales, Amazon may play on consumers’ trust in authoritative sources (Lamis et al., 2022). Customers searching for a product on Amazon, for instance, will be able to see if any well-known people have endorsed it (Feldman et al., 2020). Customers who trust the specialist are more inclined to buy from them.
Figure 2.5 in the supplemental materials illustrates this. Since customers have recognized their needs and are weighing potential solutions (Sarwar et al., 2019), it is an ideal time to present the authority bias to sway their decisions. Authority bias is present in “Stage 2 because sources’ legitimacy and perceived competence might impact the consumer’s appraisal and selection of prospective alternatives throughout their information search.
Decoy Effect Bias
The decoy effect bias occurs when consumers’ preferences change in response to adding a third, irrelevant alternative. Amazon takes advantage of this bias by providing a “dummy” option alongside the genuine one, which may influence buyers’ views and, in turn, increase sales (Marini & Paglieri, 2019). At this point, clients are weighing their alternatives, making them vulnerable to the decoy effect bias.
For laptops, Amazon may sell two different models, one for €600 and the other for €1,000 (Amazon). The introduction of a “decoy” alternative, such as a €1,200 “deluxe” model, might trick buyers into purchasing the more expensive “premium” version (Marini & Paglieri, 2019). This is because the high-end version now seems the most cost-effective choice. See Figure 2.6 in the Appendices for an illustration of this.
In the 1980s, Joel Huber and John Payne, researchers in the marketing field, introduced the decoy effect bias. This bias occurs in Stage 2: introducing a third alternative, especially a decoy option, may impact the consumer’s appraisal and consideration of the initial two options by altering their relative worth and appeal.
Bandwagon Effect
Amazon uses the bandwagon effect, the propensity of individuals to follow the majority’s actions, by highlighting the “Amazon’s Choice” label on goods that are popular with and highly rated by Amazon customers (Rahman et al., 2020). Customers may be persuaded to buy the product because of the emblem, thinking plenty of others have purchased and liked it. Farjam (2021) et al. proposed this bias in 1936.
One example is the “Amazon’s Choice” badge, which features products that are popular among Amazon.es customers. Figure 2.7 in the Appendices illustrates. At this point, we show this bias because it is crucial for consumers to understand how social influences, like the bandwagon effect, might sway their choices. The bandwagon effect occurs in “Stage 2 because consumers’ desire to adhere to popular viewpoints and trends influences their decision-making and choice assessment during the information search stage.
Availability Heuristic
Decisions are often made with just some of the information accessible, which is known as the availability heuristic bias. Amazon employs this bias by ranking search results so that high-demand items appear first. In this way, Amazon may persuade buyers to rush into purchases without carefully considering their options. In 2019, Dietz and Venmans proposed their theory of bias.
Figure 2.8 in the Appendices shows one such instance. Amazon.es may prioritize the most popular laptops in response to a customer’s search for that specific product. As a result, the purchaser might select one of the best laptops above the competition without conducting more study (Ahmad & Shah, 2020). The availability heuristic may be found in Stage 2 because customers use the ease of memory recall while making judgments about a product or brand’s quality, relevance, and popularity.
Scarcity Bias
Amazon uses the scarcity bias to make its consumers feel they need to make a quick purchase because of the limited product availability. The corporation uses techniques such as “Limited time offer” and “Only 2 left in stock” notifications on product sites to provide the impression of scarcity and raise prices. In 2019, scarcity bias was first conceptualized by Dietz and Venmans.
For instance, the Spanish version of Amazon has a large banner alerting shoppers that only a limited number of popular electronic items are still in stock. The manifestation of this bias is seen in Figure 2.9 of the appendices. Scarcity bias is included in Stage 2 because it might motivate buyers to look for information more thoroughly. As a result, they will place a higher value on the thing they want to buy and be more likely to make a speedy choice.
Default Bias
Default bias refers to the propensity to always go with the status quo. Amazon capitalizes on this propensity by signing customers up for its Prime membership program without their knowledge or consent. Prime members get quicker shipping and other perks. Customers may be less inclined to cancel if they feel they must go to some trouble to do it. This bias was suggested by Zhao et al. (2020).
When purchasing Amazon.es, for instance, customers can take advantage of a free 30-day trial of Prime. They will be charged for the subscription if they do not cancel before the trial period finishes (Moerth-Teo et al., 2021). In the Appendix, Figure 2.10 serves as an illustration. This slant emphasizes how Amazon sways customer choice and ultimately increases revenue. Default bias is categorized under Stage 2 since it encourages consumers to stick with the preselected choice rather than exploring other possibilities, such as the preselected delivery option.
Stage 3: Alternative Evaluation
Customers in the Alternative Evaluation phase have narrowed the options to a select few that they believe will adequately satisfy their requirements. Now that they know they have choices, they can make an informed buying decision. Amazon now uses several cognitive biases to sway customers in its favor. These biases include the decoy effect, social proof, the illusion of scarcity, and the endowment effect (Mrkva et al., 2020). Amazon tries to sway a customer’s purchase choice by manipulating the presentation of its items. For example, it may display a comparable but inferior product beside a customer’s selected selection to make the latter seem more appealing, or it may use timed promotions to generate a feeling of urgency.
Anchoring Bias
Amazon takes advantage of the anchoring bias by switching the positions of the regular and discount prices on a product’s page. In order to sway consumers, goods marketers often resort to this strategy (Rezaei, 2021). Amazon.es illustrates anchoring bias by showing both the list and discounted prices. Using this method, even a nominal price reduction will appear more attractive. Customers’ decision-making processes may be heavily influenced by anchoring bias when assessing the benefits and drawbacks of various solutions.
As may be seen in Figure 3.1 of the Appendices, this impact is visible on the Amazon.es website. Amazon often uses anchoring bias to influence consumer behavior and boost sales. Anchoring bias occurs in Stage 3 since it causes consumers to place excessive weight on the first piece of information they are given when evaluating alternatives.
Endowment Effect
Amazon capitalizes on this bias by offering complimentary trials of its Prime membership program. This tactic encourages clients to sign up for full membership by giving them a taste of what they are missing out on without spending a dime. For example, on Amazon.es, new consumers may sign up for a 30-day free trial of Amazon Prime in return for an email address and some basic personal information (Dietz & Venmans, 2019).
Researchers in behavioral economics, such as Daniel Kahneman and Richard Thaler, have examined and suggested the endowment effect (Mrkva et al., 2020). When buyers debate the relative merits of various options during the Alternative Evaluation phase, this document is presented to them. This phenomenon can be seen on the Amazon.es webpage, as shown in Figure 3.2 in the Appendix. The Endowment Effect is in Stage 3 because it may affect the perceived worth of alternatives, making them look less appealing than the goods one currently owns.
Decoy Effect
Amazon may boost the chance of their desired choice being picked by offering a less appealing alternative (a decoy) that makes the preferred option seem better. Dietz and Venmans (2019) assert that Amazon capitalizes on the cognitive bias of consumers by presenting a product that is marginally inferior to other alternatives but at a reduced cost, with the expectation that purchasers will ultimately opt for the pricier option. Bias manifests during the Alternative Evaluation phase of the consumer decision-making process, wherein purchasers engage in a comparative analysis of various alternatives.
Amazon employs the Decoy Effect to incentivize consumers to opt for a pricier item by introducing a comparatively unappealing alternative that functions as a “decoy.” Figure 3.3 in the Appendix illustrates this phenomena, which may be seen on the Amazon.es website. The Decoy Effect is included in Stage 3 because it gives a third alternative comparable to one of the primary choices, making one of the main options look more appealing.
Confirmation Bias
Amazon uses this prejudice to its advantage by emphasizing favorable product evaluations over negative ones, which may influence consumers’ opinions of the product (Jesse et al., 2021). Since customers are actively searching for information to support their decision-making, this bias is presented during the Alternative Evaluation stage. Products on amazon.es, for instance, that have received several five-star reviews are shown more prominently, making it more straightforward for prospective buyers to discover the overwhelmingly great comments they have received (Mrkva et al., 2020). Figure 3.4 is provided as an example in the Appendix.
In their study of how consumers use internet reviews to inform their purchases, Jesse et al. (2021) introduced the confirmation bias. Confirmation bias is present in stage 3 because it entails individuals seeking information that confirms their previous ideas and prejudices, which may impact their evaluation and decision-making process when examining multiple choices and alternatives.
Negativity Bias
Negativity bias is the propensity for people to place more weight on bad than good data (Lee et al., 2020). At this point, Amazon takes advantage of this partiality by prominently displaying negative reviews alongside good ones, which may sway customers’ perceptions of the product in an unbalanced way. This strategy can boost the authority of favorable evaluations while drawing attention to product problems.
On the Amazon.es website, for instance, both excellent and negative customer reviews are shown beside the product. This tactic may work better or worse depending on the goods and the buyer’s receptivity to the negative bias. Lee et al. (2020) recommend using the negative bias, and an example is shown in Figure 3.5 of the Supplement. Amazon uses this tactic to sway buyers when considering their options. This bias is included in Stage 3 because it might persuade consumers to avoid particular choices by focusing on the negative features of the alternatives.
Framing Effect
Viewers may have different emotions when the same information is presented differently (the “Framing Effect”). Using phrases like “best sellers,” “new releases,” and “Amazon’s choice,” Amazon tries to influence customers’ purchasing decisions (Florence et al., 2022). A good spin on a product may influence consumers’ purchasing decisions. Figure 3.6 in the appendices is an illustration of this.
Tversky and Kahneman’s (1981) study on cognitive biases included the first mention of the framing effect (Mrkva et al., 2020). This is offered when the buyer is actively weighing their alternatives and is thus more receptive to the influence of framing. On the Amazon.es website, for instance, goods labeled as “Amazon’s Choice” are more likely to sell than comparable ones. The framing effect bias occurs in stage 3 because how choices are presented or framed might impact the consumer’s decision-making process.
Bandwagon Effect
Robert K. Merton coined the term “bandwagon effect” in 1949 to explain how individuals prefer to follow the lead of the people around them. Amazon communicates this bias during the Alternative Evaluation phase by showcasing the number of times an item has been purchased, suggesting that many others have also bought it, making it a popular pick (Brandes et al., 2022). The goal is to create a feeling of social evidence that will sway consumers to make purchases. In this way, Amazon.es may label a product as a “Bestseller,” signifying that it is a top seller and, hence, a suitable option (Brandes et al., 2022). Figure 3.7 in the appendices illustrates this. The Bandwagon Effect is classified as Stage 3 because it leads buyers to pick a specific product based on its popularity and social approval, among other factors.
Availability Bias
In the late 1970s, researchers Tversky and Kahneman initially suggested the idea of availability bias. Amazon employs this bias in the Alternative Evaluation phase by recommending products often purchased together. By emphasizing the popularity of these bundles, Amazon hopes to encourage people to buy more of the advertised products. In the “Frequently bought together” section on Amazon.es, for instance, related products frequently purchased by customers who viewed the item in question are displayed (Brandes et al., 2022). Customers are more inclined to purchase complementary products if they have a positive impression of their popularity and quality, which is why this part is included. Figure 3.8 in the Appendix demonstrates this bias. Availability Bias is included in Stage 3 because customers may depend on readily accessible information, such as customer evaluations, to evaluate alternatives instead of searching out other sources of information.
Halo Effect
Various researchers have noticed the “halo effect,” or the human tendency to form an overall favorable impression of a person or thing based on just one positive quality. Amazon capitalizes on this prejudice by emphasizing items that have received celebrity or expert endorsements (Ruangkanjanases et al., 2020). The idea behind this tactic is to enhance sales by associating the product with the good qualities and skills of the celebrity.
On Amazon.es, for instance, a celebrity-endorsed perfume is highlighted, and product sales increase due to the celebrity’s widespread appeal. Figure 3.9 from the supplemental materials serves as an illustration. Amazon aims to boost revenue and earnings by capitalizing on the halo effect. The Halo Effect is in Stage 3 because it may impact a customer’s view of a product’s overall quality based on a single good trait, resulting in a biased evaluation of alternatives.
Scarcity Bias
Amazon exploits scarcity bias to influence customer behavior via persuasive techniques. When purchasers anticipate a product with a limited supply and high demand, they acquire this bias. Amazon employs the phrases “only x items left in stock” and “limited-time offer” to pressure customers into purchasing before the promotion expires (Adaji et al., 2020). Tags like “Only 2 left in stock” or “Limited time deal” alert buyers on Amazon.es that a product may soon go out of stock. (Chen et al., 2019) The goal is to encourage clients to make a speedy purchase. This strategy is shown in Figure 3.10 of the Appendices.
Psychologists in behavioral economics first proposed the idea of scarcity bias. According to Jesse et al. (2021), Amazon successfully used this prejudice to shape buyer decisions. Scarcity bias occurs in Stage 3 because it causes buyers to see a product as more valued and desired owing to its limited availability or exclusivity, creating a feeling of urgency to acquire it.
Stage 4: Purchase Decision
Customers have done all their homework and are ready to purchase at the fourth stage of the purchasing process. At this point, Amazon employs several behavioral economic biases to influence consumers’ choices (Cheng et al., 2021). Amazon uses these biases in the hopes that it would lead to more purchases and higher average order values. This step employs fallacies like scarcity bias, social proof bias, and anchoring bias. Incentives like free delivery, discounts, and time-sensitive sales are ways Amazon tries to get people to buy from them (Nagtegaal et al., 2022). The company’s recommendation system heavily influences customers’ selections, which recommends things based on their past purchases.
Default Bias
People have a propensity toward going with whatever is already set up. Amazon takes advantage of this tendency by defaulting the checkout process to provide the fastest shipping option. In 2019, Sarwar et al. put out this idea (Nagtegaal et al., 2022). Figure 4.1 in the supplemental materials illustrates how Amazon typically displays the default shipping choice. This bias is introduced throughout the decision-making process to improve the possibility that a buyer would go through with a purchase (Soroka et al., 2019).
Customers are more inclined to take the fastest delivery option without further thought if given a choice between two or more. This tactic may also boost the offer’s perceived value and create urgency. Default bias is prevalent in the “Purchase Decision” stage because customers prefer to remain with the default option even when provided with different options, which might impact their ultimate purchase decision.
Anchoring Bias
Decision-makers are vulnerable to anchoring bias by giving disproportionate importance to the initial piece of information they receive. By prominently showing both the usual and discount prices, Amazon capitalizes on this cognitive bias by increasing the perceived value of the sale price. Tversky and Kahneman were the first to suggest this bias in 1974. When given to consumers, this bias might sway them to buy products they would not have considered otherwise.
Figure 4.2 in the Appendix displays how Amazon.es displays the original price of an item alongside the discounted price (Soroka et al., 2019). Using this method, users can give the customer a sense of security regarding the product’s value, which may sway their buying decision. Anchoring bias is in the 4th stage because it may impact a buying choice by making the first price or information offered (the “anchor”) the reference point for assessing future possibilities.
Confirmation Bias
We all suffer from confirmation bias, the propensity to look for evidence that backs up our thoughts and opinions. Amazon takes advantage of this slant by prominently showcasing 5-star reviews and ratings from verified consumers to persuade potential shoppers to purchase. Sarwar et al. (2019) explore this approach further. In the Appendix, Figure 4.3 shows a product page with many five-star ratings and feedback from satisfied customers. As this bias can help customers overcome doubts about the product, its presentation in the Purchase Decision stage is crucial for Amazon (Soroka et al., 2019). A book’s product page on Amazon.es is an excellent example of this method since it has endorsements from multiple satisfied customers. Confirmation bias is significant in Stage 4 because after a decision is made, individuals prefer to seek out and concentrate on information that supports their choice while ignoring information that contradicts it.
Decoy Effect
In 1982, Joel Huber, John Payne, and Christopher Puto introduced the decoy effect, also called the asymmetric dominance effect (Doi et al., 2022). It implies that decision-making may be influenced by adding a third option that is asymmetrically dominated by one of the other options. By displaying a decoy product that is more expensive than the target product but has fewer features, Amazon uses this bias to make the target product seem more appealing (Lamis et al., 2022).
As shown in Figure 4.4 of the Appendix, a decoy choice with a higher price and fewer features is placed in the center of three laptop models, as seen on the Amazon.es website. At this point, the decoy effect steers clients toward a specific product by making it seem like the best alternative. The Decoy Effect appears in Stage 4 because it presents a third alternative to make one of the other options more appealing, increasing the possibility of a sale.
Endowment Effect
Amazon employs the endowment effect as a marketing strategy by recommending products to customers based on their prior buying behavior. According to Voramontri and Klieb (2019), the recommendation of related items can enhance the perceived value of products and elevate the average order value. The presence of bias is exemplified in Figure 4.5 of the Appendix, wherein a system recommends products to a user based on their purchasing history.
This was demonstrated in a study conducted by Ul Adin et al. in 2022. Amazon leverages the endowment effect as a strategic tool during the purchase decision-making process to incentivize customers to increase their purchase volume. Richard Thaler, a renowned behavioral economist who won the Nobel Prize, was the one who first put up the idea of the endowment effect (Kartini & Nadha, 2021). The Endowment Effect appears under Stage 4 because it makes customers appreciate products they possess more than those they do not, making them more likely to buy and harder to part with.
Social Proof
Amazon utilizes social proof as a marketing strategy by emphasizing purchase quantities, user-generated reviews, and product ratings on individual product pages. According to Ul Adin et al. (2022), prospective buyers are presented with social proof in the cumulative quantity of reviews and ratings displayed on the product page. An illustration of this can be found in Figure 4.6, located in the appendices.
The term “social proof” was introduced by Robert Cialdini in his influential publication “Influence: The Psychology of Persuasion.” According to Voramontri and Klieb (2019), the presence of social evidence indicating a product’s high quality and popularity is likely to increase the likelihood of purchase among buyers at the point of sale. The number of evaluations and critiques on a particular item can be observed on the product page of “Amazon Basics – Cable de carga para-Apple iPhone” on the Amazon.es online platform. Social proof is under Stage 4 because it uses the psychological notion that individuals follow the actions or behaviors of others to make choices, making it a successful sales tactic.
Hindsight Bias
“Hindsight bias” describes the inclination to overestimate one’s predictive powers retrospectively. As part of its business strategy, Amazon uses Hindsight bias by emphasizing and advertising goods that have been successful in the past, giving the impression that this was always the case (Fechner & Herder, 2021). This is evident in the “Best Seller” and “Customers also bought” sections of their website, which include prominently displayed successful products. Lamis et al. (2022) uncovered the existence of this bias.
Figure 4.7 in the appendices is a screenshot that exemplifies this. On Amazon.es, for instance, the most negative customer evaluations are shown front and center on the product page for “1984” by George Orwell, thereby swaying the opinions of future purchasers. The Hindsight bias section appears in Stage 4 because it might lead consumers to justify a poor purchase decision since they did not know better.
Self-Serving Bias
Amazon leverages this cognitive bias by prioritizing the presentation of predominantly positive product ratings and reviews. As illustrated in Figure 4.8 of the supplementary material, the concept was first proposed by Orsenigo (2020). The figure portrays a product that has received favorable feedback through five-star ratings and positive reviews from the Amazon.es website.
According to Doi et al. (2022), Amazon employs this bias to cultivate a positive perception of the product among customers, leading to a rise in sales. By strategically promoting favorable reviews and ratings, Amazon cultivates a self-serving bias within the consumer’s psyche, reinforcing their confidence in the accuracy of their purchase decision and fostering a sense of trust in the organization. This approach helps Amazon enhance its revenue generation while strengthening customer allegiance. The actions above exemplify how Amazon exploits bias to its benefit. The Self-serving bias is a factor in Stage 4 because it affects consumers’ propensity to ascribe their purchase’s success or failure to their talents rather than external circumstances.
Loss Aversion
Amazon capitalizes on this bias by implementing liberal return policies that facilitate exchanging or returning merchandise. According to Litovsky et al. (2022), offering a return policy can increase the likelihood of buyers purchasing goods, as it assures they can return the product if it fails to meet their expectations. Amazon’s return policy is an exemplar of this phenomenon, and it is explained comprehensively on the Help & Customer Service page of the website. In their seminal work “Prospect Theory,” Kahneman and Tversky pioneered identifying cognitive bias (Fechner & Herder, 2021). The supplementary materials are depicted in Figure 4.9. Loss aversion appears in Stage 4 because it encourages buyers to buy rather than not buy since they fear losing money if they do not.
Status Quo Bias
The Status Quo Bias is the inclination to keep things as they are rather than embrace change. Using subscription-based services like Amazon Prime, which offers free and fast shipping, streaming of movies and TV shows, music, and more, Amazon takes advantage of this cognitive bias to promote customer loyalty (Nel & Boshoff, 2020). Customers are more likely to stick with these services after they have experienced their ease and advantages. This prejudice is on full display on Amazon.es, where Prime members are given access to exclusive discounts and incentives that make continuing to buy from Amazon more tempting.
In 1988, Samuelson and Zeckhauser were the first to suggest this bias. This bias is shown here to show how Amazon manipulates cognitive biases to influence consumer behavior. Figure 4.11 in the Appendices illustrates. Status Quo bias is in Stage 4 because it makes consumers more inclined to continue with their present habit, even if better choices are available, making switching to a new product or brand more difficult.
Stage 5: Post-Purchase Evaluation
A customer’s propensity to make a repeat purchase is heavily influenced by how they feel about a product after using it. Amazon uses consumer post-purchase biases in order to influence their shopping habits. The post-purchase assessment phase is critical because it may influence consumer loyalty and word-of-mouth advertising (Boerman et al., 2021). Amazon aims to influence the buyer’s opinion of a product to encourage repeat business and good word of mouth by using techniques including tailored suggestions, social proof, and the prominence of positive reviews.
Confirmation Bias
Amazon capitalizes on this slant by highlighting positive feedback and high ratings on product sites, leading customers to create favorable opinions of the product before using it. Sarwar et al. (2019) were the first to describe this bias. Confirmation bias might affect customers’ post-purchase evaluations and subsequent purchases. Therefore, it is crucial to grasp how it works at this point.
The “The Alchemist” product page on Amazon.es indicates this bias since the reviews are mostly favorable, and the book has a high overall rating, leading customers to assume it is a worthwhile purchase (Boerman et al., 2021). This exemplifies how Amazon uses confirmation bias to sway patronage and spending habits.” Figure 5.1 in the appendices illustrates this. Confirmation bias occurs in stage 5 because consumers prefer to seek information supporting their opinions or expectations regarding the goods they bought rather than evaluating alternative information that may contradict their initial impressions.
Hindsight Bias
Amazon exploits this fallacy by making it seem as if it was simpler to predict outcomes or events that occurred in the past. Indicating the best alternative based on sales history and user ratings, the “Amazon’s Choice” logo promotes products with this Bias (Boerman et al., 2021). As a result, buyers will likely be swayed into thinking the product is superior to alternatives. This bias was first proposed by Lamis et al. (2022).
In the Appendix, Figure 5.2 illustrates this. The Amazon’s Choice badge is used as a bias during the post-purchase assessment phase to encourage repeat purchases by making consumers feel good about their purchases. If two coffee makers on Amazon.es have identical ratings and customer reviews, but one has the Amazon’s Choice label, the customer is likelier to buy the one with the label. This bias is categorized in stage 5 since it alters the customer’s impression of the purchase decision, leading them to assume that their option was evident and proper.
Loss Aversion Bias
By providing a no-questions-asked return policy, Amazon takes advantage of loss aversion, the psychological inclination to value avoiding losses more highly than attaining profits. Customers are more likely to make purchases when they know they may exchange or return items if they are unsatisfied. This bias was hypothesized by Cheng et al. (2021).
The Amazon.es website prominently displays a guarantee that if a consumer is dissatisfied with their purchase within 30 days, they may send it back at no cost to them (Apparaju, 2021). The accompanying Figure 5.3 provides more illustration of this bias. This bias has been introduced since it affects consumers’ post-purchase ratings and subsequent purchases (Hayes et al., 2021). Loss aversion bias is in Stage 5 because customers evaluate their purchases based on perceived gains and losses, and the fear of losing something they already own can motivate them to keep using it rather than looking for alternatives.
Negativity Bias
Amazon customers’ propensity to be affected more by negative than by neutral or good events and emotions. The firm takes advantage of this prejudice by concealing negative customer feedback and star ratings on product sites. This bias was first shown by Jesse et al. (2021). Because it has the potential to color the buyer’s post-purchase assessment, this bias is introduced now. If buyers go to the Spanish version of Amazon (Amazon.es), they will notice that both good and negative reviews are published, but the former is pushed farther down the page so that only the latter is accessible to readers. The illustration in Figure 5.4 can be found in the appendices. Negativity bias is in Stage 5 because consumers are more likely to recall and dwell on unpleasant experiences or product characteristics than favorable ones, which might affect their future purchase choices and satisfaction.
Positivity Bias
Positivity bias is the tendency to focus on good memories and downplay or ignore less pleasant ones. By emailing customers after making a purchase, Amazon uses this prejudice to boost a product’s star rating and sales (Kumar et al., 2022). This, in turn, may sway interested purchasers to complete the transaction. This bias is evident, for instance, on the Amazon.es website, where items with higher ratings and good reviews tend to be shown more prominently. Taylor and Brown made the 1988 proposal of this bias. The post-purchase assessment phase is when the bias manifests itself since satisfied customers are more likely to repurchase and remain loyal to a brand if they have had a good experience. In the Appendix, Figure 5.5 serves as an illustration. Positivity bias is significant in Stage 5 because buyers prefer to concentrate on the positive features of a product after buying it, which might impact their probability of favorable evaluations and repeat purchases.
Social Proof Bias
When making choices, people are prone to follow the lead of those around them due to social proof. Amazon takes advantage of this tendency by giving customers easy access to ratings and reviews written by other customers before they purchase (Cheng et al., 2021). Robert Cialdini, a social scientist, was the first to suggest this partiality. This bias manifests in the evaluation phase following a purchase because buyers want confirmation that they made the right choice. Providing social evidence of a product’s quality, the Amazon.es website displays the number of reviews and ratings it has received, along with an average rating (Vollero et al., 2021). Figure 5.6 from the supplementary materials serves as an illustration. The social proof bias occurs in stage 5 since buyers may seek out and depend on the views of others when assessing their purchase choice and post-purchase experience.
Endowment Bias
Amazon utilizes the Endowment bias by incentivizing customers to provide ratings and feedback on their acquired products. This subsequently enhances their emotional connection to the item and the probability of future purchases (Dietz & Venmans, 2019). Bias is discernible on Amazon.es, as customers are prompted to provide ratings and reviews of their purchases. Thaler (1980) first proposed the presence of this bias, as cited in Apparaju (2021). This strategy is executed post-purchase to strengthen the customer’s affective attachment to the product and encourage subsequent transactions.
The Appendices contain a visual representation, as depicted in Figure 5.7, demonstrating the manifestation of this particular bias. Amazon seeks to improve customer loyalty and increase repeat sales using Endowment bias. Endowment bias appears under Stage 5 because it relates to people’s inclination to overvalue something merely because they possess it, which might impact how they evaluate and feel about the object after acquiring it.
Choice-Supportive Bias
Amazons utilize a cognitive bias called attributing positive features to prior decisions. The company takes advantage of this tendency by encouraging customers to provide feedback through reviews and ratings, which may lead to customer post-purchase satisfaction (Alaybek et al., 2022). As a result, Amazon will see an increase in consumer happiness and loyalty. On the Amazon.es website, for instance, customers are prompted to rate and evaluate their purchases. Potential purchasers have easy access to the favorable evaluations left by past customers by reading the reviews posted on the product page.
Mather and Johnson (2000) were the first to suggest the idea of choice-supportive Bias (Apparaju, 2021). This bias is introduced at this point to demonstrate how Amazon uses consumers’ cognitive biases to affect their purchase choices. Figure 5.8 from the supplemental materials serves as an illustration. Choice-supportive bias is included in Stage 5 because it influences how consumers recall their purchase decision and promotes their feeling that they made the correct choice, resulting in increased satisfaction and perhaps more future purchases.
Omission Bias
The omission bias is a cognitive bias in which detrimental acts are seen to be more severe than inactions that may have had equivalent negative consequences. Amazon takes advantage of this misunderstanding by pushing “risk-free” items that give buyers more assurance when purchasing (Ul Abdin et al., 2022). Amos Tversky and Daniel Kahneman initially postulated this bias in perception (Alaybek et al., 2022).
If a consumer is unhappy with their purchase within 30 days, for instance, they may send it back and get their money back from Amazon. Figure 5.9 in the Appendices exemplifies this bias (Alaybek et al., 2022). Omission bias is in Stage 5 because consumers tend to regret actions they have made (like buying a product) more than actions they have not taken (like not buying a product), making inactivity seem safer.
Voluntary Response Bias
Customer feedback rates tend to increase after getting a post-purchase email, a phenomenon known as the “voluntary response bias.” Those with favorable, solid, or negative sentiments are likelier to submit feedback, whereas those with indifferent opinions may choose not to bother. Groves and Peytcheva initially proposed the concept of voluntary response bias in 2008. The report stresses the significance of this Bias because Amazon relies on consumer input for product and service development (Roodbeen et al., 2021). By being aware of this partiality, Amazon will be better able to examine customer comments and make well-informed choices about enhancing its products and services.
For instance, Amazon will contact buyers after purchasing to solicit feedback through a product review. In doing so, they give Amazon helpful information that can be used to improve the product and entice more sales. Although not all customers will leave reviews, Amazon uses statistical methods to extrapolate the general population’s opinions from the reviews they receive. This bias is included in Stage 5 because it relates to consumers who have a strong opinion, either favorable or unfavorable, to offer feedback and skew the total rating of a product, which might impact future purchase choices.
References
Achtypi, E., Ashby, N. J. S., Brown, G. D. A., Walasek, L., & Yechiam, E. (2021). The endowment effect and beliefs about the market. The Decision, 8(1), 16–35. Web.
Adaji, I., Oyibo, K., & Vassileva, J. (2020). E-Commerce shopping motivation and the influence of persuasive strategies. Frontiers in Artificial Intelligenc 3, 67. Web.
Ahmad, M., & Shah, S. Z. (2020). Overconfidence heuristic-driven bias in investment decision-making and performance: Mediating effects of risk perception and moderating effects of Financial Literacy. Journal of Economic and Administrative Sciences, 38(1), 60–90. Web.
Alaybek, B., Dalal, R. S., Fyffe, S., Aitken, J. A., Zhou, Y., Qu, X., Roman, A., & Baines, J. I. (2022). All that ends (and peaks) well? A meta-analysis of the peak-end rule and duration neglect. Organizational Behavior and Human Decision Processes, 170. Web.
Amazon. (n.d.). Amazon.es. Web.
Apparaju, A. (2021). Analyzing customer buying habits. International Journal of Scientific Research in Science and Technology, 584–587. Web.
Boerman, S. C., Kruikemeier, S., & Bol, N. (2021). When is personalized advertising crossing personal boundaries? How type of information, data sharing, and personalized pricing influence consumer perceptions of personalized advertising. Computers in Human Behavior Reports, 4. Web.
Brandes, L., Godes, D., & Mayzlin, D. (2022). Extremity bias in online reviews: The role of attrition. Journal of Marketing Research, 59(4), 675-695. Web.
Chen, Q., Feng, Y., Liu, L., & Tian, X. (2019). Understanding consumers’ reactance of online personalized advertising: A new scheme of rational choice from a perspective of negative effects. International Journal of Information Management, 44, 53-64. Web.
Cheng, H., Lambert, D. M., DeLong, K. L., & Jensen, K. L. (2021). Inattention, availability bias, and attribute premium estimation for a biobased product. Agricultural Economics, 53(2), 274–288. Web.
Curmi, T. (2021). Investigative Decision Making and Its Association With Critical Thinking Skills, Thinking Styles, and Law Enforcement Experience (Doctoral dissertation, City University of New York). Web.
Dietz, S., & Venmans, F. (2019). The endowment effect, discounting and the environment. Journal of Environmental Economics and Management, 97, 67–91. Web.
Doi, T., Doi, S., & Yamaoka, T. (2022). The peak–end rule in evaluating product user experience: The Chronological Evaluation of past impressive episodes on overall satisfaction. Human Factors and Ergonomics in Manufacturing & Service Industries, 32(3), 256–267. Web.
Dowling, K., Guhl, D., Klapper, D., Spann, M., Stich, L., & Yegoryan, N. (2020). Behavioral biases in marketing. Journal of the Academy of Marketing Science, 48, 449-477. Web.
Farjam, M. (2021). The bandwagon effect in an online voting experiment with real political organizations. International Journal of Public Opinion Research, 33(2), 412-421. Web.
Fechner, W., & Herder, E. (2021). Digital nudging for more ecological supermarket purchases. In Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. pp.284-292. Web.
Feldman, G., Kutscher, L., & Yay, T. (2020). Omission and commission in judgment and decision making: Understanding and linking action‐inaction effects using the concept of normality. Social and Personality Psychology Compass, 14(8). Web.
Fenko, A., Keizer, T., & Pruyn, A. (2017). Do social proof and scarcity work in the online context?. In 16th International Conferences on Research in Advertising (ICORIA 2017) (pp. 1-7). Web.
Florence, E. S., Fleischman, D., Mulcahy, R., & Wynder, M. (2022). Message framing effects on sustainable consumer behavior: a systematic review and future research directions for social marketing. Journal Of Social Marketing, (Ahead-Of-Print), 12(4), pp. 623-652. Web.
Fritze, M. P., Eisingerich, A. B., & Benkenstein, M. (2018). Digital transformation and possession attachment: examining the endowment effect for consumers’ relationships with hedonic and utilitarian digital service technologies. Electronic Commerce Research, 19(2), 311–337. Web.
Guo, J., Li, Y., Xu, Y., & Zeng, K. (2021). How live streaming features impact consumers’ purchase intention in the context of cross-border E-commerce? A research based on SOR theory. Frontiers in Psychology, 12. Web.
Hayes, J. L., Brinson, N. H., Bott, G. J., & Moeller, C. M. (2021). The influence of consumer–brand relationship on the personalized advertising privacy calculus in social media. Journal of Interactive Marketing, 55(1), 16-30. Web.
Ivaschenko, A., Stolbova, A., & Golovnin, O. (2020). Data market implementation to match retail customer buying versus social media activity. Advances in Intelligent Systems and Computing, Vol 1, 363–372. Web.
Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing?Journal of Personality and Social Psychology, 79(6), 995–1006. Web.
Jain, J., Walia, N., & Gupta, S. (2019). Evaluation of behavioral biases affecting the investment decision-making of individual equity investors by fuzzy analytic hierarchy process. Review of Behavioral Finance, 12(3), 297–314. Web.
Jesse, M., Jannach, D., & Gula, B. (2021). Digital nudging for online food choices. Frontiers in Psychology, Vol 12, 5870. Web.
Kartini, K., & Nadha, K. (2021). Behavioral biases on investment decision: A case study in Indonesia. The Journal of Asian Finance, Economics, and Business, 8(3), 1231-1240.
Kumar, S., Ashoka Rajan, R., Swaminathan, A., & Johnson, E. (2022). Hyper-personalization and its impact on customer buying behavior. Data Intelligence and Cognitive Informatics, 1(1) 649–664. Web.
Kumari, R. (2023). Customer buying behavior towards online retail marketer’s (Amazon vs Flipkart). International Journal For Multidisciplinary Research, 5(2). Web.
Lamis, S. F., Handayani, P. W., & Fitriani, W. R. (2022). Impulse buying during flash sales in the online marketplace. Cogent Business & Management, 9(1). Web.
Lee, E. J., Choi, H., Han, J., Kim, D. H., Ko, E., & Kim, K. H. (2020). How to “Nudge” your consumers toward sustainable fashion consumption: An fMRI investigation. Journal of Business Research, 117, 642-651. Web.
Litovsky, Y., Loewenstein, G., Horn, S., & Olivola, C. Y. (2022). Loss aversion, the endowment effect, and gain-loss framing shape preferences for noninstrumental information. Proceedings of the National Academy of Sciences, 119(34). Web.
Lo, P. S., Dwivedi, Y. K., Tan, G. W. H., Ooi, K. B., Aw, E. C. X., & Metri, B. (2022). Why do consumers buy impulsively during live streaming? A deep learning-based dual-stage SEM-ANN analysis. Journal of Business Research,147,325-337. Web.
Marini, M., & Paglieri, F. (2019). Decoy effects in intertemporal and probabilistic choices the role of time pressure, immediacy, and certainty. Behavioural processes, 162, 130-141. Web.
Moerth-Teo, J. Y., Bobek, V., Horvat, T., & Milost, F. (2021). The effects of consumers’ buying behavior on E-Commerce pricing in the highly developed emerging market: The case of Singapore. International Journal of Economics and Finance, 13(12), 55. Web.
Mrkva, K., Johnson, E. J., Gächter, S., & Herrmann, A. (2020). Moderating loss aversion: Loss aversion has moderators, but reports of its death are greatly exaggerated. Journal of Consumer Psychology, 30(3). Web.
Nagtegaal, R., Tummers, L., Noordegraaf, M., & Bekkers, V. (2020). Designing to debias: Measuring and reducing public managers’ anchoring bias. Public Administration Review, 80(4), 565–576. Web.
Nel, J., & Boshoff, C. (2020). Status quo bias and shoppers’ mobile website purchasing resistance. European Journal of Marketing, 54(6), 1433–1466. Web.
Orsenigo, L. (2020). Reflection of self-serving biases on memory representations: The unconscious effects of hedonic and utilitarian consumption, Universidade Católica Portuguesa. Web.
Pham, H. C. (2020). Factors affecting consumer goods buyers’ choice in e-commerce sites: Evidence from Vietnam. The Journal of Asian Finance, Economics and Business, 7(11), 947-953. Web.
Rahman, M. A., Islam, M. A., Esha, B. H., Sultana, N., & Chakravorty, S. (2018). Consumer buying behavior towards online shopping: An empirical study on Dhaka city, Bangladesh. Cogent Business & Management, 5(1). Web.
Renaud, K. (2021). Endowment effect. In Encyclopedia of Cryptography, Security and Privacy (pp. 1-2). Berlin, Heidelberg: Springer Berlin Heidelberg. Web.
Rezaei, J. (2021). Anchoring bias in eliciting attribute weights and values in multi-attribute decision-making. Journal of Decision Systems, 30(1), 72-96. Web.
Roodbeen, S. X., de Lacy, F. B., & Hompes, R. (2021). Response to the Comment on “Neyman’s Bias in Online Voluntary Databases!”. Annals of Surgery, 274(6), e702-e703. Web.
Ruangkanjanases, A., You, J. J., Chien, S. W., Ma, Y., Chen, S. C., & Chao, L. C. (2020). Elucidating the effect of antecedents on consumers’ green purchase intention: an extension of the theory of planned behavior. Frontiers in Psychology, 11, 1433. Web.
Sarwar, M. A., Awang, Z., & Habib, M. D. (2019). Consumer purchase regret: A systematic review. International Journal of Academic Research in Business and Social Sciences, 9(9). Web.
Soroka, S., Fournier, P., & Nir, L. (2019). Cross-national evidence of a negativity bias in psychophysiological reactions to the news. Proceedings of the National Academy of Sciences, 116(38), 18888–18892. Web.
Ul Abdin, S. Z., Qureshi, F., Iqbal, J., & Sultana, S. (2022). Overconfidence bias and investment performance: A mediating effect of risk propensity. Borsa Istanbul Review, 22(4), 780-793. Web.
Vollero, A., Sardanelli, D., & Siano, A. (2021). Exploring the role of the Amazon effect on customer expectations: An analysis of user‐generated content in consumer electronics retailing. Journal of Consumer Behaviour. 6(3), 309–433. Web.
Voramontri, D., & Klieb, L. (2019). Impact of social media on consumer behavior. International Journal of Information and Decision Sciences, 11(3), 209–233. Web.
Zhao, H., Fu, S., & Chen, X. (2020). Promoting users’ intention to share online health articles on social media: The role of confirmation bias. Information Processing & Management, 57(6). Web.
Appendices
Stage 1: Awareness or Problem Recognition
Stage 2: Information Search
Stage 3: Alternative Evaluation
Stage 4: Purchase Decision
Stage 5: Post-Purchase Evaluation
4.50
Loading...
