Digital Economy and Behavioral Bias: Psychological Traps in Online

Shopping and Platform Selection

Ruoqi An

School of Politics and International Studies, University of Leeds, Leeds LS2 9JT, U.K.

Keywords: Anchoring Effect, Framing Effect, Choice Overload.

Abstract: Since the digital economy has grown so quickly, e-commerce has emerged as a crucial element of

contemporary trade, greatly influencing consumer choice. However, cognitive biases like anchoring effects,

framing effects, and choice overload have become more prevalent due to the complexity of online information

and the sheer number of options. Through a review of the literature and a case study, this paper investigates

how these biases affect consumer behaviour and e-commerce platform strategy. The results show that framing

effects change purchasing preferences, price anchoring affects value perception, and having too many options

causes decision fatigue. While current research provides insightful information, gaps remain, especially in

long-term behavioural patterns. Platforms could improve recommendation algorithms, increase pricing

transparency, and inform users about decision-making biases in order to lessen these biases. Future studies

should look more closely at how decision-support tools and personalised recommendation systems can

encourage more sensible consumer behaviour in online buying settings.

1 INTRODUCTION

With the rapid development of the digital economy,

e-commerce has become an important component of

the modern economy, and consumers' shopping

methods and decision-making processes are

gradually shifting to online platforms. McKinsey and

Company argued that the COVID-19 pandemic has

accelerated the transformation of global shopping

methods. During the pandemic, the frequency of

online shopping by consumers increased by 2-3 times

(McKinsey & Company, 2020). This rapidly

expanding online shopping environment provides

consumers with more choices and information, but

also exacerbates the complexity they face in the

decision-making process. In this context, behavioral

biases in online shopping and platform selection

processes, such as anchoring effects, framing effects,

and choice overload, have gradually become key

factors affecting consumer decision-making. In

recent years, researchers have extensively explored

the application of behavioral bias in the digital

economy in behavioral economics. Liu et al. argue

that the anchoring effect significantly affects the

pricing decisions of online consumers, and companies

need to consider consumers' anchoring psychology

when formulating pricing strategies (Liu et al., 2020).

Hu and Li believe that by utilizing framework effects

to optimize pricing and default option strategies, e-

commerce platforms and customized services can

effectively increase consumer purchase intention and

spending (Hu & Li, 2019). Long et al. argue that when

faced with too many choices, consumers are more

likely to experience decision fatigue, which in turn

affects their purchasing decisions (Long et al., 2025).

Grewal and Roggeveen analyzed in detail the impact

of the digital shopping environment on consumer

behavior and argued that behavior biases such as

anchoring effect and loss aversion are very common

in e-commerce platforms (Grewal & Roggeveen,

2020). Although existing research provides ideas for

understanding behavioral biases in the digital

shopping environment, there are still gaps that need

to be filled. Existing research has mostly focused on

a wide range of consumer groups, neglecting the

moderating effect of individual differences (such as

age and cultural background) on behavioral biases. In

addition, further research is needed on how to reduce

decision fatigue and choice overload without

sacrificing user experience. Research has shown that

biases such as anchoring effect, framing effect, loss

aversion, choice overload, and social identity effect

significantly affect consumer decision-making and

marketing strategies of e-commerce platforms. This

article explores the impact of these biases on the

770

An, R.

Digital Economy and Behavioral Bias: Psychological Traps in Online Shopping and Platform Selection.

DOI: 10.5220/0014280300004942

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Applied Psychology and Marketing Management (APMM 2025), pages 770-774

ISBN: 978-989-758-791-7

digital economy through literature review and case

analysis, and provides optimization suggestions for

platform design, laying the foundation for future

related research.

2 CORE CONCEPTS

Kahneman argued that consumer decisions are often

influenced by cognitive biases, especially when faced

with complex information or excessive choices,

which can lead to irrational decisions (Kahneman,

2011). In the digital economy environment,

behavioral biases are mainly manifested as anchoring

effects, framing effects, loss aversion, choice

overload and other psychological factors. Anchoring

effect is a common cognitive bias where consumers

overly rely on the initial value of information to make

decisions. This effect is particularly important in the

pricing strategy of e-commerce platforms, which

often use high reference prices to enhance consumers'

attractiveness to discounted products, thereby

affecting their purchasing decisions. Liu et al. argued

that in the digital economy, the anchoring effect

changes consumers' purchase decisions by shaping

consumers' value perception and price acceptance.

Businesses can use the strategic price anchor to

optimize pricing strategies to enhance market

competitiveness and consumer conversion rate (Liu et

al., 2020). The framing effect refers to how different

ways of presenting information can affect consumers'

judgments and choices. In e-commerce platforms, the

language used in advertisements or product

descriptions may be framed in different ways, thereby

influencing consumers' psychological reactions. Hu

and Li argued that on the online platform, the

combination pricing can improve the purchase

intention better than itemized pricing, and the

degradation framework is easier to promote

consumers' acceptance of higher total price and

default options than the upgrading framework, which

is of great significance to the pricing and service

customization strategy of Internet marketing (Hu &

Li, 2019). Choice overload refers to the anxiety or

confusion that consumers feel when faced with too

many choices, leading them to make avoidance

decisions or choose simple solutions. Chernev et al.

suggest that when consumers face choice overload,

they are more inclined to choose default options or

make suboptimal decisions. This is because too many

choices can lead to decision fatigue, and consumers

are more inclined to choose "safe options" or

suboptimal options when feeling uncertain or

fatigued (Chernev et al., 2015). In order to more

clearly show how behavior bias causes psychological

traps and thus affects people's judgment in online

shopping, this paper divides it into two parts for

discussion: the first is the research on anchoring

effect and framing effect, and the second is the

research on choice overload and consumer decision-

making. In the following analysis, the present study

will explore the research content and the relationship

between these two aspects.

3 THE DUAL INFLUENCE OF

THE ANCHORING EFFECT

AND FRAMING EFFECT ON

DIGITAL SHOPPING

DECISION

As for characteristics of the research object, in the

research of anchoring effect and framing effect, many

studies focus on analyzing how consumers make

purchase decisions on e-commerce platforms. Liu et

al. studied the impact of anchoring effect on the

pricing of e-commerce products, and found that by

adjusting the price to match different anchoring

situations, consumers' value perception and product

competitiveness could be improved, and the

effectiveness of the strategy was verified by

simulation (Liu et al., 2020). Similarly, Hu and Li

studied the impact of framing effects, namely how

product descriptions framed by savings rather than

costs affect consumer choices (Hu & Li, 2019).

As for measurement methods, Liu et al.

constructed an optimal pricing model based on price

anchors using mathematical modeling, sensitivity

analysis, and simulation experiments. The results

showed that a higher price anchor would lead to a

decrease in optimal pricing, and emphasized the

importance of strategic price anchoring in e-

commerce pricing (Liu et al., 2020). Hu and Li's

research explored the impact of price display methods

on consumer decision-making through two

experiments: Experiment 1 found that combined

pricing increased purchase intention, while

segmented pricing made consumers more confused;

Experiment 2 found that offering luxury packages as

the default option would encourage consumers to

spend more, reflecting the loss aversion effect. (Hu &

Li, 2019).

As for data analysis methods, these studies used

quantitative analysis methods such as regression

analysis and experimental data analysis to verify the

impact of different biases on consumer decision-

making. Liu et al. used mathematical modeling and

Digital Economy and Behavioral Bias: Psychological Traps in Online Shopping and Platform Selection

771

optimization methods to analyze the impact of

anchoring effects on optimal pricing strategies based

on the MNL model and verified the stability of the

model through sensitivity analysis and simulation

experiments (Liu et al., 2020). Hu and Li used

independent sample t-tests to compare whether there

were significant differences in the mean values of

variables such as purchase intention, customization

frequency, and final price under different

experimental conditions. (Hu & Li, 2019).

With respect to data analysis results, The

significant impact of anchoring effect and framing

effect on consumer e-commerce platform decision-

making, especially in price evaluation and value

perception. Liu et al.'s research shows that when the

price is higher than the anchor point, the greater the

degree of anchoring, the lower the optimal pricing.

However, when the cost is high, increasing the degree

of anchoring will reduce profits (Liu et al., 2020).

The experimental results of Hu and Li show that

merger pricing increases consumer purchase

intention, and the purchase intention is significantly

higher under merger pricing conditions than under

split pricing. In another experiment, the default

downgrade framework resulted in consumers

spending more and having a lower frequency of

customization, while the upgrade framework resulted

in consumers spending less but having a higher

frequency of customization, with a significant

difference (Hu & Li, 2019).

With respect to research conclusion and

comparison with other studies, Liu et al. argued that

anchoring bias is an important determinant of

perceived product value in digital environments,

which is consistent with similar research by Tversky

and Kahneman in traditional retail environments (Liu

et al., 2020; Kahneman, 2011). Hu and Li believe that

merger pricing is more effective in promoting

consumer purchases than split pricing, while

downgrade customization is easier to maintain a

higher total price than upgrade customization. This is

because segmented pricing increases consumers'

sense of loss and reduces their willingness to

purchase, while in downgraded customization,

consumers are unwilling to give up their existing high

spending options. Research has shown that consumer

decision-making is influenced by framing effects, and

e-commerce platforms and customized services can

improve purchase rates and spending amounts by

optimizing pricing and default option strategies (Hu

& Li, 2019). Similarly, Chandran and Morwitz

studied the framing effect of price information and

argued that consumers are more likely to be attracted

by discount information such as "saving 30%" rather

than the direct price display of "original price of 100

yuan, current price of 70 yuan". This study also

indicates that subtle changes in the presentation of

prices can significantly affect consumers' purchasing

tendencies, further supporting the findings of Hu and

Li. (Hu & Li, 2019; Chandran & Morwitz, 2006).

These studies all indicate that subtle changes in the

way information is presented can significantly alter

consumer decision-making.

With respect to Research deficiencies and

shortcomings, although these studies provide

valuable insights, their limitations cannot be ignored.

For example, Liu et al.'s article has some limitations,

including assuming that consumers have

homogeneous preferences, while in reality

preferences may differ. Although the anchoring effect

has been considered, other psychological factors such

as brand loyalty and consumer emotions have not

been fully taken into account; The lack of clear

explanation of data sources and sample selection may

affect the universality and reliability of research

results (Liu et al., 2020). Similarly, Hu and Li also

have some limitations. Firstly, Experiment 1 only

used high school students, while Experiment 2 only

used MBA students as research subjects. The

representativeness of the samples may be insufficient,

and future research can be expanded to a wider range

of age groups and social groups. In addition, the

research scenario is relatively single, only examining

the purchase of electronic products and customized

travel packages. In the future, other product

categories such as luxury goods, daily necessities, and

fitness memberships can be further explored. Finally,

this study only measured consumers' purchasing

responses in the short term and did not explore long-

term shopping habits (Hu & Li, 2019). Future

research can supplement this deficiency through

long-term tracking.

4 THE IMPACT OF CHOOSING

OVERLOAD ON DIGITAL

SHOPPING DECISIONS

As for characteristics of the research object, the

research on choice overload mainly focuses on the

decision-making difficulties faced by consumers

when faced with a large number of product choices,

and this choice difficulty is particularly evident on e-

commerce platforms. Long et al. argue that moderate

recommended products are most beneficial for

improving conversion rates, while excessive selection

can lead consumers to give up purchasing, which is

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consistent with Schwartz's study on choice overload

in consumer psychology (Long et al., 2025; Schwartz,

2009). Simultaneously supporting the views of Jiang

and Benbasat, it is believed that when consumers are

faced with too many choices, they often feel decision

fatigue and either avoid making decisions or choose

default options (Jiang & Benbasat, 2007).

In regard to measurement methods, Long et al.

conducted a large-scale randomized controlled trial

on the Alibaba platform to investigate the impact of

recommended product quantity on consumer

decision-making. The experiment randomly divided

consumers into four groups, displaying 1 to 4

recommended products and tracking their click and

purchase behavior. The main measurement indicators

include search probability, purchase probability, click

through depth, and purchase conversion rate,

analyzing the impact of recommended product

quantity on consumer decision-making (Long et al.,

2025).

As for data analysis methods, in the study of

choice overload, quantitative statistical analysis and

behavioral modeling are common analytical methods

for understanding consumer decision-making

behavior when faced with a large number of choices.

Long et al. used various data analysis methods,

including two sample ratio tests to compare the

differences in click-through and purchase rates

between different groups, linear regression analysis to

control for fixed effects of merchants and dates, and

exploring the impact of recommendation quantity on

clicks and purchases; In addition, the causal forest

method was used to identify under which

circumstances the overload effect was strongest, and

randomized tests were conducted to ensure the

randomness of the experimental groups and avoid

bias in consumer attributes (Long et al., 2025).

Data analysis shows that recommending too many

products can significantly reduce consumers'

purchase and search probabilities. When

recommending two products, the purchase

probability is the highest, increasing by 67%

compared to recommending one product; When

recommending 3 or 4 products, the purchase

probability decreases, and recommending 4 products

is 16.4% lower than recommending 2 products. 64%

of the decrease in purchase rate is due to not clicking

on recommended products, reflecting the selection

overload effect (Long et al., 2025).

With respect to research conclusion and

comparison with other studies, Long et al. believe that

moderate product recommendations are most

beneficial for improving conversion rates, while

excessive selection can lead to giving up on purchases,

which is consistent with Schwartz's research on

choice overload (Long et al., 2025; Schwartz, 2009).

In regard to Research deficiencies and

shortcomings, Although research suggests that

selection overload can lead to negative outcomes,

studies are deficient in the diversity of samples and

contexts. Long et al. found that this study is limited to

the Alibaba platform, and its universality can be

verified on other e-commerce platforms in the future.

The study did not consider the long-term behavior of

consumers, nor did it analyze the negative emotions

that may arise from excessive recommendations.The

necessity of studying negative emotions can be

explained from the following perspectives: choice

overload often leads to negative emotions such as

anxiety and helplessness, which may significantly

affect consumers' decision-making behavior. For

example, too many choices may lead to decision

fatigue, leading consumers to avoid decisions or

regret. The rise of these negative emotions not only

affects the instant consumption experience, but also

may reduce the long-term loyalty of consumers.

Therefore, the study of negative emotions is helpful

to deeply understand the negative impact of over

recommendation on consumer behavior, and provide

reference for improving the design of

recommendation system. Future research can explore

the impact of long-term consumption behavior on

decision-making patterns, as well as the role of

recommended product order in decision-making

behavior (Long et al., 2025).

5 DISCUSSION AND

SUGGESTION

This article provides a comprehensive literature

review on the impact of cognitive biases on shopping

decisions, particularly the effects of anchoring,

framing, and choice overload on consumer behavior.

Research has found that cognitive biases in the digital

environment not only affect decision quality, but may

also lead to irrational behavior, such as excessive

reliance on price anchors, being misled by

information frameworks, and decision fatigue when

there are too many choices. Although the digital

economy provides a convenient shopping experience,

these biases often lead consumers to make partially

rational decisions, affecting their actual interests. E-

commerce platforms may exacerbate irrational

consumer behavior by exploiting these biases. To this

end, this article proposes several suggestions:

platforms should adopt transparent pricing strategies

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and avoid using conspicuous initial prices to

influence consumers; When displaying prices and

promotional information, it should be diversified to

help consumers evaluate comprehensively; Simplify

the shopping process and recommendation system to

reduce decision fatigue caused by selection overload.

In addition, platforms should educate consumers to

identify and address psychological biases in shopping,

ensuring transparency and fairness. For consumers,

this article suggests comparing product information

from multiple sources before shopping, setting clear

purchasing goals, reducing impulse buying, and

helping to make more rational decisions. Future

research can explore behavioral differences under

different cultural backgrounds, collect long-term data

to analyze the impact of behavioral biases on

shopping decisions and loyalty, and study cross

platform consumer behavior differences.

6 CONCLUSION

This study analyzed the impact of behavioral biases,

anchoring effects, framing effects, and choice

overload on online shopping and platform selection in

the digital economy. It was found that consumers are

susceptible to cognitive biases when faced with

complex information and choices, leading to

irrational decision-making and ultimately affecting

brand loyalty and consumption patterns. Based on

this, it is recommended that e-commerce platforms

adopt more reasonable and transparent design

strategies to reduce consumers' cognitive burden and

help them make more rational decisions. Future

research should focus on the moderating effect of

individual differences on cognitive bias, and explore

how personalized recommendation systems or

decision support tools can alleviate the impact of bias,

enhance consumer experience, and purchase behavior.

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