Price Game in Online Shopping: Analysis of Consumer Behavior in

the Double Eleven Promotions

Yimeng Yuan

Shanghai Pinghe School, Shanghai, 201206, China

Keywords: Pricing Dynamics, Consumer Behavior, E-commerce, Algorithmic Governance, Double Eleven.

Abstract: This study examines the pricing dynamics and consumer behavior during China’s “Double Eleven” shopping

festival, focusing on the Taobao platform. As the world’s largest online shopping event, Double Eleven serves

as a prime example of the intense price competition and strategic interactions between sellers, buyers, and the

platform. The research highlights the festival’s economic significance, surpassing major Western shopping

events. Combining price tracking and consumer data, this study uncovers systemic tensions between platform-

driven growth and market fairness. Through a case study of Taobao’s 2023 promotions, the paper reveals

critical issues such as deceptive pricing practices, market distortion favoring large brands, and algorithmic

control that erodes consumer trust and SME profitability. The analysis identifies underlying problems,

including information asymmetry, short-termism in pricing strategies, and regulatory gaps in algorithmic

governance. These issues collectively undermine market fairness and long-term platform credibility. To

address these challenges, the study proposes actionable solutions: enhancing pricing transparency through

historical price displays and simplified rules, rebalancing competition for SMEs via dedicated traffic

allocation, and establishing ethical algorithmic frameworks. The findings underscore the importance of

fostering a fair and sustainable e-commerce ecosystem, offering practical insights for platforms, merchants,

and policymakers. The study advances the literature on algorithmic transparency in e-commerce while

providing policy recommendations for emerging markets.

1 INTRODUCTION

1.1 Research Background

In recent years, online shopping has become an

integral part of global consumer behavior, with e-

commerce platforms experiencing exponential

growth. Among the various shopping events, China’s

“Double Eleven” (Singles’ Day) shopping festival,

initiated by Alibaba in 2009, has emerged as the

world’s largest online shopping event. In 2022,

Alibaba’s Tmall and Taobao platforms alone

generated a record-breaking 84.54 billion in gross

merchandise volume (GMV) during the 24-hour

period of Double Eleven, showcasing the immense

scale and economic impact of this event. This data

surpassed the combined GMV of Black Friday and

Cyber Monday in the same year, highlighting the

festival’s unparalleled dominance.

https://orcid.org/0009-0005-8008-0817

This phenomenon is not limited to China; similar

shopping festivals, such as Black Friday and Cyber

Monday in the United States, have also seen

significant growth. In 2022, Cyber Monday online

sales reached 11.3 billion in the U.S. alone.

1.2 Literature Review

The “Double 11” shopping festival has evolved into a

complex economic and psychological battlefield

between retailers and consumers. Huang’s analysis

reveals that while consumers are initially driven by

discounts, psychological triggers (e.g., fear of

missing out), and non-rational impulses, their

behavior has become more rational over time (Huang,

2025). By 2023, growth slowed significantly due to

consumer fatigue, overly complex pricing rules, and

broader economic pressures. This shift challenges

retailers’ traditional reliance on price discrimination

Yuan, Y.

Price Game in Online Shopping: Analysis of Consumer Behavior in the Double Eleven Promotions.

DOI: 10.5220/0013824700004708

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

In Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy (IAMPA 2025), pages 311-318

ISBN: 978-989-758-774-0

311

and bundling strategies as trust in promotional tactics

declines.

From an economic perspective, Tan explains the

festival’s initial success through classic theories:

incentives stimulate demand, price elasticity drives

volume for discount-sensitive goods, and game

theory frames the strategic interplay between buyers

and sellers (Tan, 2017). However, the globalization

of “Double 11” has introduced new challenges,

including deceptive practices (e.g., fake discounts),

resource waste from impulse buying, and intensified

competition with offline retail. Tan argues that long-

term sustainability hinges on stricter regulation and

rebuilding consumer trust.

Wang’s game theory lens further unpacks retailers’

dilemmas (Wang, 2016). Sellers are trapped in a Nash

equilibrium of price wars, where short-term

competition undermines collective profitability. To

escape this “prisoner’s dilemma,” Wang proposes

three solutions: (1)developing core competitiveness

(e.g., brand value, product differentiation), (2)

forming strategic alliances to avoid cutthroat

competition, and (3) collaborating with regulators to

ensure fair market practices.

Chen’s survey of young consumers (e.g., high

school students) confirms this paradigm shift (Chen,

2017). Younger buyers increasingly prioritize quality

and authenticity over discounts, signaling that pure

price-based strategies are losing effectiveness.

Retailers must innovate beyond discounts, focusing

on service quality, transparency, and personalized

experiences to retain this demographic.

While these studies provide valuable insights,

their methodologies predominantly rely on survey

data and theoretical modeling, lacking empirical

analysis of real-time pricing data flows within

platforms.

1.3 Research Gap

Despite extensive examination of macro-level

dynamics (e.g., Huang’s psychological analysis and

Wang’s game theory models), three critical micro-

level blind spots persist. However, few scholars have

examined the micro-level pricing mechanisms within

specific e-commerce platforms like Taobao,

particularly how real-time algorithmic pricing and

B2C price negotiation mechanisms shape outcomes

during mega-shopping events. A critical gap remains

in understanding the platform-driven pricing

ecosystem—where AI tools, regulatory policies, and

consumer tactics collide—and its implications for

market fairness and efficiency.

1.4 Research Framework

To address this gap, this study adopts a three-tiered

analytical approach: First, it dissects Taobao’s pricing

ecosystem, analyzing tools like dynamic pricing

algorithms and AI-driven promotions that sellers

employ during “Double 11”. Next, it investigates

consumer counterstrategies (e.g., price-tracking

extensions, collective bargaining) and their impact on

sellers’ pricing decisions. Finally, it evaluates

regulatory interventions (e.g., transparency policies)

and their effectiveness in mitigating issues like

algorithmic collusion or deceptive discounts.

By integrating platform technology, behavioral

economics, and policy analysis, this framework aims

to uncover the hidden rules of Taobao’s price game

and propose balanced solutions for stakeholders. The

following sections will operationalize this framework

through case studies of representative product

categories.

2 TAOBAO’S DOUBLE ELEVEN

PRICING GAME: A

MULTILATERAL DYNAMIC

COMPETITION CASE

2.1 Background and Participants

Since its inception in 2009, Taobao’s Double Eleven

has evolved from a single-day promotion to a month-

long shopping festival, with the platform acting as

both a marketplace and a rule-setter through

algorithmic governance.

Taobao “Double Eleven” is a typical multilateral

dynamic game scene; the participants include:

Platform side (Taobao/Tmall): Dominates the

rules of the game through traffic allocation,

algorithmic rules, and promotional tools (e.g., full

minus, presold), with the goal of maximizing GMV

and commission revenue.

Merchants: Strategically segmented into: (1)

Large brands (e.g., Uniqlo, Apple): Leverage brand

power to resist price wars through exclusive offers; (2)

SME sellers: Dependent on platform traffic subsidies,

often trapped in a vicious cycle of price competition.

Consumers: Rational and irrational behavior

coexist, countering merchant strategies through price

comparison tools, social communication (such as

ordering), and delaying purchases (waiting for the

lowest price).

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2.2 Core game Strategy Analysis

2.2.1 Merchants’ Prisoner’s Dilemma and

Nash Equilibrium

Price war trap: Merchants are caught in a game

similar to the “prisoner’s dilemma”. If all merchants

maintain high prices, the collective profit is the best.

However, individual merchants can gain customers in

the short term by lowering prices, which eventually

leads to a Nash equilibrium of price reduction for all

employees (for example, 53% of goods in 2023

actually do not reach the lowest price of the whole

year (Huang, 2025)). This aligns with Chen’s (2017)

findings that only 12% of surveyed merchants

achieved profit margins above 10% during Double

Eleven (Chen, 2017).

Differentiation: Leading brands (such as Apple)

jump out of the price war through limited pre-sales or

exclusive giveaways, while small and medium-sized

sellers rely on platform traffic subsidies, exacerbating

unequal competition.

2.2.2 Platform Algorithms: The Invisible

Hand of Pricing

Dynamic pricing algorithm: Taobao’s AI system

adjusts traffic allocation in real-time, prioritizing

“high conversion rate + low return rate” products,

forcing merchants to optimize prices and services.

Data monopoly advantage: The platform uses

consumer behavior data (such as shopping cart

retention) to predict demand and guide merchants to

develop “optimal discounts,” essentially an

algorithm-driven Steinkelberg game (where the

platform leads and the merchants follow), where

Taobao, as the leader, unilaterally sets traffic

allocation rules, while merchants (followers)

optimize prices within constrained options.

2.2.3 Consumer Counterstrategies:

Information and Temporal Games

Information tool game: Use price comparison plug-

ins (such as “Buy slowly”) to identify “pre-markup

before discounting”, or bypass cross-store full

reduction restrictions through social ordering.

Time game: Consumers delay payment (such as

card points using final payment coupons), forcing

merchants to release hidden benefits and forming a

late advantage in the sequential game.

2.3 Case Demonstration: Game Results

of Taobao’s “Double Eleven” in

2023

Merchant side: Large brands increase their profits

through exclusive membership prices (third-level

price discrimination), while small and medium-sized

sellers decrease their actual profit margin by 15% due

to the increase in traffic costs (Tan, 2017).

On the consumer side: With the rise of rational

consumption, 67.9% of respondents reduced impulse

purchases, resulting in the platform’s GMV growth

rate plummeting to 2.08% (15.6% in 2022) (Wang,

2016). This contrasts sharply with the 8.3% growth

rate of Douyin’s e-commerce arm during the same

period, highlighting the competitive pressure from

emerging platforms.

Platform adjustments: Taobao was forced to

simplify rules (such as eliminating “deposit inflation”)

and strengthen price regulation (such as full-cycle

insurance) to rebuild trust, a policy mandating that

post-sale prices cannot be lower than Double Eleven

prices for 15 days, reducing deceptive ‘pre-markup’

tactics.

This 2023 case demonstrates the escalating arms

race in Taobao’s pricing ecosystem: while platforms

tighten algorithmic control, merchants and consumers

develop increasingly sophisticated counterstrategies.

The following content will quantify these dynamics.

This paper will explore the price game within

China’s e-commerce platform “Taobao,” focusing on

how sellers and buyers engage in strategic pricing

interactions under the platform’s dynamic market

environment. Taobao, as one of the largest online

marketplaces in the world, operates under a highly

competitive ecosystem where millions of merchants

compete for consumer attention through pricing

strategies, promotions, and algorithmic adjustments.

The case examines the evolution of Taobao’s

pricing mechanisms, particularly during major

shopping events such as “Double 11” (Singles’ Day),

where sellers employ tactics like dynamic pricing,

flash sales, and algorithmic repricing to maximize

profits while consumers leverage tools such as price-

tracking extensions, discount coupons, and collective

bargaining to secure the best deals. Over time,

Taobao has integrated AI-driven pricing models, real-

time competitor analysis, and personalized discounts,

creating a complex yet efficient pricing ecosystem.

However, challenges such as price discrimination,

deceptive discounting, and algorithmic collusion

have emerged, raising concerns about market fairness

and consumer trust. Regulatory scrutiny has increased,

prompting Taobao to implement stricter pricing

Price Game in Online Shopping: Analysis of Consumer Behavior in the Double Eleven Promotions

313

transparency policies. This case provides insights into

the interplay between technology, competition, and

regulation in shaping modern e-commerce pricing

dynamics.

By analyzing Taobao’s pricing game, this study

aims to uncover the strategic behaviors of sellers and

buyers, assess the impact of platform algorithms, and

evaluate the effectiveness of regulatory interventions

in maintaining a balanced and fair marketplace.

3 ANALYSIS OF THE PROBLEM

3.1 Systemic Flaws in Taobao’s Pricing

Ecosystem

3.1.1 Erosion of Consumer Trust Due to

Deceptive Pricing Practices

The proliferation of manipulative pricing tactics on

Taobao has systematically eroded consumer

confidence in the platform’s pricing integrity.

Practices such as “pre-markup before discounting,”

where merchants artificially inflate original prices

before applying discounts, along with increasingly

complex promotional mechanisms during major

shopping festivals like Double Eleven, have left many

shoppers feeling misled. A comprehensive 2023

consumer behavior survey (n=12,000, covering Tier

1-4 cities) revealed that 67.9% of respondents

consciously reduced their impulse purchases due to

growing skepticism about the authenticity of

advertised discounts (Londaridze, 2024). This

distrust has manifested prominently on social media

platforms, where viral complaints about “fake

discounts” and deliberately confusing “mathematical

traps” in promotional rules have become recurring

themes during each shopping festival.

The long-term implications of this growing

consumer skepticism are particularly concerning. A

2023 JD Power report corroborates this trend,

showing a 22% year-on-year decline in consumer

trust in e-commerce discount claims (Wang et al.,

2023). As shoppers become more price-conscious and

technologically savvy, many are turning to third-party

price tracking tools to verify claims of discounts,

while others are opting out of festival shopping

altogether. This behavioral shift poses a significant

threat to the sustainability of Taobao’s sales-driven

business model. In response to mounting criticism,

the platform has implemented various transparency

measures, including price protection policies that

guarantee refunds if prices drop within 15 days of

purchase and simplified discount structures. However,

these measures remain largely superficial, failing to

address the fundamental issue of an incentive

structure that prioritizes short-term sales volume over

genuine value creation for consumers. This systemic

failure not only harms consumers but also distorts the

competitive landscape, as explored next.

3.1.2 Market Distortion and Unfair

Competition Among Sellers

The competitive landscape on Taobao has become

increasingly skewed, placing small and medium-

sized merchants at a distinct disadvantage. Recent

data indicates that 53% of products featured during

the 2023 shopping festivals did not actually offer their

lowest annual prices as claimed (Azcoitia et al., 2023).

This discrepancy is largely driven by SMEs’ inability

to absorb the costs of platform-mandated promotions,

as evidenced by while 15% of SMEs experienced

declining profit margins due to escalating costs of

acquiring customer traffic. This unsustainable

environment forces smaller merchants into

destructive price wars, often requiring them to

sacrifice profitability to remain competitive against

larger, better-resourced brands. The resulting market

distortion accelerates industry consolidation, further

entrenching the dominance of established players

with greater pricing power and financial reserves to

weather prolonged periods of thin margins.

3.1.3 Platform Dependency and Algorithmic

Control

Beyond market distortion, Taobao’s algorithmic

governance further entrenches platform dependency.

Taobao’s sophisticated dynamic pricing algorithms

have created a system where merchant success is

heavily dependent on conforming to the platform’s

invisible rules. For instance, Taobao’s ‘Price Health

Score’ algorithm penalizes listings deviating from the

platform’s expected discount range, effectively

standardizing pricing strategies (Xu & Liu, 2025).

These algorithms prioritize products with high

conversion rates, creating a self-reinforcing cycle

where merchants must continually optimize for the

platform’s metrics rather than developing authentic

competitive advantages. This centralization of power

through algorithmic control significantly limits seller

autonomy, as deviation from the platform’s preferred

pricing strategies often results in decreased visibility

and sales. More troublingly, the widespread adoption

of uniform discount thresholds across merchants,

driven by algorithmic pressures, mirrors behaviors

prohibited under Article 17 of China’s Anti-

Monopoly Law regarding algorithmic collusion, as

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the platform’s systems may inadvertently facilitate

forms of algorithmic collusion (Han et al., 2023).

3.2 Systemic Flaws: Information,

Strategy, and Governance Failures

3.2.1 Information Asymmetry Between

Stakeholders

A fundamental issue characterizing Taobao’s

marketplace is the severe imbalance in information

access between different stakeholders. Consumers

frequently lack transparent access to comprehensive

price histories, making it difficult to verify claims of

genuine discounts. Simultaneously, small and

medium-sized merchants struggle to understand the

complex algorithms that determine product visibility

and traffic allocation. This dual asymmetry creates

fertile ground for manipulation, with both the

platform and larger sellers able to exploit these

information gaps to their advantage. As a direct

consequence, the recurring “fake discount” scandals

that emerge during major shopping festivals serve as

clear evidence of how this information disparity can

be exploited to distort market perceptions and

consumer behavior. This information asymmetry

directly fuels another critical issue: the systemic

short-termism in merchant pricing strategies.

3.2.2 Short-Termism in Pricing Strategies

The current competitive dynamics on Taobao have

created a classic prisoner’s dilemma situation, much

like the classic game theory scenario, merchants face

mutual incentives to betray cooperative pricing in

pursuit of temporary traffic gains, where merchants

feel compelled to engage in aggressive price

competition despite the long-term damage to their

profitability. Research by Chen (2017) found that

only 12% of merchants achieved profit margins

exceeding 10% during Double Eleven sales events,

with the vast majority trapped in a cycle of continuous

margin erosion (updated in 2022 with similar

findings). This focus on short-term sales volume

comes at the expense of more sustainable business

investments, as merchants divert resources from

product innovation and service quality improvements

to fund ever-deeper discounts and advertising

expenditures. The resulting market environment

discourages differentiation and traps participants in a

low-value equilibrium that ultimately harms all

stakeholders, including the platform itself.

3.2.3 Regulatory Gaps in Algorithmic

Governance

The existing regulatory framework has proven

inadequate to address the novel challenges posed by

algorithmic pricing in e-commerce. Current policies,

such as the SAMR’s Provisional E-commerce Price

Regulation mandating 15-day price protection,

represent reactive measures that treat symptoms

rather than underlying causes (He, 2020). These

regulations fail to confront more systemic issues like

AI-driven price discrimination or the platform’s

monopolistic control over critical market data.

Perhaps most concerning is the complete lack of

transparency surrounding the algorithmic decision-

making processes that increasingly govern pricing

and product visibility on the platform. This opacity

raises fundamental ethical questions—unlike JD.

com’s Algorithm Disclosure Guidelines in 2021,

Taobao provides no merchant-facing documentation

on ranking criteria—as merchants and consumers

alike are subject to algorithmic judgments they cannot

understand or appeal (Shen et al., 2024). The

regulatory vacuum in this area allows potentially anti-

competitive practices to flourish unchecked while

leaving affected parties with limited recourse.

Together, these regulatory gaps compound the

issues of information asymmetry and short-termism,

creating a self-reinforcing cycle of market distortion.

4 POLICY

RECOMMENDATIONS: A

MULTI-STAKEHOLDER

APPROACH

4.1 Enhancing Pricing Transparency

and Consumer Protection

Mechanisms

To address the growing consumer distrust stemming

from deceptive pricing practices, Taobao should

implement more robust transparency measures. The

platform could mandate that all merchants display

comprehensive price histories for at least 90 days

(matching the EU's Digital Services Act standard for

price transparency), enabling consumers to verify

claims of genuine discounts. This would help

eliminate “pre-markup before discounting” tactics by

making pricing patterns clearly visible. Additionally,

Taobao should simplify its promotional mechanisms

by limiting the number of discount layers and

eliminating overly complex rules that create

Price Game in Online Shopping: Analysis of Consumer Behavior in the Double Eleven Promotions

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“mathematical traps” for consumers. The current 15-

day price protection policy should be expanded to

cover the entire pre-sale period and extended to 30

days post-purchase, with stricter penalties for

violations. These changes would help rebuild

consumer trust while maintaining the festival’s

appeal.

From a technological standpoint, Taobao could

develop an official price-tracking tool integrated

directly into the platform interface. This would

provide consumers with accurate, real-time price

comparisons while reducing reliance on third-party

extensions. The platform should also implement

machine learning algorithms to detect and flag

potential deceptive pricing patterns automatically,

with human oversight to review flagged cases. These

technical interventions should be paired with

institutional reforms: such measures would create a

more transparent shopping environment while still

allowing merchants flexibility in their pricing

strategies.

4.2 Rebalancing the Competitive

Landscape for SMEs

As the dominant market operator, to mitigate the

market distortions that disproportionately affect small

and medium-sized merchants, Taobao should revise

its traffic allocation algorithms to provide more

equitable opportunities. The platform could establish

separate promotional tracks for SMEs, with dedicated

visibility slots and lower participation thresholds for

major shopping events. This would help smaller

merchants compete without being forced into

unsustainable price wars against larger brands with

greater resources.

Taobao should also consider implementing a

tiered commission structure that reduces fees for

smaller merchants during peak shopping periods.

Additionally, the platform could provide SMEs with

subsidized access to advanced analytics tools that are

currently only affordable for larger sellers, helping

them make more informed pricing decisions. To

further level the playing field, Taobao might

introduce cooperative marketing programs where

groups of SMEs can pool resources to create

collective promotions that rival those of larger brands.

These changes should be accompanied by

enhanced educational resources for SMEs, including

training on sustainable pricing strategies and brand

differentiation. By helping smaller merchants

develop alternatives to price-based competition,

Taobao can foster a more diverse and healthy

marketplace ecosystem.

4.3 Establishing Ethical Algorithmic

Governance Frameworks

Addressing the concerns around platform

dependency and algorithmic control requires

comprehensive governance reforms. Taobao should

establish an independent algorithmic review board

(similar to Facebook's Oversight Board model

adapted for e-commerce) comprising representatives

from academia, consumer advocacy groups, and the

merchant community. This board would oversee the

development and implementation of pricing

algorithms, ensuring they adhere to principles of

fairness and competition.

The platform must increase transparency around

its algorithmic decision-making processes by

publishing regular reports detailing how pricing and

visibility algorithms function at a high level while

protecting proprietary details. Merchants should be

provided with clearer explanations of why certain

products receive more visibility than others, enabling

them to make more informed business decisions.

To prevent algorithmic collusion, Taobao should

implement safeguards that maintain minimum

variation in recommended discount rates across

similar products. The platform could also introduce

“algorithmic due process” mechanisms that allow

merchants to appeal visibility decisions and receive

human-reviewed explanations for significant changes

in their traffic patterns with mandatory compensation

for proven algorithmic errors affecting sales.

These governance changes should be

complemented by collaboration with regulators to

develop industry-wide standards for e-commerce

algorithms. By taking a leadership role in ethical

algorithmic development, Taobao can help shape the

future of fair digital marketplaces while mitigating

antitrust concerns.

4.4 Strengthening Regulatory

Collaboration and Industry

Standards

Taobao should proactively engage with regulators to

develop more effective oversight mechanisms for

online shopping festivals. The platform could work

with government agencies to create a certification

program for “genuine discounts,” with strict criteria

that prevent deceptive pricing practices. This might

include requirements for historical price consistency

and clear disclosure of all discount calculations.

The company should also support the

development of industry-wide standards for data

sharing between platforms and merchants. While

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protecting consumer privacy and proprietary

information, these standards could give merchants

access to more comprehensive performance metrics

while ensuring a balanced flow of market information.

Furthermore, Taobao could establish an industry

consortium with other major e-commerce platforms

to address systemic issues like price wars and

algorithmic transparency. By working collaboratively

with competitors, the company could help create a

more sustainable ecosystem that benefits all

stakeholders - platforms, merchants, and consumers

alike.

These regulatory and industry collaborations

should aim to create frameworks that preserve the

dynamism and innovation of online marketplaces

while ensuring fair competition and consumer

protection. Such efforts would not only address

current challenges but also position Taobao as a

leader in responsible e-commerce development.

5 CONCLUSION

5.1 Key Findings

This study decoded the intricate pricing dynamics

within Taobao’s “Double Eleven” shopping festival,

revealing a complex interplay between platform

algorithms, merchant strategies, and consumer

behavior. Key findings highlight the erosion of

consumer trust due to deceptive pricing practices,

such as pre-markup before discounting, and the

market distortion that disproportionately

disadvantages small and medium-sized enterprises

(SMEs). The analysis also identified the platform’s

algorithmic control as a central issue, creating

dependency among merchants and fostering short-

termism in pricing strategies.

To address these challenges, the study proposed

actionable suggestions: enhancing pricing

transparency through comprehensive price histories

and simplified promotional rules, rebalancing the

competitive landscape for SMEs via dedicated traffic

allocation and tiered commissions, and establishing

ethical algorithmic governance frameworks to ensure

fairness. Additionally, strengthening regulatory

collaboration and industry standards was emphasized

to promote long-term sustainability and trust in e-

commerce ecosystems. These findings carry urgent

implications for China's $2.1T e-commerce sector,

where platform governance gaps risk undermining

the entire digital economy's sustainability. This

context elevates the practical urgency of the

following research implications.

5.2 Research Significance

This research holds significant practical and social

value for multiple stakeholders. For businesses, it

provides insights into sustainable pricing strategies

beyond short-term discounts, helping merchants

navigate the competitive pressures of online

marketplaces. For platforms like Taobao, the findings

underscore the need for algorithmic transparency and

equitable policies to maintain consumer trust and

merchant loyalty. On a broader scale, the study

contributes to the discourse on fair market practices

in e-commerce, offering solutions to mitigate

deceptive pricing and information asymmetry. By

addressing these issues, the research supports the

development of a more balanced and ethical digital

marketplace, benefiting consumers, merchants, and

the industry as a whole.

5.3 Limitations and Future Studies

This study has certain limitations, primarily its

reliance on secondary data, such as existing literature

and case studies, which may not capture the full

complexity of real-time pricing behaviors. The 2023

dataset fails to capture post-pandemic consumption

pattern shifts, particularly Gen-Z's aversion to

discount fatigue. The absence of primary data, such

as surveys or interviews with merchants and

consumers, limits the depth of behavioral insights.

Future research could address these gaps by

incorporating primary data collection methods, such

as discrete choice experiments (DCEs) quantifying

consumers' willingness-to-pay under varying

discount transparency conditions or interviews with

Taobao merchants, to validate the findings and

explore nuanced perspectives. Additionally,

empirical analysis of real-time pricing algorithms and

their impact on consumer decision-making would

further enrich the understanding of this dynamic

ecosystem. Such efforts would pave the way for more

comprehensive and actionable recommendations in

the evolving landscape of e-commerce.

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