Peer Effects and Herd Behavior: An Empirical Study Based on the "Double 11" Shopping Festival

Hambur Wang

REVIEW 5 major objections 4 minor 22 references

Peer Effects and Herd Behavior: An Empirical Study Based on the "Double 11" Shopping Festival

T0 review · 5 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that a roommate's participation in the Double 11 shopping festival raises a student's probability of participating by 18.6 to 23.5 percentage points.

desk verdict The paper's headline peer effect is an artifact of using an agreement dummy as the regressor; without that, there is little new beyond a re-run of reference [5]. read the letter →

arxiv 2412.00233 v1 pith:ZSA4JIJJ submitted 2024-11-29 econ.EM

Hambur Wang This is my paper · ORCID

classification econ.EM

keywords peereffectsherdbehaviorDouble11shoppingfestivalBayesianProbitmodelonlineconsumer

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether college students shop on Double 11 because their roommates do. Using a questionnaire of 200 university students and a Bayesian Probit model, it reports that when peer effects are present, measured as the roommate's behavior matching the respondent's, the probability of participating rises by 18.6% to 23.5%, significant at the 1% level. The paper also reports that gender, prior online shopping experience, and fashion consciousness shape herd behavior, with female students about 10% more likely to conform under peer influence. If the effect is real, the basic unit of consumer behavior is not the isolated shopper but the peer group, which would make dormitories a natural target for marketing, regulation, and consumer education.

What carries the argument

The engine of the paper is a Bayesian Probit model on a binary outcome: participation in Double 11 (1/0). The main regressor is a self-reported peer-effects dummy indicating whether the roommate's behavior matched the respondent's. The paper describes the decision process as an information cascade in which later consumers imitate earlier ones, and it formalizes the trade-off as a Bayesian equilibrium between trust in personal information ($\alpha$) and trust in peer behavior ($\beta$). The model's role is to turn the survey data into marginal effects, giving the headline 18.6%–23.5% estimate while using priors to stabilize inference with 200 observations.

What would settle it

Estimate the same participation equation on data in which roommates are exogenously assigned, or instrument roommate participation with an administrative dorm-assignment rule, and test whether the marginal effect of roommate participation still falls in the 18.6–23.5% range after controlling for shared dorm-level unobservables; if it shrinks toward zero, the causal claim fails.

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Extended reading notes

Core claim

The paper's central claim is that offline peer effects directly cause herd behavior in the Double 11 shopping festival. Using both a traditional Probit and a Bayesian Probit model on survey data, it finds a marginal effect of the peer-effects variable between 18.6% and 23.5%, all significant at the 1% level, meaning a student whose roommate participates is roughly one-fifth more likely to participate. The paper further claims that online shopping experience, gender, and fashion consciousness are significant drivers of conformity, and that female consumers exhibit about 10% greater conformity under peer influence than males. The author states this as a direct, causal effect of roommate behavior on consumer decisions.

Load-bearing premise

The load-bearing premise is that the self-reported consistency between the respondent's and the roommate's behavior is an exogenous peer effect rather than a shared dorm-level cause; the paper's conclusion asserts IV corrections support this, but the methods and results sections present no IV specification or formal exogeneity test.

Editorial extensions

If this is right

Dormitories and other offline peer groups become viable marketing units: nudging one roommate can raise group participation by roughly one-fifth.
E-commerce promotions should treat offline word-of-mouth and dorm-level peer dynamics as complements to online reviews, not substitutes.
Because participation is driven partly by conformity, a negative signal about the event can spread through the same peer channel, supporting the paper's 'tide-out effect' concern.
Gender-specific communication and campaign design may be effective, since the paper reports female students are about 10 percentage points more likely to conform under peer influence.
The stability of the marginal effect across traditional and Bayesian Probit specifications suggests the qualitative result is not an artifact of estimator choice within the paper's model class.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension: randomly assign a small participation incentive to one student in each dormitory room and measure the spillover onto roommates; the paper's estimates imply a within-room multiplier that a field experiment could quantify.
A caveat beyond the paper's claims: because the peer-effect variable is the respondent's own report of behavioral consistency, the reported estimate probably captures selection into rooms and shared unobservables as well as influence, so the causal reading is stronger than the displayed identification.
A further extension: separate the two channels the paper conflates—informational influence (learning that roommates shop) and social utility (wanting to match roommates)—by collecting independent reports from both roommates rather than one self-reported alignment measure.
The conclusion states that instrumental-variable corrections confirm the effect, but no IV regression appears in the methods or results sections; readers should treat the causal language as an assertion rather than a displayed result.

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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear [name], quick take: the headline effect is not a peer effect. The 'peer effects' variable is defined as whether the roommate's behavior is consistent with the respondent (Table 1), so it is partly determined by the respondent's own participation. The stress-test note is right: with P(Y=1)=0.834 and P(D=1)=0.811, a mechanical correlation is almost forced. Table 2's coefficient on this agreement dummy cannot be interpreted as 'participating roommate raises participation by 18.6-23.5%.' That kills the paper's central claim.

What's genuinely here: new survey data from Beijing university students on Double 11, and a Bayesian Probit application to a binary outcome, which is a reasonable modeling choice. The variable definitions in Table 1 are mostly clear, and the prose is readable. That's about it. The paper does not articulate a new theoretical result or a new mechanism; its own reference [5] (Xi and Wu, 2020) is the same model, same festival, same question. The new questionnaire does not create a new contribution.

The soft spots are multiple and load-bearing. Section 2 promises model equations but none are shown; the 'Bayesian equilibrium' is a verbal restatement of the information-cascade assumption. The conclusion claims results 'remain robust after rigorous statistical corrections using instrumental variables' and mentions interaction analyses, but no IV or interaction results appear in the paper. The numbers don't line up either: abstract says 200 valid responses, Table 2 reports 204 observations; Section 4.2.2 gives 81.1% vs 18.9% participation conditional on 'peer effects present/absent,' which cannot be reconciled with the overall 83.4% participation rate. These are not typos that a good referee would fix; they undermine confidence in the whole data handling.

My verdict: this paper is not ready for peer review. The central estimand is mislabeled, the supporting analysis is internally inconsistent, and the novelty relative to [5] is not articulated. A serious referee would spend the whole report on the regressor definition and missing analyses. I would desk-reject, and tell the authors to rework the design so that the peer variable is actual roommate participation, not an agreement dummy, and to reconcile every number before resubmitting.

Hard to find a reader who gets much from this as-is. Skip it.

Referee Report

5 major / 4 minor

Summary. The paper uses questionnaire data from university students to estimate a Bayesian Probit model of participation in China's "Double 11" shopping festival. It reports that a 'peer effects' variable raises the probability of participation by 18.6% to 23.5%, and that gender, online shopping experience, and fashion consciousness are also significant. The paper frames the analysis as an information-cascade or herd-behavior study and derives marketing and policy implications.

Significance. The topic is relevant to consumer behavior and e-commerce marketing. The paper is transparent in reporting variable definitions and in presenting both classical and Bayesian estimates, and it includes a wide set of control variables. However, the central coefficient is not identified as a peer effect because the regressor is defined as agreement between the roommate's and the respondent's behavior, not as the roommate's participation. The descriptive statistics are internally inconsistent, the promised model equations are absent, and the conclusion claims an instrumental-variable procedure that is not performed. These problems undermine the paper's headline claims, so the manuscript in its current form does not provide a reliable empirical contribution.

major comments (5)

[Section 3, Table 1; Section 4.1, Table 2] The 'Peer Effects' regressor is defined in Table 1 as 'whether the roommate's behavior is consistent with the respondent,' i.e., D_i = 1{Y_i = R_i}, not as the roommate's participation R_i. The text in Section 4.1 interprets the coefficient on this variable as the causal effect of a roommate's participation on the respondent's participation. This interpretation is invalid: because D_i is constructed from the respondent's own outcome Y_i, a positive coefficient can be purely mechanical. Using the reported means P(Y_i=1)=0.834 and P(D_i=1)=0.811, under the null that Y_i is independent of R_i the implied P(R_i=1) is about 0.966, and a probit of Y_i on D_i alone would produce a large positive marginal effect even when no true peer effect exists. The paper's causal claims therefore rest on a regressor that does not measure what the conclusions say it measures.
[Section 3.2 and Table 2] The abstract and Section 3.2 state that the survey collected 200 valid responses, but every column in Table 2 reports 204 observations. The paper does not explain this discrepancy, and it compromises the descriptive statistics in Table 1 if the means are computed over a different sample than the regressions.
[Section 4.2.2] The conditional participation shares reported in this section are logically inconsistent. If 81.1% of respondents with peer effects and 18.9% of respondents without peer effects participated, then with P(peer effects)=0.811 the overall participation rate would be 0.811*0.811 + 0.189*0.189 = 0.693, not the reported 83.4%. Conversely, if 81.1% and 18.9% are shares of participants and nonparticipants with peer effects, the implied P(peer effects) is 0.708, not 0.811. The text cannot reconcile the numbers under either reading, so the descriptive claim about peer effects is not supported by the data as presented.
[Section 2.2] The two subsections under 'Specific Relationship Formulas' promise equations for the consumer decision process and the Bayesian equilibrium, with the text stating 'The specific relationship formula is as follows' and referring to 'i*' and to 'α and β'. No equations are actually displayed in the manuscript. As a result, the likelihood, the priors, and the equilibrium condition are not defined, and the reported marginal effects cannot be traced to a precise estimand.
[Section 5.1] The first paragraph of the Conclusion claims that the result 'remains robust even after rigorous statistical corrections using instrumental variables.' No instrumental-variable estimation appears anywhere in the paper; Table 2 reports only traditional and Bayesian Probit estimates. This assertion is unsupported and appears to describe a method that was not carried out.

minor comments (4)

[Table 2 note] The significance note under Table 2 reads '***p < 0.01, *p < 0.05' but the table also uses '**' for Fashion Consciousness in column (1); the note should be completed to include the '**' level.
[Section 4.2.2, chart] The bar chart labels are in Chinese ('有同伴效应' and '无同伴效应') with no English equivalents; for an English-language manuscript, the labels and the figure title should be translated.
[References [6]-[9]] References [6]-[9] concern deep learning, plant disease detection, potato production, and neural radiance fields; they are not connected to the peer-effects or consumer-behavior analysis, and the sentence citing them at the end of Section 5.2 does not motivate their inclusion. These references should be removed or properly integrated.
[Section 3.1] The list of variables in the text includes 'grade' with range 0 to 5 and 'income' with range 1 to 5, but the table notation '0' for freshman is inconsistent with the common coding 1=freshman; please clarify the coding to match the definitions.

Circularity Check

1 steps flagged · score 7.0 of 10

The 'Peer Effects' regressor is an agreement dummy built from the respondent's own participation, so the 18.6%–23.5% marginal effect is mechanically contaminated and does not identify roommate participation.

self definitional [Section 3, Table 1; Section 4.1, Table 2 and accompanying text]
"Peer Effects ... Whether the roommate's behavior is consistent with the respondent: 1 = consistent, 0 = inconsistent. ... According to the estimation results from both the traditional Probit model and the Bayesian Probit model, the marginal effect of peer effects (roommate) ranges from 18.6% to 23.5% ... compared to consumers whose roommates did not participate."

Table 1 defines the 'Peer Effects' regressor as D_i = 1{respondent's behavior is consistent with roommate's behavior}, i.e., D_i = 1{Y_i = R_i}, so the covariate is partly constructed from the outcome Y_i. Section 4.1 then interprets its coefficient as the effect of 'the participation of a roommate' on Y_i. This is mechanical: with the reported means P(Y=1)=0.834 and P(D=1)=0.811, under the null that Y_i is independent of R_i the implied P(R=1) is about 0.966, giving E[Y|D=1]−E[Y|D=0]≈0.84. A Probit on D alone can therefore produce a large positive coefficient with no peer effect.

full rationale

The central load-bearing step is Table 1's definition of the 'Peer Effects' regressor as an agreement indicator: D_i = 1 if the roommate's behavior is consistent with the respondent, i.e., D_i = 1{Y_i = R_i}. Section 4.1 treats the coefficient on this D_i as if it were the causal effect of 'the participation of a roommate' on Y_i and reports marginal effects of 18.6%–23.5%. This is a self-definitional reduction: because D_i is built from Y_i itself, regressing Y_i on D_i can produce a large positive coefficient even under no peer effect. With the reported means P(Y_i=1)=0.834 and P(D_i=1)=0.811, the null of Y_i independent of R_i implies P(R_i=1)≈0.966 and E[Y_i|D_i=1]−E[Y_i|D_i=0]≈0.84, so the estimated positive effect is not informative about roommate participation. The promised structural formulas in Section 2 are absent, so the estimand cannot be checked against a peer-effect parameter. Section 5's claim of 'rigorous statistical corrections using instrumental variables' is unsupported by any IV model in the paper; this is a missing-support problem that compounds but is not itself circular. The Bayesian equilibrium discussion in Section 2 invokes prior work without displaying its inequalities, which is an omitted derivation rather than a circular step. The end-of-paper self-citations about transformer recommendation systems are tangential and not load-bearing. Overall, the central empirical claim is compromised by the construction of the regressor itself, so the circularity score is 7.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim depends on estimated coefficients from a small convenience sample, on the untested exogeneity of the roommate-alignment regressor, and on an information-cascade behavioral assumption. No novel entities are introduced.

free parameters (3)

Peer effect coefficient (roommate behavior consistency) = 0.178 to 0.262 (Probit coefficients); marginal effect 18.6% to 23.5%
Central estimate fitted to the questionnaire data; drives the headline claim.
Coefficients on gender, online shopping experience, fashion consciousness = See Table 2
Additional fitted coefficients used to support the gender and experience claims.
Bayesian prior hyperparameters
The paper does not report the priors used for the Bayesian Probit; any prior choice affects the posterior and is a hidden free parameter.

assumptions (4)

domain assumption The roommate-behavior consistency variable is exogenous, or that Bayesian priors correct for endogeneity.
Invoked in Section 2.1 to claim the model effectively addresses potential endogeneity issues, with no formal support.
domain assumption The information-cascade model describes consumer decisions, i.e., the third consumer follows the first two when private information conflicts.
Used in Section 2.2 as the theoretical basis for herd behavior; no empirical test of this mechanism.
domain assumption The WeChat-distributed convenience sample represents Beijing university students.
Stated in Section 3.2; selection bias is acknowledged implicitly by the recruitment method, but representativeness is assumed.
standard math Standard Bayesian probability updating and Probit link functions.
Background statistical machinery, not proved in the paper.

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Cite this review

Pith. "Pith review of Peer Effects and Herd Behavior: An Empirical Study Based on the "Double 11" Shopping Festival." pith.science (2026). https://pith.science/paper/ZSA4JIJJ

@misc{pith2026241200233,
  author       = {Pith},
  title        = {Pith review of: Peer Effects and Herd Behavior: An Empirical Study Based on the "Double 11" Shopping Festival},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZSA4JIJJ}},
  note         = {Machine review of arXiv:2412.00233}
}

read the original abstract

This study employs a Bayesian Probit model to empirically analyze peer effects and herd behavior among consumers during the "Double 11" shopping festival, using data collected through a questionnaire survey. The results demonstrate that peer effects significantly influence consumer decision-making, with the probability of participation in the shopping event increasing notably when roommates are involved. Additionally, factors such as gender, online shopping experience, and fashion consciousness significantly impact consumers' herd behavior. This research not only enhances the understanding of online shopping behavior among college students but also provides empirical evidence for e-commerce platforms to formulate targeted marketing strategies. Finally, the study discusses the fragility of online consumption activities, the need for adjustments in corporate marketing strategies, and the importance of promoting a healthy online culture.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

22 extracted references · 21 canonical work pages

[5]

Double 11

Data Sources and Measurement (1) Variable Explanation In this study, we selected a series of independent variables that may influence "Double 11" online shopping behavior to comprehensively analyze consumer behavior patterns. These variables include peer effects, online shopping experience, gender, fashion consciousness, dormitory relationships, household...

work page 2000
[1]

Double 11

Introduction In recent years, despite a slowdown in China's overall economic growth, the rapid development of the internet economy has injected new vitality into the sustained and stable growth of the nation's economy. With continuous advancements in internet technologies and the rise of e -commerce platforms, online shopping has become an indispensable p...

work page 2013
[2]

One of its significant advantages is its ability to integrate prior information, providing a more flexible and precise model specification

Analytical Method (1) Theoretical Basis and Methodological Considerations for Choosing the Bayesian Probit Model The Bayesian Probit model, by incorporating prior and posterior distributions, effectively addresses potential endogeneity issues that often arise in economic and social science research. One of its significant advantages is its ability to inte...

work page 2019
[3]

information cascade

Consumer Decision-Making Process First, it is necessary to describe the consumer decision-making process in detail to clarify how consumers make purchase decisions based on personal information and observations of previous consumer behavior. This process involves a sequential decision-making model: the first consumer makes decisions entirely based on pers...

work page 2019
[4]

Double 11

Bayesian Equilibrium In a scenario where the first two consumers have made identical decisions (either participating or not participating in the "Double 11" shopping event), if the third consumer's information diverges from the choices of the first two, according to the Bayesian Probit model, the third consumer is more likely to follow the decisions of th...

work page 2020
[6]

Double 11

Results and Analysis (1) Empirical Test of Herd Behavior from the Perspective of Peer Effects Based on the Bayesian Probit model and its inference results discussed earlier, this study conducts an empirical test of herd behavior from the perspective of peer effects. To facilitate analysis, both the traditional Probit model and the Bayesian Probit model ar...

work page
[7]

Double 11

Distribution of Participation in the Event This section presents the distribution of respondents who participated in or did not participate in the "Double 11" online shopping event. According to the data, 83.4% of the respondents participated in the "Double 11" shopping event, while the remaining 16.6% did not. This high participation rate suggests that "...

work page
[8]

Double 11

The Impact of Peer Effects on Participation in Activities The chart compares the proportion of respondents who participated in the "Double 11" (Singles' Day) online shopping event under conditions of peer effects (i.e., when roommates' behaviors align) versus no peer effects. The data reveals that 81.10% of respondents participated in online shopping when...

work page

Show all 22 references

[9]

Double 11

The Relationship Between Gender and Participation in Activities According to the survey data in this study, female participants represent the majority in the "Double 11" (Singles' Day) online shopping event, accounting for 59.2% of the sample. Compared to men, women exhibit st...
[10]

Double 11

Conclusion and Implications (1) Conclusion This study first reveals the significant impact of peer effects on consumer decision- making. The data shows that when a student's roommate participates in the "Double 11" shopping event, the student's likelihood of participating incr...
[11]

tide-out effect,

The Vulnerability of Online Consumer Decision-Making This study reveals the high sensitivity of online consumer behavior to negative signals. This sensitivity stems from the nature of rational conformity behavior, distinct from the irreversibility of information cascades. Once...
[12]

Instead, they should comprehensively consider the offline peer effects and word-of-mouth impacts

Adjustments in Corporate Marketing Strategies When planning online promotional activities, businesses should not solely rely on online reviews and word -of-mouth effects. Instead, they should comprehensively consider the offline peer effects and word-of-mouth impacts. This ind...
[13]

Promoting a Healthy Online Consumption Culture Society should advocate for a healthy online consumption culture through various channels, making it a cultural trend. This not only enhances the consumer shopping experience, allowing them to enjoy discounts while also enjoying t...

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Double 11

He Jia. An Analysis of the Conformity Psychology of University Students in Online Shopping – A Case Study of the Taobao "Double 11" Event. New Media and Society, 2013, (04): 169-181

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Xi Mingming, Zhu Limeng. Consumer Behavior and Conformity Effect: Evidence from "Double 11" Online Shopping. Contemporary Finance and Economics , 2016, (07): 3 -13. DOI: 10.13676/j.cnki.cn36-1030/f.2016.07.001

2016 doi
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Double 11

Xing Ziyan, Meng Xuanzhu, Li Xinhang . Analysis of Consumer Online Shopping Characteristics during "Double 11". Modern Marketing (Late Edition) , 2019, (08): 67. DOI: 10.19932/j.cnki.22-1256/f.2019.08.037

2019 doi
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Yi Xiaoyun. A Study on Impulsive Buying Behavior of Female Consumers during the "Double 11" Online Shopping Festival. Modern Marketing (Information Edition), 2019, (01): 175

2019
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Double 11

Xi Mingming, Wu Zhijun. Peer Effects and Conformity Behavior in "Double 11" Online Shopping – Estimating with Bayesian Probit Model. Economic Management, 2020, 42(09): 95-110. DOI: 10.19616/j.cnki.bmj.2020.09.006

2020 doi
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Wang, Z., Wang, R., Wang, M., Lai, T., Zhang, M. (2025). Self-supervised Transformer-Based Pre-training Method with General Plant Infection Dataset. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15032. Springer, ...

2025 doi
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Wang, R.F., & Su, W.H. (2024). The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review. Agriculture, 14(8), 1225

2024
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Automatic Lettuce Weed Detection and Classification Based on Optimized Convolutional Neural Networks for Robotic Weed Control

Zhao, C.-T.; Wang, R.-F.; Tu, Y .-H.; Pang, X.-X.; Su, W.-H. Automatic Lettuce Weed Detection and Classification Based on Optimized Convolutional Neural Networks for Robotic Weed Control. Agronomy 2024, 14, 2838. https://doi.org/10.3390/agronomy14122838

2024 doi
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Yao, M., Huo, Y ., Ran, Y ., Tian, Q., Wang, R., & Wang, H. (2024). Neural Radiance Field-based Visual Rendering: A Comprehensive Review. arXiv preprint arXiv:2404.00714

2024 arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [5]

Double 11

Data Sources and Measurement (1) Variable Explanation In this study, we selected a series of independent variables that may influence "Double 11" online shopping behavior to comprehensively analyze consumer behavior patterns. These variables include peer effects, online shopping experience, gender, fashion consciousness, dormitory relationships, household...

work page 2000

[2] [1]

Double 11

Introduction In recent years, despite a slowdown in China's overall economic growth, the rapid development of the internet economy has injected new vitality into the sustained and stable growth of the nation's economy. With continuous advancements in internet technologies and the rise of e -commerce platforms, online shopping has become an indispensable p...

work page 2013

[3] [2]

One of its significant advantages is its ability to integrate prior information, providing a more flexible and precise model specification

Analytical Method (1) Theoretical Basis and Methodological Considerations for Choosing the Bayesian Probit Model The Bayesian Probit model, by incorporating prior and posterior distributions, effectively addresses potential endogeneity issues that often arise in economic and social science research. One of its significant advantages is its ability to inte...

work page 2019

[4] [3]

information cascade

Consumer Decision-Making Process First, it is necessary to describe the consumer decision-making process in detail to clarify how consumers make purchase decisions based on personal information and observations of previous consumer behavior. This process involves a sequential decision-making model: the first consumer makes decisions entirely based on pers...

work page 2019

[5] [4]

Double 11

Bayesian Equilibrium In a scenario where the first two consumers have made identical decisions (either participating or not participating in the "Double 11" shopping event), if the third consumer's information diverges from the choices of the first two, according to the Bayesian Probit model, the third consumer is more likely to follow the decisions of th...

work page 2020

[6] [6]

Double 11

Results and Analysis (1) Empirical Test of Herd Behavior from the Perspective of Peer Effects Based on the Bayesian Probit model and its inference results discussed earlier, this study conducts an empirical test of herd behavior from the perspective of peer effects. To facilitate analysis, both the traditional Probit model and the Bayesian Probit model ar...

work page

[7] [7]

Double 11

Distribution of Participation in the Event This section presents the distribution of respondents who participated in or did not participate in the "Double 11" online shopping event. According to the data, 83.4% of the respondents participated in the "Double 11" shopping event, while the remaining 16.6% did not. This high participation rate suggests that "...

work page

[8] [8]

Double 11

The Impact of Peer Effects on Participation in Activities The chart compares the proportion of respondents who participated in the "Double 11" (Singles' Day) online shopping event under conditions of peer effects (i.e., when roommates' behaviors align) versus no peer effects. The data reveals that 81.10% of respondents participated in online shopping when...

work page