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Institutional Overlap:

Platform Rules and Government Regulation

Wesley Wu-Yi Koo

June 2019

Institutions & Institutional Constituents

Institutions: structures that govern the behavior of individuals & organizations; rules that constrain opportunism and facilitate exchange (Scott 1987).

An institutional constituent supports an institution and exerts conformity pressures on market actors (Oliver 1991, 1992).

Institution

Constituent

Theoretical Puzzle

Theoretical Puzzle

Theoretical Puzzle

Institutional

Overlap

Theoretical Puzzle

Institutional

Overlap

Theoretical Puzzle

vs.Institutional

Overlap

Research Question

How do market actors respond to institutional overlap?

BACKGROUND

Government Regulation & Digital Platforms

Government Regulation & Digital Platforms

Government Regulation & Digital Platforms

Institutional Overlap

• Both the private constituent (the platform) and the public constituent (the government) can regulate seller behavior.

• Institutional overlap: both constituents exert a similar institutional demand on sellers (e.g., “protect data privacy”, “don’t sell counterfeits”).

Chinese Government & Digital Platforms: Censorship

Chinese Government & Digital Platforms:Censorship

Chinese Government & Digital Platforms:Consumer Rights Protection

Chinese Government & Digital Platforms:Consumer Rights Protection

• Worked with Alibaba & JD to crack down on counterfeit products.

• Incentive differences between local and central governments.

• 13th Five Year Plan: to develop standardized regulations for digital platforms and the “new economy”.

Research Question

How do market actors respond to institutional overlap?

institutional overlap = platform regulation X government regulation

HYPOTHESES

Research on private regulation

• Private constituents can provide and enforce private regulations, e.g. platform rules, to constrain opportunistic behaviors by market actors (Büthe 2010; King &

Lenox 2000).

• A legal/government presence in private regulations generally creates more coercive power (Oliver 1991).

• Example: INPO and NRC’s monitoring of nuclear power plants (Rees 1997; Reid & Toffel 2009).

H1. Relative to platform-only rules, platform rules with government regulation will be associated with a lower transgression tendency among sellers.

seller

transgression_

government

regulation in

platform rule

Rule details

• When facing a detailed issue, actors are likely to use simplified mental models to guide thinking (Gary & Wood 2011;

Gavetti & Levinthal 2000).

• Popular mental models associated with the government: incompetence and technological inadequacy to work with technology (Malesky & Taussig 2017; Nye 1997; Pasquale 2015).

—> For complex platform rules, the negative perceptions of the government will be accentuated in the minds of sellers.

H2. The mitigating effect of government regulation on seller transgression will be less pronounced for highly detailed rules.

seller

transgression_

government

regulation in

platform rule

rule details

_

METHODS

Survey Experiment

Online questionnaire answered by 3,000 Chinese e-commerce sellers in December, 2017.

Six different vignettes (500 seller respondents per vignette) systematically vary the description of the platform rule to elicit sellers’ responses.

Advantages of a survey experiment (Atzmüller & Steiner 2010; Finch

1987): • embed respondents in a realistic context;• high internal validity;• manipulation of multiple layers of treatments, e.g.

government regulation & rule complexity.

Survey Experiment

low

complexity

moderate

complexity

high

complexity

with gov.

regulationVignette A Vignette B Vignette C

without gov.

regulationVignette D Vignette E Vignette F

Survey Experiment

with government regulation

without government regulation

Survey Experiment

high details

Survey Experiment

Measures: Outcome Variable

A seller’s transgression tendency:

Measures: Predictors

Treatments/manipulations:

• GovReg: Read platform rule mentioning government regulation

• Rule Complexity: low, moderate, high complexity.

Control Variables:

• Seller Age, Seller Gender (1=male, 2=female), Seller Education.

• Store Monthly Sales (ordinal), Store Age.

• Whether seller has Worked in Government before.

• Urban-ness of store location.

• Product Category fixed effects, Prefecture fixed effects.

Model Specification

RESULTS

Government regulation ~ less transgression

Government regulation ~ less transgression

DV =

Switch Products

DV =

Switch Part of

Products

DV =

Subpar

Quality

DV =

Non-existent

Product

DV =

All OK

DV =

None OK

log(Age) 0.439

(0.397)

0.394

(0.363)

1.011**

(0.409)

0.847**

(0.399)

1.381***

(0.392)

-1.186***

(0.245)

Gender -0.747***

(0.158)

-0.518***

(0.142)

-0.485***

(0.161)

-0.702***

(0.158)

0.578***

(0.163)

0.254***

(0.098)

Sales 0.075

(0.061)

0.169***

(0.057)

0.209***

(0.064)

0.247***

(0.061)

-0.299***

(0.066)

0.010

(0.039)

Store Age -0.035

(0.032)

-0.033

(0.029)

-0.074**

(0.034)

-0.089***

(0.033)

0.023

(0.029)

0.028

(0.019)

Worked in

Government0.670***

(0.232)

0.534**

(0.221)

0.907***

(0.227)

0.666***

(0.227)

0.710***

(0.248)

-0.685***

(0.163)

GovReg-0.341**

(0.146)

-0.305**

(0.135)

-0.260*

(0.151)

-0.384**

(0.146)

0.066

(0.152)

0.224**

(0.093)

Logistic regressions. Two tailed tests: *p<0.1; **p<0.05; ***p<0.01. Standard errors in parentheses.

Rule details dampen the benefit of government regulation

Rule details dampen the benefit of government regulation

DV =

Switch

Products

DV =

Switch Part

of Products

DV =

Subpar

Quality

DV =

Non-existent

Product

DV =

All OK

DV =

None OK

All controls Yes Yes Yes Yes Yes Yes

GovReg -0.341**

(0.146)

-0.305**

(0.135)

-0.260*

(0.151)

-0.384**

(0.146)

0.066

(0.152)

0.224**

(0.093)

High Details -0.271

(0.251)

-0.317

(0.224)

-0.543**

(0.257)

-0.562**

(0.246)

0.072

(0.267)

0.208

(0.159)

High Details X

GovReg

0.490

(0.369)

0.749**

(0.325)

0.966***

(0.364)

0.835**

(0.362)

-0.012

(0.372)

-0.507**

(0.227)

Logistic regressions. Two tailed tests: *p<0.1; **p<0.05; ***p<0.01. Standard errors in parentheses.

IMPLICATIONS

Theoretical & Policy Implications

Institutional overlap:

• Having the support of multiple constituents has nuanced effects; additional support could hamper an institution.

A new approach toward platform regulation:

• Depending on the circumstance, a platform should purposively highlight/hide the role of government regulation.

AsiaEurope Middle East| |

Appendix

• Mechanism: general incompetence vs. technological inadequacy

• Evidence from panel data: police-Taobao joint operation.

Mechanism: general incompetence vs.technological inadequacy

Coefficients on High Details X

GovReg

Switch

ProductsSwitch Part

Subpar

Quality

Non-existent

Products

Sellers who were

entrepreneurs offline

(N = 658)

-0.494

(1.144)

1.822**

(0.891)

1.113

(1.004)

1.944**

(0.974)

Sellers who were not

entrepreneurs offline

(N = 2,342)

0.424

(0.415)

0.668*

(0.383)

0.899**

(0.416)

0.631

(0.428)

Sellers who worked in IT

(N = 1,016)

0.633

(0.725)

1.751***

(0.597)

0.907

(0.671)

1.148*

(0.617)

Sellers who did not work

in IT

(N = 1,984)

0.319

(0.485)

0.334

(0.451)

0.910*

(0.472)

1.038**

(0.515)

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When there is real enforcement threat:police-Taobao joint operation in 2014

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