Consumer Networks and Firm Reputation: A First Experimental Investigation



Table 1: Overview of results

trust rate

honor rate     pe

rformance rate

NO

0.21

0.19

0.05

(0.16)

(0.11)

(0.05)

0.31

0.59

0.19

DEGENERATE

(0.14)

(0.15)

(0.12)

0.44

0.48

0.23

PARTIAL

(0.17)

(0.16)

(0.14)

FULL

0.43

0.62

0.29

(0.15)

(0.23)

(0.18)

NO-DEGENERATE*

p = 0.168

adjacent treatment effects,
p
= 0.002

p = 0.005

DEGENERATE-PARTIAL*

p = 0.064

p = 0.168

p = 0.344

PARTIAL-FULL*

p = 0.468

p = 0.100

p = 0.235

NO-FULL*

p = 0.027

further treatment effects

p = 0.005

p = 0.008

NO-DEGENERATE-

PARTIAL-FULL*__________

p = 0.007

p = 0.003

p = 0.001

Standard deviations are given in parentheses. Treatment effects are tested by one-tailed
Mann-Whitney U-tests (*) and Jonckheere-Terpstra tests (#) respectively.

Our design gradually increases the amount of feedback information from treatment to
treatment. As a consequence, several observed treatment differences of neighboring
treatments are sometimes insignificant (see Table 1). However, we observe a continuous
improvement in market performance due to higher network density. The Jonckheere-Terpstra
test shows that there is an ordering for all three key variables (trust rate, honor rate and
performance rate) of the treatments according to the network’s density.3

3 The Jonckheere-Terpstra test is a non-parametric test for ordered differences among classes. The alternative
hypothesis assumes a certain ordering of the medians of
k statistically independent samples. All average values -
each of a statistically independent observation from a treatment with the same network density - are assigned to
one class.



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