Oliver Hinz Shawndra Hill Ju-Young Kim

1. Prof. Dr. Oliver Hinz
Professur für Wirtschaftsinformatik insb.
Electronic Markets
hinz@wi.tu-darmstadt.de
www.emarkets.tu-darmstadt.de
TV’s Dirty Little Secret:
The Negative Effect of Popular TV on
Online Auction Sales
Hinz, Oliver / Hill, Shawndra / Kim, Ju-Young (2016): "TV's Dirty Little Secret: The Negative
Effect of Popular TV on Online Auction Sales", Management Information Systems Quarterly,
forthcoming.

2. Second Screens
“We are moving from a world where
computing power was scarce to a
place where it now is almost
limitless, and where the true scarce
commodity is increasingly human
attention” – Satya Nadella
Higher simultaneous information
overload along with decreasing
attention span
• Content of TV programs is watched
and processed less carefully
• Commercials take a back seat if
alternative screens (smartphone,
tablet) are used instead
The average human
attention span in 2000 was
12 seconds, but by 2013 it
was only 8 seconds (1
second shorter than a
goldfish!). Microsoft
Research
http://advertising.microsoft.com/en/cl/31966/ho
w-does-digital-affect-canadian-attention-spans
2

3. Second Screening in
Germany 1/2
Second Screen – Parallelnutzung eine
Fernsehprogramm
3

4. Second Screening in
Germany 2/2
Second Screen
4

5. Causes for differences
in variance of sales
pattern?
Monday
Monday + 4
weeks
• Attention of buyers is not constant over time
• Sales possibly impacted by weather, time of day, weekday, month…
TV programs?
5
Attention

6. Let us start with…
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
2008 2009 2010 2011 2012 2013
UniqueVisitorsinComscorePanel
Year
Amazon
Ebay
6
Online Auctions…

7. TV Viewership
• Weather
• Financial Budget
• Competitive Ad
Activity
• Time of Day
• Day of Week
• Month
• Public Holidays
Online Auction
Sales
Why should you care?
• Auctions are important
• Sellers’ failure to include relevant factors affects sales prediction
• Accurately predicting sales helps seller, particularly auctioneers, to
maximize sales by timing auction ends appropriately 7
?
Research Question

8. Related Literature
Attention Economy:
• Dimmick et al. (2004) showed that Internet displaced
traditional media in daily news domain with largest
displacement in newspapers and TV
• 2nd screening: 86% of consumers owning both tablets (or
smart phone) and TV, tend to give two media attention at the
same time (Yahoo study 2011)
• How to allocate the information more efficiently and developed
or examined applications to better control or customize
information (Huberman and Wu 2007, Shapiro and Varian
1999)
8

9. Related Literature
Inattention:
• Competition, Time of day neglect: Inattention of competition
leads to peak listing hours lowering selling rates and prices
(Simonsohn 2010)
• Weekday neglect: Inattention on Fridays increases the delayed
reaction of stock prices to new information (DellaVigna and Pollet
2009)
• Number of events neglect: Inattention to a firms earnings
announcement due to a great number of earnings
announcements by other firms at the same day lead to a delayed
stock price and volume reaction (Hirshleifer et al. 2009)
• Other event neglect: Inattention towards natural disasters due to
competing events like Olympic Games lead to lower relief
(Eisensee & Strömberg 2007)
9

10. Context: Platform.com
Auction Type: Double sided auction. Buyers and sellers can
negotiate.
Time: Data between buyers and sellers on Platform.com,
covers a 211 weeks period between April 2005 and May
2009
Products: Professional sellers offer their products – such as
consumer electronics, household appliances, jewelry,
watches and cosmetics – to buyers (all products are new
and in original packaging); the prices range between 0.70 -
4,199.00 EUR with a mean price of 106.18 EUR
Unit of observation: 2-hourly period  17,023
observations
Overall, 351 different sellers sold 25,677 unique products
types in 78,068 transactions to 65,894 different buyers.
10

11. Variables and
Operationalization 1/2
Dependent Variable:
Independent variables:
Online auction
sales
Attention towards TV
Competitive activity
Temperature
1
2
4
Number of TV viewers
eBay advertising in k€
DWD – Mean Precipitation in mm
DWD – Mean Temperature in C°
Precipitation
3
11

12. Variables and
Operationalization 2/2
Dependent Variable:
Independent variables:
Online auction
sales
Weekday (e.g. Fields 1931, Jaffe and
Westerfield 1985)
Month (e.g. Lakonishok and Smidt
1988)
Time of day (e.g. Simonsohn 2010)
6
7
8
Budget (e.g. Ariel 1987, Lakonishok
and Smidt 1988)
5
Mean account balance per day
12

13. Sales and Population per Zip
13

14. Endogeneity and
Identification
Potential problems:
1) Reverse Causality Bias -- If someone wants to online shop they may turn off the TV
2) Omitted Variable Bias -- Unobserved time spent at home
Proposed approach: Different IVs and proxy variable for time spent at home towards triangulation
1) Proxy for being at home – daylight hours after 6pm
2) IV that is unrelated to error term but related to TV viewership (some exogenous shock that impacts TV viewership) – 3 IVs
0 1 2 3 4 5
6 11 11
6+i 13+i 25+i 27 28
0 0 0
β +β TVviewers+β CompetetiveAd+β Precipitation+β Temperature+β Budget+
β Weekday + β Month + β Daytime +β PublicHoliday+β t+εi i i
i i i
Sales
  
     
      
14

15. Instrumental Variables
(Local) Disasters
Dummy Variable (1/0): Special broadcasts that reported on
tragedies and made it to first channel broadcasts
15
Truly exogenous. Cannot be predicted in advance.

16. Instrumental Variables
Soccer World Cup 2006
Dummy Variable (1/0): match of the German soccer team
88% watched the matches
Nearly 30 million TV viewers at the semi-final
16

17. Instrumental Variables
US Presidential Election
2008
Dummy Variable (1/0): Nov 4, 2008
17
Could be predicted but unlikely to have large emotional impact on
viewers in Germany

18. Proxy Variable
Daylight Minutes
Number of daylight minutes after work (6pm)
18

19. Instrumental Variable
Regression
1st stage -- IV: Disasters, World Cup, US Elections
• Under-identification test  excluded instruments are relevant
(p<.01)
• Weak instruments test based on the Kleibergen-Paap Wald rk F
statistic (F=103.73)  not a weak instrument
2nd stage
TVviewersestim
=a0
+a1
×IV+a2
×CompetetiveAd+a3
×Precipitation+a4
×Temperature+a5
×Budget+
a6+i
×Weekdayi
i=0
6
å + e13+i
×Monthi
i=0
11
å + a25+i
×Daytimei
i=0
11
å +a27
×PublicHoliday+a28
×t+
0 1 2 3 4 5
6 11 11
6+i 13+i 25+i 27 28
0 0 0
Sales β +β TVviewers +β CompetetiveAd+β Precipitation+β Temperature+β Budget+
β Weekday + β Month + β Daytime +β PublicHoliday+β t+ε
estim
i i i
i i i  
     
      
19

20. Estimation Results
20

21. Advantages and
Disadvantages
21

22. Discussion & Implications
• Numerous exogenous effects on online sales like competition, budget fluctuations,
weather effects, weekday and seasonal effects
• For intermediaries like eBay hedging against weather seems reasonable: Trade
volume eBay Germany changes due to rain effect from rainy year (e.g. 2007) to
dry year (e.g. 2011): about 10 Mio EUR
• TV and Internet are rather substitutes than complements, media compete for
consumers’ scarce resource attention
• Distraction-Sales-Elasticity: increase of number of TV viewers by 1% comes with
decrease of auction sales of about 0.93% for Platform.com
• Effects are quite stable and can lead to market anomalies
• Buyers at eBay should focus on auctions that close at unanticipated “inattention
gaps” while sellers should anticipate effect of TV on auction participation
22
!

23. Thanks you very much
for your Attention!
23
Prof. Dr. Oliver Hinz
Information Systems | Electronic Markets
Faculty of Economics and Law
Technische Universität Darmstadt
Hochschulstraße 1
64289 Darmstadt - Germany
Tel.: +49 6151- 16 - 75221
Fax: +49 6151- 16 - 72220
E-Mail: hinz@wi.tu-darmstadt.de