Prof. Dr. Oliver HinzProfessur für Wirtschaftsinformatik insb.

Electronic Marketshinz@wi.tu-darmstadt.de

www.emarkets.tu-darmstadt.de

TV’s Dirty Little Secret:The Negative Effect of Popular TV on

Online Auction SalesHinz, 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.

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 NadellaHigher 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/how-does-digital-affect-canadian-attention-spans 2

Second Screening in Germany 1/2

Second Screen – Parallelnutzung eine Fernsehprogramm

3

Second Screening in Germany 2/2

Second Screen

4

Causes for differences in variance of sales pattern?

00:00

-01:59

02:00

-03:59

04:00

-05:59

06:00

-07:59

08:00

-09:59

10:00

-11:59

12:00

-13:59

14:00

-15:59

16:00

-17:59

18:00

-19:59

20:00

-21:59

22:00

-23:59

MondayMonday + 4 weeks

• Attention of buyers is not constant over time • Sales possibly impacted by weather, time of day, weekday, month…

TV programs?

5

Attention

Let us start with…

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

?

Research Question

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

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

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 2009Products: 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 observationsOverall, 351 different sellers sold 25,677 unique products types in 78,068 transactions to 65,894 different buyers.

10

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°

Precipitation3

11

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)

5Mean account balance per day

12

Sales and Population per Zip

13

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

3) Proxy for being at home – daylight hours after 6pm

4) 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 27 280 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 ii i i

Sales

14

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.

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

Instrumental Variables US Presidential Election

2008

Dummy Variable (1/0): Nov 4, 2008

17Could be predicted but unlikely to have large emotional impact on viewers in Germany

Proxy Variable Daylight Minutes

Number of daylight minutes after work (6pm)

18

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

0 1 2 3 4 5

6 11 11

6+i 13+i 25+i 27 280 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 ii i i

19

Estimation Results

20

Advantages and Disadvantages

21

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

!

Thanks you very much for your Attention!

23

Prof. Dr. Oliver Hinz

Information Systems | Electronic MarketsFaculty of Economics and LawTechnische Universität Darmstadt

Hochschulstraße 164289 Darmstadt - GermanyTel.: +49 6151- 16 - 75221Fax: +49 6151- 16 - 72220E-Mail: hinz@wi.tu-darmstadt.de