Online Social Shopping and Self-Customization

Flash Player 9 (or above) is needed to view presentations.
We have detected that you do not have it on your computer. To install it, go here.

more share options
Embed For WordPress.com

Without related presentations

Zlio looks to be in direct competition with aStore, a new service from Amazon. See Centernetwork’s coverage here from late last year. Zlio actually launched the same day as aStore. That still isn’t a good explanation as to why Amazon has shut them off, however.Amazon associate program

Long Tail: A community of esteemed professors of Business Law living in Brockton who drive Harley Davidson motorcycles

Are these really experts? – No!Can a 17 year old blogger who writes about the latest smartphone software compete with seasoned journalists? On the web, maybe

Hand over to Arnold for model summary

Explain variable (DV) backExplain source

2nd much larger dataset we are still analyzing

Controlled Experiment – spring break in cancunSide by side with livelookscreensharingcobrowsingN=182From classes in Spring 2008 – Summer I 2008Livelook Screen sharing technology

Arcs, beta, significanceDV going backwardsSo that’s pretty good; what happens when we break it into the 4 different treatments

Andy Warhol, 1968. It refers to the fleeting condition of celebrity that grabs onto an object of media attention, then passes to some new object as soon as people's attention spans are exhausted.On the Web, everyone will be famous to fifteen people – Momus, 1991Susan BoyleBritain’s got talent42600 youtube videos referencing Boyle95 million views in 7 daysI received 6 emails from different friends on 1st day video was postedSpread by facebook, twitter, texts, and maybe even emailTV exposure on CNN & Larry King AFTER web 2.0 youtube successRemember the 85 year old who unsuccessfully tried to publish letters to the editor finally publishes a blogWhats happening here? Ru bewilderedLong Tail – relevance to our paperMaslow self-esteem recognitionI am the ONE who recognized her talent. When I recommend her I feel good. The person who recvs my recommendation trusts me and opens itThis is not a fad

Problem: low conversion rates 2% (difficulties measuring this) Do folks research then buy at physical store or vice versa Learned from you last year – electronic to travel (hedonic) how should we design the interface to engage

TactileBuzz Immediate reaction from friend – what were you thinking Any reason for them to hang around a eRetailer Sterile experience online – look and go – research

Wants to know what’s happening within her “sphere of influence” What’s being browsed in a shopping siteHot product sales now by audienceTop product searches, wish listsOnline live gatherings with expertsHow customers use products, exchange ideasPrice = f(collective checkout activity)Shopcasting (embedded product code in blog)Shop with friends

What did we learn from pilot?Complex Product – PC purchaseCan the interactivity of product self-customization overcome the leanness of solo online shopping? Can the social richness of shopping in pairs overcome the leanness of solo online shopping? Is there a synergistic, interaction effect between Social Shopping and Self-Customization?Are there cultural factors at play?

Pareto principle 80/20 ruleBrynjolfsson, SloanAndersson, WiredElberse, HBSthe primary value of the internet to consumers comes from releasing new sources of value by providing access to products in the Long Tail.Brynjolfsson found by greatly lowering search costs, Internet markets substantially increase the collective share of hard-to-find products, thereby creating a longer tail in the distribution of sales.They used a theoretical model to show how a reduction in search costs will affect the concentration in product sales. By analyzing data collected from a multi-channel retailing company, they showed empirical evidence that the Internet channel exhibits a significantly less concentrated sales distribution, when compared with traditional channels. An 80/20 rule fits the distribution of product sales in the catalog channel quite well, but in the Internet channel, this rule needs to be modified to a 72/28 rule in order to fit the distribution of product sales in that channel. The difference in the sales distribution is highly significant, even after controlling for consumer differences.

1. All ideas compete on an equal footing.2. Contribution counts for more than credentials.3. Hierarchies are natural, not prescribed.4. Leaders serve rather than preside.5. Tasks are chosen, not assigned.6. Groups are self-defining and –organizing.7. Resources get attracted, not allocated.8. Power comes from sharing information, not hoarding it.9. Opinions compound and decisions are peer-reviewed.10. Users can veto most policy decisions.11. Intrinsic rewards matter most.

Engages customers

Favorites, Groups & Events

Online Social Shopping and Self-Customization - Presentation Transcript

Sawyer Business School Faculty Research Seminar April 23, 2009
“15 minutes of fame” -Warhol
Self-Customization vs. Social Shopping: User Interaction Design for Gen-Y Hedonic Online Purchases Arnold Kamis & Jonathan Frank ISOM Department SBS Faculty Research Seminar, April 23, 2009
Richness of Social Shopping Web stores can feel \"sterile and isolating” – Apple Corp
Online Shopping Expectations?
Online Shopping Research Questions
Online Shopping Long Tail •Pareto Principle 80/20 rule •Brynjolfsson (2003, 2006) •Anderson (2004) •Elberse (2008)
Web 2.0 Crowd Control
Long Tailed Self-Customization
Long Tailed Stores Online
Long Tailed Social Networks
Social Bookmarking -- Skimbit
“Trusted expert” -- ThisNext
ThisorThat
Co-browsing -- DimDim
Simplified Conceptual Model (TAM) Perceived Ease of Use + + + (Marketing) (TPB) (TAM) + Information Perceived Intent to + Seeker Trait Effective- Purchase ness + + + + Perceived (Flow) Enjoyment
Research Design  Controlled experiment  Two travel web sites: StudentCity and Expedia  Cobrowsing with LiveLook  Student Subjects from classes in Spring 2008 – Summer I 2008  N=182
for Solo or Paired Online Shoppers Packaged Customized
Overall 57.6% Perceived Ease of Use -0.087 0.220 0.212 0.608 0.403 Information Perceived 0.306 Intent to Seeker Trait Effective- Purchase ness 16.3% 50.6% 0.267 0.738 0.097 Perceived Enjoyment 61.2%
Simplest Combination: solo & packaged R2 = 52.7% Perceived Ease of Use -0.182 0.091 -0.089 (4.30, 1.64) 0.731 0.532 Perceived Information 0.124 Intent to Effective- Seeker Purchase ness R2 = 28.3% R2 = 26.8% 0.472 0.729 0.132 Perceived Enjoyment R2 = 65.2%
social & packaged R2 = 69.1% Perceived Ease of Use 0.016 0.194 0.342 (4.34, 1.68) 0.655 0.553 Perceived Information 0.563 Intent to Effective- Seeker Purchase ness R2 = 30.6% R2 = 77.5% 0.036 0.759 0.115 Perceived Enjoyment R2 = 68.6%
solo & customizable R2 = 70.9% Perceived Ease of Use 0.020 0.299 0.159 (4.84, 1.76) 0.576 0.444 Perceived Information 0.033 Intent to Effective- Seeker Purchase ness R2 = 19.7% R2 = 52.6% 0.563 0.713 0.165 Perceived Enjoyment R2 = 63.9%
social & customizable 32.9% Perceived Ease of Use -0.337 0.186 0.385 (5.02, 1.32) 0.380 0.182 Perceived Information 0.434 Intent to Effective- Seeker Purchase ness 3.3% 57.2% 0.117 0.607 -0.007 Perceived Enjoyment 36.7%
Zoomerang  Excellent company for panels of online users  Still analyzing new dataset (n=606) from Field Experiment  United States: $5.50 / subject  Going Global o China: $18 / subject! o Russia: $20 / subject! o India: $27 / subject! o Brazil: $32 / subject!
Conclusions  The social dimension and the customizability dimension explain to a significant extent intent to purchase online travel  The two dimensions influence the Gen-Y subject(s) differently.  We can support and influence both dimensions with careful design of the user interface and the social process.  We expect these dimensions to vary by culture.  There are additional variables we have yet to identify.  Any suggestions?
What’s next?