Wharton, Baker retailing seminar, AMA CB-SIG, MSI, Jari Salo, online social shopping, social shopping platforms, ecosystems, online recipes, food purchasing

1. Maatalous-metsätieteellinen tiedekunta
ONLINE SOCIAL SHOPPING: THE IMPACT OF
SOCIAL AND COMMERCIAL ACTIVITIES ON USER
BEHAVIOR ON A SOCIAL SHOPPING PLATFORM
ONLINE SOCIAL SHOPPING: THE IMPACT OF
SOCIAL AND COMMERCIAL ACTIVITIES ON USER
BEHAVIOR ON A SOCIAL SHOPPING PLATFORM
Jari Salo,
Professor of Marketing,
University of Helsinki
jari.salo@helsinki.fi
Wharton Baker retailing seminar –
co-sponsored by MSI and AMA CB-SIG

2. Maatalous-metsätieteellinen tiedekunta
INTRODUCTION
• Using different types of online communities e.g. social media and
virtual worlds are popular way to spend time (e.g. Geogaching.com /
Facebook)
• Previously stickiness of the media spent was key factor to be
measured
• Now firms are adding shopping and transactional elements on such
platforms or social activities to their own platforms which has given rise
to the phenomenon called online social shopping
• Social shopping e.g. sCommerce (in-social, in-app or generating
transactions elsewhere)
• Still relatively little of the social action turns into commercial success
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3. Maatalous-metsätieteellinen tiedekunta
RESEARCH QUESTIONS
§ How to promote
§ Consumers’ social activities?
§ Consumers’ commercial activities?
§ Individual level customization makes it difficult for unified
framework to develop such social shopping sites
§ How to quantify the success of social shopping sites?
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4. Maatalous-metsätieteellinen tiedekunta
LITERATURE REVIEW
• Brand communities (Schouten and McAlexander, 1995)
• Online social network communities (Algesheimer et al., 2010)
• Online social shopping constitutes three key elements: social
media technologies, social interaction, and commercial activities
(Yadav et al., 2013; Yadav and Pavlou, 2014).
• Consumers share their ideas on these platforms often providing
customers with customized shopping experience based on their
profiles, preferences and the wisdom of crowds and their friends
(Wall Street Journal, 2013).
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5. Maatalous-metsätieteellinen tiedekunta
LITERATURE REVIEW
From social perspective, studies look into impact of consumers’ :
• online social participation on their behavior (Algeshiemer et al., 2010; Rishika et al., 2013),
• measuring online word of mouth (Godes and Mayzlin 2004; Kozinets et al., 2010),
• diffusion of user-generated contents (Susarla et al., 2012)
• impact of information technologies on online knowledge creation (Ma and Agarwal, 2005).
From commercial perspective, studies look into consumers’:
• shopping activities through the lens of social media (Trusov et al., 2009; Stephen and Toubia, 2009;
Trusov et al., 2010; Fischer and Reuber, 2011)
• multi-channel coordination (Pozza, 2014)
From more technological perspective, studies look into
• interactive decision aids (Häubl and Trifts, 2000),
• predicting internet buying behavior (Sismeiro and Bucklin, 2004),
• turning website visitors into customers (Venkatesh and Agarwal, 2006; Reibstein, 2002; Hauser et al.,
2009)
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CONCEPTUAL FRAMEWORK
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DATA
§ Digital Foodie (origins in
Finland, owned by Black Dragon
Capital US)
§ multiplatform service
Foodie.fm targeted towards
making grocery buying in a
multichannel social
environment
§ Foodie recipe
§ Foodie local
§ Foodie quality
§ Foodie store
§ Foodie delivery
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8. Maatalous-metsätieteellinen tiedekunta
SOCIAL AND COMMERCIAL ACTIVITIES
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DATA DESCRIPTIVE
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DATA DESCRIPTIVE
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MODEL
§ Rank order the consumer actions based on consumer visit
§ Shortest path taken to visit between states
§ Consumer visit (incidence to a state)
§ Time spent on each state
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12. Maatalous-metsätieteellinen tiedekunta
RANKING OF USER ACTION &
SHORTEST DISTANCE
§ Google page rank algorithm
§ Ranking the user actions
§ Based on in-degree and out-degree of the actions
§ Dijkstra’s algorithm
§ Shortest distance between two nodes
§ Weight of the link is the cost of visiting the link
§ Based on total clicks required
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MODELING OF STATE VISIT AND
TIME SPENT
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RESULTS: RANKING OF
CONSUMER ACTIONS AND
DISTANCE BETWEEN STATES
• Commercial actions are most important
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RESULTS: STATE VISIT
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RESULTS: TIME SPENT
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SUMMARY OF RESULTS: (1/2)
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SUMMARY OF RESULTS: (2/2)
§ Ranking and Distance between States are good predictors of consumers’ online
shopping behavior (incidence and time spent)
§ Rank and Distance have higher impact on incidence than time spent
§ Ranks of other states positively affects incidence and time spent on a particular
state
§ Distance from other states negatively affects time spent on a particular state
§ The effects of inter-state distance on shopping state have all negative effects
indicating that shopping is highly indulgent activity that consumers perform
online and therefore should be distraction free
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