Remote Shopping Advice: Crowdsourcing In-Store Purchase Decisions

Recent Pew reports, as well as our own survey, have found that consumers shopping in brick-and-mortar stores are increasingly using their mobile phones to contact others while they shop. The increasing capabilities of smartphones, combined with the emergence of powerful social platforms like social networking sites and crowd labor marketplaces, offer new opportunities for turning solitary in-store shopping into a rich social experience.We conducted a study to explore the potential of friendsourcing and paid crowdsourcing to enhance in-store shopping. Participants selected and tried on three outfits at a Seattle-area Eddie Bauer store; we created a single, composite image showing the three potential purchases side-by-side. Participants then posted the image to Facebook, asking their friends for feedback on which outfit to purchase; we also posted the image to Amazon’s Mechanical Turk service, and asked up to 20 U.S.-based Turkers to identify their favorite outfit, provide comments explaining their choice, and provide basic demographic information (gender, age).

Study participants posted composite photos showing their three purchase possibilities; these photos were the posted to Facebook and Mechanical Turk to crowdsource the shopping decision.
Study participants posted composite photos showing their three purchase possibilities; these photos were the posted to Facebook and Mechanical Turk to crowdsource the shopping decision.

Although none of our participants had used paid crowdsourcing before, and all were doubtful that it would be useful to them when we described what we planned to do at the start of the study session, the shopping feedback provided by paid crowd workers turned out to be surprisingly compelling to participants – more so than the friendsourced feedback from Facebook, in part because the crowd workers were more honest, explaining not only what looked good, but also what looked bad, and why! They also enjoyed the ability to see how opinions varied among different demographic groups (e.g., did male raters prefer a different outfit than female raters?).

Although Mechanical Turk had a speed advantage over Facebook, both sources generally provided multiple responses within a few minutes – fast enough that a shopper could get real-time decision-support information from the crowd while still in the store.

Our CSCW 2014 paper on “Remote Shopping Advice” describes our study in more detail, as well as how our findings can be applied toward designing next-generation social shopping experiences.

For more, see our full paper, Remote Shopping Advice: Enhancing In-Store Shopping with Social Technologies.

Meredith Ringel Morris, Microsoft Research
Kori Inkpen, Microsoft Research
Gina Venolia, Microsoft Research

How Much Does Facebook Really Know About You? Predicting Motives For Facebook Use From Logged Data

People consume media for many different reasons and, indeed, individuals use a wide range of channels to achieve diverse ends: entertainment, edutainment, information seeking, communication, socializing, and surveillance to name but a few. Uses and Gratifications (U&G) is a theoretical framework that facilitates studying these motives and outcomes – casting light on the how and why of media consumption. U&G has been applied to formats as diverse as tabloids, reality TV, mobile phones and, lately, social network sites. A U&G study typically involves three elements: motives for media use, social and psychological antecedents, and cognitive, attitudinal, or behavioral outcomes. The value of U&G lies in combining these factors: the motives people have for using a service are seen in the context of specific kinds of users (e.g. via antecedents such as demographics) and the behaviors they engage in (e.g. outcomes such as purchasing content).

The combination of the three elements is at the heart of our approach.
The combination of the three elements is at the heart of our approach.

Most U&G studies collect this information from surveys or questionnaires. While this technique is good at eliciting motives – asking why – it is weaker at capturing antecedents and outcomes. Indeed, data for these categories is typically limited to a few basic demographic and usage questions: asking about age, gender, and time or frequency of use. Beyond being coarse-grained, such information can also be hard to recall accurately. To address these problems, we studied Facebook motivations with a data-driven approach. We surveyed motives and simultaneously collected a rich dataset from the Facebook API. This included eleven usage metrics (e.g. number of photographs, “likes” given, events attended) and eight personal network metrics (e.g. network size, density, connected components) that we used as novel outcomes and antecedents. This data provided novel insights into Facebook use.

– In line with prior work, we identified seven motives for Facebook use: social connection, shared identities, photographs, content, social investigation, social network surfing and newsfeed.

– We showed that data gathered from the Facebook API predicts these motives. For instance, we can determine if Facebook users are primarily interested in connecting with others, looking at photographs, or stalking, based directly on their usage patterns and network structure.

– We gained a deeper understanding of motives for using Facebook. For example, the Social Investigation motive was linked to longer times spent on site but negatively linked with posting status updates. This profile highlights the “lurkers” on Facebook: the silent majority who observe but don’t post.

For more, see our full paper, Understanding Motivations for Facebook Use: Usage Metrics, Network Structure, and Privacy.

Tasos Spiliotopoulos, Madeira Interactive Technologies Institute, University of Madeira
Ian Oakley, Ulsan National Institute of Science and Technology

Online Social Networking across the Life Span

The surging popularity of social networking sites (SNSs) provides new means for staying in touch with close and distant relations. Most of us have the notion that SNS users are primarily young adults and college students; but online social networking has quickly gained popularity among people of all ages. Currently, there is a disproportionately large growth among users who are 50 years or older (Zickuhr and Madden, 2012)

Socioemotional selectivity theory (SST) posits that social goals and motivations differ across the life span due to individuals’ increasing awareness of limitations on future time. So what are the main age-related differences across the life span in regards to social goals? Well,

Younger adults prioritize future-oriented goals such as gathering information and establishing new relationships.
Older adults prioritize present-oriented goals such as emotional well-being and meaningful relationships.

Keeping this in mind, we wanted to examine the patterns of online social networking use across the life span through a nationally representative sample of demographically diverse Facebook users.

To explore the effects of age on online social networking on SNSs, we took data that was collected through a national telephone survey, with a sample of 1000 adults, ranging from 18 years to 93 years old. We looked at age differences in their network composition, their use of SNSs, and the relationship between social network composition and social isolation and loneliness.

Findings suggest that:

• The selectivity of Facebook social partners increases with age.
• Compared to younger adults, friend networks of older adults are smaller but contain a greater proportion of people who are considered to be actual friends.
• A higher proportion of actual to total Facebook friends is associated with lower levels of social isolation and loneliness across the life span.

Natalya Bazarova, Cornell University
Pamara Chang, Cornell University
Yoon Hyung Choi, Cornell University
Corinna Loeckenhoff, Cornell University

Self-Censorship on Facebook

Ever start writing a post or comment on Facebook, but ultimately decide against sharing it? You wouldn’t be alone. We found that 71% of Facebook users exhibited some form of “last-minute” self-censorship over 17 days.

More specifically, users refrained from sharing 33% of the posts, and 13% of the comments, that they began writing.

Overall self-censorship rates, broken down across product usage. Comments are represented in the left chart, and posts are on the right.
Overall self-censorship rates, broken down across product usage. Comments are represented in the left chart, and posts in the right. The aggregation of all comments and posts are represented with the “comments” and “posts” label, respectively.

While last minute self-censorship is generally prevalent, the frequency of self-censorship does seem to vary by the nature of the content (e.g., is it a post or a comment?) and the context surrounding it (e.g., is it a status update or an event post?). Indeed, status updates (34%) and posts within Facebook groups (38%) were censored far more frequently than posts on friends’ timelines (25%) or events (25%).

The frequency of self-censorship for comments did not vary as drastically as with posts, though comments on photos (15%) and on group posts (14%) were censored more than comments on timeline posts (12%) and status updates (11%).

The decision to self-censor, thus, seems to be partially driven by two simple principles:

  • People censor more when their audience is harder to define, and
  • People censor more when the relevance or topicality of a CMC “space” is narrower.

In other words, undirected content that might be read by anyone is censored frequently, but so is very specifically directed content. After all, knowing one’s audience is only one part of the battle. A known audience is a double-edged sword: Topics relevant to the group may be easier to share, but fewer thoughts, statements, or photos may be considered relevant to the group.

Overall, a user’s “perceived audience” does indeed seem to lie at the heart of the matter, but the effect is not always straightforward.

We also found that:

  • People with more boundaries to regulate self-censor more;
  • Males self-censor more posts than females;
  • People who exercise more control over their audience self-censor more content; and,
  • Users with more politically and age diverse friends self-censor less, in general.

For more, see our full paper, Self-Censorship on Facebook.

Sauvik Das, Carnegie Mellon University
Adam Kramer, Facebook