Conflict resolution initial findings #1: which values and norms matter the most when it comes to sharing location?

I’m still quite busy analyzing the large dataset (~1600 cases) collected from the conflict resolution online user study conducted earlier this year. Before delving into detailed model building, and findings related to specific types of normative conflicts, I thought I’d present two simple yet quite clear findings that appeared upon the initial inspection of the data:

1. Which of the five values in the experiment were found to matter the most, in the general sense?

We have asked participants to use a pie chart to indicate, in the general sense, and assuming a role (either a parent or a child), their preference for five human values that we found to matter the most in the location sharing domain. Below was the description we provided for these values.

Friendship: for you, or your family members to build friendships, a social life, and be recognized amongst others in the social circle.

Privacy: for you, or your family members to be free from unwanted outside intrusion, and undesirably shared information.

Safety: for you, or your family members to be free from dangers or harm.

Independence: for you, or your family members to be capable of doing what they need to do without other’s control or support.

Responsibility: for you, or your family members to know and be able to do the tasks they’re expected to do.

The pie chart below shows how, on average, users ranked the importance of these values:


What I find interesting is that (1) the fact that there was a significant preference for some values over others and (2) that privacy, long considered a pivotal value in social data sharing (especially location!), was ranked lowest. Now, the domain of the experiment is indeed family life, so that makes this finding a little less surprising, yet still interesting as privacy ranked last amongst all five values, not just second to safety, the expected winner.

2. Obligations vs. Prohibitions (to share and receive data)?

Throughout the experiment we asked participants to create conflicting normative statements regarding sharing and receiving location, and we then asked them to indicate their preference (and by how much, using a slider), in the case a conflict occurs. Now, a conflict always included an obligation commitment (e.g. I want someone to share/receive data with/from me/somebody else under some circumstances), and a prohibition (e.g. I want someone to not share/receive data with/from me/somebody else under some circumstances). Again, before going into details on predicting user preference using statistical models, another simple yet clear finding presented itself upon early data inspection:


Data here was modified so that all obligations are to the left side (negative values), and all prohibitions are to the right side (positive values). In the experiment itself the order of course was random. We can see that there is a clear tendency for obligations of sharing and receiving data to be preferred to prohibitions. If we make this discrete, obligations were preferred around 63% of the time:


So, and without drawing any detailed conclusion yet, these two simple findings could alone increase prediction accuracy in conflict resolution in location sharing, by quite some margin.

Website for conflict resolution user study is now online!

We have at last finalized the website for the conflict resolution user study mentioned here, and it is online now for anyone to participate. We have also launched a campaign on to add more participants. You can check it out at:

Don’t forget to watch the instructional video(s)!

Journal article: “A social commitment model for location sharing applications in the family domain”.

Just submitted a journal article titled “A social commitment model for location sharing applications in the family domain”, to the International Journal of Human Computer Studies (IJHCS). In this article I discuss the creation of the social commitment model and lifecycle (see photo below), throughout the conception phase, evaluation through CCS, then the enhancement, creation of an alternative lifecycle, and then evaluation results using SWT.

Will update this post with a preliminary version once it’s been published. If you would like a copy already, leave a comment with your email.


Gave a talk at Social Media Week Rotterdam, 2015!

Was very glad this week to have given a talk @Social Media Week conference in Rotterdam! This event is a part of an international Social Media Week, which takes place in different cities simultaneously around the globe, on 4 different occasions every year (Rotterdam was during the same week as London, Miami, and Sao Paolo). I talked about the results of two experiments, the usability/usefulness study conducted using the crowd sourcing platform, and the app testing at the day care centers with children.

I was at the main room (zaal 1), and the talk was streamed live online and was archived as well. You can check it out below. Great experience overall, looking forward for coming back next year as well!

Generalized Estimating Equations (GEE) models using R

I’ve been having the feeling lately that using SPSS for running multilevel analysis may not be the greatest idea. I had to switch to something that is more robust and better documented. Enter R: free, open source, greatly documented, tons of well maintained packages, great for both simple and complex analyses, mathematics in general, plotting capabilities, as well as pretty much anything you would have used MATLAB to do.

Here’s a quick explanation on how to perform the same analysis in here which was done using SPSS, this time using R. The package I’m using here is geepack, and It’s documented here.

require(xlsx) #to read excel sheets
require(geepack) #the gee package

#reading my data file in excel, with a first row of column headers
J1 <- read.xlsx("J1\ multimodel.xlsx", 1)

#making sure I read my predictors as factors
#default would treat them as covariates.
J1$N <- as.factor(J1$N)
J1$A <- as.factor(J1$A)
J1$Cr <- as.factor(J1$Cr)
J1$Cd <- as.factor(J1$Cd)

#constructing the model:
#slider as response,
#N, A, Cr, and CD as fixed effects
#and pp as random effect
#with slider's distribution as gaussian
# and an unstructured covariance matrix
gee01 <- geeglm (slider ~ N*A*Cr*Cd,
id =pp, data = J1,

#and finally, to see the results:

ICT Open 2015

Presented both a poster and a demo at ICT Open 2015! This is my second participation in ICT Open after the 2012 edition, and this time it was held in De Flint theater, Amersfoort, over the course of two days. There was around 50 or more demos in the hallway, and plenty of interesting talks/events, however I had to stay by my demo (and poster) almost the whole time. Overall, there was plenty of interest especially in my app, and plenty of research/industry contacts made. It was also great to see my supervisor at TU Delft awarded the Dutch prize for ICT in 2014, though unfortunately she couldn’t come to accept it in person. See below the poster, and more photos from the event here.


Simulated Work Tasks: an online study of usability and contribution of a Social Commitments model

About to run a Simulated Work Tasks (SWTs) experiment using the online platform The goal of this experiment is to assess the usability and domain contribution to the social commitments (SC) model that we created. Participants will perform 4 tasks each, and at the end of each task, they will try to solve the family-life domain problem through creating an agreement using a menu representation of the SC model. After that, they will have to rate how well did the options in the SC menu contribute towards the solving the problem in the scenario, using a slider.

You can try it out yourself as well, using this link. Make sure you see the explanation video first!

Video to help explain to children how to answer questionnaire

Just created a new video (again, using iMovie and the GoAnimate website), to try and bridge the gap for children between the app they are going to be testing, and the futuristic questions they have to answer in the questionnaire. I have to admit it is difficult to imagine how a 7-10 year old child can answer a “fill in the blank” questionnaire with a continuous slider like this shown in the video. Hopefully this tutorial will ease the transition!

Location sharing app for families: a formal(ish) description

Finally the app is done. Here’s a formal description, as it went in one of the papers we wrote:

The application permits its users to share check-ins in certain locations with other users of the system (such as family members and school friends), similar to the popular application Foursquare. The check-in feature was selected for our case study based on the analysis previous qualitative data with members of our target group.

Basic features and preferences

Users can place other users of the system in one of two lists (“family” or “friends”), otherwise they are placed in the list “others”. Users can select with which lists they share their check-ins, and from which lists they view shared check-ins.

Users can create locations either through placing a marker on an integrated Google Map, or simply by detecting the current GPS position. The application will create a 50m*50m location surrounding the resulting GPS coordinates, to which the users can assign a name.

When a user wants to check-in, a list of nearby, already created locations is displayed (with the option of adding a new location). The user can select their location, and confirm their check-in, to be shared with the users in the lists our user is sharing with, according to their preferences. Users who receive this check-in will get a pop-up with the sharer’s name and location information, assuming that they opted (in their own preferences) to view check-ins from the list to which the sharer belongs.

Social commitments

The application allows for additional, norm-based behavior customization. Drawing from the social commitments framework by Singh, and our analysis of user data, we have created the following commitment model:

A commitment (S, T, t, n, e, d) consists of S the source (creator of the commitment), T the target that has to comply with it, t the triggering condition that activates the commitment, n the normative effect (an obligation or a prohibition of sharing or viewing a check-in, from someone or a group of people), e the expiry condition that deactivates the commitment, and d the deadline by which an obligation commitment should be fulfilled.

For example, Mark (source) can create the following commitment: (1) I want Paula (target) to share her check-ins with me (normative effect), if she enters the park (triggering condition). Another example is Mark creating the commitment: (2) I want Paula to “not view” check-ins from the group “friends” (normative effect) after 9 pm (triggering condition). In the current app version, the deadline ASAP is used for obligation commitments. Expiry condition in (1) is not required, while in (2) it is implied as a certain hour (9 am).

Upon creation, commitments are sent from source to target, whereby the target can either accept or delete the commitment, or “decide later”. The commitment enters its “active” state when it’s been accepted by the target, and the trigger condition has been met. The commitment leaves its “active” state when the expiry condition has been met, but can re-enter that state if the triggering condition was met again, provided that it was not deleted.

Conflicts between preferences and an active commitment are decided in favor of the commitment. For example, if Mark is in Paula’s family list, and Paula opted in her preferences to “not share check-ins with family”, accepting commitment (1) above means Paula’s check-in will be shared with Mark if she enters the park. Conflicts between two active commitments would be solved in favor of the commitment most recently accepted.

A 3-minute tutorial video of the application (with subtitles) can be seen here.