Alipay and WeChat Pay are everywhere in China – new paper for CSCW 2020 + reflections on cross-cultural research

This is a super-weird week to be submitting the camera-ready version of this research paper for publication at CSCW 2020. On Thursday, the “Executive Order Addressing the Threat Posed by WeChat” set a countdown of 45 days until the Tencent app would be “banned,” along with ByteDance’s TikTok. It recognizes what we document – the central role that these apps’ financial transactions play in the U.S.-intertwined Chinese economy.

Of course: I agree that apps such these, and Alipay and WeChat Pay, collect a lot of data about us while we go about using them for both fun and serious self-expression, and that this data is obtainable through various processes by the government of the country in which their parent companies are headquartered. I’ve long worried about our data security and privacy with regards to a constellation of mobile social media and short-form video apps, along with mobile payment options such as Apple Pay, Google Wallet Google Pay, PayPal, Venmo, Zelle, Square Cash, and Facebook’s Messenger and Novi. (Disclosure: I work at Facebook this summer, on marketing/ad data literacy.)

I felt a grief, however, at thinking of our global internet shrinking just a bit more from fully embracing the marvel of how newly connected so many of us can live and work despite our physical boundaries and limitations. The pandemic has sharpened my keen appreciation for how WeChat and other social media help family and friends bridge great distances, and for how much education, business and other knowledge work depends on reliable and usable communication software being available to everyone, everywhere.

It has been a joy and fascination to help pilot and design research into a very different manifestation of internet-enhanced life than the one I know in the U.S., directed by lead author Hong Shen (also a graduate of the University of Illinois College of Media) and with fellow HCII Phd researcher Haojian Jin and my awesome advisors, Laura Dabbish and Jason Hong. In China, you don’t have to go out with your wallet, just your phone! Even street vendors have QR codes for you to scan! Which gives rise to new forms of communication, such as attaching a message with a transfer equal to a penny! and new threat models, such as thieves coming in the night and replacing the QR code printout with their own!

And that was just from the pilot interviews. Read the preprint version of the paper for specifics on what my Chinese co-authors discovered when they conducted a survey (n=466) and interviews (n=12) in China about the advantages and the pitfalls of moving to a largely mobile and cashless economy.

I spoke up about my interest in the project in part thanks to Dan Grover, whose blogging (in English, thankfully 🙂 ) about his experience of working at WeChat as a product manager had piqued my interest in the various advances in the Chinese social media ecosystem. I couldn’t agree with him more in his tweeted responses to the EO on Thursday night:

“Matching Up Adults in Work Groups” – Exploratory Survey Research for Spring 2020 Class Project

One advantage of still (STILL) taking courses toward my Phd is that I can leverage our group projects to explore research questions outside of my core area. This one got a little “meta” – we looked for the factors that are key to students creating groups for successful course projects!

The following is a blog post that we created for our final project in Social Web in HCI, taught by Geoff Kaufman and Hiro Shirado. My teammates are Ruiqi Hu and Endong Zhu.

In current society, collaboration is a vital component of daily life. People collaborate for diverse personal purposes such as romantic dating, pursuing shared interests, addressing community issues, and solving technical problems. This has led to the rise of dozens of computational systems for “social matching” (Terveen & McDonald, 2005). The rise of team-oriented productivity structures in academia and industry has similarly motivated work to create tools for professional social matching (Olsson et al., 2020). While socio-technical research has led to useful solutions for instructors matching up students in group projects – such as CATME ( and Pair Research ( – we seek to create a computational tool for students who want to self-organize their project groups.

To help us better envision what such a tool might need for its data inputs, we undertook an exploratory research project in Spring 2020 for the Social Web course in the Human-Computer Interaction Institute at Carnegie Mellon University. First, we undertook a literature search through Google Scholar and our existing reference libraries, and we interviewed subject matter experts and gathered feedback from classmates on what competitor tools exist and what other published research was relevant. From this process, we identified several key variables such as team size, fraction of newcomers and incumbents, team skills, and personality traits, from which to create a statistical model of which input variables mattered most for the desired outcomes of excellent grades and group-work satisfaction.

Then, we designed a pilot survey to help us explore these variables with a real-world dataset. We crafted a codebook of survey items corresponding to these variables, from which we then designed and wrote an online questionnaire in Google Forms. We then recruited survey respondents from among our class and personal networks, and we cleaned and prepared the resulting data for statistical analysis using multilinear regression. Finally, we produced charts and graphs to visualize the most important inputs for determining our respondents’ stated grade and satisfaction outcomes.

Our results showed, first, that the more “weak ties” or acquaintances that were reported in the group, the lower were the project outcomes. We theorize that this is because working with acquaintances will lower people’s expectations for the project – students may just want to “hang out” with their school friends instead of focusing on the quality of their projects.

Figure 1: The number of weak ties in a group is negatively associated with top-percentile project outcomes.

Second, our results show that the personality trait of “negative emotionality,” such as a tendency to anxiety, is positively associated with both project outcomes AND satisfaction. This finding is surprising to us, because we assumed that this trait would have negative effects on outcomes due to creating psychological obstacles or group friction. However, it may be that students who worry more tend to care more and devote more efforts to the project.

Figure 2: The “negative emotionality” personality trait is positively associated with top-percentile project outcomes AND satisfaction.

This work has validated our initial hunch that using a psychometric and skills-profiling tool may help students to self-assemble a group for their course projects that is more likely to lead to excellent grades and high satisfaction. We see the need in the future to collect a larger survey sample, with a monetary incentive for participation rather than “social capital” among the convenience sample, in order to test whether we can replicate the results.

“Date Assist” Plug-in for Self-Affirmation in Online Dating Apps – Fall 2019 Class Project

Yes I am STILL taking courses towards my doctorate in human-computer interaction (HCI) here at Carnegie Mellon! It’s annoying that my department does not count my 2017 master’s degree in HCI from Indiana University as an acceptable credential. The flip side, though, is that I have experience with group projects, so these feel like a breeze – plus, I get to meet and work directly with PhD students outside my area and with our amazing master’s degree students!

The following is a blog post that we created for our final project in Persuasive Design in HCI, taught by Geoff Kaufman. My teammates are Aaron Bishop, Brandon Fiksel, Samantha Reig, Bidisha Roy, and Molly Schaefer.

A summary image of our "Date Assist" plugin. The QR code links to the interactive Figma prototype.
A summary image of our “Date Assist” plugin. The QR code links to the interactive Figma prototype.

Having anxiety about meeting the right romantic partner in today’s online dating apps? You’re not alone. A Google search of the phrase “online dating is stressful” yielded close to 10 million results! Moreover, our research shows that young adults in a college setting struggle with three issues with dating: finding a mutually agreeable time for the first date, bolstering their mental state before the date, and coming up with conversation starters during the date. 

Enter “Date Assist.” Our plugin for online dating apps uses a touch-screen calendar interface to help you and that special someone easily find a meeting time that works for both. Once the date is scheduled, we help you remember what’s most important to you with a simple quiz about your values. We also help you to build a personalized, meaningful playlist to get you feeling great before your date — and, if you so choose, to share one of your favorite songs to break the ice and ease conversation. Finally, we push out reminders to you that help you to stay positive and remember: you’re a catch!

The psychological mechanism that our “Date Assist” plugin uses is called self-affirmation. In this process (Steele, 1988), a person reflects on valued aspects of the self, allowing those positive aspects to counter the negative effects of seeing other aspects of the self as negative. Such a reflection in one domain can reduce threats in unrelated domains by shifting from a narrow focus on the immediate threat to an expanded perspective of one’s self-worth. 

Our “Date Assist” plugin was developed in Sept.-Dec. 2019 using an iterative, user-centered research and design process:

  • We identified the need to test a self-affirmation in a context that is a threat to an individual’s positive self-concept. 
  • We found a novel research domain for this threat — first dates — that is relevant to the research population that we have immediate access to, young adults. 
  • We used interviews to define this population’s need for help in finding a mutually agreeable time for the first date, in bolstering their mental state before the date, and in coming up with conversation starters during the date. 
  • We created sketches and gathered user feedback. We then consolidated these into a high-fidelity prototype with the Figma collaborative design tool. 
  • To test the effectiveness of “Date Assist,” we propose a large-scale 4×2 experimental study.
Our team met weekly to discuss our ideas, interpret interview data and share low-fidelity sketches. Three images: One of team members Bidisha Roy and Aaron Bishop with others reflected in the lab mirror; a sketch of possible app screens; a menu design in progress.
Our team met weekly to discuss our ideas, interpret interview data and share low-fidelity sketches.

Our process gives us confidence that “Date Assist” can make two contributions to the fields of psychology and human-computer interaction. First, our research extends the existing literature on self-affirmation to the context of online dating. Second, our research provides a novel operationalization of self-affirmation with the creation of the “Date Assist” plugin. 

Most importantly, our work may significantly improve the experiences of those who seek romantic connections via dating apps. We hope that “Date Assist” helps to ease the stressful process of finding love and companionship for brave first-daters everywhere!