AIS Early Career Award

About the Award

The AIS Early Career Award was established in 2014 and was granted for the first time in 2015. This award recognizes individuals in the early stages of their careers who have already made outstanding research, teaching, and/or service contributions to the field of information systems.

Qualifications for the Award

Nominees for the AIS Early Career Award should have made global contributions to the discipline as well as local contributions in the context of their country and region. Nominees should be role models to colleagues and students within the discipline, as well as garner the respect of individuals from outside the discipline and be esteemed for their high levels of professional and personal integrity.

Nominees must have been AIS members for at least two consecutive years and be current members of AIS at the time of nomination and receipt of recognition. Individuals may only win the Early Career Award once. For the purpose of this award, AIS defines “early career” as individuals who, at the time of nomination:

  • have received their doctoral degrees no more than seven (7) years prior to nomination; OR
  • be within the first seven (7) consecutive years of their academic career if they do not have a doctoral degree.

2025 Recipients

Zhi (Aaron) Cheng
London School of Economics

Aaron Cheng is an Assistant Professor of Information Systems and Innovation at the London School of Economics and Political Science.

An AIS Distinguished Member and AIS Early Career Award recipient, he is an active contributor to the community. He serves on the Editorial Review Boards of ISR and JAIS, has been Associate Editor for a MISQ special issue, and has received Best Reviewer and Best AE awards at ICIS and ECIS. His research examines the economics of digitisation and AI and has appeared in Management Science, ISR, JMIS, JAIS, and JIT.

I’m genuinely honoured to receive the award. In the early years of an academic career, much of the work happens quietly, such as shaping ideas, revising papers, designing courses, and learning how to review and serve well. Progress is often gradual and not always visible from the outside. This award offers a moment to pause and reflect on that journey. At the same time, it reminds me that an early career is still just the beginning. I see it as both encouragement and responsibility to keep doing careful work and to contribute in ways that help others grow.

One challenge was learning what not to take on. Early on, I was excited by many ideas, collaborations, teaching opportunities, and service roles, and it was tempting to say yes to everything. Over time, I realised that time and attention are limited, and without clear priorities, it is easy to feel busy but unfocused. Becoming more intentional about where I could contribute meaningfully, and where I needed to step back, was an important shift. Another challenge was developing patience with the pace of growth across research, teaching, and service. I learned to focus less on immediate outcomes and more on steady improvement over time. Having generous senior mentors and supportive junior peers also made a tremendous difference.

There was no single turning point, but a gradual clarification. Across different empirical contexts, I realised my work pointed to the same underlying mechanism: digital technologies reallocate scarce resources such as attention, time, space, and labour, reshaping individual choices and institutional dynamics rather than simply complementing or substituting inputs. Recognising that pattern gave my research intellectual coherence. At the same time, invitations to speak at the UK House of Parliament, to mentor scholars at a JAIS workshop on policy impact, and to publish a policy editorial made the societal relevance of my work tangible. Developing a course at LSE on managing AI further shifted my focus toward designing responsible institutions with digital technologies. Together, these experiences shaped my identity beyond what writing papers alone could.

Right now, I’m most excited about projects with my PhD students on how inclusive institutions shape AI innovation. By inclusive institutions, we mean policies and governance structures that protect broad societal interests, such as privacy regulation in the EU/US or open data initiatives in China. Using patent and contract data, we study how institutional choices redirect the trajectory of AI innovation. What makes them especially meaningful is the collaboration itself. Working closely with doctoral students creates a space where ideas evolve through dialogue, debate, and shared problem-solving. Substantively, the work aligns with my broader interest in responsible digital innovation. I also hope it encourages our field to engage more deeply with institutional and policy-level questions about how innovation is shaped, not just how it performs.

First, be patient with your growth. Strong research, thoughtful teaching, and meaningful service take time. Early career can feel like a race, but chasing speed rarely builds depth. Focus on doing work you are proud of, not just work that is fast.

Second, build coherence. Your agenda does not need to be narrow, but it should be anchored in questions that genuinely matter to you. When opportunities arise, ask yourself whether they strengthen that core or distract from it.

Finally, treat academia as a community, not just a publication pipeline. Review carefully. Mentor generously. Show up for others. The field is shaped not only by what we publish, but by how we contribute to one another’s growth.

Randy Wong
University of Auckland

Randy Wong is a Lecturer (Assistant Professor) in Information Systems at the University of Auckland. Her research focuses on the societal and ethical implications of digital technologies. She is an active member of the AIS community and values the supportive network it provides. She is honored to receive the AIS Early Career Award and grateful to the AIS community for its role in shaping her academic journey.

Receiving the AIS Early Career Award was honestly a surprise to me. I remember hearing about it when I was a PhD student and thinking of it as something distant, something that belonged to scholars I admired. To be nominated and receive it feels both unexpected and extremely meaningful.
At this stage of my career, the award feels like reassurance and encouragement. After graduation, building my own research identity came with uncertainty. This recognition encourages me by affirming that the work I have been doing has contributed.

More than anything, I see this award as a reflection of collective support from mentors, collaborators, students, and colleagues. It is not an arrival point, but momentum, and a reminder to continue contributing with care and purpose.

The early-career stage has been both exciting and demanding. One of the biggest challenges was transitioning from being guided as a PhD student to building my own identity as an independent researcher. As a faculty member, you are expected not only to develop your own research program but also to mentor others. Stepping into that role while still refining my own research voice required a shift in mindset and learning to trust my judgment.
Moving to a new university also meant adapting to different systems and expectations while balancing research, service, and teaching. There were moments of doubt, especially during difficult revision cycles and heavy workload. What helped most was mentorship and perspective. Over time, I learned that steady persistence and long-term focus matter more than immediate outcomes.

I am still learning myself, but one thing I have been reflecting on is how quickly our landscape is changing, especially with AI now able to do things that once took us much longer. It makes me think about the importance of intellectual identity: the kinds of questions we care about and the perspective we uniquely bring. Tools and methods will continue to evolve, but clarity about that identity can provide direction.

I have also found that working on topics that interest me helps sustain motivation over time. Research can move slowly, and progress is not always immediately visible. Patience and long-term thinking seem important.

Collaboration and simple kindness have also shaped my experience in meaningful ways. Some of the most rewarding work has come from shared thinking and mutual trust. The people we meet along the way often become long-term collaborators, colleagues, or unexpected sources of support.

The most rewarding part of my time as an AIS volunteer has been the sense of community. Being involved behind the scenes allowed me to see how much care and quiet dedication sustain our field. Conferences and journals may appear seamless, but they are made possible by many people who generously give their time and energy.

Working with program teams, AIS colleagues, review coordinators, and student volunteers has been especially meaningful. I have seen their patience, responsibility, and willingness to step in whenever needed. Those shared efforts, sometimes late nights, sometimes small details, created a strong sense of trust and teamwork.

These experiences made me feel more connected to AIS, not just as a researcher, but as part of a community that supports one another. That sense of belonging has been the most rewarding part for me.

One of my favorite memories at AIS events is something quite simple: reconnecting with old friends and meeting new ones. Over time, I have come to appreciate how conferences feel less like formal gatherings and more like a community coming together. Between sessions, over coffee, or during small conversations, you hear about each other’s progress, new ideas, and even personal milestones.

After becoming more involved behind the scenes, I also started to notice the quiet dedication of those who make these events possible. Sitting in a ceremony and realizing how many people contribute behind the scenes felt unexpectedly moving. Those shared moments of connection and gratitude are what I treasure most.

Marta Stelmaszak Rosa
University of Massachusetts Amherst

Marta Stelmaszak Rosa is an Assistant Professor of Information Systems at the Isenberg School of Management, University of Massachusetts Amherst. Her research examines digital data in organizations, with a focus on their innovation, responsibility, and value. Her work has appeared, among others, in MIS Quarterly, Information Systems Research, the Journal of the Association for Information Systems, and the Journal of Management Studies. She co-leads the Data Studies Bibliography, an international community advancing scholarship on data as an object of inquiry.

Receiving the AIS Early Career Award is a very meaningful acknowledgment of the work I have been developing around digital data across research, teaching, and service. Since the beginning of my career, I have focused almost entirely on this topic, guided by a conviction that understanding data is one of the central questions in our discipline. It is incredibly affirming to see that engagement with one core area, despite the allure of many other directions, can bear fruit! At this stage of my career, the award strengthens my commitment to this research trajectory and I hope it encourages other early career scholars to pursue focused research programs with persistence.

Like many early career scholars, I began with a relatively narrow network of collaborators. What helped was being proactive in building relationships: reaching out, offering friendly reads, and contributing where I could. Another challenge was staying focused amid competing demands on time and attention. Developing a structured research practice has been essential for me to protect research time. Finally, building a coherent scholarly identity required alignment by ensuring that my research, teaching, and service reinforced one another instead of pulling in different directions.

Rather than a single turning point, my scholarly interest has grown out of a lifelong curiosity about data. As a teenager, I had the opportunity to witness how databases are developed, and I became fascinated by the structures that organize information. That curiosity has stayed with me and continues to energize me in my work. I have, however, experienced a defining shift when I learned how to turn curiosity into a disciplined research practice through the guidance of my generous mentors. Their advice helped me transform puzzling questions into a scholarly identity.

First, seek out mentorship. Mentors can offer complementary perspectives on a range of issues, from practical career navigation to long-term intellectual trajectory. I have benefitted immensely from their advice. Second, invest in building a strong network of peer relationships: research is both intellectually richer and more rewarding when done with others, be it as collaborators, friendly readers, or accountability partners. Finally, develop a scholarly practice that prioritizes research time and enables depth. Protecting space for thinking allows you to pursue the questions you are genuinely passionate about.

I think it is essential to cultivate at least one source of joy outside of work. For me, that means activities that are embodied rather than cognitive. I’m a keen runner, and I also enjoy creative hobbies such as painting by numbers or building miniature book nooks. I’m not particularly skilled at any of them, but the point is to create space for activities that feel playful and tangible. This helps me return to research refueled, while maintaining a fulfilling life outside of academia.

Jiaheng Xie
University of Delaware

Jiaheng Xie is an Assistant Professor of MIS at the University of Delaware’s Alfred Lerner College of Business and Economics, specializing in interpretable AI, deep learning, and generative AI with applications in health risk and business analytics.

Receiving the AIS Early Career Award is a deeply humbling milestone. As I transition into a more established researcher role, this recognition validates the revisions and ideas that characterize my post-PhD journey. It reinforces my commitment to advancing interpretable AI and health analytics, affirming that my work resonates within the IS community. Most importantly, it highlights the incredible support I’ve received from my mentors, co-authors, and the University of Delaware.

A major challenge was balancing the high-risk nature of methodological research with rigorous tenure publication timelines. Developing novel interpretable deep learning models for complex health problems requires extensive technical validation and time to articulate IS contributions. I navigated this by working with a network of collaborators with complementary strengths. Leaning on senior mentors for strategic advice also helped structure my pipeline effectively.

I am particularly excited about my forthcoming paper in Information Systems Research, “Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model.” With platforms like TikTok soaring in popularity, there are well-founded concerns regarding their impact on youth mental health. To address this, we developed a knowledge-guided neural topic model that incorporates established medical knowledge to predict a video’s potential to induce suicidal thoughts at the exact time of upload. This project is especially meaningful—and recently garnered significant media attention—because of its direct real-world implications. It advances methodological boundaries while equipping platforms with a proactive, practical tool to moderate harmful content and protect vulnerable viewers before harm occurs.

A turning point was when I applied deep learning to healthcare challenges. Initially, my focus was primarily on improving predictive accuracy. However, engaging with healthcare professionals revealed a critical gap: predictive power means little in high-stakes clinical settings without transparency. This realization pivoted my scholarly identity toward interpretable AI. I committed to designing models that predict outcomes while explaining the “why” behind them, making human-centered AI the cornerstone of my research.

Outside of academia, I enjoy spending time outdoors, whether hiking, traveling, or taking a long walk to clear my head.

  • Dominik Gutt, Erasmus University
  • Hyeokkoo Kwon, Nanyang Technological University
  • Tapani Rinta-Kahila, University of Queensland
  • Dandan Qiao, National University of Singapore
  • Lusi Yang, Georgia State University
  • Mingwen Yang, University of Washington
  • Carolina Alves de Lima Salge, University of Georgia
  • Yue Katherine Feng, Hong Kong Polytechnic University
  • Dongwon Lee, The Hong Kong University of Science and Technology
  • Chen Liang, University of Connecticut
  • Warut Khern-am-nuai, McGill University
  • Jingchuan Pu, University of Florida
  • Van-Hau Trieu, Deakin University
  • Melody Zou, University of Warwick
  • Sofia Bapna
  • Tommy K. H. Chan
  • Yong Jin
  • Yu-Kai Lin
  • Harris Kyriakou
  • Sagar Samtani
  • Panagiotis Adamopoulos, Emory University
  • Taha Havakhor, Temple University
  • Zach Lee, Durham University
  • Yixin Lu, The George Washington University 
  • Yenni Tim, University of New South Wales Sydney
  • Lior Zalmanson, Tel Aviv University
  • Zachary Steelman, University of Arkansas
  • Vilma Todri, Emory University
  • Nina Huang, University of Houston
  • Matthias Soellner, University of Kassel
  • Markus Salo, University of Jyväskylä
  • John Dong, University of Groningen
  • Marten Risius, University of Queensland
  • Christian Maier, University of Bamberg
  • Gene Lee, University of British Columbia
  • Hailiang Chen, The University of Hong Kong
  • Keongtae Kim, Chinese University of Hong Kong
  • Liangfei Qiu, University of Florida
  • Miguel Godinho de Matos, Universidade Catolica Portuguesa
  • Till Winkler, Copenhagen Business School
  • Xiao Xiao, Copenhagen Business School
  • Brad Greenwood, University of Minnesota
  • Kevin Hong, Arizona State University
  • Thomas Kude, ESSEC Business School
  • Mari-Klara Stein, Copenhagen Business School
  • Lynn Wu, University of Pennsylvania
  • David (Jingjun) Xu, City University of Hong Kong
  • Gordon Burtch, University of Minnesota
  • Jeffrey Jenkins, Brigham Young University
  • Aaron Baird, Georgia State University
  • Jaime Windeler, University of Cincinnati
  • Alvin Leung, City University of Hong Kong
  • Robert Gregory, IESE Business School
  • Michelle Carter, Washington State University
  • Daniel Schlagwein, UNSW Australia
  • John Tripp, Baylor University
  • Greg MoodyUniversity of Nevada Las Vega
  • James GaskinBrigham Young University
  • Jennifer GerowVirginia Military Institute 
  • Attila MartonCopenhagen Business School
  • Ning SuUniversity of Western Ontario 

AIS Early Career Award Committee

The Early Career Award committee is comprised of AIS members as established in the Council Policy Manual.

The Selection Process

A description of the nomination and selection process can be found in Council Policy. To submit a nomination for this award, please use the online nomination form that will be available until October 7.