1. No onboarding, having to piece together all the info
How do the printers work? What is FAIR data? For most of us, there was no comprehensive onboarding that'd explain how the academic environment works
💡Tip: team up with a more senior PhD student to get some lifehacks
2. Feeling frustrated, lost, lacking progress
Most of us have to define the project ourselves. An overwhelming amount of information and no clear "rules of the game" take their toll
💡Tip: narrow down, define your niche, read the best articles from your field, get feedback early
3. Ghosting supervisors
Ever felt ignored by your supervisors? Didn't see them for weeks? Unfortunately, a ghosting supervisor is a reality for >50% of us
💡Tip: try planning weekly short meetings (max 15 mins), don't skip them (or reschedule if your supervisor doesn't show up)
4. Limited/not constructive feedback
Sometimes it seems like scholars are taught to be harsh on each other
💡Tip: supervisors: ask to use a certain feedback framework (e.g., McKinsey feedback model); reviewers: accept it, reflect on it, embrace your feelings
5. Feeling like a failure when experiments don't go as planned
Data didn't support the hypotheses? An experiment failed? Many fall into a trap of associating their worth with research output
💡Tip: remember that you are not your research, discuss your experience with colleagues
How do you deal with these? Any strategies, tools, or exercises that help you get through? 👇
• • •
Missing some Tweet in this thread? You can try to
force a refresh
1⃣ Data challenges: new privacy regulations, no access to third-party cookies, and new post-covid advertising models
Some research questions to look at:
• What business models can help companies adapt to changing privacy policies?
• What available data (e.g., search data) should firms use to improve targeting?
• Can firms optimally combine pre and post covid advertising models?