Writing Academic Papers

Stop having opinions. Start having hypotheses. Everyone in business has opinions. But the best executives work with hypotheses. An opinion is a point of view, sometimes even backed by data. “We’re losing customers because we’re more expensive.” It sounds logical. You check the numbers, and yes, you are more expensive. But that doesn’t prove that price is the reason they leave. That’s just a correlation. A hypothesis, on the other hand, is testable. “If price is really driving churn, then the customers who pay the highest premium should be the ones leaving fastest.” See the difference? The opinion describes the symptom. The hypothesis seeks the cause and tells you what data will confirm or kill your assumption. That’s what makes hypothesis thinking so powerful: It forces you to move to test. From “I think” to “let’s check.” From debating opinions to discovering truth. Here’s how to apply it like a master 1. Start every discussion with an “if–then.” “If X is true, then we should observe Y.” It makes your thinking structured and measurable. 2. Define what would make you change your mind. Don’t just say “we’ll look at data.” Be specific about what evidence would disprove your idea. 3. Refine fast. Good consultants don’t cling to their first hypothesis. They update it every time new facts appear. In short: Opinions sound smart. Hypotheses make you smarter.

How great hypothesis help you learn fast, and pick the right path. At Moonpig, I’ve been thinking a lot about how we can make testing more purposeful — not just to move metrics and see what happened, but to deeply understand customer behaviour, and why customers do what they do. So I recently introduced a 💡 Hypothesis Framework 💡 to help our product teams write better, bolder hypotheses, that help us understand the most important things about our customers. The goal? 1️⃣ Learn faster (through various means, both quant and qual) 2️⃣ Take bigger (but smarter) risks where it matters most 3️⃣ Focus on testing to learn, not just to prove 4️⃣ And shift from vague test ideas to more opinionated bets that help us pick a direction We want teams to ask themselves: “What would we learn about our customers if this fails?” and “what will I do differently based on the outcome of this test?” Because every test should move us forward — regardless of outcome… meaning we never ‘fail’, we always learn. The hypothesis canvas I created guides teams to define... 🧠 What they believe to be true about why our customers do what they do 🧪 How they will validate their assumption quickly (either through research, or testing) 📈 What they will measure (thinking about leading UX metrics such as engagement or clicks) 🤩 How they’ll know they are right (what % users need to agree or exhibit the desired behaviour) 🗺️ What they’ll do next based on what they have learned Finally, it’s important to remember that hypothesis aren’t created after you come up with the idea, they are created before. Hypothesis are tools to help you generate ideas to prove your assumptions true or false, ways to learn about your customers, not just to prove that your idea worked. #ProductDesign #ProductManagement #Experimentation #HypothesisDrivenDesign #Moonpig #innovation #uxdesign

When you’re building something new, it’s easy to confuse validation with resonance, and even easier to confuse resonance with truth. You talk to a few people, see some heads nod, hear a “yes, I’d use that,” and take it as confirmation. But often, what you’re getting isn’t problem validation; it’s bias validation. You’ve introduced your idea, framed the problem in your terms, and received affirmation that sounds like alignment, but really, you’ve just validated your own framing. This has been top of mind for me lately. I’m in the thick of building, and I know how easy it is to let personal experience harden into certainty. A strong hypothesis can quickly become a strong bias. So I’ve been staying open, listening more and questioning more. The key is to ask: Are we learning from users, or are we leading them? Most people don’t intend to mislead. If you pitch them your product, they’ll often affirm it because you’ve set the terms. If you describe a problem compellingly, they’ll agree, not because that’s how it shows up for them, but because you’ve primed them to see it that way. This is why the early stages of discovery matter. Before introducing your idea, you need to understand their context. What are their workflows like now? How do they describe their struggles, in their own words? What’s frustrating? What’s working? What have they already tried? Your goal is not to validate your pitch; it’s to understand the shape of the problem in their world. That means asking open, neutral questions. Use the first 10–15 minutes of any conversation to get clean signals — how someone actually behaves, not how they respond to a concept. Once I understand what’s true for them, then I introduce a hypothesis and observe how it lands. Even when they agree, I dig deeper. I ask: Why does that resonate with you? When has this shown up for you? What did you do the last time that happened? What was difficult about it? I’m not looking for approval. I’m looking for behavioural evidence. This is the key difference between feedback and insight. Feedback is surface-level. Insight requires digging; asking the same thing in different ways, noticing inconsistencies, listening for what’s missing. The “five whys” technique is a simple yet useful approach. Keep peeling until you get to something that holds weight across contexts. That’s when something better can emerge...and when it works, it’s worth it. By sitting with the problem longer, holding our assumptions more loosely, and following the signal wherever it leads, I’ve never felt more grounded in our direction than I do now. The clarity we’ve found didn’t come from getting louder; it came from listening better.

A few of my PhD students are nearing completion, and I wanted to share a few themes we're focusing on: • Tell a story: It’s not just about raw data. Interpret, engage, and pan out to demonstrate the wider significance of your findings. • Be concise: Lengthy theses aren’t necessary. Focus on clear communication. Edit to remove, say, 10% of the words, whilst aiming to convey the same message. • Abstracts: In 300 words, highlight your findings and methodology—not background info. • Method vs. Methodology: Method explains how, methodology explains why. Be clear about what you did, why you did it, and your approach's limitations. • Use visuals: Diagrams help to explain complex ideas quickly. • Conclusions: Treat them like an extended abstract—state your findings, then zoom out to show their broader impact. You must reflect on what the data tells us, and what it all means. Extrapolate. • Plan for time: Completing the thesis takes longer than expected, so allow for contingencies. Your supervisors might need 3-4 weeks to read and comment on the final draft; the examiners might need 8 weeks before the viva. Before you know it, 3 months have passed - and this is after you've submitted and before any corrections. As always - It’s a privilege to witness the brilliant minds behind these exciting projects and discoveries!

When a business grows rapidly, the cracks in your processes start to show. That’s exactly what happened to us As our team scaled, it became clear: not everyone understood the hypothesis-generation process in the same way. This caused confusion, inconsistent problem-solving, and slowed down decision-making So, we developed a clear format to align everyone, newcomers and veterans alike, around structured, high-impact hypotheses. It starts with identifying the bottleneck In ecommerce, this might mean noticing that users drop off before completing a purchase The first instinct? "Add trust badges at checkout" But that’s too vague Is the real issue trust? A confusing checkout? Delivery costs? We learned to dig deeper: Problem: Low checkout conversion because users lack trust Action: Add trust badges (e.g., privacy policy, money-back guarantees) Expected result: Increase conversion from 20% to 40% 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 + 𝗔𝗰𝘁𝗶𝗼𝗻 + 𝗘𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝗥𝗲𝘀𝘂𝗹𝘁 This structure keeps our hypotheses focused and testable We prioritize using the ICE framework (Impact, Confidence, Ease). Doesn’t matter if we sum or multiply the values; the important part is consistent prioritization Then, we hold regular meetings: 1) Prepare hypotheses with a defined problem and goal 2) Refine and discuss existing ideas 3) Only brainstorm new ones when we’ve addressed the current list The result? A ready-to-implement hypothesis that’s documented from start to finish. This documentation becomes gold when reviewing what worked and what didn’t Fast growth demands clarity. Rebuilding internal processes isn’t just helpful, it’s necessary What’s your go-to method for hypothesis generation?

You're not bad at academic writing.  You just don't have a system. Everyone says "read more papers, write more often." But nobody shows you how to actually improve. Here's how: 1. Find your accountability partner  → You don't get extra points for struggling alone.  → Find someone who writes well and will give you real feedback. 2. Identify your weak spots → Don't try to fix "bad writing."  → Fix concrete things: Is your challenge structure, flow, clarity, or vocabulary?  → You can’t fix what you can’t name. 3. Read good papers AND bad papers → Good papers show you what works.  → Bad papers teach you what to avoid.  → Study how they structure arguments, not just what they say. 4. Read beyond your field → Reading academic papers alone won't teach you writing craft.  → Read actual books on writing, blog posts, and articles.  → Great writing anywhere teaches clarity everywhere. 5. Write every single day → 15 minutes minimum.  → A short reflection, a random thought, a summary of anything.  → Writing fluency comes from repetition. 6. Translate your research for non-experts  → If you can't explain it simply, you don't understand it well enough.  → Write blog posts or LinkedIn articles about your work. 7. Stop editing while you draft  → First draft = get ideas down.  → Second draft = make it good.  → Third draft = polish.  → Trying to be perfect while writing first draft kills momentum. 8. Get feedback early and often  → Waiting for "complete" drafts slows your growth.  → Share rough paragraphs and messy outlines. → Fast feedback beats slow perfection every time. Writing isn't a talent you're born with. Every great academic writer you admire once wrote terrible first drafts too. The difference is they kept writing. If you're struggling right now, don't be too hard on yourself. Follow these steps. Read → Write → Feedback → Reflect → Iterate PS: What helped you improve your academic writing? _____ (🔁) REPOST. Someone in your network needs this. 

You know that sinking feeling… Someone interrupts your carefully prepared presentation with “But what about...?” and raises a point you never considered. Everyone is looking at you, and you feel the weight of the world on your shoulders. In that moment, the idea or solution you’ve been presenting weighs in the balance. Address the resistance well, and your idea will likely be adopted with even more optimism than before. Address it poorly, and your idea is as good as gone. Here’s a quick overview of my “RAP” formula that you can use in these moments to turn blindside objections into “aha” moments. 1. R: Recognize the type of resistance you’re facing: - Logical resistance (conflicting data or reasoning) - Emotional resistance (values or identity challenges) - Practical resistance (implementation concerns) 2. A: Address it proactively in your presentation: - For logical resistance: Acknowledge competing viewpoints before they’re raised. "Some might point to last quarter’s numbers as evidence against this approach. Here’s why that perspective is incomplete..." - For emotional resistance: Connect your idea to their existing values. "This initiative actually strengthens our commitment to customer-first thinking by..." - For practical resistance: Demonstrate you’ve considered the real-world constraints. "I know this requires significant change. Here’s our phased implementation plan that accounts for..." 3. P: Provide a path forward that transforms resistance into alignment: - Give them space to voice concerns (but in a structured way) - Incorporate their perspective into the solution - Show how addressing their resistance actually strengthens the outcome The most powerful thing you can say in a presentation isn’t "trust me", it’s "I understand your concerns." When you genuinely see resistance as valuable feedback rather than an obstacle, you’ll find your ideas gaining traction where they previously stalled. #CommunicationSkills #BusinessCommunication #PresentationSkills

You're wasting your time submitting research without a strategic approach. Yes, there are millions of academic submissions to journals each year, which means there is a lot of potential publication opportunity for you to get. But over the last decade, 95% of research papers submitted to top-tier journals get rejected. Chances are, you are going to be in the 95% rejection bucket. So, does this mean you shouldn't pursue academic publishing? No, it just means you are doing it wrong. The old way of academic publishing, which is what most researchers focus on is... - They write traditional papers using conventional methodologies. - They submit without understanding journal-specific requirements. - They hope for the best. That's why most researchers don't succeed in getting published. That model doesn't work. Academic publishing is valuable and worth doing ONLY IF you do it right. What's the right way? Here are 7 proven steps: 1. Select the right journal Target journals specifically aligned with your research domain and impact factor. 2. Understand submission guidelines Meticulously review and follow every single journal requirement. 3. Craft a compelling title and abstract Your first impression matters - make it crisp, clear, and captivating. 4. Develop rigorous methodology Ensure your research methodology is transparent, replicable, and innovative. 5. Present clean, structured data Use impactful visualizations and statistical analyses that tell a clear story. 6. Write with academic precision Maintain professional language, eliminate grammatical errors, and follow citation standards. 7. Prepare for peer review Anticipate potential questions, be open to constructive feedback, and be ready to revise. Pro Tip: Remember, persistence is key. Even renowned researchers face initial rejections. Have you successfully published in a top-tier journal? What was your biggest challenge? #Research #Science #Scientist #Publishing #Professor #PhD #postdoc #postgraduate #ChemicalEngineering

The presentation isn't over when the slides stop. It's over when you survive the Q&A. Weeks of effort can crumble if you can't defend your work. Confidence is not a feeling — it's the byproduct of preparation. Instead of hoping for an easy Q&A, use an LLM to get ready for battle. Here's how: 1. Profile the Room: Use the LLM to map stakeholder fears and generate their "killer questions." Direct the LLM to adopt the persona of the most skeptical VP in the audience. 2. Conduct the Pre-Mortem: Assume failure and force the LLM to find the fatal flaws in your logic. Command the AI to focus on execution risk and overlooked data biases. 3. Run the Roleplay Loop: Simulate the fight. Practice delivering concise, data-backed answers under pressure. Demand the LLM pushes back aggressively until your answers are irrefutable and under 30 seconds. Get the exact LLM prompts to bulletproof your next presentation here: https://bit.ly/4a3ymNg Prepare for war so you can present in peace. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Make writing a proposal for research funding easy. Here is how. There is a tendency to rapidly begin filling in the parts of the application form as soon as possible. With a deadline looming, I used to ask all the partners in a consortium project to state filling in their work packages right away after the first meeting. I had a sooner the better mentality. My plan would be that once we had work packages written I would piece them together. The result. Frankenstein projects. Work packages that did not align, and objectives that sounded like they were each describing different projects. It was a writing nightmare. I was trying sew different ideas together. Reviewers see stitches. Like a good scientific paper, a funding proposal has to have a good logical flow. I now realize that the panicked approach I took previously to funding proposal development is not how to do it. It is much better to be 100% certain of the concept. Then write. For some projects this happens very quickly. Other projects take much more time. Sometimes what you are aiming to do is just complicated and full of uncertainties. Take that time. For scientific papers an outline works. For funding proposals the first step is to get all those involved aligned on the concept. This is not to say you don't write anything at all. To the contrary writing is a way to think. But you need to build up the layers. 1️⃣ Describe the problem and what you will do on a high level. 2️⃣ Then the impacts, outcomes and outputs you intend to have 3️⃣ Then the methods. ➡️ Methods are where you often uncover subtleties and problems that were not apparent at first. You need to solve those problems and the accompanying doubts before you can really begin to write. 4️⃣ Then you can build a project plan. Not before. "Give me six hours to chop down a tree and I will spend the first four sharpening the axe." -Abraham Lincoln Take the time to get the concept right, then write.