What Nobody Tells You Before Starting an IS PhD

The day I started my PhD at UNT I had a fairly clear picture of what the next four years would look like. I would read important papers. I would develop ideas. I would write research. I would become a scholar. This picture was not wrong exactly, but it was missing most of the texture of what it actually feels like to go through a doctoral program. Nobody had described the texture. Or maybe they had, and I had not been ready to hear it.

So here is my attempt to describe it honestly, for whoever is considering this path.

The first year is survival, not research. This is the thing I most wish someone had said plainly. You will spend the first year reading more than you have ever read, attending seminars, adjusting to a new institution, and probably teaching undergraduates for the first time while simultaneously trying to make a good impression on faculty you barely know. Actual research, meaning a question you are developing yourself with a plan to answer it, tends to start later than the program timeline suggests. Some programs have you writing a working paper by the end of year one. In practice, year one is about building the vocabulary. You need to understand the literature well enough to have a research question, and building that understanding takes longer than the syllabus admits.

You will read more than you thought was physically possible. This is not a complaint. It is a factual warning. My first seminar assigned roughly 200 pages of dense IS and management theory per week. A lot of it was genuinely difficult, not because the writing was bad but because the ideas were layered and the debates between authors were invisible to a newcomer. I read papers twice not because I wanted to but because one pass left me unsure what I had read. By year two this gets easier, not because you read faster but because you build a mental map of the literature and each new paper connects to something you already know.

Theory is not the scary thing. Operationalization is. People who have not done this assume theory is the abstract, difficult part and measurement is the straightforward technical execution. My experience is exactly backwards. Reading and understanding structural equation modeling, or why a particular theoretical lens makes sense for your question, is hard but learnable. The truly difficult part is figuring out how to measure what your theory is actually about. How do you operationalize "effective use"? What survey items capture a person's perception of an algorithm's legitimacy? What does "digital transformation" mean precisely enough to build a study around? The gap between the concept and the measurement is where most IS research lives, and it is where most of my intellectual energy has gone.

Reviewers will reject papers for reasons that seem arbitrary, and sometimes the reasons actually are arbitrary. I say this not to be cynical but because the expectation management matters. The peer review process in IS is slow and uneven. You can submit a paper that you and your advisor think is solid, wait six months for a response, and receive a rejection based partly on a misreading of your methods section and partly on a reviewer who wanted you to use a different theoretical lens. This is normal. It has happened to everyone I respect in this field. The response is to revise and resubmit somewhere else, not to conclude you are not capable of doing research. The rejection rate at top IS journals is high enough that even very good work gets rejected regularly.

Your advisor's network matters more than your institution's ranking, at least in some ways. I am at UNT, which is a solid research university but not a top-five IS program in any ranking. What I have found is that the placement outcomes for IS PhD graduates depend a lot on the visibility of the advisor, the quality of the working papers at the time of graduation, and the connections the advisor has to search committees at other schools. A well-connected advisor at a mid-ranked program can open more doors than an inaccessible advisor at an elite one. This is not a universal rule, and I am hedging it deliberately because I have not studied placement data systematically. But it is the consistent story I hear from people who have been through the market.

Around year two or three, something strange happens. You know enough to see how much you do not know. The literature that felt manageable in year one now reveals all its gaps and disputes. You have read enough to know that your initial research question was too vague, that the construct you wanted to study has three competing operationalizations with active debates about which is best, and that the method you planned to use has serious threats to validity that the best papers in the area do not fully solve. This is the valley of despair, and I think it is actually a sign that your training is working. You are becoming sophisticated enough to see the complexity. The risk is concluding that good research is impossible rather than concluding that you need to make careful choices about which problems to work on.

The small wins are what keep you going. A paper accepted at a conference. A faculty member from another university who liked something you said in a seminar. A student who comes to office hours because they actually found your topic interesting. Your committee saying that a draft is "moving in the right direction." None of these are big enough to report to family as achievements. Collectively they are the entire emotional economy of a PhD program, and ignoring them to focus only on the large milestones is a mistake.

What I wish someone had told me before I started is simpler than all of this: the degree changes how you think, not just what you know. I read differently now. I write differently. I ask different questions about things I see in the news, in organizations, in how technology gets described and justified. That change is slow and mostly invisible while it is happening. But it is the actual thing the program is for.