I regularly see both worlds. What I find more troubling in academia is that it is difficult to openly talk about how flawed the system is, that people make mistakes, that papers have mistakes (the own ones as well as others'). We know all software has bugs; but the code that a PhD student hacks together over night is assumed to be flawless (the more senior people rarely even glance at it), otherwise the paper is all wrong, and papers are set in stone... So I genuinely struggle with the lack of a proper failure culture in academia, as it is designed as a system that is fundamentally geared against openly discussing failure.

If we don't admit that there are fundamental problems that affect all of us (academics) and instead pretend it is only the lesser people who f things up, we'll all be screwed sooner or later.

1) Even with stable mean and median, NPS tends to vary month over month, at least for my B2B settings where samples are probably much smaller than for B2C. Then, management goes nuts because of very subtle shifts in the distribution caused by NPS' arbitrary aggregation into promoters, neutrals, detractors. Of course, often investors are married to NPS, so educating management does not solve the problem.

2) NPS varies unreasonably across cultures. We used to say, somewhat tongue-in-cheek, that NPS is a US-centric metric, where things are either amazing or awful (with little space in between). E.g., in northern/central Europe, an 8 can be pretty amazing.

Science is, obviously, not a "monotonic" process in which every single paper adds to the truth; this is practically not even the case for mathematics, which is at least monotonic on object-level (but mistakes happen all the time). As a prominent example, consider this impressive list of Feynman errata: https://www.feynmanlectures.caltech.edu/info/flp_errata.html.

That's not the case for the people who are in the key phase of establishing themselves as academics and who are generally not on permanent contracts. And for tenured staff, performance decides access to resources that may be as mundane as one's own research time. Conversely, the problem is a mix of extremely shallow performance metrics and a paper-pusher system in which most people get bogged down by bureaucracy by the time they get tenure (or even earlier). This means that very few people have the peace of mind that allows them to focus on high-risk fundamental research. In European AI and CS, I would claim that the ones who manage to maintain focus essentially do this in their free time and get little reward for this; the alternative, i.e., building a strong franchise, is more rewarding in terms of performance metrics.