The UX of why people hate their new job after three months

a row of doors

My first-day advice to new members of the team boils down to, “Don’t worry, you are going to be totally miserable in about three months.”

We all know the excitement of that first day at work. There are new people to meet, new projects to learn and new roles and responsibilities to master. Part of the intoxication of those early weeks is they are often the only time in our adult lives we are allowed to ask stupid questions and not have to pretend to know everything.

And despite that opportunity our learning plan is often simply, “Give me a few weeks to get up to speed and I will own the place.” That approach boosts our short-term confidence but also sets unrealistic expectations for our ability to quickly understand and master the complexity of the organization and its needs.

In UX research, we think a lot about mental models – how a user believes a process works v.s. the system model of how it was actually designed; and framing – the context in which an interaction is interpreted. In essence, your satisfaction within an experience can depend a lot on your initial expectations.

For new employees, the expectation we need to set is that while the initial learning curve is steep, the on-boarding process actually never ends nor does it move in a predictably straight line.

I describe it like this:

The first month is like sitting in a waiting-room. There are lots of doors with people passing through, plenty to read and free coffee if you can figure out how to operate the machine.

And while reading and caffeinating is great, you will soon be excited to pick a door, enter a side room and get down to work. This is a most glorious time. You will start to feel productive, in control, valued and effective.

Then one afternoon, maybe three months later, you will inevitably walk up to another door. Behind it lie some unpleasant new realities. Maybe your first big project missed a deadline and you feel responsible. Or your boss has grown distant since those early days of interviewing, training and goal setting. Or your commute is worse than you thought. Whatever the cause, the honeymoon is over and some early doubts creep in.

Deciding how to open this door is key. Because everyone experiences it. And after months of gaining competence and confidence, you are going to feel a bit miserable realizing you don’t know nearly as much as you thought. How could you have been so mistaken? So self-deluded? So in the wrong career? It can feel like an actual crisis in the moment.

Fortunately, at this point most of us actively re-invest in the job. With effort, the organization reveals new puzzle pieces (system model) and we improve our understanding (mental model) of how to navigate the challenges. We open the door, discover new skills and grow more confident and productive than we had before.

But here is the trick: This cycle repeats itself endlessly. Maybe first at three months, then 18 months, then four years. Each time the door seems heavier and the crisis more dire. Each time you will feel miserable and each time you will wonder if you should go update your LinkedIn profile.

We can’t avoid the doors and we can’t avoid some amount of psychic pain and misery each time. But learning to recognize the pattern is transformative. It can turn a month’s worth of anxious lunch hours lost to checking into a few days of introspection followed by a fulfilling action plan.

The challenge is, people often think their crisis of confidence in these situations is a personal failing. That their impostor syndrome is showing. That they have picked the wrong job or career. This is almost never true. So how do we avoid people falling for that conceit?

Tell them well in advance you know they will find some doors too heavy to open alone. “Don’t worry, you are going to be totally miserable in about three months. But it won’t last – if you see it coming.”

Tell them how you think it might happen and when. Set the expectation so they can recognize the pattern. And then give them a hand when they do. In this case, knowing is half the battle.

The problem with personalization

The challenge of personalizing the news is the heterogeneity of our personal interests and the weakness of the signal we expose to the recommendation engine.

As an example, below are four stories from around the country I was interested in today, for reasons ranging from ‘possibly obvious’ to ‘unknowable’ by an algorithm. And by ‘unknowable’ I mean how likely would a machine be to affirmatively pick any of these four at a very low signal to noise ratio.

The dot-Boston domain is now open
Why am I interested? I was working at the Globe when we originally bid on and won the rights to sell this TLD. (I proposed renaming to com.Boston. Just because.)
Could a machine have affirmatively predicted my interest? No.

Micro-apartments proposed for former mill building on Saco Island
Why am I interested? We have relatives that live in the complex and I spent some time working in the area. Could a machine have affirmatively predicted my interest? Not likely.

NBC moves 130 Premier League games to streaming service
Why am I interested? I am a big fan of the Premier League and watch games on NBC – but found this by random chance at our paper in Sacramento. Could a machine have affirmatively predicted my interest? Yes, I probably leave a wide paper trail on this topic.

Heavy traffic, cellphone service disruption expected in Charleston for total solar eclipse
Why am I interested? We will be in town for the eclipse. Could a machine have affirmatively predicted my interest? If the algorithm knew my calendar, correlated the travel with the geographically-specific eclipse event and put the two together – yes. In the current reality, no.

So what’s the ‘problem’ here? Personalization depends mostly on observed web behaviors. Much of our interest in the news is based on real life experiences and events. So to provide me a list of recommended stories you need to know not what I clicked on last week, but where I lived in 1998 and my level of interest in urban planning issues.

So even though Facebook knows ostensibly everything about me, and Twitter is packed with people I know/trust and rely on for news – neither of those platforms or any other app I am aware of is going to identify those four stories on a given day and surface them in a unified newsfeed.

In fact I will will be suitably impressed if a machine will ever be able to perform at that level of serendipity.  But, if you invent it, I would pay for that convenience as a service.

Product Management and proverbs

I can’t remember the first (or last time) I heard this proverb but I am convinced it is an allegory for product management and technical debt.

For want of a nail, the shoe was lost;
For want of the shoe, the horse was lost;
For want of the horse, the rider was lost;
For want of the rider, the battle was lost;
For want of the battle, the kingdom was lost;
And all for the want of a horseshoe nail

Newspapers are still steam powered

Yesterday, the BBC podcast “50 Things That Made The Modern Economy” covered the invention of the electric dynamo. The episode was partly based on a 1990 academic paper by economist Paul A. David who studies “scientific progress and technical change.

His thesis is that the initial lack of productivity gains related to the introduction of the electricity in factories was the common practice of simply replacing the one centralized steam engine with an electric motor. That approach maintained the disadvantages of steam while bringing none of the value of electricity.

The paper is here but read the excerpt below and mentally find and replace steam/electric with print/digital.

The proximate source of delay in the exploitation of the productivity improvement potential incipient in the dynamo revolution was, in large part, the slow pace of factory electrification.

The latter, in turn, was attributable to the unprofitability of replacing still serviceable manufacturing plants embodying production technologies adapted to the old regime of mechanical power derived from water and steam.

Thus it was the American industries that were enjoying the most rapid expansion in the early twentieth century (tobacco, fabricated metals, transportation equipment and electrical machinery itself) that afforded greatest immediate scope for the construction of new, electrified plants…



Shining a light in Plato’s Product Office

One of my favorite things about ONA is the session pitch. It is a great chance to wonder, “do I actually have anything to say?” but more importantly “what do I want to learn more about?”

The suggestion box opened recently and I am still thinking about this post from October on the challenges of building great products at legacy news organizations.

Here’s the pitch:

Shining a light in Plato’s Product Office

We can all agree – creating great products requires an understanding of and empathy for our users. That is a core tenet of Design Thinking and all Human Centered Design approaches to product development.

But empathy for users (readers, viewers, customers?) is not enough. The products we develop are an emergent property of the people, processes and cultures of our teams. And creating great products in any organization, especially one with a hundred-plus years of history, requires understanding ourselves as much as it does users.

We will talk about Plato’s Cave, empathy, fundamental attribution error, and the unique challenges of shepherding ideas through the complex web of intra-organizational boundaries, stakeholders, silos and workflows that are typical in modern media companies.