Two stories that often get blended
A familiar story says that email grew until it became unmanageable, and that workplace messaging apps arrived as the fix. A second, more recent story says that chat tools produced a new kind of overload of their own. Both have a grain of truth, but both are usually told with confident numbers that deserve a closer look.
This article is about how claims on message volume are gathered and what they tend to leave out. It does not argue that one tool is better than another.
What “overload” can mean
Overload is not a single quantity. Researchers and commentators typically use it to mean one or more of the following:
- Volume. The number of messages received or sent.
- Time. How many hours go on reading and replying.
- Interruption. How often attention is broken by notifications.
- Expectation. How quickly a reply is assumed to be due.
- Relevance. How many messages are unnecessary or poorly targeted.
A large inbox of automated notifications can be less taxing than a small number of urgent, ambiguous requests. A count of messages alone says little about which kind of load a person experiences.
Where the figures come from
System logs. Company email servers and chat platforms record message counts, timestamps and sometimes response times. This is precise for what it covers. It does not cover communication on other channels, and it generally cannot tell which messages mattered. Access to such data is limited, so published analyses often rely on particular firms or small groups.
Surveys. People are asked how many emails they receive, how long they spend on them, or whether they feel overwhelmed. Estimates from memory are imprecise, and people often report how they feel instead of what happened. A survey that asks about feelings and a log that records counts can legitimately disagree.
Diary and time-use studies. Participants record what they do through the day. This can show how email and chat fit with other tasks, but samples are usually small and recording itself can change behaviour.
Vendor reports. Software companies publish usage statistics about their own products. These may be genuine, but they describe users of that product, and the publisher has an interest in the story they tell. Platform data from a single provider says little about workers who use other tools.
What message counts leave out
Message type. Email includes newsletters, automated alerts and copied-in messages, while chat includes quick replies and reactions. Counting them equally treats a one-word acknowledgement the same as a detailed proposal.
Who is counted. Averages hide wide differences between roles. Managers, customer-facing staff and technical specialists often experience communication very differently, and an average may describe nobody well. This is the kind of mistake the ecological fallacy warns about: group-level patterns do not necessarily describe individuals.
Time of day. Messages sent in the evening or at weekends may matter more to how workers feel than the same number sent mid-morning, but simple totals do not distinguish them.
Substitution. If a message that once went by email now arrives in chat, the total volume could remain about the same while the channel changes. Studies that look at only one tool can wrongly suggest a rise or fall.
Meetings and calls. Some communication shifts to video calls or to hallway conversation, which logs rarely capture.
Why the move to messaging is hard to evaluate
Messaging apps are often described as faster and more conversational. They also tend to produce many short exchanges, along with notifications that can pull attention away from other work. Whether this lowers or raises the burden depends on how teams use the tools, which is a matter of norms as much as software.
Assessing the move runs into familiar evidence problems:
- Before-and-after comparisons. Teams that adopted chat at a certain time may also have changed their working patterns, staffing or location, so confounding variables make it difficult to isolate the tool’s effect.
- Selection. Organisations that move to messaging are not a random sample. Early adopters may be more tech-oriented or have different communication needs.
- Self-assessment. Workers who say chat helps them may be reflecting a preference for the format or an improved sense of connection, rather than less total communication load.
- Moving targets. Both email and messaging tools keep adding features such as summaries, filters and scheduled sending, so a study from a few years ago may describe different software.
How people feel versus what is measured
Perceived overload can rise even when measured volume is steady, and the reverse is also possible. Feeling overloaded may relate to unclear priorities, constant availability expectations, or being included in too many threads, rather than raw counts. Researchers who want to understand experience often combine logs with interviews or short surveys for this reason.
It is also worth noting that complaints about email have a long history, and people tend to remember the present burden more vividly than past ones. That does not mean the concern is unfounded; it means that “more overloaded than ever” is difficult to confirm without comparable measures over time.
Reading a claim about message volume
- Does the figure come from logs, surveys or a vendor, and who is covered?
- Is the claim about quantity, time spent, interruptions or feelings?
- Does the comparison account for messages that moved between channels?
- Is an average being used to describe people whose roles differ widely?
- Could another change, such as team size or working location, explain the difference?
Overload is real enough as an experience for many workers, but the numbers used to describe it are narrower than they sound. Treating them as partial views, each with its own gaps, is a safer approach than treating any single figure as the answer.