How can researchers capture the daily emotions and decisions that shape family firms?

Most of what we know about family firms comes from snapshots. A survey filled in once. Archival accounts pulled together years after the fact. A founder interviewed about a handover that happened a decade ago. These approaches built the field, and they did real work. They also share a blind spot: they cannot watch the firm as it actually runs, hour by hour, where tempers rise, a decision gets made before lunch, and a family heir and a long-serving non-family manager walk away from the same meeting with opposite readings of what just happened.

The authors describe a method they call ESME, short for the event-sampling method with experimental design. The principle is easy to state. Run small, controlled experiments inside the ordinary workday, then measure how people react — again and again, across roughly two weeks. It is a tool built for researchers, but the questions it can answer are ones that owners and advisors wrestle with constantly and rarely get clean answers to.

What We Studied

The paper is conceptual rather than empirical. The authors are not reporting a dataset; they are making the case for a method and showing how to run it without falling into the usual traps. ESME builds on experience sampling, a technique psychologists have used since the late 1970s to capture experiences as they happen rather than relying on memory. What ESME adds is the experiment: participants receive deliberately designed prompts woven into their working day.

The mechanics are straightforward. Over the study, each person encounters both an experimental condition — say, recalling a moment of belonging to the owning family — and a neutral control condition, delivered on different days. Short follow-up surveys then record their thoughts, mood and behaviour. Because everyone passes through both conditions, each participant serves as their own comparison. That single design choice strips out the noise of individual temperament: the difference between an experimental day and a control day cannot be explained away as one person simply being more cheerful or more anxious than another.

Researchers can distribute those conditions in different ways, and the choice carries consequences. A grouped approach clusters all the experimental days together, which is useful when the question is whether an effect builds up or wears off over repeated exposure. A daily approach alternates or randomises conditions instead, which guards against day-of-week patterns and against the risk that a participant who drops out halfway leaves behind unusable data. The authors are candid that the grouped design is more exposed to attrition, since an early departure can mean a person never experienced both conditions at all.

Key Insights

A laboratory that travels to the workplace

The real innovation is that ESME splits the difference between the lab and the field. Lab experiments offer tight control but little realism; people behave differently when nothing is at stake. Field studies have the stakes but surrender control. ESME keeps participants in their normal workday while the researcher decides when, and to whom, the experimental prompt arrives. The interruption is mild and brief, designed to resemble the kinds of small psychological events — a reminder, a reflection, a passing slight — that happen to workers anyway.

Small samples, surprisingly large datasets

Family firms are typically small, private and wary of outsiders, which has long made them hard to study at scale. ESME sidesteps the problem through repetition. The authors give a concrete illustration: persuade five firms to contribute ten employees each, survey those fifty people three times a day for ten working days, and you generate roughly 1,500 datapoints. That is enough to support the kind of sophisticated multilevel analysis that a one-time survey of fifty people could never sustain. The burden on any single participant stays light — individual surveys may take only three or four minutes — even as the dataset grows rich.

A handful of cooperative firms can yield statistically serious research, which lowers the barrier that has kept so much family business inquiry locked in case studies.

Settling the horse-or-cart question

Cross-sectional surveys are stuck with correlation. ESME can probe causation. Because a prompt can be delivered in the morning and its effect measured in the afternoon, the independent and dependent variables are separated in time. Researchers can then test whether A drives B or whether the relationship in fact runs the other way, comparing how well each direction fits the data. The authors frame this as addressing the proverbial horse-or-cart problem, and it is a genuine advance for a field that has often had to assume the direction of its own arrows.

Where family and non-family part ways

The design is built for the question that sits at the centre of family business research: do family and non-family employees experience the firm differently? In a split-block variation, half the participants switch conditions at the midpoint, letting researchers see whether priming a sense of family belonging lands differently on a relative than on a hired manager. The authors suggest belonging itself may act as a boundary condition — a manipulation that moves family members while leaving outsiders untouched, or one that only reaches non-family staff after several days of repeated exposure. What the field has long assumed becomes something it can measure.

Takeaways

For researchers weighing this design, the authors are generous with practical guidance. Repeated exposure to the same prompt can distort results: a confidence boost on day one might tip into overconfidence by day four, or fade to nothing. The suggested safeguards are to pilot the study first, vary the wording of the manipulation, randomise the order of conditions, and add a study-day control variable to absorb any drift. Survey language deserves equal care — asking how someone feels "right now" captures something quite different from asking about "the day so far," and the choice should follow the theory, not convenience.

For owners and advisors who might be asked to host such a study, the pitch is reassuring. The demand on staff is modest, the prompts are mild, and participation often comes with a return: firms can receive an aggregated, anonymised read on their own employees' attitudes. The authors do flag a shared danger they call scope creep. Owners, understandably, want to bolt on extra questions; researchers, wanting to oblige, let the survey swell until it drives participants away. Protecting the study means keeping it disciplined.

Impact

The deeper argument is about direction. Family business scholarship has spent decades on "why" questions — why these firms behave differently — answered largely through theory and historical data. ESME pushes toward "how": how socioemotional wealth actually shapes a decision as it is being made, how a parent's mood colours the office, how a sense of belonging takes hold or spreads from one person to the next.

Socioemotional wealth, the field's signature concept, is usually inferred rather than observed, and scholars still argue over what it even contains. A method that could watch it operate in real time — and test whether it behaves as a single thing or as several distinct components — would settle questions that have stayed open for years. The authors also point to construal theory and perspective-taking as ready candidates: both have well-established ways of being manipulated in the lab, and both speak directly to how family and non-family members read the same situation. ESME does not replace the case study or the large survey. It fills a gap in the toolkit, and it does so precisely where the field has been weakest.

Recommendations

1. Treat ESME as a serious alternative when the research question is about emotions, daily cognition, or the difference between family and non-family experience — rather than defaulting to another cross-sectional survey.
2. Run a short pilot before the full study to confirm the manipulation works, gauge the time burden on participants, and expose timing problems early.
3. Guard against cumulative or diminishing effects by varying the wording of the prompt, randomising the order of conditions, and including a study-day control.
4. Agree the scope with the host firm up front, offering an aggregated feedback report while resisting the slow expansion of the survey that drives attrition.
5. Secure ethical clearance early, recognising that research touching both livelihoods and family relationships demands a higher standard of care than ordinary workplace studies.