Desirability and feasibility: meaning and application

Many people consider a course, a new way of working or a career move worthwhile and still keep postponing the decision. Often the why clashes with the how: the value of the outcome with the effort needed to get there. The distinction between desirability and feasibility helps you to research and address those two sides separately.

Desirability concerns the value of the end result of a choice: why is this worthwhile? Feasibility concerns the means of reaching that result: does it fit my time, resources, skills and priorities?

According to construal level theory, desirability weighs more heavily when a decision is still far away, and feasibility when it comes close. Recent replication research found the underlying distance effect to be much smaller than previously thought. Use the distinction as an analytical framework, not as a fixed law.

Almost every choice involves two kinds of considerations. Desirability is about the value of the end result: why is it worth it? Feasibility is about the route there: how much time, money, energy and skill does it take, and does it fit alongside everything else?

Trope and Liberman describe desirability as the value of an action's end state and feasibility as the means used to reach that end state (Trope & Liberman, 2010). In plain terms: desirability is the why, feasibility the how.

Something can be highly desirable and still feel unfeasible, such as a course that would advance your career but takes three evenings a week. Asking only whether people like an idea misses half the story.

The distinction was developed within construal level theory by Nira Liberman and Yaacov Trope. A key publication from 1998 carries it in its title: the role of feasibility and desirability considerations in near and distant future decisions (Liberman & Trope, 1998).

The theory is about psychological distance: the subjective experience that something is close to or far from yourself, here and now. Trope and Liberman distinguish four dimensions: time, space, social distance and hypotheticality, meaning how likely something is (Trope & Liberman, 2010). The idea is that the greater the distance, the more abstractly people represent something in their minds.

An abstract representation (high construal level) answers the question of why an action is performed, a concrete representation (low construal level) the question of how. Desirability belongs to the abstract level, feasibility to the concrete one.

When a decision is far away, you mainly think about what it will deliver. As it approaches, the practical details come into focus: which evenings, which deadlines, who looks after the children.

Liberman and Trope studied this with, among other things, a guest lecture. The further in the future the lecture was, the more its attractiveness depended on desirability (how interesting the lecture was) and the less on feasibility (how convenient the timing was), as Trope and Liberman later summarised (Trope & Liberman, 2010).

In January you agree to a project in September; in August your diary is full. Your view of the value has not changed, but the weight of feasibility has. For research and communication, this means the timing of the decision matters.

The shift towards feasibility ties in with a broader problem: people do not always do what they intend to do. Sheeran and Webb summarised research on this intention–behaviour gap and estimate that intentions are translated into action about half of the time (Sheeran & Webb, 2016).

According to their review, things go wrong when getting started (forgetting, missing opportunities, not preparing), when staying on track (competing goals, habits) and when finishing. Many are feasibility questions. Sheeran and Webb also report a meta-analysis of 94 studies in which concrete if-then plans (implementation intentions) had a medium-to-large effect on goal attainment (d = 0.65) compared with merely forming an intention.

This link is an interpretation, as the gap has many causes.

Expectancy-value models also place value and effort side by side. They explain choices by the expectation that something will succeed and the value a person attaches to it. Eccles and Wigfield distinguish within that value the personal importance of a task, the enjoyment of it, its usefulness for future goals and its cost. That cost includes the effort required, lost opportunities and anxiety or fear (Eccles & Wigfield, 2002).

Expectancy-value models describe which ingredients feed a choice; construal level theory adds a prediction about when each weighs more.

A meta-analysis by Soderberg and colleagues covered 106 papers with a total of 267 experiments and found a reliable, medium-sized effect of psychological distance on the level of abstraction in mental representation and on its downstream consequences (Soderberg et al., 2015).

Recent replication research qualifies that picture. In a registered report with more than 11,000 participants, an international consortium tested four predictions of the theory, including a direct replication of a study from Liberman and Trope (1998). For temporal distance, the researchers found d = 0.08 (95% confidence interval 0.003 to 0.16), compared with d = 0.92 in the original research. They conclude that there is limited evidence for the theory's predictions (Calderon et al., 2026).

This replication tested the effect of distance on abstraction, not the weighting of desirability and feasibility directly, but that weighting relies on the same mechanism. Treat "desirability counts at a distance, feasibility up close" as a hypothesis to test in your context. The distinction between the why and the how remains a useful analytical framework.

"I don't have time for it" is a feasibility objection, "I don't see the point" a desirability objection; they call for different answers. See also What are a target group's wants and goals? and What are objections in a target group profile?.

Asking "What do you think of this idea?" mostly yields answers about desirability. Feasibility surfaces when you ask about it specifically or when the decision is close.

• Split the prompts. In an association task, ask separately about meaning ("What does this mean to you?") and about execution ("What comes to mind if you were to start next month?"). See How do you measure associations?.
• Code by content. Is an association about value and outcome, or about time, effort and resources? Fix coding rules in advance and have two coders code independently.
• Record the distance to the decision and compare groups only at a similar distance.
• Look at the combination. Positive about meaning and negative about execution is a different problem from the reverse.

This is an editorial application, not a validated instrument. Context determines the coding: "evenings" can also concern desirability.

The distinction suggests a working hypothesis: respond to the question that is relevant at that moment.

• Up close: make the how concrete. Provide schedules, time investment, first steps and support. This ties in with the if-then plans described by Sheeran and Webb.
• At a distance: make the why clear, without hiding the practical side.
• Do not confuse the two. A better explanation of the benefits does not solve a time problem, and a schedule does not resolve doubts about the benefits.

This is editorial advice, not a proven effect. Test messages before rolling them out widely; see From target group insights to testable messages.

1. Describe the choice and the moment: which decision, for whom, and how far away?
2. Formulate separate questions about meaning (why) and execution (how).
3. Fix coding rules for associations about value and about time, effort and resources.
4. Collect data and record each participant's distance to the decision.
5. Analyse both sides separately and then in combination.
6. Formulate hypotheses about where people drop out.
7. Test an adjustment with an outcome measure chosen in advance.

This example is fictional. No results are known.

Situation and decision question. A fictional organisation offers employees a two-year part-time course alongside their job. Many employees call the course valuable, but few sign up. Do people not see the point, or does it not fit their lives?

Available information. Sign-up figures and loose remarks from conversations; nothing that separates desirability from feasibility.

Suitable approach. A short association task with two prompts: "A course alongside your job means to you…" and "If you were to start next quarter, you think of…". Associations are coded as meaning or execution according to rules agreed in advance. Participants are also asked whether they are considering starting within six months or later.

Possible interpretation. If associations with the first prompt are mainly about growth and prospects, and with the second about evenings, family and workload, that points to a feasibility threshold. That is a hypothesis, not a finding.

Next step. Alongside the existing information about the why, a version is created that makes the how concrete: hours per week, study leave, exam schedule. The number and quality of sign-ups are fixed in advance as outcome measures.

• Reading agreement as willingness: "good idea" says something about desirability, not about behaviour.
• Researching only the why: feasibility thresholds then remain invisible.
• Ignoring the timing: research long before the decision can underestimate feasibility.
• Seeing feasibility as a lack of motivation: someone who drops out over time may still see the value.
• Treating the distance prediction as a law: the evidence is mixed.

• You can: show whether doubt is mainly about value or about execution, and link hypotheses about the moment of dropping out to that.
• You can't: predict what one person will do; group patterns are not individual predictions.
• You can't: conclude from correlation that a feasibility threshold causes people to drop out; that requires a test.
• You can't: assume that the distance prediction holds for your target group.

Desirability is about the why of a choice, feasibility about the how. Construal level theory predicts that the why weighs more while a decision is far away and the how once it comes close. Recent replication research calls for caution with that prediction. Research both sides separately, pay attention to the timing of the decision and test what you change in communication.