Setting Expectations and Goals — Practices

Making expectations and goals explicit — stating what “done” looks like, by when, and who owns it, instead of leaving them implied — is the everyday work of clarity. A large industry of branded practices promises to do this well: SMART goals, written-goal exercises, priority frameworks, realistic job previews, communication charters, team charters. This page sorts those practices by how strong their evidence actually is, keeps the effect sizes and sources attached, and flags the two claims that are repeated everywhere but do not hold up.

One honest distinction runs through everything below. The underlying mechanism — that specific, challenging goals beat vague “do your best” ones — is one of the best-replicated findings in work psychology (Locke and Latham 2002; effect sizes about d = .42–.82), and it is documented on the research and evidence page and in the goal-setting-theory glossary entry. The practices that claim to deliver it vary widely in how well that claim is supported. Because this page mixes verified, industry-consensus, and directional evidence, each section carries its own confidence label rather than a single page-wide one.

Does the SMART goals format actually improve performance?

Making expectations explicit and specific reliably helps performance, but the SMART acronym itself has essentially no rigorous evidence that the format improves outcomes — the evidenced mechanism is goal specificity and difficulty, not the mnemonic.

The picture is two-layered. The underlying mechanism — that specific, challenging goals beat vague “do your best” ones — is one of the best-replicated findings in work psychology (Locke and Latham 2002; effect sizes about d = .42–.82), and it belongs to the Clarity science material. But the SMART acronym itself is essentially untested. The format was coined by consultant George Doran in 1981 — originally Specific, Measurable, Assignable, Realistic, Time-related, with “Assignable” since drifting to “Achievable” — and Doran offered it as a practical heuristic; he never claimed it was research-based. A 2022 narrative review (Swann and colleagues) concluded the acronym “is not based on scientific theory… is not consistent with empirical evidence… has redundancy in its criteria… and has a risk of potentially harmful effects.” A 2024 randomized comparison (Pietsch and colleagues) found SMART goals no more effective than do-your-best or open goals for creative performance.

The practical upshot is that the value sits in the mechanism — specificity, an owner, a deadline, an appropriate challenge level — not in ritual adherence to five letters. The “Realistic/Achievable” element can even backfire by nudging people toward safe rather than stretching goals, the opposite of what the science recommends. SMART is a reasonable, low-cost scaffold for first-time supervisors, with no evidence it is harmful in ordinary performance management, but “we use SMART goals” is not itself evidence that expectations are well-set. The substance — is the goal specific, owned, time-bound, and appropriately challenging — is what the evidence supports.

Confidence: industry-consensus (the specificity/difficulty mechanism is verified; the SMART acronym specifically is untested).

Sources: Locke & Latham, Building a Practically Useful Theory of Goal Setting and Task Motivation, American Psychologist (2002); Doran, There’s a S.M.A.R.T. Way to Write Management’s Goals and Objectives, Management Review (1981); Swann et al., Health Psychology Review (2022); Pietsch et al., Educational Psychology (2024).

Does writing goals down actually make them more likely to happen?

Merely writing a goal down has only weak, uncertain support, but forming an implementation intention — an if-then plan specifying when, where and how — is well-evidenced, with a medium-to-large effect (d = .65) on goal attainment. The widely repeated story that a 1953 Yale or 1979 Harvard study found the 3 per cent of graduates who wrote down their goals out-earned everyone else is a documented fabrication; do not cite it.

Myth flag — do not cite. The widely repeated story that a 1953 Yale (or 1979 Harvard) study found the 3 per cent of graduates who wrote down their goals later out-earned the other 97 per cent combined is a fabrication. No such study was ever conducted. Yale’s own library confirms “no ‘goals study’ of the Class of 1953 actually occurred,” and a 1996 investigation traced the tale to motivational speakers with no original source.

The real evidence splits in two. First, “writing a goal down” on its own has only weak, uncertain support. The most-cited genuine study (Gail Matthews, 2015, Dominican University of California) found participants who wrote goals plus action commitments plus weekly progress reports to a friend achieved markedly more — about 76 per cent reported success versus 43 per cent for unwritten goals — but it is a small study, never peer-reviewed, with high attrition, and its strongest signal is accountability and action, not writing as such. It should be treated as suggestive only.

Second, and far more robust, are implementation intentions. Specifying the when, where, and how of action — “If situation X arises, then I will do Y” — reliably improves follow-through (Gollwitzer 1999). The Gollwitzer and Sheeran 2006 meta-analysis found that across 94 independent tests, implementation intentions had a medium-to-large effect (d = .65) on goal attainment, with a separate large effect (d = .61) specifically on getting started. This is the genuinely evidenced practice: not “write the goal,” but “plan the cue and the response.” It is free and immediately usable — in one-on-ones, converting vague intentions into if-then plans (“When I sit down Monday at 9, I’ll draft the proposal first”) and adding a light check-in. The effect sizes here are among the strongest in this whole body of research.

Confidence: verified (the implementation-intentions effect; the “writing alone” claim is weak and the 3 per cent study is fabricated).

Sources: Gollwitzer & Sheeran, Implementation Intentions and Goal Achievement: A Meta-Analysis of Effects and Processes, Advances in Experimental Social Psychology (2006); Gollwitzer, Implementation intentions: Strong effects of simple plans, American Psychologist (1999); Matthews, Dominican University of California (2015, unpublished conference paper); Yale University Library / Fast Company, debunking of the 1953 Yale goals study (2011).

Does narrowing to fewer, clearer priorities improve focus?

The cognitive cost of switching between tasks (attention residue) is well-evidenced, which supports protecting focus and limiting how many priorities a person juggles at once. The broader strategy-execution consulting claims about prioritization are directional practitioner findings, not causal proof.

Two different claims hide inside “focus on fewer priorities,” and they carry very different evidence. The rigorous half is the cost of switching. Sophie Leroy’s 2009 experiments demonstrated “attention residue”: when people switch from Task A to Task B — especially when Task A is unfinished or under time pressure — part of their attention stays stuck on Task A, and performance on Task B suffers, becoming slower and less accurate. This is controlled lab evidence with a clear mechanism, and it underwrites limiting how many things a person juggles and protecting blocks of focused time. It is lab-task evidence, not field productivity.

The softer half is the strategy-execution framing. Sull, Homkes and Sull (2015, Harvard Business Review) drew on a multi-year program — “more than 40 experiments… and a survey of nearly 8,000 managers in more than 250 companies” — to argue execution fails less from misalignment than from poor coordination, reporting that only 11 per cent of managers believed all their company’s strategic priorities had the resources to succeed. This is useful, widely cited practitioner work, but it is a consultancy survey program in a business magazine, not peer-reviewed causal research; the specific figures should be treated as directional and self-reported.

The honest synthesis: “fewer priorities improve focus” is well-supported at the individual cognitive level (attention residue) and is sound management consensus organizationally, but the leap to “narrowing priorities causes better business execution, by this much” is not established by rigorous causal evidence. The cheap, evidence-aligned moves are to cap concurrent priorities per person, batch similar work, and protect uninterrupted blocks; the grander execution prescriptions are reasonable but should be held as directional. Where people wear several hats, attention-residue costs are arguably higher than in large organizations with narrow roles.

Confidence: industry-consensus (attention residue is well-evidenced; the strategy-execution figures are directional and self-reported).

Sources: Leroy, Why is it so hard to do my work? The challenge of attention residue when switching between work tasks, Organizational Behavior and Human Decision Processes (2009); Sull, Homkes & Sull, Why Strategy Execution Unravels — and What to Do About It, Harvard Business Review (2015).

Do realistic job previews reduce turnover?

Realistic job previews reliably but modestly reduce turnover and improve role clarity, met expectations, honesty perceptions, and commitment — a small, low-cost, well-replicated effect, with voluntary-turnover correlations around r = −.06 to −.09, not a dramatic one.

The practice is to give candidates, or brand-new hires, a balanced, honest preview of the job — the bad with the good — rather than overselling it. It is one of the better-evidenced clarity-of-expectations practices, supported by four meta-analyses spanning roughly 40 years and field experiments that randomly assigned candidates to receive a preview or not.

The effects are real but small. Phillips (1998), meta-analyzing 40 studies, found realistic job previews (RJPs) related to lower voluntary turnover (r = −.06), lower process attrition, more accurate initial expectations, and modestly higher performance (r = .05). The voluntary-turnover correlations are consistent across reviews — roughly r = −.06 to −.09 — confirming RJPs are “a low-investment strategy for modestly influencing turnover.” Earnest, Allen and Landis’s 2011 path analysis identified the dominant mechanism: previews raise the perception that the organization is honest (r = .11), with role clarity also improving.

There are honest caveats. The magnitudes are single-digit correlations, and a preview also reduces perceived attractiveness — a real risk where attracting applicants is already hard. The QIC-WD umbrella summary notes the most effective preview is delivered orally or in writing shortly after hire, which captures the honesty and role-clarity benefit without shrinking the applicant pool. Previews work best when the job is complex. In practice, an RJP is essentially free — a candid conversation about the hardest parts of the role, or an hour of shadowing — and because small employers feel each departure acutely and rarely have deep applicant pools, the post-hire preview during onboarding is the lower-risk option. The expectation should be a small, reliable nudge plus better-calibrated new hires, not a large turnover reduction. The dollar value of prevented turnover is handled separately on the costs and consequences page.

Confidence: verified (a small, well-replicated effect across four meta-analyses).

Sources: Phillips, Effects of Realistic Job Previews on Multiple Organizational Outcomes: A Meta-Analysis, Academy of Management Journal (1998); Premack & Wanous, A meta-analysis of realistic job preview experiments, Journal of Applied Psychology (1985); Earnest, Allen & Landis, Mechanisms Linking Realistic Job Previews with Turnover: A Meta-Analytic Path Analysis, Personnel Psychology (2011); Graef (QIC-WD), Umbrella summary: Realistic job previews (2020).

What communication norms keep expectations clear on a hybrid or remote team?

Explicit communication norms are sensible and address a documented failure mode — the mutual-knowledge problem in dispersed teams — but the causal evidence that writing down communication guidelines improves outcomes is thin and largely cross-sectional. Most communication best practice is consultant consensus.

The practice is to agree explicitly on who communicates what, when, through which channel, how fast people respond, and how to escalate — instead of letting norms form by accident. The rigorous anchor for why this matters is Catherine Cramton’s 2001 study of 13 dispersed teams, which identified the “mutual knowledge problem”: dispersed collaborators fail to establish and maintain shared knowledge, through failure modes such as unevenly distributed information, difficulty conveying what is salient, uneven speed of access, and difficulty interpreting silence. It is a careful study of the mechanism explicit norms are meant to fix — but it does not test whether a written charter improves performance.

On whether communication practices causally improve outcomes, the strongest evidence is adjacent and correlational. Marlow and colleagues’ 2018 meta-analysis — roughly 150 studies and 9,700 teams — found communication quality relates more strongly to team performance than communication frequency: richer, elaborated information-sharing beats sheer volume. Crucially, the included studies were cross-sectional, so this is association, not causation. Most “remote communication best practices,” such as response-time rules and channel norms, rest on consultant consensus rather than experiments.

The honest assessment is that this is one of the thinner-evidenced clarity practices. The problem it targets — coordination failure — is real and documented; the specific prescription, writing explicit norms, is sensible and low-cost but not proven to lift performance in controlled studies. Hybrid is now structural: per Statistics Canada, in November 2024 about 12.5 per cent of employed Canadians usually worked exclusively at home and 11.5 per cent were hybrid, with Ontario among the highest. A one-page, co-created norm — “acknowledge requests within 24 hours,” “decisions go in the shared channel, not DMs” — is cheap and targets a genuine failure mode; it just should not be oversold as a proven performance driver. The belonging dimension of remote work is covered separately in the Connecting practice material.

Confidence: directional (the mechanism is documented; causal evidence for written norms is thin and cross-sectional).

Sources: Marlow, Lacerenza, Paoletti, Burke & Salas, Does team communication represent a one-size-fits-all approach? A meta-analysis of team communication and performance, Organizational Behavior and Human Decision Processes (2018); Cramton, The Mutual Knowledge Problem and Its Consequences for Dispersed Collaboration, Organization Science (2001); Statistics Canada, Labour Force Survey, working-from-home data (2024).

Do team charters improve team performance?

Team charters reliably improve team processes — cohesion, communication, effort, satisfaction — but generally do not improve hard performance directly. They help only conditionally, paired with a task strategy or as a substitute for low conscientiousness, and the evidence base is thin and mostly student samples.

A team charter is a document agreed at team formation that makes expectations, roles, norms, and ways of working explicit — a natural clarity practice. It is widely recommended, but the causal evidence on hard performance is weaker than its popularity suggests.

The strongest longitudinal study, Mathieu and Rapp (2009), tracked 32 MBA teams in a strategy simulation and found charter quality had no strong standalone effect; the highest sustained performance came only from teams high on both charter quality and a task-performance strategy — a significant interaction. Courtright and colleagues (2017), studying 239 undergraduate teams, found charter quality affects performance only indirectly, via task cohesion, and mainly for teams low on conscientiousness — meaning charters substitute for what conscientious members already do. The 2025 National Academies review synthesizes the field candidly: charters “positively predict team functioning and processes” (communication, effort, cohesion, satisfaction) but “tend not to influence team performance directly,” and the literature is thin and mostly student samples. No meta-analysis isolates a charter-to-performance effect.

The honest assessment is directional. Charters reliably improve the soft, process side and are low-cost, but claims that they directly boost output are not supported. A short charter is cheapest and most useful when a team is newly formed, has less experienced members, or works hybrid — exactly the conditions where the process and substitution benefits appear — and is best paired with an explicit task strategy (Mathieu and Rapp) rather than expected to move results on its own.

Confidence: directional (reliable on team processes; not supported as a direct performance driver; thin, mostly student-sample evidence).

Sources: Mathieu & Rapp, Laying the foundation for successful team performance trajectories: the roles of team charters and performance strategies, Journal of Applied Psychology (2009); Courtright, McCormick, Mistry & Wang, Quality charters or quality members? A control theory perspective on team charters and team performance, Journal of Applied Psychology (2017); National Academies of Sciences, Engineering, and Medicine, The Science and Practice of Team Science, Ch. 3 (2025).

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This page is general information for managers and employers, not legal or professional advice. The practices above vary in how strongly they are evidenced — verified, industry-consensus, and directional are not the same thing — and two widely repeated claims (that the SMART acronym is proven, and that a Harvard or Yale study found written goals made the 3 per cent who wrote them out-earn everyone else) do not hold up. Read each practice for the strength of evidence behind it before adopting it as more than a low-cost, sensible default.