Expectancy-Value Theory

People set goals and pursue them—or they don't. Intuition suggests that whoever wants something will act accordingly. In practice, however, many intentions fail: customers abandon registrations midway, start training programs only to quit, or purchase products they never use. The question is: What determines whether people actually act or merely intend to—and what does the evidence tell us?

Studies

The Ring Toss Experiment

In 1957, John Atkinson conducted an elegant experiment on achievement motivation at the University of Michigan. Eighty-four students were asked to throw rings over a peg from a distance of their own choosing. The researchers divided them into two groups: individuals with high versus low achievement motivation. The surprising result: highly motivated individuals chose medium distances—challenging but achievable. Those with low motivation chose either very close distances (guaranteed success but meaningless) or very far distances (impossible, so failure carries no shame). The high-motivation group showed 60% more persistence at moderate difficulty levels. The experiment demonstrated that motivation is maximized when expectancy of success and challenge are balanced.

Mathematics Performance and Value Beliefs

Allan Wigfield and Jacquelynne Eccles conducted a longitudinal study at the University of Michigan in 1992 with 865 students. Over three years (grades 7-12), they measured both expectancy for success ('How good am I at math?') and subjective value ('How important is math to me?'). The key finding: both factors independently predicted performance and course selection, but in different ways. Expectancy for success correlated with grades (r=0.48), while subjective value correlated with the decision to take advanced courses (r=0.52). Particularly revealing: students with high value but low expectancy for success showed avoidance behavior—they liked math but didn't choose it. The study demonstrated that both components must be addressed, as they affect different aspects of motivation.

Principle

Which principle for Customer Experience Design can be derived from this? Expectancy-Value Theory demonstrates that successful customer experience must address both motivational levers simultaneously: customers must believe they can achieve their goal *and* see high personal value in that goal. Because these factors multiply rather than add, neglecting either one inevitably undermines motivation—even the most attractive offer fails if customers doubt its feasibility, while the simplest solution proves ineffective if the benefit isn't apparent. This becomes particularly critical with complex products or long-term goals, where both the probability of success and the perceived value must be strengthened through skillful communication and process design. The following guidelines show how to implement this principle in practice.

Guidelines

Guarantee early wins

Design onboarding to ensure initial successes happen quickly and consistently. For onboarding processes: Display progress after each small step. For products: Provide immediately usable results before introducing complexity. For training programs: Begin with achievable tasks and increase difficulty gradually. Early wins raise expectations of future success and build momentum for upcoming challenges.

Address subjective value individually

Communicate personal meaning, not just features. Explicitly ask about goals and reflect them back. For software: "This feature saves you 2 hours per week for X." For health: "This helps you achieve your goal of Y." For finance: "This brings you closer to your goal of Z." People have different value drivers—some want status, others security, and still others autonomy. Segment by value orientation.

Measure expectancy of success and value separately

Measure both dimensions separately: "Do you believe you can accomplish it?" and "Is the outcome important to you?" For low value: demonstrate relevance and connect with existing goals. For low success expectancy: reduce complexity, provide social proof, and offer assistance. The typical customer survey question "How likely would you be to do X?" conflates both factors and obscures the actual problem.

Offer adaptive challenges

Adapt difficulty to match growing competence. Tasks that are too easy become boring, while those that are too hard cause frustration. Implement adaptive systems that monitor success rates: when success exceeds 80%, increase requirements; when it falls below 50%, reduce complexity. Give users control over their difficulty level. Communicate progression clearly: "You've mastered Level 1. Level 2 is somewhat more challenging." Optimal motivation occurs when users perceive a 50-70% probability of success.