Expectancy Theory

What is the Expectancy Theory?

Expectancy theory posits that individuals choose to engage in certain behaviors based on the expected outcomes. According to this theory, the decision to act in a particular way is influenced by the anticipated rewards and the belief that the behavior will lead to the desired result. Developed by Victor Vroom, this theory highlights three key components: expectancy (belief that effort leads to performance), instrumentality (belief that performance leads to rewards), and valence (value placed on the rewards).

The Basic Idea

When people ask me how I spent last summer, I tell them that I studied for the LSAT from May through August. This typically results in musings of how terrible it must have been and questions of how I motivated myself. While studying for the test was a stressful experience, it was nowhere close to being terrible. I was motivated to study because I knew that it would be a valuable step toward my larger goal, which was getting into law school. I expected that increasing my efforts to study would increase my chances of a high score, which I believed would help me achieve my overall goal.

My experience was one that followed expectancy theory, which assumes that people are motivated to engage in certain behaviors because of their expected outcomes.1 Thus, the values associated with said outcomes are the discerning factors for choosing one behavior over another. Expectancy theory separates the decision making process into expectancy (efforts will lead to high performance), instrumentality (performance will lead to predicted outcomes), and valence (predicted outcomes are desirable).

Motivation depends on how much we want something and how likely we think we are to get it.

– Victor Vroom, pioneer of the expectancy theory of motivation

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Key Terms

Expectancy theory: A theory which proposes that behaviors result from conscious choices among alternatives, based on the expected utility and rewards of said behaviors. Expectancy theory consists of expectancy, instrumentality, and valence.

Expectancy: The belief that increased efforts will lead to better performance (E -> P).

Instrumentality: The belief that if better performance is achieved, it will result in a certain outcome (P -> O).

Valence: The significance or value expected of an outcome.


Developed by Canadian organizational behavior and motivation researcher Victor H. Vroom, expectancy theory was inspired by the realization that employee performance is based on individual factors like personality, past experiences, confidence, skills, and knowledge.1 This theory recognizes that what motivates one employee to complete their work may not work for all employees. In other words, the relationship between behaviors and goals was not as simple as previously believed.2

When choosing behaviors to engage in, motivation is the most important factor.1 Vroom suggested that people can be motivated toward their goals if they believe that:

  • There is a positive correlation between efforts and performance, such that as one increases, so does the other;
  • Better performance will result in a desirable reward;
  • Rewards will be valuable and satisfy important needs; and/or,
  • The desire to satisfy the need is strong enough to make their efforts worthwhile.2

Vroom introduced three variables that drive expectancy theory: expectancy, instrumentality, and valence.1 The relationship between these variables was also clearly defined. Expectancy refers to the belief that one’s effort (E) will lead to a desirable and improved performance (P). Thus, expectancy is based on the relationship from E -> P. Expectancy is theorized to be influenced individual factors including:

  • Self-efficacy: One’s belief about their ability to perform the behavior;
  • Goal difficulty: Whether one has the necessary skills to perform the behavior; and,
  • Perceived control: One’s belief about how much control they have over the outcome.3

Following expectancy, instrumentality is the perceived probability that good performance (P) will lead to desired outcomes (O), so it is based on the relationship from P -> O.1 Notably, instrumentality is low when rewards remain the same across all levels of performance. Factors that influence instrumentality include:

  • Trust for those who distribute rewards based on performance;
  • Control over how the decision is made; and,
  • Evidence of policies that reflect an acceptable correlation between performance and outcomes.3

Finally, valence is the perceived value of the rewards of an outcome, based on one’s needs, goals, values, and preferences.1 Importantly, the expected satisfaction of an outcome can vary depending on the person who will experience it. Vroom developed expectancy theory to explain the process of motivation: rather than simply stating what will motivate someone, the theory defines how motivation comes about.3 Ultimately, process theories are models of the decision making processes that people perform to decide whether they will be motivated to expend the energy needed to pursue a certain activity.


Vroom’s expectancy theory is generally supported by empirical evidence and is one of the most commonly used theories of motivation in the workplace, highlighting how the intensity of work effort depends on one’s belief that it will be valuable.3 Although originally developed for employee motivation in the field of management,4 expectancy theory has been applied to other fields such as education.5,6,7

Expectancy theory can help managers understand how employees are motivated to choose among behavioral alternatives.4 To enhance the connection between performance and outcomes, expectancy theory suggests managers tie valuable rewards closely to performance, as well as increase self-efficacy by training employees to improve their abilities. Managers should not discount individuals’ proclivity to act in their self-interest: maximizing valuable rewards will increase opportunities to satisfy both employee needs and organizational requirements.

Educators are constantly trying to understand what enables and motivates students to excel in their learning, and expectancy theory has been used to assess motivation.5 Research has found that the potential for a higher GPA, greater post-university job performance, and increased self-esteem are important motivators for students in university. Notably, it was found that valence tied to the aforementioned variables is more influential on a student’s effort levels, compared to the sole expected probability of attaining said outcomes. In fact, researchers have found that valence is the best overall predator of university students’ academic performance, in terms of cumulative GPA.6

Motivation is especially important in adult education due to the increased external demands placed on students.7 Applying the expectancy theory, researchers have suggested that increased planning and structure will provide educators with more opportunities to manipulate expectancy variables, while providing students with clear learning expectations. Clear rewards and achievements from work can be maximized through marketing and teaching strategies that connect adult education to personal outcomes, such as employment opportunities.


While expectancy theory provides a good framework to assess, interpret, and evaluate employee behavior as it applies to learning, decision making, motivation, and attitude formation, some critics have suggested that the variables remain misunderstood.3 This has resulted in different interpretations, applications, and methods of statistical analysis when researchers explore expectancy theory. As a result, the validity of Vroom’s expectancy theory has been questioned.

For example, some have suggested a need to distinguish between intrinsic and extrinsic rewards on the basis of effort and performance.3 Extrinsic outcomes would be rewards distributed by someone else, such as a manger, while intrinsic outcomes are personal rewards like self-fulfillment and a boost in self-esteem. As an extension of this distinction, other researchers have suggested that intrinsic outcomes are better predictors of job performance and satisfaction than extrinsic outcomes,8,9 while others have suggested that intrinsic outcomes are more powerful motivators than extrinsic outcomes.10

It’s important that expectancy theory is not interpreted too simplistically: managers must ensure that rewards are perceived to be valuable, taking valence into account.11 Since valence is subjective, executives who hope to motivate employees should consider personal characteristics such as culture. One example of culturally inappropriate rewards comes from ASMO, a Japanese motor company that opened a manufacturing plant in the United States. Despite maintaining a large workforce of Japanese employees, a team of American managers was hired to oversee plant operations.

Wanting to motivate employees, the American managers at the ASMO plant implemented an employee of the month program that required employees to nominate their coworkers.11 However, Japanese culture values conformity, teamwork, and modesty, so it was embarrassing for Japanese employees to be named the employee of the month. If the management team had considered the influence of such cultural characteristics on valence, they could have developed a more motivating reward system.

Case Study

Implementing information systems

Expectancy theory has been applied to user acceptance of new information systems, with implementation research suggesting that user attitudes toward technological systems are critical.12 If organizations hope to implement expert systems, employees must believe the outcomes associated with learning the new system are valuable.

A team of American researchers used expectancy theory to examine the necessary motivations for successful implementation of new information systems.12 They were curious about four potential outcomes:

  1. Improved and more effective decision making;
  2. More efficient decision making;
  3. Higher frequency of making correct decision; and,
  4. Increased job insight based on the learning stimulated by the system.

Ninety-five MBA students completing an information systems course were given a case study, where they were loan officers at a commercial bank.12 They were told that a new expert system was available to help determine eligibility of loan applications. This problem-solving computer program assisted in making loan approval decisions by judging financial attributes of companies. Regardless of whether the system was used, the loan officers would be responsible for loan decisions. Using the expert system was voluntary and participants could decide the extent to which they used the system.

Participants were asked to judge the overall attractiveness of using the system to its maximum extent, and the perceived likelihood that it would help users achieve the four outcomes listed above.12 This judgement was meant to reflect valence, and participants also rated their motivation to use the new system, based on the four outcomes. The researchers found that greater valence was associated with increased motivation to use the new expert system, showing that expectancy theory can be applied early in the design phase of system development.

Tenure and productivity

Universities are increasingly relying on performance assessments to determine salaries and tenure awards for faculty members.13 Being denied tenure can result in personal, professional, emotional, and financial consequences for the faculty member, and can decrease their investment in the institution. On the other hand, being awarded tenure will increase job security, and prior motivation research has suggested that productivity tends to suffer on the basis of long-term job security.14

As a result, some worry that once tenure is achieved and job security increases, motivation levels of faculty members will be negatively affected.13 Considering the potential impact of tenure on a university’s effectiveness, researchers have applied expectancy theory to examine the motivation and productivity implications of awarding tenure.

Pre-tenure and post-tenure data on research productivity levels was compared among a group of twenty-four faculty members.13 Productivity was defined as scholarly research activity in the form of published journal articles. Among these faculty members, there was a significant decline in research publications for tenured faculty, equating to a 42% reduction. Thus, the results supported expectancy theory predictions on increased job security and decreased productivity.

Notably, this effect is also tied to valence, similar to findings in academic achievement5,6 and implementing information systems.12 Since tenured faculty already achieved research success, it is unlikely that productivity declines were attributed to expectancy effects, such as doubt in their ability to achieve the outcome.13 It is also unlikely that there is no value placed on research productivity, ruling out instrumentality effects. Rather, productivity declines were likely attributed to value perceptions of higher sustainability of published research, reflecting valence. While there are other possible reasons for publication declines, the results appear to support expectancy theory and the emphasis on valence, consistent with other case studies.

Related TDL Content

Observer-expectancy effect

Our expectations are powerful and can extend beyond motivation to impact the behavior of others. The observer-expectancy effect describes how awareness of being observed can cause biased behavior aimed to satisfy the observer.


  1. Vroom, V. H. (1964). Work and Motivation. Wiley.
  2. Vroom, V. H., & Deci, E. L. (1989). Management and Motivation. Penguin.
  3. Chiang, C., & Jang, S. (2008). An expectancy theory model for hotel employee motivation. Hospitality Management, 27, 313-322.
  4. Isaac, R. G., Zerbe, W. J., & Pitt, D. C. (2001). Leadership and motivation: The effective application of expectancy theory. Journal of Managerial Issues, 13(2), 212-226.
  5. Geiger, M. A., & Cooper, E. A. (1996). Cross-cultural comparisons: Using expectancy theory to assess student motivation. Issues in Accounting Education, 11(1), 113-129.
  6. Geiger, M. A., & Cooper, E. A. (1995). Predicting academic performance: The impact of expectancy and needs theory. The Journal of Experimental Education, 63(3), 251-262.
  7. Howard, K. W. (1989). A comprehensive expectancy motivation model: Implications for adult education and training. Adult Education Quarterly, 39(4), 199-210.
  8. Graen, G. B. (1969). Instrumentality theory of work motivation: Some experimental results and suggested modifications. Journal of Applied Psychology, 53, 1-25.
  9. Mitchell, T. R., & Albright, D. W. (1972). Expectancy theory predictions of the satisfaction effort, performance, and retention of naval aviation officers. Organizational Behavior and Human Performance, 8, 1-20.
  10. Wahba, M., & House, R. (1974). Expectancy theory in work and motivation: Some logical and methodological issues. Human Relations, 27, 121-147.
  11. Lumen Learning. (2020). Process-Based Theories. Motivating Employees. https://courses.lumenlearning.com/wmopen-introductiontobusiness/chapter/process-based-theories/
  12. Burton, F. G., Chen, Y., Grover, V., & Stewart, K. A. (1992). An application of expectancy theory for assessing user motivation to utilize an expert system. Journal of Management Information Systems, 9(3), 183-198.
  13. Estes, B. C., & Polnick, B. (2012). Examining motivation theory in higher education: An expectancy theory analysis of tenured faculty productivity. International Journal of Management, Business, and Administration, 15(1), 1-7.
  14. Estes, B. C. (2011). Predicting productivity in a complex labor market: A sabermetric assessment of free agency on Major League Baseball player performance. Business Studies Journal, 3(1), 23-58.

About the Authors

Dan Pilat's portrait

Dan Pilat

Dan is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. Dan has a background in organizational decision making, with a BComm in Decision & Information Systems from McGill University. He has worked on enterprise-level behavioral architecture at TD Securities and BMO Capital Markets, where he advised management on the implementation of systems processing billions of dollars per week. Driven by an appetite for the latest in technology, Dan created a course on business intelligence and lectured at McGill University, and has applied behavioral science to topics such as augmented and virtual reality.

Sekoul Krastev's portrait

Dr. Sekoul Krastev

Sekoul is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. A decision scientist with a PhD in Decision Neuroscience from McGill University, Sekoul's work has been featured in peer-reviewed journals and has been presented at conferences around the world. Sekoul previously advised management on innovation and engagement strategy at The Boston Consulting Group as well as on online media strategy at Google. He has a deep interest in the applications of behavioral science to new technology and has published on these topics in places such as the Huffington Post and Strategy & Business.

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