Many organizations strive to motivate and train their employees effectively, yet many are halted by the myriad options available to them. One theory, expectancy, may provide a solution to organizations who want a simple training methodology that is backed by well-researched data. To determine the effectiveness of expectancy theory, one must explore the challenges and costs associated with training and motivation. Investigating the origins of this theory and how it holds up when tested could be beneficial to anyone developing a training program. Furthermore, it is not enough to simply know what a theory is or how it works without knowing how to apply it effectively. One must investigate the potential outcomes of using this theory in their organization. While expectancy theory represents one method of motivating and training employees—and is by no means the only method—it may be the foot in the door that organizations need to get their employees moving in the right direction.
Employee training can be overwhelming for many organizations. A quick Google search of the term employee training reveals over 347 million web links—far too many for an organization to determine the most effective. Each site claims that it holds the truth. Yet, finding the metaphorical needle in a haystack might be too daunting a task. According to Iyengar (1999), too many choices can lead to indecision. One of the biggest challenges industrial and organizational (I/O) psychologists face is helping organizations sift through mountains of information to find well-researched data to support their training initiatives. Additionally, factors like age, relationships, and perceptions about training can impact its effectiveness.
In place of exploring all of the motivation and training options that are available, and to narrow the scope in helping organizations find at least one solution to the training challenges they face, this article will focus on the following research questions:
How can expectancy theory improve an organization's training programs?
How can I/O psychologists contribute to the success of an organization in the area of expectancy theory?
It might be best to begin answering these questions by looking at the challenges organizations face when it comes to motivation and training. Additionally, one should give a homogenous view to the origins of expectancy theory and its place in the world of training.
The degree in which the knowledge that was shared in training leads to improved job performance is known as learning transfer (Jayakumar & Sulthan, 2014). To test this concept, Jayakumar and Sulthan used a census survey with 475 participants to determine employee perceptions on training. The results showed that for training to be effective it needs to help employees develop skills, it must transfer knowledge, and it needs to provide a method for ongoing support (Jayakumar & Sulthan). The results of this study yield a high Cronbach alpha, which indicates that the results are very reliable. Additionally, the results are valid when accounting for inter correlation.
Muchinsky (2012) claims that transfer is facilitated by an individual’s motivation to sustain a new behavior. Renta-Davids, Jiménez-González, Fandos-Garrido, and González-Soto (2014) found through a self-reported survey given at two distinct intervals that transfer can only occur when training is relevant and effective—a step-by-step regression analysis and Cronbach’s alpha indicate these results to be both reliable and valid. In other words, it does not help to train employees on information or processes they will not use. Not only are relevance and effectiveness important to transfer, but the post training environment can also play a role. Muchinsky says that limitations in one’s environment can also affect transfer.
Age can also impact training effectiveness. Zwick (2015) suggests that older employees are expected to perform at the same levels as their younger peers. However, many older employees do not have the same goals as younger employees. When it comes to career development, earnings, flexibility, adoption of new skills, and job security, older employees may not take an interest (Zwick). It may be best to design training to accommodate employees at every level in the organization.
In the pursuit of factors that affect training effectiveness, one must not forget participation. Training is no good without participation; both from managers and employees. Jayakumar and Sulthan (2014) demonstrate in their research that an employee’s perception of the training can impact whether he or she participates. This is not the only variable that affects participation. If managers do not see training as valuable, their employees are likely to see the training in a similar light (Franca & Pahor, 2012). Unfortunately, when this occurs, participation suffers. Leaders carry a strong influence over their peers. If employees believe they have a good relationship with their managers, that their job is secure, and knowing their leader’s expectations can influence whether an employee participates in training (Afsar, Shahjehan, & Rehman, 2010). More importantly, this relationship affects transfer; the whole purpose for the training (Afsar, Shahjehan, & Rehman).
Transfer and participation are important—that much has been established, but one must not forget content. Content needs to be relevant to be effective (Renta-Davids et al., 2014). This is precisely why expectancy theory fits into training as a strong motivator. When leaders understand this theory, they may be more inclined to pay attention to it. Additionally, they might be more willing to design training programs with expectancy in mind. Expectancy theory originated in the 1930’s with Tolman. He claimed that humans develop expectancies about the outcomes of their behaviors and consequently behave in a manner that is likely to create the desired outcome (Oliver, 1974). In 1964, expectancy theory made it into the mainstream when Vroom introduced it as a model for work motivation (Muchinsky, 2012). Vroom demonstrated that employees are motivated by job outcomes (e.g. pay, promotions, recognition, accomplishments, and etc.) (Muchinsky). Additionally, Vroom believed that instrumentality (Perceived degree of connections between performance and outcome), valence (one’s feelings), expectancy (the perceived connections), and force (the amount of effort needed) were all contributors to an individual’s motivation (Muchinsky).
Modern researchers suggests that there is a hidden cost to expectancy theory. Flake, Barron, Hulleman, McCoach, and Welsh (2015), used focus groups and nine-point-scale surveys to gather student’s perceptions on the cost of expectancy. To ensure content validity, eight judges (experts in the field) analyzed the data to ensure it was relevant to cost dimensions; the higher the agreement, the more likely the content had validity. Additionally, Cronbach’s alpha indicated high reliability. Flake et al. found that when exploratory factor analysis was contrasted with confirmatory factor analysis, individuals expressed task effort, outside effort, loss of valued alternatives, and emotional costs. They suggest that people express these costs in terms of too much or too hard and other similar phrases (Flake et al.).
Another factor of expectancy theory is the impact it has on one’s intrinsic motivation. Hsu, Shinnar, and Powell (2014) took a sample of 433 usable surveys from entrepreneurial undergraduate. The surveys covered 33 subtopics of entrepreneurship and expectancy and were designed to measure self-efficacy and intention. Cronbach’s alpha was acceptable at 0.76 and a confirmatory factor analysis confirmed validity. Hsu, Shinnar and Powell found that expectancy, instrumentality, and valence precede motivation. In essence, if one expects to a high degree that his or her behaviors to produce a desired outcome, and his or her feelings are in alignment with those predictions, he or she will be intrinsically motivated to pursue it (Hsu et al.).
Theoretical Framework, Recommendations, and Predictions
Training is only as effective as it is perceived (Jayakumar & Sulthan, 2014). For transfer to occur, the content needs to be relevant and effective (Renta-Davids et al., 2014). Additionally, trainers need to keep age and other variables in mind when designing a training program (Zwick, 2015). When it comes to expectancy, trainers can use outcomes (i.e. rewards, recognition, etc.) to motivate employees to apply the training (Muchinsky, 2012). Extrinsically, these could act as incentives to drive employees. However, trainers need to keep intrinsic motivation in mind and demonstrate to employees the instrumentality, valence and expectancy they could use to be internally driven (Hsu et al., 2014). Research also shows that when tests are administered during training that transfer is stronger when a similar test is given at a later period (Carpenter, 2012). Trainers/leaders and could develop tests that anchor the information in sequentially as it is being learned.
Furthermore, training should be designed while keeping the employee/leader relationship in mind. Implementing ideas from Franca and Pahor’s (2012) study, manager and leaders need to find value in the training and the outcomes it creates before presenting it to their employees. Perhaps this is where I/O psychologists can help. Managers and leaders may not be versed in all of the studies that are available when it comes to training and expectancy theory. Additionally, the may not know the best practices for designing curriculum or tests. Therefore, I/O psychologists can assist in finding information that is relevant to the organization and work closely with them to implement the training in an effective manner. After all, the goal is transfer of learning.
Organizations that work with I/O psychologists, value their training, have strong valence, expect a return on their efforts, and test for transfer will have a higher probability of success than those who do not. If they keep employee differences in mind and build strong relationships, they will be more inclined to see employee’s expectations strengthen and transfer will occur.
Organizations have myriad options to choose from when it comes to training. However, many are ill-equipped to decide which ones are the most effective. I/O psychologists can help organizations make the best choices for their teams. Research suggests that relevant and effective information transfers. Additionally, training needs to help employees develop new skills and provide them with ongoing support. Otherwise, transfer may not stick. Leaders impact the degree of transfer and the motivation of their employees. If employees feel they have a strong relationship with their leaders, they may be more inclined to practice or implement what is taught. Moreover, employee’s perceptions of training can be influenced by the perceptions of their leaders. Not all employees learn the same way or have the same drives. Trainers should keep this in mind and make adaptations for age and other variables. When employees have high instrumentality, expectation, and valence, they will be more intrinsically motivated and will be more likely to be self-driven. Trainers can use incentives and recognition to help employees become more motivated. Extrinsic and intrinsic motivators can be designed with expectancy in mind. In short, organizations who help their employees see the benefits of their behaviors, design programs around transfer, pay attention to their employee’s needs, and offer support will be the ones that make the greatest advances.
Afsar, B., Shahjehan, A., & Rehman, Z. U. (2010). Leader’s influences on training effectiveness. Annales Universitatis Apulensis : Series Oeconomica, 12(2), 597-607. Retrieved from http://search.proquest.com/docview/856208500?accountid=458
Carpenter, S. K. (2012). Testing enhances the transfer of learning. Current Directions in Psychological Science, 21(5), 279-283. doi:10.1177/0963721412452728
Flake, J., Barron, K., Hulleman, C., McCoach, B., & Welsh, M. (2015). Measuring cost: The forgotten component of expectancy-value theory. Contemporary Educational Psychology, 41(1), 232-244. doi:10.1016/j.cedpsych.2015.03.002
Franca, V., & Pahor, M. (2012). Influence of management attitudes on the implementation of employee participation. Economic and Industrial Democracy, 35(1), 115-142. doi:10.1177/0143831X12464070
Hsu, D. K., Shinnar, R. S., & Powell, B. C. (2014). Expectancy theory and entrepreneurial motivation: A longitudinal examination of the role of entrepreneurship education. Journal of Business and Entrepreneurship, 26(1), 121-140. Retrieved from http://search.proquest.com/docview/1635000526?accountid=458
Iyengar, S. S., & Lepper, M. R. (1999). Rethinking the value of choice: A cultural perspective on intrinsic motivation. Personality and Social Psychology, 76(3), 349-366. doi:http://dx.doi.org/10.1037/0022-3518.104.22.1689
Jayakumar, G. S., & Sulthan, A. (2014). Modelling: Employee perception on training and development. SCMS Journal of Indian Management, 11(2), 57-70. Retrieved from http://search.proquest.com/docview/1543741421?accountid=458
Muchinsky, P. M. (2012). Psychology Applied to Work (10th ed.). Summerfield, NC: Hypergraphic Press, Inc..
Oliver, R. L. (1974). Expectancy theory predictions of salesmen's performance. Journal of Marketing Research, 11(3), 243-253. doi:10.2307/3151139
Renta-Davids, A., Jiménez-González, J., Fandos-Garrido, M., & González-Soto, A. (2014). Transfer of learning: Motivation, training design and learning-conducive work effects. European Journal of Training and Development, 38(8), 728 - 744. doi:http://dx.doi.org/10.1108/EJTD-03-2014-0026
Zwick, T. (2015). Training older employees: What is effective? International Journal of Manpower, 36(2), 136-150. doi:http://dx.doi.org/10.1108/IJM-09-2012-0138