For technical organizations Kochanski and Ledford found 15 predictors of retention that were associated with five types of rewards that affect turnover. The five generalized types of rewards were: job content, direct financial (cash), benefits, careers, and affiliation. These are shown in the “Rewards of Work Model” figure. This model identifies the types of rewards that scientists and engineers consider important in making a decision about whether or not to remain with their current employer.
Fifteen areas that management can improve upon to reduce turnover in innovation organizations are shown in the “Predictors of Turnover” figure. Note that these improvements or remedies differ slightly from what might be used with other types of staff groups.
In the area of DIRECT FINANCIAL REWARDS nothing more clearly demonstrates the money is not the only factor in retaining scientific and technical employees than the finding that actual pay level is a less important predictor of retention than are feelings about pay raises and the processes used to minister pay. As such leaders need to pay careful attention to the way pay changes are made and provide education to employees that effectively tells them how they can earn pay increases. It is also important to know that in innovation organizations, scientific and technical employees have a culture that is very receptive to stock options, and are more likely than others to be motivated by stock options, and are most much more likely than other professionals to base retention decisions on stock option opportunities.
In the area of CAREER REWARDS it was found that career opportunity was a most important predictor, followed by satisfaction with training opportunities and the employees’ relationship with his or her supervisor in predicting retention. Thus leadership should focus on the quality of supervision, and when employees leave look to training or replacement of the supervisor to prevent future problems. As an aside note that in innovation organizations job title is not a significant factor.
For WORK CONTENT, scientific and technical workers care greatly about the work they do. The most important predictor in this category is feedback from coworkers and supervisors. From a leadership standpoint good performance management and other systems to provide useful performance feedback from supervisors and peers is essential.
Two strong predictors of retention in the AFFILIATION category reflect two sides of the same coin, organizational commitment and organizational support. In technical organizations ongoing budget support for projects is a primary driver, as well as supervisor’s and mentor’s support of project efforts.