Explicating Individual Training Decisions

Abstract

In this paper, we explicate individual training decisions. For this purpose, we propose a framework based on instrumentality theory, a psychological theory of motivation that has frequently been applied to individual occupational behavior. To test this framework, we employ novel German individual data and estimate the effect of subjective expected utility (SEU) from continuing vocational training (CVT), the effect of restricting factors, and the effect of personal characteristics on the willingness to pay for CVT. Our results imply that SEU is generally the main driver of training decisions. In contrast, financial restrictions are most decisive for persons who are more likely to participate in training (i.e., training tendency). Time restraints also help explain why some individuals are entirely unwilling to participate. Moreover, regional infrastructure is a crucial training determinant. We also find that age and vocational qualifications do not directly affect training decisions. However, persons in specific occupational settings (e.g., low occupational status, overly challenging workplace situations) do exhibit a lower training tendency. Additionally, the training behavior of these persons appears to be more rigid, making them less likely to react to changes in their cognitive attitudes to training.

Appendix

Composition of Subjective Expected Utility: Algebraic formulation

$$ SEU=\left({E}_j\times \left[{\displaystyle \sum_k\left({V}_k\times {I}_{jk}\right)}\right]\right)\left({\displaystyle \sum_l\left({V}_l\times {I}_l\right)}\right) $$

(1)

At first, all valences V were weighted by their respecting instrumentalities I. Then the individual IxV-weights of the extrinsic outcomes were multiplied with the mean measure of all three expectancy values. Note that, in contrast to extrinsic goals, intrinsic outcomes are directly linked to the action (i.e., CVT participation) itself. Thus, the sum of the products V_l × I_l immediately represents the observed individuals’ intrinsic motivation for CVT participation, whereas the sum of the products V_k × I_jk merely represents the valence of the actions’ first-order outcome (i.e., training success). Weighted also with the respective expectancy value, V_k × I_jk represents an individuals’ extrinsic motivation for CVT. The multiplicative combination of valences, instrumentalities, and expectations implies that actions are attractive only when personal goals are rated relatively high and when the respective action is simultaneously perceived to foster the attainment of highly rated goals. However, additive combinations between variables have also been adopted in the empirical literature (Wahba and House 1974).

The summarization and multiplication of instrumentalities, expectancies and values has been criticized as being overly deterministic. Following a common criticism, individuals do not calculate SEU-scores for different actions before they take an action. Indeed, theories of rational action do not postulate that individuals multiply expectancies and values for various potential actions. The theories rather assume that individuals choose, among alternatives, those actions, which lead to both desirable and achievable goals. Multiplication of V, I, and E measures is simply the method used in empirical investigations to approximate this behavior.

Nevertheless, those calculations have been criticized for another reason: when summarizing and multiplying the measures of expectancy, instrumentality and valence, one would disregard the limitations of the scales used (Mitchell 1974; Vroom 2005).

Formal regression model to test hypotheses 1–3:

$$ \ln \frac{P\left(WTP>m\Big|x\right)}{P\left(WTP\le m\Big|x\right)}={\beta}_o+{\beta}_{SEUm}SEU+{\beta}_{RESTRm}RESTR+{\beta}_{PERSm}PERS+\varepsilon, m=1,2,\dots, 6 $$

(2)

The left-hand side of the equation specifies the logarithmic odds in favor of willingness to pay being greater than the threshold level m (i.e., the ratio of the probability that the willingness to pay (WTP) is greater than m to the probability that willingness to pay is less than or equal to m), given the values of the predictors. The right-hand side of the equation includes the SEU indicator, a set of restrictions (RESTR), and a vector of personal characteristics (PERS).

Running an approximate LR test (Gould and Wolfe 1998) and a Wald test (Brant 1990) indicates that the parallel lines assumption only holds for 15 variables in our specified model. Therefore, we use a partial proportional odds model (Williams 2006) for estimation.

Formal regression model to test hypotheses 4 and 5:

$$ \ln \frac{P\left(WTP>\left.m\right|x\right)}{P\left(WTP\le \left.m\right|x\right)}={\beta}_o+{\beta}_{SEU}SEU+{\beta}_{RESTR} RESTR+{\beta}_{PERS} PERS+{\beta}_{PERS\times SEU} PERS\times SEU+\varepsilon $$

(3)

“Imagine you could participate in a continuing training course. The course is about [SUBJECT]. In particular, this may be [TOPIC]. The course [SCOPE] and you would visit it outside of your regular working schedule on weekdays and/or on weekends.”

	SUBJECT	TOPIC	SCOPE
Scenario 1	extending your qualification and enabling you to lead other employees	Case sensitive a	duration accounts for about 800 h, e.g., 100 work days
Scenario 2	computer software	, dependent on your background knowledge, a beginners, advanced or expert course, for example word processing, e-mail, Computer Aided Design etc., which is used in your professional field
Scenario 3	improving your social skills	a course about taking speeches, paralanguage, presenting, communicating, negotiating, team working or coping with stress	duration accounts for 16 h
Scenario 4	expanding knowledge in your occupational field	any course about technical aspects in your occupational field

1. ^aThe interviewers gave examples dependent on the employment status and qualification of the employees. For example, if the employee had been a craftsman without tertiary degree, the TOPIC would have been: a course which prepares you for the master craftsman’s examination.