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3.3. Operacionalización / categorización

3.3.2. Definición operacional de las variables de estudio

Method. Respondents were 103 students (20 men, 83 women, M age = 20.02 years, SD

= 4.67) from Leiden University. We worked with the same two independent factors as in Study 4.1; participants’ personal regulatory orientations and the type of intervention they received. The whole study procedure was also similar to Study 4.1, except that participants now had to perform a promotion task.

Personal regulatory orientation. Participants’ personal regulatory orientations were assessed with the same measure as in Study 4.1 (Higgins et al., 2001). Both scale constructs were reliable (prevention orientation: alpha = .82 ; promotion orientation: alpha = .61). We again established their dominant orientation by means of difference scores, with lower scores indicating a preferred prevention orientation and higher scores indicating a preference for a promotion orientation.

Promotion task.After the filler task, participants again had to imagine that they were the owners of biological fast-food restaurant. This time, however, they had to come up with as many ways as possible to make their restaurant known to the public. This task represents a variation of the established “brick” creativity task (Guilford, 1950), which other researchers have also used to examine performance on promotion-type tasks (e.g. Friedman & Förster, 2001). The task requires eagerness, creativity and open mindedness (Van Dijk & Kluger, 2011). Making a mistake on this task does not have immediate consequences for the restaurant, whilst a good performance (developing an outstandingly engaging promotional campaign) could potentially enhance publicity and revenues to a great extent (Jacobs, 1981).

Performance incentives. Before participants had to start with the task, they were randomly assigned to one of the two performance incentive conditions. They either received a prevention incentive or a promotion incentive.

In this study, participants received a bogus article on research conducted by the London Business School, which compared 500 starting businesses that had either used or did not use an original promotional campaign. The promotion incentive stated that an

Testing the effectiveness of interventions to enhance performance on regulatory tasks | Chapter 5

117 original promotional campaign would help their businesses to make large profits and be very successful. The prevention incentive stated that an original promotional campaign would help prevent their businesses from making insufficient profits and therefore being unsuccessful. Full descriptions of these performance incentives are provided in Appendix B. After participants read this article, they had to execute the promotion task and develop as many ideas as they could to promote their restaurant.

Measures. Objective performance was measured by counting the distinct number of ideas that participants generated, as well as assessing the quality of the ideas (i.e. the originality and feasibility of the ideas). We followed the coding procedure developed by Rietzschel, Nijstad and Stroebe (2010), and trained two raters who were blind to our conditions to rate all ideas on ‘originality’ and ‘feasibility’( on a five-point scale ranging from 1 = not at all original / feasible, to 5 = very original / feasible). A two-way random model with consistency definition (ICC2) showed that the Intra Class Correlations between the raters can be considered excellent on both dimensions (.87 for originality and .91 for feasibility; see Cicchetti & Sparrow, 1981).

After the task was finished, participants filled in the same questionnaire as in Study 4.1, which contained our manipulation checks and measures to capture participants’ subjective task experience (self-reported performance, alpha = .87; self-reported motivation, alpha = .85). All statements again had to be answered on a 7-point Likert scale ( 1 = strongly disagree to 7 = strongly agree).

Results

Manipulation checks. The results of a one-way ANOVA on the message quality check (alpha = .73), confirmed that in this study too, both incentive messages were equally convincing to participants (Maverage = 3.90, SD = 1.01, F (1,102) = .29, p = .60). A series of one-way ANOVAs on the statements that checked whether participants correctly remembered which incentive they had received, again did not yield any significant effect (lowest F (1,102) = .25, p = .62). This could mean that the messages did not communicate the intended regulatory incentive successfully, an issue that we will come back to later.

We followed the same procedure to analyze the dependent measures as in Study 4.1. That is, for each measure, we performed two-step regression models. In Step 1, we entered participant’s chronic regulatory orientation and the type of incentive they received as independent factors in the model. In Step 2, the interaction between these two factors was added as a predictor to the model.

Objective performance. In this study, the two independent factors did not significantly predict the number of distinct ideas that participants generated to promote their fast-food restaurant. Participants’ personal orientation and the type of incentive they received also had no joint influence on this measure (lowest p-value in the regression analysis, b = .02, p = .82). This means that the effectiveness of the performance incentives did not depend on people’s dominant regulatory orientation. We also did not obtain any significant effects on the originality and feasibility of the ideas that participants developed (lowest p-value across regression analyses, b = -.03, p = .90). Hence, we did not find any support for one of the two hypotheses on the objective performance measure.

Subjective measures. We also did not find any significant main effects for the participant’s personal regulatory orientation and the type of incentive they received on their self-reported performance and motivation levels. Nor did the two factors interactive affect participants’ subjective task experience measures (lowest p-value across both regression analyses, b = -.04, p = .90).

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