Construal-level theory (CLT) proposes that psychological distance influences the level of abstraction at which something is mentally construed: Things perceived as less probable (likelihood) or further away from the here (spatial distance), now (temporal distance), or self (social distance) are thought about more abstractly. In this international multilab study, we tested four basic hypotheses derived from core assumptions of CLT and explore potential moderators and boundary conditions of the effects. Participants ( N = 11,775) from 27 countries and regions were randomly assigned to one of four experimental protocols focused on different types of psychological distance (temporal, spatial, social, or likelihood), and each experiment manipulated psychological distance (close vs. distant). The protocols for temporal distance ( n = 2,941) and spatial distance ( n = 2,973) were direct replications of Liberman and Trope (Study 1) and Fujita et al. (Study 1), respectively. The remaining two protocols were paradigmatic replications, applying to social distance ( n = 2,926) and likelihood ( n = 2,936). The effects of psychological distance on construal level for the four present studies were as follows (positive effects are consistent with hypotheses): temporal, d = 0.08, 95% confidence interval [CI] = [0.003, 0.16] (effect in original study: d = 0.92); spatial, d = 0.04, 95% CI = [−0.03, 0.11] (effect in original study: d = 0.55); social, d = −0.27, 95% CI = [−0.34, −0.19]; and likelihood, d = 0.03, 95% CI = [−0.05, 0.11]. Pretests indicated that valence and abstraction were confounded in response options on the outcome measure. Controlling for this confound eliminated the hypothesis-inconsistent effect of social distance, d = 0.006, 95% CI = [−0.05, 0.07]. These findings provide limited evidence for the predictions of the theory and present a critical challenge for CLT.
Police organizations depend on applicants and officers whose career decisions are grounded in realistic and sustainable motivational orientations. This study examined career choice motivation for policing across three career-stage groups in the German Federal Police: police commissioner candidates at the beginning of university-based training (n = 232), candidates after six months of training (n = 159), and experienced officers entering career-advancement training (n = 164). Career choice motivation was assessed with the BEWAPOL questionnaire, a policing-specific measure that operationalizes motivational dimensions within an expectancy-value framework. One-way analyses of variance indicated no significant differences between the two candidate groups, but several differences between candidates and career-advancement officers. Supplementary analyses of covariance controlling for age and gender confirmed group differences for perceived ability/fit, challenge, security and pay, and reputation/status. The effect for nature of the work was no longer significant after controlling for age and gender. No group differences emerged for society/justice. The findings indicate broadly similar motivational profiles during the early training period, but distinct motivational patterns between candidates and experienced officers entering career advancement. Results are discussed in relation to expectancy-value theory, occupational socialization, selection processes, realistic recruitment, and differentiated police education.
Business-to-X (B2X, "X" for business, customer, etc.) mobile applications ("apps") show various mobile-specific chances and challenges that must be ad-dressed in the whole development process. Knowledge about mobile app devel-opment models' usage remains restricted, although various process models for B2X app development have been published already. This article first reviewed available process models for mobile app development. In addition, 28 expert in-terviews with various stakeholders involved in typical B2X mobile app develop-ment processes were conducted to examine this research-practice knowledge di-vide. Since hybrid process models are often advantageous for B2X mobile app development, technical backgrounds or communication processes, are also cru-cial. Since no process model can be used unadopted, this study theorizes mobile-specific characteristics and challenges of app development process models to a reference model generally valuable for management decision support. The find-ings create avenues for better theorizing toward successful B2X mobile app de-velopment.
Abstract The development and evaluation of optimization algorithms critically rely on representative benchmark functions. However, existing benchmarks often offer limited coverage of the diverse landscape characteristics encountered in real-world problems. Moreover, real-world black-box optimization tasks involve costly or time-consuming evaluations, highlighting the need for efficient surrogate models. This work presents a novel, feature-driven method for the automated and targeted generation of synthetic benchmark functions using Deep Exploratory Landscape Analysis (Deep ELA) features. The generated functions are neural network models tailored such that their Deep ELA feature vectors closely match user-specified targets. Unlike previous approaches that depend on handcrafted ELA features and computationally expensive surrogate modeling, our method leverages pretrained transformer-based embeddings and gradient-based optimization to directly produce functions exhibiting desired feature combinations. Our method can generate functions whose Deep ELA vectors resemble those of BBOB problems across multiple dimensions, posing similar structural properties and yielding comparable algorithmic performances. Furthermore, our approach supports the generation of novel benchmark instances, with structural characteristics whose feature representations extend beyond those covered by existing test suites. Overall, our proposed approach offers a scalable methodology for creating efficient surrogate models of existing benchmark libraries or expensive real-world problems, and for generating novel optimization problems with much more diverse landscape characteristics.
