This chapter discusses main opportunities and challenges of assessing and utilizing personality traits in personalized interactive systems and services. This unique perspective arises from our long-term collaboration on research projects involving three groups on human-computer interaction (HCI), psychology, and statistics. Currently, personalization in HCI is often based on past user behavior, preferences, and interaction context. We argue that personality traits provide a promising additional source of information for personalization, which goes beyond context- and device-specific behavior and preferences. We first give an overview of the well-established Big Five personality trait model from psychology. We then present previous findings on the influence of personality in HCI associated with the benefits and challenges of personalization. These findings include the preference for interactive systems, filtering of information to increase personal relevance, communication behavior, and the impact on trust and acceptance. Moreover, we present first approaches of personality-based recommender systems. We then identify several opportunities and use cases for personality-aware personalization: (i) personal communication between users, (ii) recommendations upon first use, (iii) persuasive technology, (iv) trust and comfort in autonomous vehicles, and (v) empathic intelligent systems. Furthermore, we highlight main challenges. First, we point out technological challenges of personality computing. To benefit from personality awareness, systems need to automatically assess the user’s personality. To create empathic intelligent agents (e. g., voice assistants), a consistent personality has to be synthesized. Second, personality-aware personalization raises questions about user concerns and views, particularly privacy and data control. Another challenge is acceptance and trust in personality-aware systems due to the sensitivity of the data. Moreover, the importance of an accurate mental model for users’ trust in a system was recently underlined by the right for explanations in the EU’s General Data Protection Regulation. Such considerations seem particularly relevant for systems that assess and utilize personality. Finally, we examine methodological requirements such as the need for large sample sizes and appropriate measurements. We conclude with a summary of opportunities and challenges of personality-aware personalization and discuss future research questions.

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates.

This research investigates some unintended consequences of different mass customization formats on individual processing styles. Two studies provide novel empirical evidence that conventional attribute-wise configuration formats increase local processing, whereas prespecified configuration formats increase global processing. Importantly for marketers, we show that a global (vs. local) processing style leads to more mental simulation of the configured product and, as a consequence, to increased choice satisfaction, pride of authorship, and purchase intention. These findings highlight important process variables that should be considered when designing mass customization systems.

Algorithms increasingly replace humans in evaluating consumers with consequences for consumer thinking style and decision-making. Combining research on dual-system theory and lay beliefs about algorithms, we propose the phenomenon of anticipated algorithmic evaluation (AAE) which is demonstrated to increase analytical and decrease experiential thinking compared to the human status quo. We offer some preliminary evidence that this change in thinking styles might affect task performance.

There is growing evidence that psychological characteristics are spatially clustered across geographic regions and that regionally aggregated psychological characteristics are related to important outcomes. However, much of the evidence comes from research that relied on methods that are theoretically ill-suited for working with spatial data. The validity and generalizability of this work are thus unclear. Here we address two main challenges of working with spatial data (i.e., modifiable areal unit problem and spatial dependencies) and evaluate data-analysis techniques designed to tackle those challenges. To illustrate these issues, we investigate the robustness of regional Big Five personality differences and their correlates within the United States (Study 1; N = 3,387,303) and Germany (Study 2; N = 110,029). First, we display regional personality differences using a spatial smoothing approach. Second, we account for the modifiable areal unit problem by examining the correlates of regional personality scores across multiple spatial levels. Third, we account for spatial dependencies using spatial regression models. Our results suggest that regional psychological differences are robust and can reliably be studied across countries and spatial levels. The results also show that ignoring the methodological challenges of spatial data can have serious consequences for research concerned with regional psychological differences.

Supervised machine learning (ML) is becoming an influential analytical method in psychology and other social sciences. However, theoretical ML concepts and predictive-modeling techniques are not yet widely taught in psychology programs. This tutorial is intended to provide an intuitive but thorough primer and introduction to supervised ML for psychologists in four consecutive modules. After introducing the basic terminology and mindset of supervised ML, in Module 1, we cover how to use resampling methods to evaluate the performance of ML models (bias-variance trade-off, performance measures, k-fold cross-validation). In Module 2, we introduce the nonlinear random forest, a type of ML model that is particularly user-friendly and well suited to predicting psychological outcomes. Module 3 is about performing empirical benchmark experiments (comparing the performance of several ML models on multiple data sets). Finally, in Module 4, we discuss the interpretation of ML models, including permutation variable importance measures, effect plots (partial-dependence plots, individual conditional-expectation profiles), and the concept of model fairness. Throughout the tutorial, intuitive descriptions of theoretical concepts are provided, with as few mathematical formulas as possible, and followed by code examples using the mlr3 and companion packages in R. Key practical-analysis steps are demonstrated on the publicly available PhoneStudy data set (N = 624), which includes more than 1,800 variables from smartphone sensing to predict Big Five personality trait scores. The article contains a checklist to be used as a reminder of important elements when performing, reporting, or reviewing ML analyses in psychology. Additional examples and more advanced concepts are demonstrated in online materials (https://osf.io/9273g/).