Contributions published at Marketing and Market Research (René Algesheimer)

Both the ICD-11 and the DSM-5 (Section III) classification systems introduced dimensional models of personality disorders, with five broad domains called the Pathological Big Five. Nevertheless, despite large congruence between the two models, there are also substantial differences between them, with the most evident being the conceptualization of the fifth dimension: Anankastia in the ICD-11 vs. Psychoticism in the DSM-5. The current paper seeks an answer to the question of which domain is structurally better justified as the fifth trait in the dimensional model of personality disorders. For this purpose, we provided both a conceptual and empirical comparison of the ICD-11 and the DSM-5 models, adopting the Circumplex of Personality Metatraits—a comprehensive model of personality structure built on the basis of the higher-order factors of the Big Five—as a reference framework. Two studies were conducted: the first on a sample of 242 adults (52.9% female; Mage = 30.63, SDage = 11.82 years), and the second on a sample of 355 adults (50.1% female; Mage = 29.97, SDage = 12.26 years) from the non-clinical population. The Personality Inventory for ICD-11 (PiCD), the Personality Inventory for DSM-5 (PID-5), and the Circumplex of Personality Metatraits Questionnaire–Short Form (CPM-Q-SF) were administered in both studies, together with the PID-5BF+M algorithm for measuring a common (ICD-11 + DSM-5) six-domain model. Obtained empirical findings generally support our conceptual considerations that the ICD-11 model more comprehensively covered the area of personality pathology than the DSM-5 model, with Anankastia revealed as a more specific domain of personality disorders as well as more cohesively located within the overall personality structure, in comparison to Psychoticism. Moreover, the results corroborated the bipolar relations of Anankastia vs. Disinhibition domains. These results also correspond with the pattern of relationships found in reference to the Big Five domains of normal personality, which were also included in the current research. All our findings were discussed in the context of suggestions for the content and conceptualization of pathological personality traits that flow from the CPM as a comprehensive model of personality structure including both pathological and normal poles of personality dimensions.

The circumplex model of affect, defined by the dimensions of Valence and Arousal, is commonly used to describe the emotional experience. The usefulness of both dimensions has been confirmed, but Arousal has structural problems. In this study, we systematically investigated the structure of affect in terms of meeting the requirements of (a) full circumplex (communalities and angles are equal), (b) quasi-circumplex with equal angles, (c) quasi-circumplex with equal communalities, (d) circular model (both communalities and angles can be unequal) and we inspected which scales deviated from their theoretically expected location. We examined the structure of state affect based on the Chinese circumplex model of affect by Yik (2009), trait-like affect (actual) and ideal affect measured with the affect valuation index by Tsai et al. (2006). Structural equation modeling and Procrustes analyses were used to examine the structure of affect in a sample of N = 863. There was no evidence of a circumplex structure in any of the models of affect, instead a circular model was supported in all cases. In all models, the Arousal dimension was systematically associated with discrepancies from the theoretical predictions derived from circumplex model of affect, resulting in an ellipse structure.

Objective The circular structure of values has been verified mostly at a between-person level and on measures of general value preferences. In this manuscript, we argue that it is a simplification that neglected significant aspects of the value structures and distinguish four different types of structures: (a) between-person structure of value traits, (b) within-person structure of value traits, (c) between-person structure of value states, and (d) within-person structure of value states. We argue that the within-person structure of value states addresses the circular structure of values most accurately. Method To compare all four structures, we collected three partially dependent samples (N1 = 449, N2 = 293, N3 = 218) of adults (age 17–57, M = 24). At three time points, separated by 5–7 weeks, respondents completed a questionnaire measure (Portrait Values Questionnaire-Revised [PVQ-RR]) of value preferences (value traits) and reported the importance of values in their everyday actions (value states) for 1 week in an experience sampling study. Results The four types of value structures were stable over time. All four were also consistent with Schwartz's value model to some degree, but at the same time, there were some deviations. Conclusions It is important to distinguish four types of value structures and be aware of their different interpretations that we outlined in this paper.

Most of the countries in Europe are experiencing a rapid aging of their populations and with this an increase in mental health challenges due to aging. Comparative research may help countries to assess the promotion of healthy aging in general, and mentally healthy aging in particular, and explore ways for adapting mental health policy measures. However, the comparative study of mental health indicators requires that the groups understand the survey questions inquiring about their mental health in the same way and display similar response patterns. Otherwise, observed differences in perceived mental health may not reflect true differences but rather cultural bias in the health measures. To date, research on cross-country equivalence of depression measures among older populations has received very limited attention. Thus, there is a growing need for the cross-country validation of existing depression measures using samples of the older population and establishing measurement equivalence of the assessment tools. Indeed, insights on mental health outcomes and how they compare across societies is paramount to inform policy makers seeking to improve mental health conditions of the populations. This study, therefore, aims to examine measurement equivalence of self-reported depressive symptoms among older populations in 17 European countries and Israel. The data for the current analysis are from the sixth wave (2015) of the Survey on Health, Ageing and Retirement in Europe (SHARE) and consist of the population of respondents 50 years of age and older. The measurement of depression is based on the EURO-D scale, which was developed by a European consortium. It identifies existing depressive symptoms and consists of the 12 items: depression, pessimism, suicidality, guilt, sleep, interest, irritability, appetite, fatigue, concentration, enjoyment, and tearfulness. We examine the cross-country comparability of these data by testing for measurement equivalence using multigroup confirmatory factor analysis (MGCFA) and alignment. Our findings reveal partial equivalence thus allowing us to draw meaningful conclusions on similarities and differences among the older population across 18 countries on the EURO-D measure of depression. Findings are discussed in light of policy implications for universal access to mental health care across countries.

Objective There is a considerable body of evidence from the last 20 years, indicating the need for the reconceptualization of the highest level of the personality structure that the Big Five/Five‐Factor Model (FFM) was assumed to occupy. The main goal of the presented study was to test the relationships between two models that have been developed in this respect: The Circumplex of Personality Metatraits (CPM), based on the higher‐order factors of the Big Five, and the HEXACO model including a sixth basic personality dimension (Honesty‐Humility). Method The sample consisted of 500 respondents (56.8% females; Mage = 31.9, SDage = 14.0), all of whom completed the CPM, HEXACO, and FFM measures. Results The results corroborated the expectation that the HEXACO model can be coherently located within the CPM model, despite the latter is rooted in the FFM research tradition. However, this substantial integration has been made possible by a relatively slight but crucial modification of the CPM, already suggested by previous research. Conclusion After the modification, which concerned the location of the Neuroticism/Emotional stability trait, the CPM enables a comprehensive integration of major models of personality structure encompassing the Two‐Factor Model, the FFM, and the HEXACO.

Customer base analysis of noncontractual businesses builds on modeling purchases and latent attrition. With the Pareto/NBD model, this has become a straightforward exercise. However, this simplicity comes at a price. Customer-level predictions often lack precision. This issue can be addressed by acknowledging the importance of contextual factors for customer behavior. Considering contextual factors might contribute in two ways: (1) by increasing predictive accuracy and (2) by identifying the impact of these determinants on the purchase and attrition process. However, there is no generalization of the Pareto/NBD model that incorporates time-varying contextual factors. Preserving a closed-form maximum likelihood solution, this study proposes an extension that facilitates modeling time-invariant and time-varying contextual factors in continuous noncontractual settings. These contextual factors can influence the purchase process, the attrition process, or both. The authors further illustrate how to control for endogenous contextual factors. Benchmarking with three data sets from the retailing industry shows that explicitly modeling time-varying contextual factors significantly improves the accuracy of out-of-sample predictions for future purchases and latent attrition.

When making the decision to use a service for personal benefits, consumers are fast to underestimate privacy-related costs and hence, freely share their personal data (uninformed consent). This cost-benefit analysis shifts, when focusing on data sharing for a social good. We show that asking people to use a service that serves a social good (containing the spread of the coronavirus), they overestimate the costs and rather not use the service due to privacy concerns (misinformed dissent). To increase data sharing for a societal cause, we test two interventions on how privacy-related information should be communicated. Our results indicate that providing additional information on a service (1) is not processed thoroughly when consumers already have a strong prior conviction about using the service; (2) increases knowledge and positive attitude only if the information is processed thoroughly; or (3) information is presented in a comparative manner compared to single information.

How does the complexity of the world around us affect the reliability of our opinions? Motivated by this question, we quantitatively study an opinion formation mechanism whereby an uninformed observer gradually forms opinions about a world composed of subjects interrelated by a signed network of mutual trust and distrust. We show numerically and analytically that the observer’s resulting opinions are highly inconsistent (they tend to be independent of the observer’s initial opinions) and unstable (they exhibit wide stochastic variations). Opinion inconsistency and instability increase with the world’s complexity, intended as the number of subjects and their interactions. This increase can be prevented by suitably expanding the observer’s initial amount of information. Our findings imply that an individual who initially trusts a few credible information sources may end up trusting the deceptive ones even if only a small number of trust relations exist between the credible and deceptive sources.

Binary random variables are the building blocks used to describe a large variety of systems, from magnetic spins to financial time series and neuron activity. In statistical physics the kinetic Ising model has been introduced to describe the dynamics of the magnetic moments of a spin lattice, while in time series analysis discrete autoregressive processes have been designed to capture the multivariate dependence structure across binary time series. In this article we provide a rigorous proof of the equivalence between the two models in the range of a unique and invertible map unambiguously linking one model parameters set to the other. Our result finds further justification acknowledging that both models provide maximum entropy distributions of binary time series with given means, auto-correlations, and lagged cross-correlations of order one. We further show that the equivalence between the two models permits to exploit the inference methods originally developed for one model in the inference of the other.

Nestedness refers to a hierarchical organization of complex networks where a given node’s neighbors tend to form a subset of the neighborhoods of higher-degree nodes. Although nestedness has been traditionally interpreted as a macroscopic property that involves all the nodes of the network, recent works have reinterpreted it as a mesoscopic network property, by revealing that interactions in diverse empirical networks are often arranged into blocks that exhibit an internally nested structure. Inspired by the popular modularity function, these works rely on a quality function – called in-block nestedness – that assumes a partition of the nodes into blocks that exhibit an internal nested structure. A potential limitation of this approach is that the optimization of modularity (and related quality functions) notoriously suffers from resolution limits that impair the detectability of small blocks. Yet, we do not know whether the in-block nestedness function may exhibit similar resolution limits. Here, we provide numerical and analytical evidence that the in-block nestedness function lacks a resolution limit, which implies that our capacity to detect correct partitions in networks via its maximization depends solely on the accuracy of the optimization algorithms.

A substantial body of evidence has identified higher-order factors above the five basic dimensions of personality known as the Big Five. Indeed, two higher-order factors, and also one General Factor of Personality (GFP), have been found in many studies in data collected by using instruments intended to measure the Big Five in the Five- Factor Model (FFM) framework. To date, however, there is a lack of studies that apply the methodology for identifying higher-order factors of the Big Five/FFM to the Big Six/HEXACO model, even though the latter is currently deemed a serious alternative to the former. The goal of the presented studies was to fill this gap. Namely, we tested whether similar higher-order factors to those identified above the Big Five/FFM can be found above the Big Six/HEXACO basic personality traits by applying analytical methods elaborated within the Big Five/FFM framework. We conducted two studies: one with 805 participants, and another with 502 respondents, using three different Big Six/HEXACO measures and three Big Five/FFM measures. The obtained results suggest the existence of two higher-order factors in the Big Six/HEXACO, similar to those found in the Big Five/FFM, and indicate no presence of the GFP in the former.

The 3-factor model of narcissism is generally agreed upon within the literature. However, only a limited number of studies have investigated its structure. We investigated the internal structure of the measure using exploratory factor analysis on the Polish adaptation of the Five Factor Narcissism Inventory (FFNI). This article reports results of 2 studies conducted in Poland, including a total of 793 adults. The results of both studies provided evidence for the 3-factor structure of narcissism. Nevertheless, there were also some deviations: Grandiose fantasies, thrill seeking and arrogance do not load appropriately on any factor, and manipulativeness and reactive anger were better indicators of agentic extraversion and narcissistic neuroticism than self-centered antagonism. The validity of the modification of the FFNI scoring was assessed in regard to the Big Five personality traits and other measures of narcissistic personality. Results provide evidence that the composite scores of the 3 factors are valid and that the modification of scoring improves the measurement precision of the FFNI. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

We perform all possible removals of nodes from networks of size , then we identify and measure the largest connected component left in every case. The smallest of these components represents the maximum possible damage (on a network of vertices), limited to the removal of nodes, and the set that produces such damage is called the optimal set of size . We apply the procedure in a series of networks with controlled and varied modularity. Then, we compare the resulting statistics with the effect of removing the same amount of vertices according to state of the art methods of network fragmentation, i.e., High Betweenness Adaptive attack, Collective Influence, and Module-Based Attack. For practical matters we performed mainly attacks of size on networks of size , because the number of all possible sets () is at the verge of the computational capability of standard desktops. The results show, in general, that the resilience of networks to attacks has an inverse relationship with modularity, with being the critical value, from which the damage of the optimal attack increases rapidly. Networks are highly vulnerable to targeted attacks when the modularity is greater than the critical value of each heuristic method. On the other hand, for modularities lower than , all the heuristic strategies studied have a similar performance to a random attack.

Circumplex models are widely utilized in the field of personality and individual differences research. Although within the literature one could find many suggestions on how to analyze such models, none of them are comprehensive enough. Within the current paper we propose a three-step procedure, which will systematize and standardize the analysis of circumplex models and the localization of external variables within such circumplex models. First, we propose to verify the underlying circumplex structure through structural equation modeling. Second, we propose to test the possibility to locate external variables in the space of the empirical circumplex model through the investigation of their structural summary profiles. Finally, we propose to test the congruence between empirical locations and theoretical predictions within the circumplex structure through the Procrustes rotation. The three-step procedure is described using the example of narcissism embedded within the Circumplex of Personality Metatraits model. This paper is supplemented by pedagogical tutorials assisting other researchers in applying the three-step procedure to their own data.

This study covers the evolutionary development of blockchain technologies over the last 11 years (2009–2019) and sheds lights on potential areas of innovation in heretofore unexplored sub-components. For this purpose, we collected and analyzed detailed data on 107 different blockchain technologies and studied their component-wise technological evolution. The diversity of their designs was captured by deconstructing the blockchains using the Tasca-Tessone taxonomy to build what we call the “tree of blockchain” composed of blockchain main and sub-components. With the support of information theory and phylogenetics, we found that most design explorations have been conducted within the components in the areas of consensus mechanisms and cryptographic primitives. We also show that some sub-components like Consensus Immutability and Failure Tolerance, Access and Control layer, and Access Supply Management have predictive power over other sub-components. We finally found that few dominant design models—the genetic driving clusters of Bitcoin, Ethereum, and XRP—influenced the evolutionary paths of most of the succeeding blockchains.

System dynamics (SD) is an established discipline to model and simulate complex dynamic systems. The primary goal of SD is to evaluate and design new policies that can impact the system under study in a desired way. Policy design, that is, identifying effective model levers, however, is a challenge and in many cases trial-and-error driven. In this article, we introduce a new and coherent framework for model analysis, called structural analysis methods (SAM), to facilitate the policy design process in complex SD models. SAM provides a resource-efficient and effective means for the detection of candidate policy parameters. It enables to identify intended and unintended effects of activating these policy parameters, and to discover candidate structural changes such as introducing new variables and links in SD models. The main innovation of SAM is that it translates the structure of SD models into weighted digraphs allowing algorithmic tools from the realms of graph theory and network science to be applied to SD. SAM is validated on the basis of two well-known simulation models of increasing complexity: the third-order Phosphorus Loops in Soil and Sediment (PLUM) model and the fifth-order World2 model. The validation shows that SAM seems to be most valuable for the analysis of more complex simulation models (World2) and is less suited for the analysis of low complexity models (PLUM).

Artificial intelligence-based voice assistants (VAs) such as Amazon Alexa deliver personalized product recommendations in order to match consumers’ needs. The use of voice assistants for shopping purposes in corporates elements of risk affecting when and how they are considered trusted relationship partners. In this uncertain environment, it is unclear ‘when’ voice assistants are capable of gaining trust and ‘how’ the development of such a trusted relationship affects decisions. This research explores the effect of trusting beliefs towards voice assistants on decision satisfaction through the indirect effect of consideration set size (n.of options), in the context of voice shopping. Findings of an individual-session online experiment (N = 180) show a positive direct effect of trust on customer’s satisfaction and a mediating role of set size, confirming consumers’ bias towards default choices. This study highlights the consequences of trust in AI-enabled voice assistants for decision-making during utilitarian purchases.

AI-based voice assistants such as Amazon Alexa deliver personalized product recommendations in order to match consumers' needs. The use of voice assistants for shopping purposes incorporates elements of risk affecting when and how they are considered trusted relationship partners. In this uncertain environment, it is unclear 'when' voice assistants are capable of gaining trust and 'how' the development of such a trusted relationship affects decisions. This research explores the effect of trusting beliefs towards voice assistants on decision satisfaction through the indirect effect of consideration set size (n. of options), in the context of voice shopping. Findings of an individual-session online experiment (N=180) show a positive direct effect of trust on customer's satisfaction and a mediating role of set size, confirming consumers' bias towards default choices. This study highlights the consequences of trust in AI-enabled voice assistants for decision-making during utilitarian purchases.