Contributions published at Marketing and Market Research (René Algesheimer)

The Action–Trait theory of human motivation posits that individual differences in predispositional traits of action may account for variance in contemporary purposeful human behavior. Prior research has supported the theory, psychometric properties of scales designed to assess the motive dimensions of the theory, and the utility of these scales to predict an array of behaviors, but this is the first study to evaluate the cross-linguistical invariance of the 15-factor theoretical model. This study evaluated translations of the English language 60-item Quick AIM in 5 samples – Croatian (N = 614), French (N = 246), German (N = 154), Polish (M = 314), and U.S. English (N = 490) – recruited from 4 countries (Croatia, Poland, Switzerland, and the U.S.). Exploratory structural equation modeling (ESEM) supported the theoretical model on which the traits of action are based and scrutinized the measurement invariance (configural, metric, scalar invariance) of the scale across the languages.

Cryptocurrencies are distributed systems that allow exchanges of native (and non-) tokens between participants. The availability of the complete historical bookkeeping opens up an unprecedented possibility: that of understanding the evolution of a cryptocurrency's network structure while gaining useful insights into the relationships between users' behavior and cryptocurrency pricing in exchange markets. In this article we review some recent results concerning the structural properties of the Bitcoin Transaction Networks, a generic name referring to a set of three different constructs: the Bitcoin Address Network, the Bitcoin User Network, and the Bitcoin Lightning Network. The picture that emerges is of a system growing over time, which becomes increasingly sparse and whose mesoscopic structural organization is characterized by the presence of an increasingly significant core-periphery structure. Such a peculiar topology is accompanied by a highly uneven distribution of bitcoins, a result suggesting that Bitcoin is becoming an increasingly centralized system at different levels.

Many real networks feature the property of nestedness, i.e. the neighbours of nodes with a few connections are hierarchically nested within the neighbours of nodes with more connections. Despite the abstract simplicity of this notion, various mathematical definitions of nestedness have been proposed, sometimes giving contrasting results. Moreover, there is an ongoing debate on the statistical significance of nestedness, since random networks where the number of connections (degree) of each node is fixed to its empirical value are typically as nested as real ones. By using only ergodic and unbiased null models, we propose a clarification that exploits the recent finding that random networks where the degrees are enforced as hard constraints (microcanonical ensembles) are thermodynamically different from random networks where the degrees are enforced as soft constraints (canonical ensembles). Indeed, alternative definitions of nestedness can be negatively correlated in the microcanonical one, while being positively correlated in the canonical one. This result disentangles distinct notions of nestedness captured by different metrics and highlights the importance of making a principled choice between hard and soft constraints in null models of ecological networks.

Identifying risk spillovers in financial markets is of great importance for assessing systemic risk and portfolio management. Granger causality in tail (or in risk) tests whether past extreme events of a time series help predicting future extreme events of another time series. The topology and connectedness of networks built with Granger causality in tail can be used to measure systemic risk and to identify risk transmitters. Here we introduce a novel test of Granger causality in tail which adopts the likelihood ratio statistic and is based on the multivariate generalization of a discrete autoregressive process for binary time series describing the sequence of extreme events of the underlying price dynamics. The proposed test has very good size and power in finite samples, especially for large sample size, allows inferring the correct time scale at which the causal interaction takes place, and it is flexible enough for multivariate extension when more than two time series are considered in order to decrease false detections as spurious effect of neglected variables. An extensive simulation study shows the performances of the proposed method with a large variety of data generating processes and it introduces also the comparison with the test of Granger causality in tail by Hong et al. (2009). We report both advantages and drawbacks of the different approaches, pointing out some crucial aspects related to the false detections of Granger causality for tail events. An empirical application to high frequency data of a portfolio of US stocks highlights the merits of our novel approach.

We propose a method to infer lead-lag networks of traders from the observation of their trade record as well as to reconstruct their state of supply and demand when they do not trade. The method relies on the Kinetic Ising model to describe how information propagates among traders, assigning a positive or negative ‘opinion’ to all agents about whether the traded asset price will go up or down. This opinion is reflected by their trading behavior, but whenever the trader is not active in a given time window, a missing value will arise. Using a recently developed inference algorithm, we are able to reconstruct a lead-lag network and to estimate the unobserved opinions, giving a clearer picture about the state of supply and demand in the market at all times. We apply our method to a dataset of clients of a major dealer in the Foreign Exchange market at the 5 minute time scale. We identify leading players in the market and define a herding measure based on the observed and inferred opinions. We show the causal link between herding and liquidity in the inter-dealer market used by dealers to rebalance their inventories.

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The objective of the current study was to examine the relations between narcissism and Adler’s community feeling. Based on theoretical considerations, we claim that community feeling can be treated as an opposite pole of narcissism and we expected that: (1) both grandiose and vulnerable narcissism would be negatively related to community feeling and that (2) grandiose and vulnerable narcissism would be positively related to anti-community domination and isolation. A sample of 520 university students (Mage D 21.37, SDage D 4.31) completed the Community Feeling Questionnaire (CFQ), the Narcissistic Admiration and Rivalry Questionnaire (NARQ) and the Hypersensitive Narcissism Scale (HSNS). Structural equation modeling largely confirmed our expectations. These results suggest that narcissism can be understood in terms of a deficit in community feeling. It turned out that community feeling and narcissism are related constructs but they are not reducible to each other.

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In this paper, we have presented our proposal for reconceptualization and operationalization of Type C (cancer-prone) personality. Based on theoretical analyses, taking into account both the literature on Type C and models of personality structure, we have proposed a two-facet structure of Type C, comprising Submissiveness (the interpersonal aspect) and Restricted Affectivity (the intrapersonal aspect). The study devoted to the validation of the measure of Type C involved 232 participants aged 18–70 (M = 29.35, SD = 8.93; 54% male). We used (a) our proposed measure of Type C personality and (b) the Circumplex of Personality Metatraits Questionnaire (CPM-Q-SF; Strus and Cieciuch, 2017), assessing personality metatraits. The measure of Type C proved to have acceptable internal consistency (Cronbach’s alpha was 0.85 for Submissiveness and 0.78 for Restricted Affectivity). The measurement model in confirmatory factor analysis with two latent variables proved to be well-fitted to the data. We have also confirmed the hypothesis concerning the location of the two facets of Type C personality close to each other in the theoretically predicted area between the Delta-Plus/Self-Restraint and Beta-Minus/Passiveness metatraits (in the Circumplex of Personality Metatraits). The clinical value of the theoretically refined Type C can be tested in the next step in research on patients with cancer.

This paper takes new energy vehicles as the research object, building the technical cooperation innovation network of new energy vehicles based on the patent perspective by establishing the related technology patent search expression, and analyzing the processes of the invulnerability and optimization in the actual technology cooperation innovation network by using the simulation analysis method. The research results show that the harmfulness of the degree value priority attack in the technical cooperation innovation network of new energy vehicles is stronger than the weighted degree value priority attack and random attack, and the attacks of the State Grid and other hub nodes have an important impact on the network invulnerability. During the network optimization process of three types of connection preferences, the “weak”-“weak” connection is the best connection mode given the situation of an unweighted network without considering the weight of the connected edge. However, the “strong”-“weak” connection is the best mode given the situation of a weighted network considering the weight of the connected edge. In addition, compared with the weighted network situation, the “strong”-“weak” connection has better network optimization results given the situation of an unweighted network. Finally, we propose counter measures and suggestions to promote the innovation network invulnerability capabilities of technical cooperation in new energy vehicles.

Multi-group Bayesian structural equation modeling (MG-BSEM) gained considerable attention among substantive researchers investigating cross-group differences and methodologists exploring challenges in measurement invariance testing. MG-BSEM allows for greater flexibility by applying elastic rather than strict equality constraints on item parameters across groups. This, however, requires a specification of user-defined prior variances for cross-group differences in item parameters. Although prior selection in general Bayesian settings is well-studied, guidelines with respect to tuning the normal prior variances in MG-BSEM approximate measurement invariance (AMI) analysis are still largely missing. In a Monte Carlo simulation study we find that correctly specifying prior variances results in more precise credibility intervals (CI) and posterior standard deviations, while prior misspecification has little influence on point estimates. We compared the BIC, DIC, and PPP fit measures and found in our simulation scenarios that the DIC measure was most effective, when a proper threshold for model selection was applied.

The Dark Triad of personality is most commonly studied model of dark personality traits. The current study attempts to empirically compare the Dark Triad to other catalog of dark personality traits, namely the seven deadly sins, and locate them within the broader model of personality – the Circumplex of Personality Metatraits model. We examined this problem from two perspectives: self- (N = 280) and other-report (N = 412) using the Short Dark Triad, Vices and Virtues Scales, and the Circumplex of Personality Metatraits Questionnaire. The Dark Triad and the seven deadly sins were substantially interrelated. Moreover, both analyzed models of dark personality traits were strongly associated with Alpha-Minus (both, in self- and other-report), providing evidence about their dark character. The expected locations within the Circumplex of Personality Metatraits were generally supported, nevertheless there were some discrepancies between self- and other report. Results of our study reveals that the Dark Triad of personality does not fully exhaust the possible catalog of the dark personality and future research is needed to fill this gap.

The Bitcoin Lightning Network (BLN), a so-called "second layer" payment protocol, was launched in 2018 to scale up the number of transactions between Bitcoin owners. In this paper, we analyse the structure of the BLN over a period of 18 months, ranging from 12th January 2018 to 17th July 2019. Here, we consider three representations of the BLN: the daily snapshot one, the weekly snapshot one and the daily-block snapshot one. By studying the topological properties of the three representations above, we find that the total volume of transacted bitcoins approximately grows as the square of the network size; however, despite the huge activity characterising the BLN, the bitcoins distribution is very unequal: the average Gini coefficient of the node strengths (computed across the entire history of the Bitcoin Lightning Network) is, in fact, ~0.88 causing the 10% (50%) of the nodes to hold the 80% (99%) of the bitcoins at stake in the BLN (on average, across the entire period). This concentration brings up the question of which minimalist network model allows us to explain the network topological structure. Like for other economic systems, we hypothesise that local properties of nodes, like the degree, ultimately determine part of its characteristics. Therefore, we have tested the goodness of the Undirected Binary Configuration Model (UBCM) in reproducing the structural features of the BLN: the UBCM recovers the disassortative and the hierarchical character of the BLN but underestimates the centrality of nodes; this suggests that the BLN is becoming an increasingly centralised network, more and more compatible with a core-periphery structure. Further inspection of the resilience of the BLN shows that removing hubs leads to the collapse of the network into many components, an evidence suggesting that this network may be a target for the so-called split attacks.

Interpersonal values are defined in terms of preferences for achieving specific interpersonal outcomes and waysof dealing with social interactions. They form an octant circumplex based on two dimensions–Agency andCommunion, analogous to the Interpersonal Circumplex model. The purpose of this paper was twofold: (1) theintegration of two different models that organize values in two diverse circular structures–interpersonal valuesand basic human values and (2) the inspection of the personality underpinnings of interpersonal and basichuman values.The study was conducted on a group of 816 participants in Poland aged from 16 to 72.The results indicate (1) convergence between the dimension of Agency in interpersonal values and the di-mension of Openness to Change vs. Conservation in basic values model, (2) convergence between the dimensionof Communion in interpersonal values and the dimension of Self-Transcendence vs. Self-Enhancement values, (3)relations of interpersonal values with Agreeableness and Extraversion, as theoretically predicted on the basis ofthe key assumptions of the Interpersonal Circumplex, and (4) relations between basic human values and per-sonality traits, suggesting there are common personality underpinnings of interpersonal and basic values.

The opportunity to capitalize on social contagion has led many firms to invest significant resources in designing viral products and identifying the best seeding strategies. While extensive literature has been devoted to addressing both topics, incorporating this knowledge to predict and engineer product virality remains a difficult task. In this article, we examine whether the diffusion of a new product can be predicted based on individual, product and social network characteristics. To this end, we integrate a lab experiment with an agent-based model of product diffusion, and validate our results on empirical data. In the lab experiment, we use a conjoint design to measure the individual susceptibility to social influence from observed product choices. We show that susceptibility is dependent on the interplay between product and individual characteristics. We use the experimental results to calibrate an agent-based model of new product diffusion in a social network. We quantify the success predictability of different products, the potential outcome and risk associated with seeding strategies, and the role played by product and network characteristics on cascade size. Furthermore, we propose a method to construct an optimal portfolio of seed nodes with an ordinary number of contacts, and show that it outperforms seeding high degree nodes. We validate our results on susceptibility inference and diffusion predictability in an empirical study of an online food community (1M users) where we observe the diffusion of over 75’000 user-generated recipes over 10 years. Overall, our findings shed light on the drivers of social contagion, establish a link between micro-level observations and macro-level outcomes, and provide insight into designing more effective viral marketing campaigns.