Alice Ramos, Eldad Davidov, Peter Schmidt, Marta Vilar Rosales, Dina Maskileyson, Immigration from the Immigrants’ Perspective: Analyzing Survey Data Collected among Immigrants and Host Society Members, Social Inclusion, Vol. 7 (4), 2019. (Journal Article)
Immigration has been one of the most crucial global phenomena, changing the fabric of many societies, and a topic of substantial research. Much of this research has focused on how the host society views immigrants and immigration, or on the societal factors influencing the latter. The goal of this thematic issue is to present different studies focusing on various aspects of immigration from a perspective that has not been often viewed under the magnifying glass so far, but which is of major importance: looking at immigration from the immigrants’ point of view. |
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Alex Mari, Voice Commerce: Understanding Shopping-Related Voice Assistants and their Effect on Brands, In: IMMAA Annual Conference, IMMAA, 2019-10-04. (Conference or Workshop Paper published in Proceedings)
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Matus Medo, An Zeng, Yi-Cheng Zhang, Manuel Mariani, Optimal timescale for community detection in growing networks, New Journal of Physics, Vol. 21 (9), 2019. (Journal Article)
Time-stamped data are increasingly available for many social, economic, and information systems that can be represented as networks growing with time. The World Wide Web, social contact networks, and citation networks of scientific papers and online news articles, for example, are of this kind. Static methods can be inadequate for the analysis of growing networks as they miss essential information on the system's dynamics. At the same time, time-aware methods require the choice of an observation timescale, yet we lack principled ways to determine it. We focus on the popular community detection problem which aims to partition a network's nodes into meaningful groups. We use a multi-layer quality function to show, on both synthetic and real datasets, that the observation timescale that leads to optimal communities is tightly related to the system's intrinsic aging timescale that can be inferred from the time-stamped network data. The use of temporal information leads to drastically different conclusions on the community structure of real information networks, which challenges the current understanding of the large-scale organization of growing networks. Our findings indicate that before attempting to assess structural patterns of evolving networks, it is vital to uncover the timescales of the dynamical processes that generated them. |
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Martin Kindschi, Jan Cieciuch, Eldad Davidov, Alexander Ehlert, Heiko Rauhut, Claudio Tessone, René Algesheimer, Values in adolescent friendship networks, Network Science, Vol. 7 (4), 2019. (Journal Article)
Values—the motivational goals that define what is important to us—guide our decisions and actions every day. Their importance is established in a long line of research investigating their universality across countries and their evolution from childhood to adulthood. In adolescence, value structures are subject to substantial change, as life becomes increasingly social. Value change has thus far been understood to operate independently within each person. However, being embedded in various social systems, adolescents are constantly subject to social influence from peers. Thus, we introduce a framework investigating the emergence and evolution of value priorities in the dynamic context of friendship networks. Drawing on stochastic actor-oriented network models, we analyze 73 friendship networks of adolescents. Regarding the evolution of values, we find that adolescents’ value systems evolve in a continuous cycle of internal validation through the selection and enactment of goals—thereby experiencing both congruence and conflicts—and external validation through social comparison among their friends. Regarding the evolution of friendship networks, we find that demographics are more salient for the initiation of new friendships, whereas values are more relevant for the maintenance of existing friendships. |
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Manuel Mariani, Searching for Individuals Whose Early Adoptions Signal Future Success in a Nationwide Socio-Economic System, In: 4th European Conference on Social Networks. 2019. (Conference Presentation)
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Artur Pokropek, Eldad Davidov, Peter Schmidt, A Monte Carlo Simulation Study to Assess The Appropriateness of Traditional and Newer Approaches to Test for Measurement Invariance, Structural Equation Modeling, Vol. 26 (5), 2019. (Journal Article)
Several structural equation modeling (SEM) strategies were developed for assessing measurement invariance (MI) across groups relaxing the assumptions of strict MI to partial, approximate, and partial approximate MI. Nonetheless, applied researchers still do not know if and under what conditions these strategies might provide results that allow for valid comparisons across groups in large-scale comparative surveys. We perform a comprehensive Monte Carlo simulation study to assess the conditions under which various SEM methods are appropriate to estimate latent means and path coefficients and their differences across groups. We find that while SEM path coefficients are relatively robust to violations of full MI and can be rather effectively recovered, recovering latent means and their group rankings might be difficult. Our results suggest that, contrary to some previous recommendations, partial invariance may rather effectively recover both path coefficients and latent means even when the majority of items are noninvariant. Although it is more difficult to recover latent means using approximate and partial approximate MI methods, it is possible under specific conditions and using appropriate models. These models also have the advantage of providing accurate standard errors. Alignment is recommended for recovering latent means in cases where there are only a few noninvariant parameters across groups. |
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Manuel Mariani, Matúš Medo, François Lafond, Early identification of important patents: Design and validation of citation network metrics, Technological Forecasting and Social Change, Vol. 146, 2019. (Journal Article)
One of the most challenging problems in technological forecasting is to identify as early as possible those technologies that have the potential to lead to radical changes in our society. In this paper, we use the US patent citation network (1926–2010) to test our ability to early identify a list of expert-selected historically significant patents through citation network analysis. We show that in order to effectively uncover these patents shortly after they are issued, we need to go beyond raw citation counts and take into account both the citation network topology and temporal information. In particular, an age-normalized measure of patent centrality, called rescaled PageRank, allows us to identify the significant patents earlier than citation count and PageRank score. In addition, we find that while high-impact patents tend to rely on other high-impact patents in a similar way as scientific papers, the patents' citation dynamics is significantly slower than that of papers, which makes the early identification of significant patents more challenging than that of significant papers. In the context of technology management, our rescaled metrics can be useful to early detect recent trends in technical improvement, which is of fundamental interest for companies and investors. |
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Hui Liu, Naiding Yang, Zhao Yang, Yanlu Zhang, Jianhong Lin, Modeling and simulations of the cascading failure of multiple interdependent R&D networks under risk propagation, Physics Letters A, Vol. 383 (21), 2019. (Journal Article)
In this paper, we study the robustness of multiple interrelated R&D networks under risk propagation. Firstly, using a bi-partite graph to represent the interrelated R&D networks is emphasized and proposed. Secondly, a risk propagation model is built by defining risk load and risk capacity of each enterprise on a specific R&D network, Thirdly, we use simulations to study risk propagation in interrelated R&D networks. Our results indicate that there exist three critical thresholds to quantify the robustness of R&D networks. Risk propagation in R&D networks is highly affected by the heterogeneity of all enterprises' scales and risk capacities. |
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Radu Petru Tanase, Zhao Yang, René Algesheimer, Switch or Repeat? The Hidden Effect of Social Influence on Purchase Behavior, In: INFORMS MARKETING SCIENCE – 2019. 2019. (Conference Presentation)
Understanding how people influence or are influenced by their peers can help us understand the flow of market trends, product adoption and diffusion processes. Most existing work on social influence considers change in purchase behavior as a dependent variable and thus an individual is influenced if she was determined to change her behavior. However, nowadays people are faced with countless buying options, thus repeatedly purchasing the same product can be considered the exception rather than the norm. In this paper, the authors propose a theoretical framework in which the decision to repurchase or switch to a new product is related to two types of stimuli: intrapersonal (related to variety seeking and loyalty behavior) and interpersonal (related to exposure to the social group). They distinguish between two influence processes: to switch to a new product (visible influence) and to repurchase (hidden influence). By analyzing data on consumption decisions, the authors show that hidden influence has a positive effect on the probability to repurchase. The authors explore the effect further and show that for variety seeking consumers the hidden influence has a stronger effect on the purchase decision compared to visible influence. The results challenge classical findings by showing that the effect of social influence on switching behavior is only one facet of social influence and a potentially equally important aspect is its effect on repeated behavior. Focusing only on the visible side and ignoring the intrapersonal motivation to switch and repurchase has lead to framework in which the total social influence effect can often be over or underestimated. |
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Zhao Yang, Radu Petru Tanase, René Algesheimer, The Differential Effect of Social and Content Related User Generated Content on Customer Acquisition, In: 41st Annual ISMS Marketing Science Conference. 2019. (Conference Presentation)
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Manuel Mariani, Zhuo-Ming Ren, Jordi Bascompte, Claudio Tessone, Nestedness in complex networks: Observation, emergence, and implications, Physics Reports, Vol. 813, 2019. (Journal Article)
The observed architecture of ecological and socio-economic networks differssignificantly from that of random networks. From a network science standpoint, non-random structural patterns observed in real networks call for an explanation of their emergence and an understanding of their potential systemic consequences. This article focuses on one of these patterns: nestedness. Given a network of interacting nodes, nestedness can be described as the tendency for nodes to interact with subsets of the interaction partners of better-connected nodes. Known since more than 80 years in biogeography, nestedness has been found in systems as diverse as ecological mutualistic systems, world trade, inter-organizational relations, among many others. This review article focuses on three main pillars: the existing methodologies to observe nestedness in networks; the main theoretical mechanisms conceived to explain the emergence of nestedness in ecological and socio-economic networks; the implications of a nested topology of interactions for the stability and feasibility of a given interacting system. We survey results from variegated disciplines, including statistical physics, graph theory, ecology, and theoretical economics. Nestedness was found to emerge both in bipartite networks and, more recently, in unipartite ones; this review is the first comprehensive attempt to unify both streams of studies, usually disconnected from each other. We believe that the truly interdisciplinary endeavor – while rooted in a complex systems perspective – may inspire new models and algorithms whose realm of application will undoubtedly transcend disciplinary boundaries. |
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Manuel Mariani, Discoverers of success in temporal networks, In: NetSci 2019. 2019. (Conference Presentation)
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Marta Białecka-Pikul, Małgorzata Stępień-Nycz, Iwona Sikorska, Ewa Topolewska-Siedzik, Jan Cieciuch, Change and consistency of self-esteem in early and middle adolescence in the context of school transition, Journal of Youth and Adolescence, Vol. 48 (8), 2019. (Journal Article)
Self-esteem is continuous and has stable characteristics, but it may also change, e.g., during transitions from one educational level to the next. In a prospective cross-sectional study over a year and a half, 250 Polish early adolescents (N = 109, 54 girls; mean age at T1 = 12.68 years, SD = 0.49) and middle adolescents (N = 141, 107 girls; mean age at T1 = 15.80, SD = 0.44) were tested three times using Harter’s Self-Perception Profile for Adolescents, assessing both global self-esteem and self-evaluation in eight domains. The change and consistency of self-esteem were analyzed, at both group and individual levels. At the group level, the following results were found: (1) continuity of self-esteem in five domains (scholastic competence, athletic competence, physical appearance, close friendship, and romantic appeal) and in global self-esteem and discontinuity in only three domains (social acceptance, job competence, and behavioral conduct); (2) significant inter- individual variation in the change not explained by age; and (3) higher self-esteem (in five domains) in early adolescents. At the individual level, the stability in most domains was weak, but was restored over the second year at the new school. The complexity of the developmental change and consistency in self-esteem in adolescence was highlighted, emphasizing the need for analyzing both group and individual change. |
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Eldad Davidov, Direct and indirect effects of values on attitudes and behavior, In: Research workshop of the Israel Science Foundation: Understanding personal values: Personality, context and culture. 2019. (Conference Presentation)
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Hao Liao, Ming-Kai Liu, Manuel Mariani, Mingyang Zhou, Xingtong Wu, Temporal similarity metrics for latent network reconstruction: The role of time-lag decay, Information Sciences, Vol. 489, 2019. (Journal Article)
When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag function strongly affects the metrics’ reconstruction accuracy, depending on the network’s clustering coefficient, and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks. |
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Shilun Zhang, Matúš Medo, Linyuan Lü, Manuel Mariani, The long-term impact of ranking algorithms in growing networks, Information Sciences, Vol. 488, 2019. (Journal Article)
When users search online for content, they are constantly exposed to rankings. For example, web search results are presented as a ranking of relevant websites, and online bookstores often show us lists of best-selling books. While popularity-based ranking algorithms (like Google’s PageRank) have been extensively studied in previous works, we still lack a clear understanding of their potential systemic consequences. In this work, we fill this gap by introducing a new model of network growth that allows us to compare the properties of networks generated under the influence of different ranking algorithms. We show that by correcting for the omnipresent age bias of popularity-based ranking algorithms, the resulting networks exhibit a significantly larger agreement between the nodes’ inherent quality and their long-term popularity, and a less concentrated popularity distribution. To further promote popularity diversity, we introduce and validate a perturbation of the original rankings where a small number of randomly-selected nodes are promoted to the top of the ranking. Our findings move the first steps toward a model-based understanding of the long-term impact of popularity-based ranking algorithms, and our novel framework could be used to design improved information filtering tools. |
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Fang Zhou, Linyuan Lü, Manuel Mariani, Fast influencers in complex networks, Communications in Nonlinear Science and Numerical Simulation, Vol. 74, 2019. (Journal Article)
Influential nodes in complex networks are typically defined as those nodes that maximize the asymptotic reach of a spreading process of interest. However, for practical applications such as viral marketing and online information spreading, one is often interested in maximizing the reach of the process in a short amount of time. The traditional definition of influencers in network-related studies from diverse research fields narrows down the focus to the late-time state of the spreading processes, leaving the following question unsolved: which nodes are able to initiate large-scale spreading processes, in a limited amount of time? Here, we find that there is a fundamental difference between the nodes – which we call “fast influencers” – that initiate the largest-reach processes in a short amount of time, and the traditional, “late-time” influencers. Stimulated by this observation, we provide an extensive benchmarking of centrality metrics with respect to their ability to identify both the fast and late-time influencers. We find that local network properties can be used to uncover the fast influencers. In particular, a parsimonious, local centrality metric (which we call social capital) achieves optimal or nearly-optimal performance in the fast influencer identification for all the analyzed empirical networks. Local metrics tend to be also competitive in the traditional, late-time influencer identification task. |
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Ronald Fischer, Maria Cristina Ferreira, Nathalie van Meurs, Kubilay Gok, Ding-Yu Jiang, Johnny R J Fontaine, Charles Harb, Jan Cieciuch, Mustapha Achoui, Ma Socorro D Mendoza, Arif Hassan, Donna Achmadi, Andrew A Mogaji, Amina Abubakar, Does organizational formalization facilitate voice and helping organizational citizenship behaviors? It depends on (national) uncertainty norms, Journal of International Business Studies, Vol. 50 (1), 2019. (Journal Article)
Prosocial work behaviors in a globalized environment do not operate in a cultural vacuum. We assess to what extent voice and helping organizational citizenship behaviors (OCBs) vary across cultures, depending on employees’ perceived level of organizational formalization and national uncertainty. We predict that in contexts of uncertainty, cognitive resources are engaged in coping with this uncertainty. Organizational formalization can provide structure that frees up cognitive resources to engage in OCB. In contrast, in contexts of low uncertainty, organizational formalization is not necessary for providing structure and may increase constraints on discretionary behavior. A three-level hierarchical linear modeling analysis of data from 7,537 employees in 267 organizations across 17 countries provides broad support for our hypothesis: perceived organizational formalization is weakly related to OCB, but where uncertainty is high; formalization facilitates voice significantly, helping OCB to a lesser extent. Our findings contribute to clarifying the dynamics between perceptions of norms at organizational and national levels for understanding when employees may engage in helping and voice behaviors. The key implication is that managers can foster OCB through organizational formalization interventions in uncertain environments that are cognitively demanding. |
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Alex Mari, The Rise of Machine Learning in Marketing: Goal, Process, and Benefit of AI-Driven Marketing, Swiss Cognitive, Zurich, https://swisscognitive.ch/2019/05/09/the-rise-of-machine-learning-in-marketing-goal-process-and-benefit-of-ai-driven-marketing/, 2019. (Published Research Report)
This independent research report describes the goal, process, and benefit of AI-driven marketing. It explores how marketing leverages machine learning models to automate, optimize, and augment the transformational process of data into actions and interactions with the scope of predicting behaviors, anticipating needs, and hyper-personalizing messages. |
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Dina Maskileyson, Moshe Semyonov, Eldad Davidov, In search of the healthy immigrant effect in four west european countries, Social Inclusion, Vol. 7 (4), 2019. (Journal Article)
The present research examines whether the ‘healthy immigrant effect’ thesis observed in the American context prevails also in the West European context. According to this thesis, immigrants are likely to be healthier than comparable nativeborn.
Data for the analysis are obtained from the Generations and Gender Survey for the following countries: Austria,
France, Germany, and the Netherlands. Ordered logit regression models are estimated to compare the health of immigrants with the native-born population. The findings reveal that in all countries, immigrants tend to report poorer health than comparable third generation native-born Europeans, and that health disparities between second and third generation are smaller than health disparities between first-generation members and native-born regardless of second- or thirdgeneration membership. The findings in the West-European countries do not lend support to the healthy immigrant effect. We attribute the differences between the United States and the West European countries to differential selection processes and differences in healthcare policies. |
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