Contributions published at Marketing and Market Research (René Algesheimer)

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In the last years an increasing attention has been devoted to the identification of influential individuals and their use as super-spreaders of products or ideas. Most often, from the observed data researchers construct an influence network and identify the influencers as the most central individuals in this network or as the key players in the development of a dynamical process. However, in most practical situations there are at least two potential issues with this approach. First, the construction of the influence network is non-trivial as most often the influence relationships between people are not directly observable but rather aspects of their behavior. Second, even if the influence relationships were observable, the static network representation cannot capture their time-dynamical aspect. We present a new approach to identify influential individuals from time varying social interactions which does not require constructing the influence network nor modeling social influence as a dynamical process. We consider that individuals become influential due to unobserved features they posses, which we call the latent potential to influence. This potential is revealed during social interactions and acknowledged by other participants through rewards (e.g. upvotes on discussion platforms). We uncover the latent potential to influence from the observed rewards using the influence potential (IP), a novel index we introduce here. To illustrate our approach we analyze two real-world systems: a news discussion forum (CNN) and a business review platform (Yelp). In both datasets we find few influencers, which is in agreement with the existing theory. We compare the results against a null model and show that the presence of such individuals is very unlikely to have occurred by chance. We validate the approach by dividing the datasets into a training and a test set and showing that the users with the highest IP in the training set are also the most influential in the test set.

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Customer Lifetime Value (CLV) is an important metric used in marketing to assess the value of the relationship with every customer. This thesis proposes and compares eleven different customer retention models to calculate the CLV in a multi-service, contractual setting. Eight of these models are constructed at a customer-relationship-level i.e. focusing on weather a customer maintains the relationship with the company or not. The three remaining models are constructed at a service-level, focusing on the purchase likelihood of individual services within the company and paying especial attention to cross buying. The performance of the models is tested on two datasets that contain information about the customers of two direct insurance companies, for which the future customers expenditures are calculated for a period of four years. Simple relationship-level models and frailty relationship-level models show the overall best performance. The service-level Markov model achieves the highest performance when it comes to the categorization of customers according to their value.

Measurement invariance is a necessary precondition for meaningful cross-country comparisons, and three levels have been differentiated: configural, metric, and scalar. Unfortunately, establishing the most stringent form, i.e., scalar measurement invariance, across groups is very difficult. This was also the case for human values in European Social Survey (ESS). As was shown by Davidov (Davidov, Schmidt, Schwartz, 2008; Davidov, 2008, 2010) mean values measured by PVQ-21 in the European Social Survey (ESS) could not be compared across all countries because scalar measurement invariance was not supported. Recently, Muthén and Asparouhov proposed testing for approximate rather than exact measurement invariance as this may be sufficient for meaningful comparisons. Following their strategy, the results of cross-country approximate measurement invariance tests of the PVQ-21 scale to measure values in ESS are presented (respondents from 15 countries participating in six rounds). Applying the new approximate method for the test of measurement invariance showed that although exact measurement invariance cannot be established, approximate measurement invariance is present for some values across subsets of countries. In particular, approximate measurement invariance was established in almost all rounds for self-enhancement (8 countries), self-transcendence (12 countries), and conservation (10 countries).

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Which are the mechanisms that underlie the formation of networks? This is a key question in network science, pervading the most variegated disciplines, and extensively ap- proached both, theoretically and empirically. Strikingly, most results depend on assuming a large number of network constituents and little is known for networks in which this is implausible. For instance, most existing statistical network analyses rely on large sample properties of estimators, where sample is defined on the network size. However, as we show in this Paper, several statistical network studies based on observational data suffer from two shortcomings: (1) they are not replicable, since parameters are not constant on sample size – as opposed with other regression models- and (2) for the exponential random graph model (ERGM) – among the most used statistical network models – large sample properties remain unknown. Here, we address the first problem by determining the functional form of the parameters on the number of agents for given ERGMs. We use these results to con- struct a class of models termed finite exponential random graph model (fERGM), which do not make assumptions on the network size, but on the number of observed networks. This exchange of assumptions proves fundamental for the study of the influence the network size on the network formation. We also demonstrate that a recent methodology for addressing environment effects (e.g. like the network size) has on the formation of network lacks on fundamental statistical properties, and thus some empirical results need to be revised. Finally, we demonstrate how to use fERGM to test for the effect that the network sizes has on the simple mechanisms for the formation of networks, i.e. reciprocity and transitivity.

Werte sind eine wichtige Komponente der menschlichen Natur, welche genutzt werden kann, um Individuen zu charakterisieren und um die Motivation von Einstellungen und Handlungen zu erklären (Schwartz, 2012). Für sämtliche Beurteilungen, sei das über Personen, Situationen oder Geschehnisse, bedient man sich stets seiner Werte (Schwartz, 2012). Werte als ein Teil der Persönlichkeit, welcher jedem Individuum eigen ist, bringen somit bei jedem Menschen andere Verhaltensausprägungen zum Vorschein. Dies wird im Zitat von Elvis Presley am treffendsten beschrieben mit: „Werte sind wie Fingerabdrücke. Keiner hat dieselben, aber Du hinterlässt sie bei allem, was Du tust.“ In anderen Worten sind Werte also nicht direkt sichtbar, jedoch erkennt man sie, indem man die Verhaltensweisen der Menschen beobachtet. Lassen sich folglich bestimmte Werte spezifischen Verhaltensweisen zuordnen? Diese Bachelorarbeit liefert einen Beitrag an den aktuellen und unausgeschöpften Forschungsbereich der Werte und deren Zusammenhang mit dem Verhalten von Menschen. Im Fokus liegt die Verwendung dieser Beziehung in einem unternehmerischen Umfeld, insbesondere im Bereich der wertebasierten Marktsegmentierung. Im Folgenden werden anhand eines Frameworks, durch Anknüpfung an bereits bestehende Literatur, Werte bestimmten Verhaltensweisen zugeordnet. Nach der Aufstellung von Hypothesen, werden diese Zusammenhänge daraufhin untersucht mit empirischen Daten aus mehreren Schulklassen. Diese Daten werden mittels eines eigens für Oberstufenschüler konzipierten Fragebogens erhoben. Schliesslich werden die Ergebnisse der Analyse präsentiert und diskutiert, um dann im letzten Teil ein Fazit zu ziehen und einen Ausblick zu geben.

Understanding how people influence or are influenced by their peers can help us understand the flow of market trends, product adoption and diffusion processes. Recent research has shown that social networks can be leveraged to accelerate behavior change, improve organizational efficiency, enhance social change and improve dissemination and diffusion of innovation. The objective of this thesis is threefold. In the first place the student will search the literature for methods to identify opinion leaders in a wide variety of contexts. In the second step he will analyze a dataset collected from a social media platform and identify the opinion leaders. To this end he will first perform a text mining analysis of the data. Based on the results, he will select a subset of the data and manually identify the opinion leaders. In the last step he will evaluate the accuracy of existing methods by comparing the results with the manual identification. He will conclude with a recommendation of the best algorithms together with an overview of the challenges raised by using such methods to analyze social media data. After finishing the thesis the student will have a good knowledge of social networks and will be familiar with the use of R for scientific research.

Understanding how people influence or are influenced by their peers can help us understand the flow of market trends, product adoption and diffusion processes. In the last years an increas- ing attention has been devoted to identifying the most influential individuals and using them as super-spreaders of products or ideas. Most often, from the observed data researchers construct an influence network and identify the influentials as the most central individuals in this network or as the key players in the development of a dynamical process. However, in most practical situations there are at least two potential issues with this approach. First, the construction of the influence network is non-trivial as most often the influence relationships between people are not directly observable but rather aspects of people’s behavior. Furthermore, even if the influence relationships were observable, the static network representation cannot capture their time-dynamical aspect. Second, identifying influential individuals based on their role in a dynamical process is sensitive to the model chosen and to the assumed role influentials should play in the process. In this article we present a model-free approach to identify influential individuals from time varying social in- teractions which does not require constructing the influence network nor modeling social influence as a dynamical process. We start by introducing the influence potential (IP), a novel index that captures the intrinsic ability of individuals to consistently influence others while controlling for the total number of individuals in the process. We validate our results by computing an adapted version of the area under the curve (AUC) as indicator of the in-sample prediction accuracy and further use this to identify how many influential individuals are in a dataset. To illustrate our approach we analyze two real world systems: a news discussion forum extracted from cnn.com and a business review platform extracted from yelp.com. In both datasets we identify a low number of users with high influence potential relative to the entire user base. This implies that if we are interested to steer the user behavior on the platform, we can design intervention campaigns tar- geting influential individuals but with very few reliable targets. Furthermore, we compare the two datasets and find that Yelp has a higher percentage of individuals with high influence potential. This suggests that in the Yelp dataset it is relatively easier to locate potential targets, which might have important implications for comparing the intervention costs on the two platforms.

In accordance with the classic – developmental – approach to identity originated by Marcia (1966), there are two basic identity formation processes: exploration and commitment. The first step on the way to mature identity is exploration. The aim of the present study was to analyze the dynamics of exploration in the period when it begins: in early adolescence. The participants in the longitudinal study (with three measurements at half-year intervals) were 327 adolescents aged 11 to 15 (M = 13.26, SD = 1.20) – elementary and middle school students. The sample was balanced in terms of gender (45% were girls). The instrument we used was the Early Identity Exploration Scale (EIES; Kłym & Cieciuch, 2015), enabling the measurement of identity exploration in 12 domains: physical appearance, free time, family of origin, work, boyfriend–girlfriend relationships, own opinion formation, perception of own place in the life cycle, self-reflection, future, future family, outlook on life, and attitude toward rules. The analysis was performed using a latent growth curve model. It turned out that in some domains (physical appearance, work, boyfriend–girlfriend relationships, and outlook on life) the level of exploration systematically increased, despite the relatively short time of the study; the domain of boyfriend–girlfriend relationships was the only one in which we found no interpersonal differentiation in the intensity of this increase. It also turned out that there was interpersonal differentiation in the level of exploration at the outset in all the domains analyzed.

The process of adolescence, including identity formation, differs significantly from what it was in previous generations. This is a consequence, among other things, of the demographic and economic changes that have taken place in recent years, locally as well as globally. This introduction is devoted to a review of these problems, and the current issue comprises texts addressing selected questions in detail. Some scholars identify groups of young people characterized by a tendency to consciously avoid making commitments typical of adults. However, research results point to the positive impact of taking on adult roles on perceiving oneself as an adult. The person’s identity develops from childhood, encompassing various domains. During the transition from childhood to adolescence, the increase in self-awareness is accompanied by an intensification of seeking information about oneself and one’s own functioning and of making plans for the future. The stage of adulthood involves the verification of previously made choices by resuming exploratory activities, whose intensity is in proportion to the amount of significant changes in the individual’s environment

This chapter bridges academic and managerial perspectives on the use of social media platforms for the creation of strategic consumer–brand relationships and their effect on brand equity. In particular, the chapter explores some of the recurring barriers that marketing managers have identified when discussing the implementation of relationship-based initiatives linked to marketing communications objectives. According to Fournier (1998: 344), the fact that brands are “animated, humanized, or somehow personalized” supports the idea that brands can be relationship partners. Kent and Taylor (1998) suggested that organizations have an opportunity to build dialogic relationships with stakeholders through the use of strategically designed websites. Although previous studies have investigated the potential of social media platforms for building and maintaining relationships with the public (Bortree and Seltzer, 2009; Park and Reber, 2008), there has been little empirical exploration on the evolution of consumer–brand relationships resulting from the advent of social channels (e.g., Mandelli and La Rocca, 2014). Social media platforms have had a remarkable impact in the evolution consumer–brand relationships. This phenomenon is expected to play a leading role in the creation of economic and social innovation during this decade (Tapscott, 2014). As Gummesson (2004: 139) noted, “when relationship marketing, CRM, and services marketing are combined with a network view, they become drivers of a paradigm shift in marketing.” The reason for the shift is the advancement of information technology, which has resulted in the use of information to understand and enhance customer relationships. The author of the present chapter examines several cases to better understand the advantages of a total relational approach enhanced by digital technology.

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„And for seizing the reins of the global media, for founding and framing the new digital democracy, for working for nothing and beating the pros at their own game, TIME’s Person of the Year for 2006 is you.“ LEV GROSSMAN, in TIME MAGAZINE In 2006, the TIME Magazine selected “You” as the magazine’s Person of the Year. This award acknowledged the contribution millions of people worldwide make by co-creating the web-of- information through their participation to pages such as Wikipedia, YouTube, Facebook, Pinterest, or Twitter. It is the era of user-generated content, but also the era of self-branding. Wait! This article was published ten years ago. Hand on heart: Haven’t you thought you have missed the opportunities of social media in the recent past? In a time period in which everyone seems to be talking about the importance of the digital transformation and the relevance of the social. Haven’t you realized that almost everyone else is already utilizing the power of social media? That they are making money through their social engagement? That all others have already hundreds of followers? And that your first attempts to setup accounts and grow on Twitter, Facebook, Pinterest, or Instagram failed? That you have missed the first-mover advantage and that it seems impossible to cope with the growth speed of accounts who have already a significantly large audience? At least, we can tell you that you are not the only one. We have discovered this need through numerous discussions we had with industry managers, as well as with our university students. Thus, we decided to act and do something against it. First, we teamed up a marketing scientist (René), a data scientist (Radu) and a social media agency nerd (Niklas) to share different perspectives on this topic. Second, we have created a seminar “Internet and Social Media Marketing” at the University of Zurich sponsored by the University Research Priority Program Social Networks that selected twenty-four talented young students to participate in this endeavour. Third, we decided to focus on six platforms (Twitter, Facebook, Instagram, Pinterest, Facebook Ads, Google Analytics) and structure each platform by four substantial dimensions (feed, grow, automatize, analyze). Fourth, the students scanned the web for the most innovative, rocking hackz for each of these platforms and dimensions and collected more than 700 tips. Fifth, in several iterations, the editors have framed the idea, evaluated and finally edited all the content to come up with the 250 tips that enable you to rock social media. Sixth, we have created a platform to advertise the book and students are asked to campaign the book. The learning effects for the students are therefore twofold: They learn how to manage the platforms, but in parallel apply their knowledge to a real existing product. Finally, 100% of the revenues of this book are donated to the non-profit organization IMFUNDO – Teachers WITHOUT FRONTIERS[2] to foster international education. In sum, by purchasing this ebook, you will not only learn a lot, but you will also support the engagement of this young organization. The purpose of this book is to offer a collection of hands-on smart tips & tricks utilizing social media tools that enable you to create fast, and sustainable growth. Maybe, not gigantic growth as the title indicates, but that was just catchier. For this, we assume that you are familiar with the fundamental social platforms such as Twitter, Facebook, Instagram, Pinterest or even Facebook Ads and Google Analytics and have a basic understanding of how they work. There will be two versions of the book: At first, we will offer an electronic book with many hyperlinks. Later on, a printed version will follow. We hope you enjoy reading our book, apply and practice our hands-on ideas and realize gigantic growth.

This paper investigates the process of knowledge exchange in inter-firm Research and Development (R&D) alliances by means of an agent-based model. Extant research has pointed out that firms select alliance partners considering both network-related and network-unrelated features (e.g., social capital versus complementary knowledge stocks). In our agent-based model, firms are located in a metric knowledge space. The interaction rules incorporate an exploration phase and a knowledge transfer phase, during which firms search for a new partner and then evaluate whether they can establish an alliance to exchange their knowledge stocks. The model parameters determining the overall system properties are the rate at which alliances form and dissolve and the agents' interaction radius. Next, we define a novel indicator of performance, based on the distance traveled by the firms in the knowledge space. Remarkably, we find that - depending on the alliance formation rate and the interaction radius - firms tend to cluster around one or more attractors in the knowledge space, whose position is an emergent property of the system. And, more importantly, we find that there exists an inverted U-shaped dependence of the network performance on both model parameters.