Success factors of social influencers – multiple dimensions and contingencies of a prosperous campaign

Social Influencer haben sich zu einem mächtigen Mittel der Marketing-Kommunikation entwickelt. Gegenwärtig übersteigt die Höhe der Ausgaben für Social Influencer Marketing die der traditionellen Werbung (wie Fernsehspots, Print- oder Plakatwerbung). Angesichts des großen Einflusses, den Social Influencer auf Konsumenten haben können, stellt sich die Frage, wie man eine Influencer-Kampagne erfolgreich durchführt. Erste Ansätze haben Engagement-Variablen berücksichtigt - z. B. die Anzahl der Follower eines Influencers. Allerdings haben sich diese Ansätze oft genug als zu schlicht und eindimensional erwiesen. Tatsächlich beruht der Erfolg eines Influencer-Endorsements auf einem komplexen System von Erfolgsfaktoren, deren Bedeutung variieren kann. Dazu gehören unter anderem Faktoren, die in der Person des Influencers liegen, das Zusammenspiel zwischen Influencer und Zielgruppe, das Setup von Influencer und Marke/Produkt, der Kommunikationsstil des Influencers und die Vermeidung von Influencer-Fehlverhalten. Diese Elemente können miteinander verbunden sein und auch in gegenseitigem Konflikt stehen. Die vorliegende Dissertation widmet sich der Erforschung dieses komplexen Systems und der Schließung von Forschungslücken. Das erste Modul (1 Beitrag) legt ein Fundament, indem die drei Faktoren Attraktivität, Expertise und Vertrauenswürdigkeit untersucht werden. Im zweiten Modul, das zwei Forschungsarbeiten umfasst, wird das Zusammenspiel zwischen Influencer, Konsument und Marke/Produkt behandelt. Das erste Paper fokussiert die Persönlichkeit und untersucht die Übereinstimmung der Influencer-Persönlichkeit mit dem tatsächlichen und gewünschten Selbstkonzept des Konsumenten sowie mit der Markenpersönlichkeit. Dabei wird auch die moderierende Rolle des Produktinvolvements berücksichtigt. Im zweiten Beitrag wird das Zusammenspiel von Influencer- und Konsumentenattraktivität sowie Geschlecht untersucht. Das dritte Modul (4 Beiträge) konzentriert sich auf die Erfolgsfaktoren für verschiedene Produktarten bzw. Endorsement-Anlässe; dabei wird ein starker Bezug zur Kommunikation des Influencers hergestellt. Paper 1 und 2 ziehen eine grundsätzliche Grenze zwischen hedonischen und utilitaristischen Produkten und untersuchen die Bedeutung von Kommunikationsstil, Faktizität, Expertise und demographischer Ähnlichkeit. Der dritte Beitrag untersucht die Rolle der Attraktivität und Expertise von Influencern für attraktivitätsbezogene und nicht-attraktivitätsbezogene Produkte. Der vierte Beitrag schließlich diskutiert die Besonderheiten eines Influencer-Endorsements im Non-Profit-Kontext. Im letzten Modul werden die Schattenseiten des Influencer-Marketings, nämlich die schädliche Wirkung von Skandalen, in einem Beitrag beleuchtet. Diese Arbeit verdeutlicht die Vielfalt und Kontingenz der Faktoren, die ein erfolgreiches Influencer Endorsement ausmachen. Alle Faktoren müssen gegeneinander abgewogen und diskutiert werden; dabei spielen Unterschiede wie die angesprochene Zielgruppe oder das beworbene Produkt bzw. Anliegen eine große Rolle. Die Ergebnisse liefern wertvolle Implikationen für Praktiker vieler Branchen, um ihre Influencer-Kampagnen erfolgreich zu gestalten und umzusetzen. Ebenso eröffnen die Ergebnisse viele Perspektiven für zukünftige Forschung. Ein großes Forschungspotenzial kann in einer qualitativen Ergänzung der durchgeführten quantitativen Studien liegen. Auf diese Weise könnten die Gedanken, Gefühle und Handlungsabsichten von Influencern, Konsumenten und Praktikern, die die Grundlage der vorliegenden Ergebnisse bilden, aufgedeckt werden

The complex triad of congruence issues in influencer marketing

Finding a fitting endorser has proven to be one of the most delicate and critical tasks of influencer marketing. This research explores the relevance of the congruency of the influencer personality with (1) brand personality and consumers' (2) actual/(3) ideal selves. Additionally, the (4) moderating role of involvement is considered, the impacts on post attitude/belief, brand trust and purchase intention are thereby studied. The novelty of this study lies in the integral examination of the types of congruencies and involvement in the context of influencer marketing as well as the consideration of their impact on the brand-related variables. Based on an online survey with 547 participants analyzed by means of structural equation modeling in SmartPLS, partly counterintuitive findings were produced. When the involvement level rises, congruence with consumers' actual selves becomes more important. Under low-involvement conditions, practitioners should pay more attention to influencers' fit with consumers' ideal selves. An adequate fit between brand and endorser is paramount and becomes even more important under high-involvement conditions. Overall, this study reveals that the three types of congruency and involvement interact in a very unique way in the context of influencer marketing. © 2021 The Authors. Journal of Consumer Behaviour published by John Wiley & Sons Ltd

ROBUST DECISION SUPPORT SYSTEMS WITH MATRIX FORECASTS AND SHARED LAYER PERCEPTRONS FOR FINANCE AND OTHER APPLICATIONS

The recent financial crisis showed the need for more robust decision support systems. In this paper, we introduce a novel type of recurrent artificial neural network, the shared layer perceptron, which allows forecasts that are robust by design. This is achieved by not over-fitting to a specific variable. An entire market is forecast. By training not one, but many networks, we obtain a distribution of outcomes. Further, multi-step forecasts are possible. Our system uses hidden states to model internal dynamics. This allows the network to build a memory and hardens it against external shocks. Using a single shared weight matrix offers the possibility of interpreting system output. An often cited disadvantage of neural networks, the black box character, is not an issue with our approach. We focus on two case studies: determining value at risk and transaction decision support. We also present other applications, including load forecast in electricity networks

A presidência e a separação dos poderes no Brasil (1999-2002)

Este trabalho discute as relações entre a presidência brasileira e os outros poderes da União durante a segunda gestão FHC de 1999 a 2002. A hipótese central é de que a crescente separação dos poderes, apontada por vários autores desde a Constituição de 1988, também caracteriza processos importantes ocorridos durante essa gestão de FHC. Argumentamos que esse processo empírico exige uma reavaliação de conceitos sobre a democracia brasileira e que teorias "separacionistas" de poder e governo oferecem novas perspectivas para o estudo das instituições políticas brasileiras.This paper discusses the relationship between the Brazilian Presidency and other Union powers during Fernando Henrique Cardoso's second term in office, from 1999 to 2002. The central hypotheses is that the growing separation of powers, highlighted by various researchers since the 1988 Constitution, also characterizes important processes, which took place during this time. It is argued that this empirical process demands a reevaluation of concepts on Brazilian democracy and that theories that separate power and government offer new perspectives for the study of Brazilian political institutions

INDUSTRIALIZATION OF DERIVATIVE DESIGN: INTEGRATED RISK MANAGEMENT WITH THE FINANCIAL INFORMATION SYSTEM WARRANT-PRO-2

Risk management is essential in a modern financial services industry. Derivative instruments like options have a particular status. Appropriate derivatives allow financial service providers to redistribute risks towards others. The process of creating customer tailored derivatives is not wellinvestigated today. With the financial information system (FIS) WARRANT-PRO-2 derivative prices are computed for given payments. The deviation, for example, from a predefinable Delta of an option can be minimized. Automatic creation of optimally synthesized options is very promising for buyer and seller. An example is presented to show the easy process of creating a customer tailored option

Load Management in Power Grids - Towards a Decision Support System for Portfolio Operators

Decentralized renewable energy sources become more and more common. This leads to stability problems in power grids. Conventional energy sources are easy to control. In contrast, wind and solar power are much more difficult to forecast. Forecasts are only possible short term and are more imprecise. Producers and consumers of energy can try to help reducing stability problems. Contributions towards a decision support system are proposed and recommend how to alter the behavior of producers and consumers. On the producer side centrally controlled heat and power plants are able to shift load in a virtual power plant. The plant operator offers a load curve based on forecasts. The centrally controlled heat and power plants help to mitigate the effect of revised forecasts. An incentive based control on the consumer side is also proposed. Smart appliances react to pricing information. They alter their execution window towards the cheapest time slot, if possible. The exact behavior of appliances in the expected field experiment is still partially unknown. It is necessary to simulate the behavior of these appliances and to train an artificial neural network. The artificial neural network allows computing the pricing signal leading to a desired load shift

Decision Support for the Automotive Industry: Forecasting Residual Values using Artificial Neural Networks

The leasing business is one of the most important distribution channels for the automotive industry. This implies that forecasting accurate residual values for the vehicles is a major factor for determining monthly leasing rates: Either a systematic overestimation or underestimation of future residual values can incur large potential losses in resale value or, respectively, competitive disadvantages. In this paper, an operative DSS with the purpose of facilitating residual value related management decisions is introduced, with a focus on its forecasting capabilities. Practical implications are discussed, a multi-variate linear model and an artificial neural network approach are benchmarked and further, the effects of price trends and seasonal influences are investigated. The analysis is based on more than 150,000 data sets from a major German car manufacturer. We show that artificial neural network ensembles with only a few input variables are capable of achieving a significant improvement in forecasting accuracy