Web-Marketing 3.0 in Social Network using MDA Approach and Pulse Detector

Die Bedeutung der großen Marketingplattformen nimmt stetig zu; inzwischen haben die meisten Unternehmen erkannt, dass diese im Hinblick auf ihre Marketing-Strategie viele Potenziale bereithalten. Eine besondere Rolle nehmen hier das soziale Netzwerk „Facebook“ und die Suchmaschine „Google“ ein. Der Beitrag „Facebook und Google. Wie Community und Suchportal in das Web-Business integriert werden“ von Prof. Dr. Tilo Hildebrandt, Geschäftsführer der DTH Beratungs- und Beteiligungs GmbH, stellt die Möglichkeiten dar, wie Unternehmen Facebook und Google für ihren Erfolg im Web nutzen können. Zunächst stehen das Pull-Marketing, wie wir es mit Blick auf Google erkennen, sowie das Push-Marketing, das Facebook zusätzlich hierzu nutzt, im Fokus. Im Anschluss erläutert Dr. Hildebrandt die Potenziale einer Community für das Web-Marketing. Darüber hinaus veranschaulicht er, inwiefern Facebook einen besonderen Nutzen als Marketing-Instrument aufweist und geht auf die dort auffindbaren Synergiegruppen ein. Abschließend verdeutlicht Dr. Hildebrandt die Verwendung spezifischer Controlling-Größen, die im Kontext von Facebook und Google als Grundlage für Optimierungsmaßnahmen genutzt werden können. The significance of the great marketing platforms is constantly increasing; by now most of the corporations have recognized that these platforms have many potentials with regard to their marketing strategy. A special role is assumed by the social network „Facebook“ (market share of 85% on all accesses to communities) and the search engine „Google“ (market share of more than 95% referring to the searches in Germany). The article „Facebook and Google. How to integrate community and search portal in the web business“ by Dr. Tilo Hildebrandt, CEO of DTH Beratungs- und Beteiligungs GmbH, represents the ways that companies can use Facebook and Google for their success in the web. Keywords: web business, wachstum, pull und push marketing, optimierung, community

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The social network

Skin & Allergy News ◽

10.1016/s0037-6337(13)70045-7 ◽

2013 ◽

Vol 44 (2) ◽

pp. 22

Author(s):

ALAN ROCKOFF

Keyword(s):

Social Network ◽

The Social

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Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

Endocrine Practice ◽

10.1016/s1530-891x(20)42480-2 ◽

2015 ◽

Vol 21 ◽

pp. 301

Author(s):

Armand Krikorian ◽

Lily Peng ◽

Zubair Ilyas ◽

Joumana Chaiban

Keyword(s):

Social Network ◽

Network Data ◽

Social Network Data

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Correlating Personality and Actual Phone Usage

Journal of Individual Differences ◽

10.1027/1614-0001/a000139 ◽

2014 ◽

Vol 35 (3) ◽

pp. 158-165 ◽

Cited By ~ 38

Author(s):

Christian Montag ◽

Konrad Błaszkiewicz ◽

Bernd Lachmann ◽

Ionut Andone ◽

Rayna Sariyska ◽

...

Keyword(s):

Big Data ◽

Social Network ◽

Mobile Phone ◽

Mobile Phones ◽

Self Report ◽

Psychological Variables ◽

New Approach ◽

Report Data ◽

Self Report Data ◽

Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

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Extraverts Have Larger Social Network Layers

Journal of Individual Differences ◽

10.1027/1614-0001/a000048 ◽

2011 ◽

Vol 32 (3) ◽

pp. 161-169 ◽

Cited By ~ 74

Author(s):

Thomas V. Pollet ◽

Sam G. B. Roberts ◽

Robin I. M. Dunbar

Keyword(s):

Social Network ◽

Social Relationships ◽

Outer Layer ◽

Network Size ◽

Emotional Intensity ◽

Emotional Closeness ◽

Network Layer ◽

Network Layers ◽

Social Network Size

Previous studies showed that extraversion influences social network size. However, it is unclear how extraversion affects the size of different layers of the network, and how extraversion relates to the emotional intensity of social relationships. We examined the relationships between extraversion, network size, and emotional closeness for 117 individuals. The results demonstrated that extraverts had larger networks at every layer (support clique, sympathy group, outer layer). The results were robust and were not attributable to potential confounds such as sex, though they were modest in size (raw correlations between extraversion and size of network layer, .20 < r < .23). However, extraverts were not emotionally closer to individuals in their network, even after controlling for network size. These results highlight the importance of considering not just social network size in relation to personality, but also the quality of relationships with network members.

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The Multilevel p2 Model

Methodology ◽

10.1027/1614-2241.2.1.42 ◽

2006 ◽

Vol 2 (1) ◽

pp. 42-47 ◽

Cited By ~ 47

Author(s):

Bonne J. H. Zijlstra ◽

Marijtje A. J. van Duijn ◽

Tom A. B. Snijders

Keyword(s):

Social Network ◽

Network Data ◽

Mcmc Algorithm ◽

Independent Observation ◽

Single Observation ◽

Multiple Networks ◽

Social Network Data ◽

Multiple Observations ◽

Practical Support ◽

The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

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Learning Conversation and Shared Network Leadership

Journal of Personnel Psychology ◽

10.1027/1866-5888/a000015 ◽

2010 ◽

Vol 9 (4) ◽

pp. 181-194 ◽

Cited By ~ 11

Author(s):

Jürgen Weibler ◽

Sigrid Rohn-Endres

Keyword(s):

Qualitative Research ◽

Grounded Theory ◽

Social Network ◽

Interorganizational Networks ◽

Original Network ◽

High Quality ◽

Qualitative Research Design ◽

Collective Processes ◽

Shared Network ◽

Practical Implications

This paper develops an understanding of how shared leadership emerges in social network interactions. On the basis of a qualitative research design (grounded theory methodology – GTM) our study in two interorganizational networks offers insights into the interplay between structures, individuals, and the collective for the emergence of shared network leadership (SNL). The network-specific Gestalt of SNL appears as a pattern of collective and individual leadership activities unified under the roof of a highly developed learning conversation. More importantly, our findings support the idea that individual network leadership would not emerge without embeddedness in certain high-quality collective processes of relating and dialogue. Both theoretical and practical implications of this original network leadership perspective are discussed.

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