Social Media Analytics

Stieglitz, Stefan; Dang-Xuan, Linh; Bruns, Axel; Neuberger, Christoph

doi:10.1007/s12599-014-0315-7

Cited by 218 publications

(104 citation statements)

References 16 publications

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“…The SMA approach is interdisciplinary and seeks to combine, extend and adapt methods for analysis of social media data (Stieglitz et al, 2014). While the purposes of applying SMA vary across disciplines, innovation management has been suggested to represent one of the settings where this methodological approach is particularly useful.…”

Section: Methodsmentioning

confidence: 99%

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Analysing Uber in Social Media — Disruptive Technology or Institutional Disruption?

Laurell

Sandström

2019

Digital Disruptive Innovation

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Extant literature suggests that market disruptions take place because of two main reasons: technological disruption or institutional change. In view of these two alternative explanations, this paper aims to explore how the recent rise of the collaborative consumption platform Uber is perceived by consumers and whether this platform is primarily regarded as a technological innovation or as an institutional disruption. Drawing from a dataset of more than 6500 user-generated contents in social media, our findings suggest that Uber is not primarily perceived as a technological innovation, but rather as an institutional disruption.

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Section: Methodsmentioning

confidence: 99%

“…Thus, the tool allows for researchers to collect data from a relatively broad set of social media applications found in the social media landscapes in a structured manner. This also means that the researcher does not need to use problematic data collection methods, such as scraping techniques (Stieglitz et al, 2014).…”

Section: Data Collectionmentioning

confidence: 99%

Analysing Uber in Social Media — Disruptive Technology or Institutional Disruption?

Laurell

Sandström

2019

Digital Disruptive Innovation

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“…Cluster VII covers mobile enterprise tools for the interaction and collaboration among employees in a company. Known concepts are co-creation and computer supported collaboration cooperative work tools as well as social media applications [38,39]. Applications in Cluster VIII provide employees with intelligent user interfaces for an effective human-computer interaction.…”

Section: Information and Knowledgementioning

confidence: 99%

“…Intelligent search and prediction algorithms are needed. Well-known concepts are Social Media Analytics or Artificial Intelligence [39].…”

Section: Information and Knowledgementioning

confidence: 99%

Taxonomy of Enterprise-Related Mobile Applications

Brockmann

Stieglitz

Lattemann

2014

Social Computing and Social Media

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Abstract. The increasing diffusion of mobile devices is changing the working environment and implicating new challenges for enterprises. Mobile applications specifically designed to the enterprises' needs, so-called Enterprise Apps, conquer the market. Enterprise Apps support e.g. enterprise processes to enhance the communication possessing interface to existing enterprise systems, like CRM or ERP. Mobile workers obtain thereby an ideal tool for the accomplishment of their operations in interaction with enterprise system. The applications areas in enterprises throughout Enterprise Apps are complex and widespread. This paper sheds light in the complexity of Enterprise Apps presenting a framework for specific Enterprise Apps. It contributes to research by providing a first approach of a classification scheme to the scenery of Enterprise App usage.Keywords: Enterprise Apps, Taxonomy, Mobile, Classification. IntroductionOn average, each inhabitant of Germany already owns more than one mobile device 17. However, this development is not limited to Germany, smart phones and other mobile devices are widely spread around the world (c. f. table 1). The average penetration and usage rate of mobile devices in major emerging markets, such as Russia, Brazil, China and India and in the developed markets such as United States, Japan, and Germany are between 67 per cent and 130 per cent. Within the next years the diffusion of mobile devices will further increase worldwide. The traditional feature phone, whose functionality is usually limited to phone calls and short message transmission, is increasingly being replaced by smartphones. Market researchers are forecasting the number of smartphones at one billion in 2016. 350 million smartphone owners will use their devices for business purposes 31. A survey by the University of St. Gallen substantiates this, as 40 percent of respondents state they have used a smartphone for business purposes, and their number is still rising 10.Consequently, the ubiquitous availability of information due to mobile devices makes employees increasingly independent of their workplace. According to a survey * Corresponding author. 38T. Brockmann, S. Stieglitz, and C. Lattemann by BITKOM, 58% of employees prefer flexible working conditions concerning the place of value performance 16. The traditional workplace is becoming less important and an increasing part of the occupational activities occur outside of offices. Enterprises therefore feel the need to attune to these modified conditions and to allow their employees mobile access to enterprise systems 22. CIOs are recognizing this trend. The Gartner CIO Report 2012 states that enterprises now prioritize the adaption of mobile technology and that they are going to invest a large part of their budget in mobile solutions and concepts 23. Especially the emergence of mobile applications (apps) has created novel applications which enterprises can capitalize on. The survey "Mobile-Web-Watch-2011", released by the consulting company Accenture, proves that 31% of...

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“…Social features have their origin in (public) Social Media and include subscribing (following) information or people, commenting or tagging content as well as simple annotations such as recommendations or likes [34]. Social Media Analytics is the term suggested by Stieglitz et al [42] for the study of these public, openly accessible platforms.…”

Section: Introductionmentioning

confidence: 99%

Metrics for Analyzing Social Documents to Understand Joint Work

Schubert¹,

Mosen²,

Schwade³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Social Collaboration Analytics (SCA) aims at measuring and interpreting communication and joint work on collaboration platforms and is a relatively new topic in the discipline of Information Systems. Previous applications of SCA are largely based on transactional data (event logs). In this paper, we propose a novel approach for the examination of collaboration based on the structure of social documents. Guided by the ontology for social business documents (SocDOnt) we develop metrics to measure collaboration around documents that provide traces of collaborative activity. For the evaluation, we apply these metrics to a large-scale collaboration platform. The findings show that group workspaces that support the same use case are characterized by a similar richness of their social documents (i.e. the number of components and contributing authors). We also show typical differences in the "collaborativity" of functional modules (containers).

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Social Media Analytics

Cited by 218 publications

References 16 publications

Analysing Uber in Social Media — Disruptive Technology or Institutional Disruption?

Analysing Uber in Social Media — Disruptive Technology or Institutional Disruption?

Taxonomy of Enterprise-Related Mobile Applications

Metrics for Analyzing Social Documents to Understand Joint Work

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