Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users’ expectations

Căpăţînă, Alexandru; Kachour, Maher; Lichy, Jessica; Micu, Adrian; Micu, Angela-Eliza; Codignola, Federica

doi:10.1016/j.techfore.2019.119794

Cited by 76 publications

(54 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Sentiment analysis deals with the analysis of "people's opinions, sentiments, evaluations, appraisals, attitudes, and emotions" (Liu 2012, p. 415) and has been recognized as a highly dynamic research field (Yue et al 2019;Capatina et al 2020). Consequently, automated sentiment analysis is a discipline linked to Natural Language Processing, Text Mining, Web Mining and Information Retrieval for instance (Al-Ghamdi 2021;Liu 2012;Yue et al 2019).…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Whereas "task-oriented" efforts focus on the tasks to be conducted for determining the sentiment of social media content (e.g., feature identification, subjectivity detection, etc. ), the "granularity-oriented" approaches differ in whether the analysis is performed on a document, a sentence or a single word (Yue et al 2019;Capatina et al 2020). Then finally, "(semi-)supervised" and "unsupervised" methods are summarized as "methodology-oriented" approaches in this respect (Yue et al 2019).…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Additionally, the use of artificial intelligence (AI) for social media monitoring has come up as a further research stream in recent years (cf. Perakakis et al 2019;Capatina et al 2020;Al-Ghamdi 2021;Micu et al 2018;Davenport 2018). For instance, Micu et al (2018) analyze correlations between users' experience in social media marketing and their knowledge about machine learning algorithms as well as the frequency of applying these procedures in day-to-day business.…”

Section: Clusteringmentioning

confidence: 99%

“…The authors' major aim is to derive future AI functionalities for social media monitoring software in the areas of audience, image and sentiment analysis (cf. Micu et al 2018;Capatina et al 2020). Perakakis et al (2019) emphasize the potential of AI for social media monitoring and introduce a software tool called "Social Intelligence Advisor" to support digital marketers.…”

Section: Clusteringmentioning

confidence: 99%

See 3 more Smart Citations

UR: SMART–A tool for analyzing social media content

Schwaiger

Hammerl

Johannsen

et al. 2021

Inf Syst E-Bus Manage

View full text Add to dashboard Cite

The digital transformation, with its ongoing trend towards electronic business, confronts companies with increasingly growing amounts of data which have to be processed, stored and analyzed. Instant access to the “right” information at the time it is needed is crucial and thus, the use of techniques for the handling of big amounts of unstructured data, in particular, becomes a competitive advantage. In this context, one important field of application is digital marketing, because sophisticated data analysis allows companies to gain deeper insights into customer needs and behavior based on their reviews, complaints as well as posts in online forums or social networks. However, existing tools for the automated analysis of social content often focus on one general approach by either prioritizing the analysis of the posts’ semantics or the analysis of pure numbers (e.g., sum of likes or shares). Hence, this design science research project develops the software tool UR:SMART, which supports the analysis of social media data by combining different kinds of analysis methods. This allows deep insights into users’ needs and opinions and therefore prepares the ground for the further interpretation of the voice. The applicability of UR:SMART is demonstrated at a German financial institution. Furthermore, the usability is evaluated with the help of a SUMI (Software Usability Measurement Inventory) study, which shows the tool’s usefulness to support social media analyses from the users’ perspective.

show abstract

Section: Sentiment Analysismentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

Section: Clusteringmentioning

confidence: 99%

Section: Clusteringmentioning

confidence: 99%

See 2 more Smart Citations

UR: SMART–A tool for analyzing social media content

Schwaiger

Hammerl

Johannsen

et al. 2021

Inf Syst E-Bus Manage

View full text Add to dashboard Cite

show abstract

“…All around the world, smartphones with intelligent-based software packages, social networking sites (SNSs), and a variety of mobile applications (apps) have taken over the global market (Capatina et al, 2020;Lai et al, 2011). The development of information technologies has led to applications that allow the user to carry out various tasks (Malik et al, 2017) or access services from all over the globe.…”

Section: Introductionmentioning

confidence: 99%

Ubiquitous Role of Social Networking in Driving M-Commerce: Evaluating the Use of Mobile Phones for Online Shopping and Payment in the Context of Trust

et al. 2020

View full text Add to dashboard Cite

The article analyzes the role of driving m-commerce with social networking and therefore provides insight into how the application of mobile apps influences customers’ perceptions on purchasing products online and on the mode of payment. The consumers are engaged in social interactions through the internet by the new opportunities provided by social media. These interactions provide and generate certain values for both businesses and consumers. An upsurge in the application of social media on mobile phones by users is evident, giving optimism and the ability to view the role of the integration of m-commerce into social media. Certain criteria like mobile app compatibility, trust, perceived value of mobile phone apps for online shopping, and online payment are examined from the point of view of consumers who purchase products, save purchase time, and provide easy use and security through social networking sites and m-commerce. Adoption of a digital mode of payment is affected by the education level of the consumers as, if they are internet savvy, they will be more inclined to use the digital payment mode. The article not only discusses the role of education in the better understanding of consumers toward the application of online modes of transaction through mobile phones, but also indicates that there are security issues, although these have been resolved to some extent by technological advances. Yet, there is need for the retailers as well as the consumers to achieve further technological progress.

show abstract