A classification of user-generated content into consumer decision journey stages

Examining social media live stream’s influence on the consumer decision-making: a thematic analysis

Social media live streaming, in the form of live video and user stories, is widely used by influencers, organisations and individuals to connect with their audiences. Its popularity is well-established in a range of theoretical and managerial contexts. However, there is a lack of scholarship on the role of this phenomenon on consumer decision-making. Filling this gap in the research is essential due to the importance of consumer decision-making in marketing and brand strategy development in organisations. Therefore, the purpose of this paper is to explore and outline the nature of the influence of live stream on the consumer decision-making. The study was part of a 12-month Netnography consisting of participant observation and social media monitoring of brand pages and branded hashtags on social media platforms, Facebook, YouTube, Twitter and Instagram. A thematic analysis revealed five main themes and a conceptual model is proposed which outlines the social media live stream’s influence on consumer decision-making at each stage.

Multi-Label Annotation and Classification of Arabic Texts Based on Extracted Seed Keyphrases and Bi-Gram Alphabet Feed Forward Neural Networks Model

In natural language processing, text classification is a fundamental problem. Multi-label classification of textual data is a challenging topic in text classification where an instance can be associated with more than one label. This paper presents a multi-label annotation and classification methodology for Arabic text data that is not currently classified as multi-label, aiming to analyze and compare the performance of various multi-label learning approaches. The current work includes two phases: The first involves automatic annotation of hotel reviews with more than one label based on the aspects found in the reviews. In this phase, review data instances were automatically annotated as multi-label based on the extracted seed keyphrases clusters. The second phase involves experiments to compare the performance of various multi-label classification learning methods. In this phase, we introduced different models including feed-forward networks model that learns a vector representation based on the bi-gram alphabet rather than the commonly used bag-of-words model. The bi-gram alphabet vector representation model has the advantage of having reduced feature dimensions and not requiring natural language processing tools. The results indicated that employing the bi-gram alphabet vector representation feed forward neural network is a competitive solution for the multi-label text classification problem. It has achieved an accuracy of about 75.2%, and standard deviation (0.062).

CURIE: Towards an Ontology and Enterprise Architecture of a CRM Conceptual Model

Companies face the challenge of managing customer relationships (CRM) in a context marked by a drastic digital transformation and unbridled evolution of consumer behavior, exacerbated by the COVID-19 pandemic. The customer is more demanding, has access to the global market and interacts with companies through multiple digital channels, such as email, social networks, mobile apps or instant messaging. In this situation, the success of a CRM implementation highly depends on information technology and the applications used. To harmonize this new business context with the development of information systems (IS), a suitable CRM ontology and enterprise architecture (EA) is needed. While an ontology-based conceptual model provides a unifying framework, aids sharing and reusing knowledge, and facilitates communication within a domain, an EA-based model unequivocally describes, analyzes, and visualizes how an organization should operate from the perspective of business, application, and technology. The purpose of the paper is the proposal of CURIE-O, a CRM OntoUML UFO-based ontology, together with CURIE-EA, a CRM ArchiMate-based EA to serve business managers and IS specialists an updated unifying framework of reference in the CRM domain as well as a highly efficient tool to support application development and maintenance in this changing and increasingly digital context. Modeling has proven to be an essential element to achieve high-performance information systems. In order to apply the ontology and the EA proposed here, the authors developed a CRM task management application prototype that was implemented as a case study in a consulting company. The methodology followed was design science research (DSR), in order to design and validate the artifacts. Within the DSR framework, other complementary research methods have been used, in particular literature research, interviews and focus groups carried out with several hotel chains in Tenerife (Canary Islands). The main existing CRM models in the scientific literature have also been analyzed together with the leading CRM market solutions.

Managerial Responses and Customer Engagement in Crowdfunding

Despite the widespread recognition of the importance of customer behavior in crowdfunding performance, empirical research concerning the importance of managerial responses in user-generated content is scarce. How do managerial responses affect backers’ comments? Does user-generated content affect following backers’ behavior? Using a dataset of backers’ comments and creators’ managerial responses from Kickstarter.com, we attempt to clarify the relationships among creator responses to comments, comment volume, linguistic features of comment text and crowdfunding performance. Our results show creator responses have a significant positive effect on customer engagement and crowdfunding performance. Moreover, creator response is an effective advertising strategy to improve crowdfunding performance.