IoT big data value map : how to generate value from IoT data

Huge sources of business value are emerging due to big data generated by the Internet of Things (IoT) technologies paired with Machine Learning (ML) and Data Mining (DM) techniques' ability to harness and extract hidden knowledge from data and consequently learning and improving spontaneously. This paper reviews different examples of analyzing big data generated through IoT in previous studies and in various domains; then claims their business Value Proposition Map deploying Value Proposition Canvas as a novel conceptual tool. As a result, the proposed unprecedented framework of this paper entitled "IoT Big Data Value Map" shows a roadmap from raw data to real-world business value creation, blossomed out of a kind of three-pillar structure: IoT, Data Mining, and Value Proposition Map. The result of this study paves the way for prototyping business models in this field based on value invention from huge data analysis generated by IoT devices in different industries. Furthermore, researchers may complete this map by associating proposed framework with potential customers' profile and their expectations

Tracing the evolution of digitalisation research in business and management fields: Bibliometric analysis, topic modelling and deep learning trend forecasting

Research on digitalisation trends and digital topics has become one of the most prolific streams of research within the fields of business and management during the course of the past few years. The purpose of this study is to provide a general picture of the intellectual structure and the conceptual space of this research realm. To this purpose, 6067 publications related to digital topics, indexed in the business and management categories of Web of Science (WoS), and dated from 1990 to 2020 are explored based on the approaches of bibliometric analysis, topic modelling and trend forecasting. The results of the bibliometric analysis comprise insights into the publication and citation structure, the most productive authors, the most productive universities, the most productive countries, the most productive journals, the most cited studies and the most prevalent themes and sub-themes on digitalisation in business and management. In addition, the outcomes of the topic modelling give new knowledge on the latent topical structure along with the rising, falling and fluctuating trends of this literature. In addition, the results of the trend forecasting enable readers to have a glimpse of how the underlying trends of the literature will probably change within the next years until 2025. These results provide guidance and orientation for both academics and practitioners who are initiating or currently developing their efforts in this discipline.info:eu-repo/semantics/acceptedVersio

Digital transformation: towards new research themes and collaborations yet to be explored

This study aimed at providing an overview of research themes and collaborations in the digital transformation scholarship. The methods of co-word analysis, co-author analysis, and network analysis were employed to network-analyze the keywords, countries, and institutions of 2820 research articles published on the digital transformation topic and indexed by the Web of Science database. Our main results indicated that researchers have mostly focused on three aspects of the digital transformation phenomenon including Technological and Industrial View, Organizational and Managerial View, and Global and Social View. Also, it was realized that Technology, Sustainability, Big Data, Information and Communications Technology, Innovation, Industry 4.0, Artificial Intelligence, Business Model, Social Media, and Digitization are the most recurring themes in this field of research. Besides, Small and Medium-Sized Enterprises, Blockchain, Machine Learning, Knowledge Management, and Sustainable Development were respectively identified as the five hottest issues in the digital transformation scholarship. The contribution of our study highlights that European countries and specially the institutions of northern Europe have had better performance in the research collaborations in digital transformation.info:eu-repo/semantics/acceptedVersio

Discovering IoT implications in business and management: A computational thematic analysis

IoT as a disruptive technology is contributing toward ground-breaking experiences in contemporary enterprises and in our daily life. Rapidly changing business environment and phenomenally evolving technology enhancement necessitate a robust understanding of IoT implications from business and management perspective. The current study benefits from an explanatory sequential mixed-method approach to represent and interpret the inductive topical framework of IoT literature in business and management with emphasis on business model. Bayesian statistical topic model called latent Dirichlet allocation is employed to conduct a comprehensive analysis of 347 related scholarly articles to reveal the topical composition of related research. Further, we followed a thematic analysis for interpreting the extracted topics and gaining in-depth qualitative insights. Theoretical implications with emphasizing on research agenda for future study avenues and managerial implications based on influential themes are provided

Business model analytics: technically review business model research domain

Purpose Although the business model field of study has been a focus of attention for both researchers and practitioners within the past two decades, it still suffers from concern about its identity. Accordingly, this paper aims to clarify the intellectual structure of business model through identifying the research clusters and their sub-clusters, the prominent relations and the dominant research trends. Design/methodology/approach This paper uses some common text mining methods including co-word analysis, burst analysis, timeline analysis and topic modeling to analyze and mine the title, abstract and keywords of 14,081 research documents related to the domain of business model. Findings The results revealed that the business model field of study consists of three main research areas including electronic business model, business model innovation and sustainable business model, each of which has some sub-areas and has been more evident in some particular industries. Additionally, from the time perspective, research issues in the domain of sustainable development are considered as the hot and emerging topics in this field. In addition, the results confirmed that information technology has been one of the most important drivers, influencing the appearance of different study topics in the various periods. Originality/value The contribution of this study is to quantitatively uncover the dominant knowledge structure and prominent research trends in the business model field of study, considering a broad range of scholarly publications and using some promising and reliable text mining techniques