Search results

This paper refines the Digital Media–Arena (DMA) framework to address the diversity of stakeholders contributing to the production, (re)appropriation and (re)distribution of organisational messages in digital environments. It also presents a case analysis for the purpose of demonstrating the applicability of the revised conceptual framework to a critical situation.

Grounded in key public relations, corporate communication and strategic communication research, this study first extends the DMA framework by introducing six new forms of media-arenas. Next, the study takes a public sector perspective to analyse the revised framework against a critical situation involving the Finnish prime minister in summer 2022.

The application of the revised DMA framework to analyse the critical situation shows the importance of mapping and understanding diverse discourses across multi-arenas and their communication role in a rapidly unfolding scandal surrounding the prime minister of Finland. Findings also reveal the diversity of stakeholder voices forming their own versions of organisational messages and sometimes converging organisational messages within and across DMAs.

The DMA framework can offer practical suggestions to guide communicators to make strategic choices in what, where, how and with whom they can communicate.

The revised DMA framework contributes expanding the field's knowledge of the strategic communicative use of the digital environment in typically highly volatile and multi-vocal situations by offering instrumental understanding of the conflicting challenge between subjugating and liberating organisational messages across the digital spectrum.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

Generative pretrained transformers (GPTs), soaring to one million users at lightning speed, outpaced social media giants (15 times faster) (Buchholz, 2023). Despite this, scant research explored GPT’s impact on the digital entrepreneurial intentions (EIs) of students and tech-savvy generations. This study aims to pioneer a fusion of the technology acceptance model (TAM) and the theory of planned behavior (TPB), bridging the gap in research.

In this bold quantitative quest, business administration students became fearless participants, engaging in a survey of profound significance. Guided by the mighty powers of G*Power and Stata’s structural equation modeling builder, the intricate relationships within a robust sample of (n = 400) were unraveled.

The mediating power of GPT usefulness and GPT ease of use part of the TAM emerges, paving the way for a future brimming with digital entrepreneurship (DE) boundless possibilities. Predictably, the study found that TPB constructs also affect the EI of students.

This groundbreaking study brings together the powerful combination of TAM and TPB, while pioneering the exploration of GPT models’ mediating role. Its findings offer invaluable contributions to the field of DE and policymakers.

This study aims to give a glimpse of the existing blockchain applications across industries and add to a complete knowledge of the blockchain’s properties.

Systematic literature review is used as the research strategy for this investigation and other aspects of the preferred reporting items for systematic reviews and meta-analyses framework have been incorporated to create a scholarly publications evaluation of the blockchain-based application in the financial arena and its future. The research looks at 86 studies published between 2018 and 2022.

There has been a steady but noticeable increase in the study of blockchain’s potential in many application domains over the past few of years. This rising tendency illustrates the newness and potential of blockchain technology, as well as the increasing attention from academics. According to the findings, blockchain is an appropriate solution for processing transactions using cryptocurrencies; nevertheless, it still has significant technical issues and limits that require to be exploring and solving before it can be considered a viable option. It is therefore, necessary to have a high level of reliability for payments and confidentiality, in addition to maintaining the anonymity of nodes, to stop assaults and efforts to disrupt transactions in the blockchain.

This study has several important theoretical and practical implications. First, it adds to the body of knowledge on blockchain and Fintech, focusing on the transaction side. While much blockchain research has focused on how the technology may affect strategic choices, this study has shed light on its potential from the perspective of financial reporting. Second, by highlighting the importance of the demand for the prompt identification of losses, this work adds to the body of knowledge on the factors that influence transaction frauds involving paper money. Additionally, by establishing the link between transparency and virtual transactions, the author backs up the asymmetric responses of investors to different investment possibilities. It looks at the evolution of financial technology (Fintech) and shows how it can be used to take the advantage of unique opportunities.

The study is different and novel from the previously published literature on this topic mainly because of its comprehensiveness, as it revolves around all industrial and commercial areas. The three main lines of research have been outlined, namely, classifying the many blockchain-based innovations that will alter the financial landscape in many industries; identifying whether these industries are a good fit for blockchain’s wealth creation potential; and directing researchers by outlining prospective study pathways.