Search results

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

The purpose of this study is to conceptually integrate business to consumer (B2C) into business to business (B2B), with a holistic consumer-centric, technology-reinforced, long-term vision for tourism industries and companies to survive and succeed in the era of new technologies 4.0. The research suggests that the tourism-marketing-new technologies decision-making involves customers as the center of the design and decision process.

The research design includes a qualitative study with 94 in-depth interviews, a literature analysis and a conceptual proposition. The qualitative study follows the tourism consumer desire data analysis, from categorization to integration. The literature analysis applies a systematic literature review approach based on the 29 most up-to-date new-tech papers from peer-reviewed journals. The analysis compares qualitative research findings and literature analysis results and matches the new technology applications with consumer desire understanding. The conceptual framework of tourism marketing/advertising is proposed based on qualitative research and literature analysis.

The qualitative research deciphers that consumers, based on their imagination and memorization, desire therapy and sceneries and connect such desires to the empathetic and resonating advertising messages. The literature analysis synthesizes the new tech applications in tourism and matches the qualitative research findings with the deciphered desires in tourism. The conceptual model proposes that B2C should be integrated into B2B to provide value for both consumers and businesses and opens avenues of research on this topic.

This research has made the following theoretical contributions: it offers an in-depth understanding of consumer desire, often hidden or subconscious, in the field of tourism. Consumer desires regarding tourism are mostly subconscious and exist long before consumers are exposed to advertising messages. These desires reflect the search for therapy and sceneries and become “embodied” – they exist on multisensorial levels and become part of the body and life and will lead consumers into positive perceptions when marketing communications/advertisements resonate with them. In the latter case, they will subjectively judge advertising as “good,” regardless of the advertising design quality. The research also connects consumer research with a new technologies research review and proposes a conceptual framework to integrate business to consumer (B2C) with business to business (B2B). As such, the research makes theoretical contributions to the integration or the “boundary blurring” between B2C and B2B research and practical suggestions that involved industries and consumers may all benefit from such integration. Conceptually, there is a lack of discussions of the pitfalls of new technologies, a dearth of empirical verification of the applications of new technologies in the proposed fields and a shortage of discussions about ethical issues. Qualitative methods, offering an efficient tool for understanding consumer desires in the tourism industry, have their own limits, as discussed in previous research. The sample is limited to the state of New York population and may be influenced by geographic, demographic and psychological characteristics related to the region.

This research provides advertising practitioners, new technology innovators and tourism industries with a framework to face the combined challenges of understanding hidden consumer desires and applying adequate technologies that resonate with consumer desires to tackle relevant issues. The conceptual proposition of this research fills the gap between qualitative consumer research without concrete practical resolution and new technologies applications without in-depth consumer understanding. Through the conceptual framework, the author provides insights into how industries may benefit from consumer understanding. The business relationships among the industries of marketing, tourism and new technologies should be centered around consumers. Thus, B2C and B2B should be naturally integrated into business practices.

Social implications of this research include three major points: first, the understanding of consumer desire for therapeutic power in tourism, which invites more attention to tourism as part of social well-being design instead of a purely for-profit business. Second, a profound comprehension of what consumers need and desire, without which the applications of new technologies may cause severe societal problems. Third, a way to tailor to consumers’ individuality and desires for advertising/marketing that may be considered abusive, stressful and socially destructive if applied in a nonpersonal manner.

Conceptually, this research adds consumer desire, an originally B2C concept, to the B2B context regarding the new technology applications in tourism marketing/advertising. It contributes to the B2B literature by proposing a strong consumer-centric approach, especially the consumer desire understanding, that is not yet investigated in the B2B literature; and a combination of empirical study and literature analysis and the matching of the two for better practice of advertising/marketing, tourism and new technologies applications.

The purpose of this paper is to explore the impact of ChatGPT on the development of corporate social responsibility (CSR).

Based on the current applications of ChatGPT in enterprises and existing challenges in CSR, the paper analyzes how the tool promotes corporate sustainable development and what potential risks and challenges are in the practical application.

This paper finds that ChatGPT can promote the development of CSR from four aspects: to support social responsibility activities, to strengthen internal control, to promote external supervision and to convey the notion of sustainability and other corporate philosophies for enterprises.

This paper provides the ground for applying ChatGPT to CSR and includes the potential risks and challenges of the practical applications that enterprises can use for reference.

ChatGPT is becoming increasingly popular and mature, but its applications and effectiveness are less mentioned in CSR. In the future, the research in this area needs to be further advanced.

This paper makes contributions to the link between ChatGPT and CSR. To the best of the authors’ knowledge, this is one of the first studies that explore the applications, impacts, challenges and opportunities of the technology in the area of social responsibility.

The ability of generative artificial intelligence (AI) tools such as ChatGPT to produce convincing, human-like text has major implications for the future of corporate reporting, including sustainability reporting. As the importance of sustainability reporting continues to grow, this study aims to critically analyse the benefits and pitfalls of automated text generation and processing.

This study develops a conceptual framework to delineate the field, assess the implications and form the basis for the generation of research questions. This study uses Alvesson and Deetz’s critical framework, considering insight (a review of literature and practice in the field), critique (consideration of the influences on the production and use of non-financial information and the implications for assurers of such information) and transformative redefinition (considering the implications of generative AI for sustainability reporting and proposing a research agenda).

This study highlights the implications of generative AI for sustainability accounting, reporting, assurance and report usage, including the risk of AI facilitating greenwashing, and the importance of more research on the use of AI for these matters.

The paper highlights to stakeholders the implications of AI for all aspects of sustainability reporting, including accounting, reporting, assurance and usage of reports.

The implications of AI need to be understood in society, which this paper facilitates.

This study critically analyses the potential use of AI for sustainability reporting, construct a conceptual framework to delineate the field and develop a research agenda.

This paper aims to manage the word gap in information retrieval (IR) especially for long documents belonging to specific domains. In fact, with the continuous growth of text data that modern IR systems have to manage, existing solutions are needed to efficiently find the best set of documents for a given request. The words used to describe a query can differ from those used in related documents. Despite meaning closeness, nonoverlapping words are challenging for IR systems. This word gap becomes significant for long documents from specific domains.

To generate new words for a document, a deep learning (DL) masked language model is used to infer related words. Used DL models are pretrained on massive text data and carry common or specific domain knowledge to propose a better document representation.

The authors evaluate the approach of this study on specific IR domains with long documents to show the genericity of the proposed model and achieve encouraging results.

In this paper, to the best of the authors’ knowledge, an original unsupervised and modular IR system based on recent DL methods is introduced.

In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task.

To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning.

Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets.

This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.

In times of crisis, such as the Covid-19 global pandemic, journalists who write about biomedical information must have the strategic aim to be clearly and easily understood by everyone. In this study, we assume that journalistic discourse could benefit from language redundancy to improve clarity and simplicity aimed at science popularization. The concept of language redundancy is theoretically discussed with the support of discourse analysis and information theory. The methodology adopted is a corpus-based qualitative approach. Two corpora samples with Brazilian Portuguese (BP) texts on Covid-19 were collected. One with texts from a monthly science digital magazine called Pesquisa FAPESP aimed at students and researchers for scientific information dissemination and the other with popular language texts from a news Portal G1 (Rede Globo) aimed at unspecified and/or non-specialized readers. The materials were filtered with two descriptors: “vaccine” and “test.” Preliminary analysis of examples from these materials revealed two categories of redundancy: paraphrastic and polysemic. Paraphrastic redundancy is based on concomitant language reformulation of words, sentences, text excerpts, or even larger units. Polysemic redundancy does not easily show material evidence, but is based on cognitively predictable semantic association in socio-cultural domains. Both kinds of redundancy contribute, each in their own way, to improving text readability for science popularization in Brazil.