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This study aims to explore whether Chat Generative Pre-training Transformer (ChatGPT) can produce quantitative data sets for researchers who could behave unethically through data fabrication.

A two-stage case study related to the field of tourism was conducted, and ChatGPT (v.3.5.) was asked to respond to the first questionnaire on behalf of 400 participants and the second on behalf of 800 participants. The artificial intelligence (AI)-generated data sets’ quality was statistically tested via descriptive statistics, correlation analysis, exploratory factor analysis, confirmatory factor analysis and Harman's single-factor test.

The results revealed that ChatGPT could respond to the questionnaires as the number of participants at the desired sample size level and could present the generated data sets in a table format ready for analysis. It was also observed that ChatGPT's responses were systematical, and it created a statistically ideal data set. However, it was noted that the data produced high correlations among the observed variables, the measurement model did not achieve sufficient goodness of fit and the issue of common method bias emerged. The conclusion reached is that ChatGPT does not or cannot yet generate data of suitable quality for advanced-level statistical analyses.

This study shows that ChatGPT can provide quantitative data to researchers attempting to fabricate data sets unethically. Therefore, it offers a new and significant argument to the ongoing debates about the unethical use of ChatGPT. Besides, a quantitative data set generated by AI was statistically examined for the first time in this study. The results proved that the data produced by ChatGPT is problematic in certain aspects, shedding light on several points that journal editors should consider during the editorial processes.

本研究旨在探讨ChatGPT是否能够为那些可能通过数据伪造行为不道德的研究人员生成定量数据集。

本研究进行了与旅游领域相关的两阶段案例研究, 并要求ChatGPT（v.3.5.）代表400名参与者回答第一个问卷, 以及代表800名参与者回答第二个问卷。通过描述统计、相关分析、探索性因子分析、验证性因子分析和哈曼的单因素测试对人工智能生成的数据集的质量进行了统计测试。

结果显示, ChatGPT能够按照所需的样本大小水平回答问卷, 并以表格格式呈现生成的数据集, 以便进行分析。还观察到ChatGPT的回答是系统性的, 并且它创建了一个在统计上理想的数据集。然而, 本研究注意到所产生的数据在观察变量之间存在较高的相关性, 测量模型未能达到足够的拟合度, 并出现了共同方法偏差的问题。本研究得出的结论是, ChatGPT目前不能生成适用于高级统计分析的数据, 或者说不适合这样做。

本研究表明, ChatGPT可以为试图不道德地伪造数据集的研究人员提供定量数据。因此, 它为关于ChatGPT不道德使用的持续争论提供了一个新而重要的论点。此外, 在本研究中首次对由人工智能生成的定量数据集进行了统计检验。结果表明, ChatGPT生成的数据在某些方面存在问题, 为期刊编辑在编辑过程中考虑的几个要点提供了启示。

This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.

The empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.

The results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.

First, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”

Denial-of-service (DoS) attacks develop unauthorized entry to various network services and user information by building traffic that creates multiple requests simultaneously making the system unavailable to users. Protection of internet services requires effective DoS attack detection to keep an eye on traffic passing across protected networks, freeing the protected internet servers from surveillance threats and ensuring they can focus on offering high-quality services with the fewest response times possible.

This paper aims to develop a hybrid optimization-based deep learning model to precisely detect DoS attacks.

The designed Aquila deer hunting optimization-enabled deep belief network technique achieved improved performance with an accuracy of 92.8%, a true positive rate of 92.8% and a true negative rate of 93.6.

The introduced detection approach effectively detects DoS attacks available on the internet.

Employee training and learning have transformed over the years. The movement from classroom training to the blended format represents the magnitude of this evolution. This has placed much attention on self-regulated learning. This study aimed to understand the individual and organizational mechanisms that sustain the formal learning process in organizations. It explored the goals the organizations and employees strive to achieve by investing in learning. Through this, the authors investigated how technology assistance makes learning more goal-oriented, despite the possibility of different goals for different stakeholders. They also examined how person-job fit can be achieved in employee training.

The study adopted a grounded theory-based inductive approach using a qualitative inquiry that used in-depth interviews of employees working in the Indian IT/ITES sector. This sector is knowledge-intensive and engages in constant skill development. A content analysis of the interview transcripts unraveled the most relevant themes from the participants' discussion.

Individual learners use dimensions of self-regulated learning to set and achieve goals such as better performance and career development. On the other hand, organizations use learning support mechanisms such as better access and flexibility to direct employee learning behavior to achieve organizational goals. Focusing on goal congruence leads to better achievement of results. Goal congruence also implies good person-organization fit.

This research established how aligning individual and organizational mechanisms can help achieve training goals that ultimately contribute to organizational performance. The study differentiated itself by investigating training goal setting and goal achievement at two levels – organizational and individual – using a qualitative approach. It also showed how goal congruence is vital in improving organizational performance and how technology-enabled training practices rely on self-regulated learning and help achieve goal congruence.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

The contemporary business environment is characterised by an increasing reliance on artificial intelligence, automation, optimisation, efficient communication and data-driven decision making. Based on the limited academic literature that examines the managerial perspective on enterprise chatbots, the paper aims to explore organisational needs and expectations for enterprise chatbots from a managerial perspective, assesses the relationship between managerial knowledge and managerial opinion regarding enterprise chatbots, and delivers a framework for integrating chatbots into the digital workforce.

The paper presents a quantitative design. An online, self-administered survey yielded 111 valid responses from managers in service and manufacturing organisations based on convenience and snowball sampling strategies. Given the nature of the data and the research questions, the research was conducted using principal component analysis, parallel analysis, correlation, internal consistency and difference in means tests.

This research explores the managerial perspective on enterprise chatbots from multiple perspectives (i.e., adoption, suitability, development requirements, benefits, barriers, performance and implications), presents a heat map of the average level of chatbot need across industries and business units, highlights the urgent need for education and training initiatives targeted at decision makers, and provides a strategic framework for successful chatbot implementation.

This study equips managers and practitioners dealing with enterprise chatbots with knowledge to effectively leverage the expected benefits of investing in this technology for their organisations. It offers direction for developers in designing chatbots that align with organisational expectations, capabilities and skills.

Insights for managers, researchers and chatbot developers are provided. The work complements the few academic studies that examine enterprise chatbots from a managerial perspective and enriches related commercial studies with more rigourous statistical analysis. The paper contributes to the ongoing discourse on decision-making in the context of technology development, integration and education.

This study evaluated the impact of a faculty training program on student assessment using the Kirkpatrick model.

A self-reported survey assessed 111 Saudi and non-Saudi participants' satisfaction. Subjective and objective measures (self-reported measures, assessment literacy inventory and performance-based assessment tasks) gauged participants' learning level. Pre- and post-training data were collected from 2020 to 2022.

A highly significant effect on satisfaction (>80%) and learning levels was observed, as manifested by workplace practices of student assessment (>70%, the cut-off score). Pre- and post-training comparisons of participants' satisfaction and assessment literacy scores showed significant improvements following training. Multiple regression analyses showed no significant effects for gender and educational attainment but a substantial impact of academic cluster on participants' student assessment skills.

Long-term effects of training faculty on assessment practices and student achievement will be studied at the institutional level in future research.

The current study contributes to human capital investment via faculty training on student assessment, helping them comply with assessment best practices. This assures the quality, fairness and consistency of assessment processes across disciplines in higher education institutions, enhances assessment validity and trust in educational services and may support institutional accreditation.

This study provides opportunities for sharing best practices and helps establish a community of practice. It enhances learning outcomes achievement and empowers higher education graduates with attributes necessary to succeed in the labor market. The human capital investment may have a long-term impact on overall higher education quality.

This study contributes to the scarce literature investigating the impact of training faculty from different clusters on student assessment using subjective and objective measures. It provides developing and evaluating a long-term student assessment program following the Kirkpatrick model.

This study examines the relationship between streamers' product descriptions, customer comments and online sales and focuses on the moderating effect of streamer–viewer relationship strength.

Between June 2021 and April 2022, the structured data of 965 livestreaming and unstructured text data of 42,956,147 characters from two major live-streaming platforms were collected for the study. Text analysis and regression analysis methods were employed for data analysis.

First, the authors' analysis reveals an inverted U-shaped relationship between comment length and product sales. Notably, comment volume and comment emotion positively influence product sales. Furthermore, the semantic richness, emotion and readability of streamers' product descriptions also positively influence product sales. Secondly, the authors find that the strength of streamer–viewer relationship weakens the positive effects of comment volume and comment emotion without moderating the inverted U-shaped effect of comment length. Lastly, the strength of streamer–viewer relationship also diminishes the positive effects of emotion, semantics and readability of streamers' product descriptions on product sales.

This study is the first to concurrently examine the direct and interactive effects of user-generated content (UGC) and marketer-generated content (MGC) on consumer purchase behaviors in livestreaming e-commerce, offering a novel perspective on individual decision-making and cue utilization in the social retail context.