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With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic data science learning experiences with their students.

Interviews with 12 practicing high school mathematics and statistics teachers were conducted and video-recorded. Teachers were given two different data sets about the same context and asked to explain which one would be better suited for an authentic data science experience. Following knowledge analysis methods, the teachers’ responses were coded and iteratively reviewed to find themes that appeared across multiple teachers related to their aesthetic judgments.

Three aspects of authenticity for data sets for this task were identified. These include thinking of authentic data sets as being “messy,” as requiring more work for the student or analyst to pore through than other data sets and as involving computation.

Analysis of teachers’ aesthetics of data sets is a new direction for work on data literacy and data science education. The findings invite the field to think critically about how to help teachers develop new aesthetics and to provide data sets in curriculum materials that are suited for classroom use.

This paper contends that data generated by research on supervision are often taken as authentic data. Through an examination of studies that use audio/visual recordings to investigate supervision, the paper both promotes and problematises the recording of supervision meetings as a useful technique for doctoral supervision research. This paper aims to encourage a critical evaluation of methodological choices in research on supervision, and both promotes and problematises the practice of recording supervision meetings to enhance nuance in research on supervision practices.

This paper reviews how prior studies have adopted different research methods to construct the space of supervision, and how the chosen methods have been justified. The paper draws on data from an empirical study which included interviews with supervisors in China, based on recordings of their supervision meetings.

Presenting a single case with one participant to explore the recording and interview process in detail, this study demonstrates how hearing the supervision meeting can present a multi-faceted picture of supervision practice. This multi-faceted picture underpins the alternative understanding of authentic data that this study unpacks.

Drawing on the tradition of poststructuralist critiques of traditional research methodology, this study is presented as a methodological paper, with a core aim of interrogating and problematising methodological decisions taken in studies of doctoral supervision. This study reviews research methods that were used in prior studies on supervision, investigating how the chosen methods were justified and how these methods affect the resultant construction of supervision.

This paper aims to understand the impact of big data on the UAE audit profession. Mainly exploring whether the emergence of big data threatens the reliability of audit standards and whether audit standards need to be improved. Also, exploring the impact of big data on the collection of audit evidence.

Semistructured interviews were used to collect data, mainly targeting the audit-related workers of the Big Four and Non-Big Four audit firms in the UAE. Thematic analysis is adopted to analyze the original data, and the main factors affecting the audit standard and audit evidence collection.

This study found that the reliability of audit standards and the way audit evidence is collected can be affected by big data. It concludes that audit standards need to be improved and strengthened to include detailed essential elements associated with big data to ensure audit reliability, legitimacy and regularity. The results also identify the impact of big data on audit evidence in terms of adequacy, appropriateness, authenticity, consistency and reliability, as well as the impact on the validity and completeness of evidence collection. The research highlights the importance of big data skills and knowledge education, the contribution and challenges of big data to auditing, and the use of big data in future auditing.

This research provides specific empirical evidence from both Big Four and Non-Big Four audit firms in the UAE, which is lacking in the literature on the use of big data technology by auditors to assist audit works in UAE. It may serve as a reference for other researchers or those interested in relevant research.

The purpose of this study is to examine how existing supply chain finance challenges confronting SMEs are affected by the emergence of smart fintech providers. In so doing the paper aims at uncovering critical role of fintech service provision in SCF and associated mechanisms that affect the SCF partners.

An in-depth case study approach has been applied in this study. The overall design is informed by a 5-stage-based case study approach developed in operation management, including the literature review and research question, followed by case selection and instrument development, the data gathering, the analysis and findings and dissemination.

The study shows that fintech service provider is capable of offering different digital technologies adapted to specific needs while concomitantly orchestrating the information flow across the partners. Key mechanisms that influence the establishment of trust-based relationships among the SCF partners, and related service processes and value creation based on the platform system architecture are explained.

Several practical implications for digital platform management and other key digital SCF partners are identified.

This paper contributes a novel perspective on the importance of digital trust in SCF and also contributes to the existing literature by filling up a gap with a new and fine-grained understanding of the role of fintech companies in SCF.

The quality of service determines whether service firms can satisfy customers and achieve business quality and sustainability. As contemporary service firms are dependent on both team and employee to serve customers, it is important to investigate how to simultaneously facilitate team service performance (TSP) and employee service performance (ESP). Our aim is to build a multilevel model of the curvilinear effect of task conflict (TC) on TSP and ESP, as well as the moderating effects underlying the above curvilinear relationships.

Two-sourced data were obtained from 47 team leaders and 326 employees in Chinese hotels. Multilevel structural equation modeling was utilized for validating the model.

The results revealed that TC exerted a curvilinear effect on both TSP and ESP. Ethical climate (EC) and internal knowledge transfer (IKT) served as moderators strengthening the curvilinear nexus between TC and ESP.

We contribute to the conflict-performance stream in management literature by unmasking the curvilinear effects of TC on both TSP and ESP, and the moderation mechanisms underlying such curvilinear effects.

This article explores brand positioning and authenticity within the global-local continuum, utilizing the evolution of the Italian rock band, Måneskin, as a case study.

Employing Greimas’s (1987) semiotic framework, I analyze social media and media articles on Måneskin’s success, unveiling consumer perceptions of global, local and intermediate brand positionings and related authenticity dimensions. I particularly uncover a narrative centered on “global” versus “local” brand positioning and their counterparts (i.e. “not global” and “not local”), forming a semiotic square.

In the “global” perception, the band is evaluated in terms of conforming to global standards, while, in the “local” understanding, the emphasis shifts to connections to local roots. In the “glocalization” perspective (global and local), the band’s activities are assessed concerning an integration between global conformity and local connections. The “glalienation” viewpoint (neither global nor local) is related to consistency, in the sense of being unique and avoiding a commitment to either global or local values. The data also highlight issues of inconsistency regarding brand positioning’s contradictions, such as the band’s incoherently merging local and non-local elements.

The proposed structural semiotics approach enriches previous theories by examining authenticity within global-local dynamics, offering insights into various authenticity dimensions and their interplay. It underlines shifts in authenticity perceptions and challenges binary brand positioning, advocating for strategic placement across the global-local continuum. Moreover, it emphasizes leveraging cultural elements and semiotics to effectively communicate authenticity.

Blockchain, a groundbreaking technology that recently surfaced, is under thorough scrutiny due to its prospective utility across different sectors. This research aims to delve into and assess the cognitive elements that impact the integration of blockchain technology (BT) within library environments.

Utilizing the Stimulus–Organism–Response (SOR) theory, this research aims to facilitate the implementation of BT within academic institution libraries and provide valuable insights for managerial decision-making. A two-staged deep learning structural equation modelling artificial neural network (ANN) analysis was conducted on 583 computer experts affiliated with academic institutions across various countries to gather relevant information.

The research model can correspondingly expound 71% and 60% of the variance in trust and adoption intention of BT in libraries, where ANN results indicate that perceived possession is the primary predictor, with a technical capability factor that has a normalized significance of 84%. The study successfully identified the relationship of each variable of our conceptual model.

Unlike the SOR theory framework that uses a linear model and theoretically assumes that all relationships are significant, to the best of the authors’ knowledge, it is the first study to validate ANN and SEM in a library context successfully. The results of the two-step PLS–SEM and ANN technique demonstrate that the usage of ANN validates the PLS–SEM analysis. ANN can represent complicated linear and nonlinear connections with higher prediction accuracy than SEM approaches. Also, an importance-performance Map analysis of the PLS–SEM data offers a more detailed insight into each factor's significance and performance.

ChatGPT is a new development in this technological era. This artificial intelligence-based tool responds to individuals’ queries and produces the requested content within seconds. Therefore, it is becoming popular among academics, the research community and library professionals. This study aims to test (through personal interaction with the tool) the authenticity of the ChatGPT’s produced records. Another objective of the research is to check the relevance between the individuals’ queries and the tool’s results. The research also intends to identify the challenges in retrieving information through ChatGPT.

The five researchers from different countries and organizations experienced ChatGPT by asking questions on more than 70 subjects. The responses were recorded in Notepad and converted into MS Excel and MS Access to standardize and analyze the data. The investigators consulted 11 reputed databases/sources, including Web of Science and Scopus, to assess the authenticity of the data retrieved through ChatGPT.

The findings confirmed that over 90% of results produced by ChatGPT were fake (the information did not exist in the literature). Similarly, the study sheds light on the discrepancies, such as irrelevant and incomplete information in the data generated by ChatGPT.

This is a unique study that shares the findings based on the different regions’ researchers’ personal experiences with ChatGPT. The researchers covered different subject areas (above 70) while asking questions to ChatGPT. The paper shares implications for researchers, students, faculty members, academic/research organizations and policymakers.

The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).

Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.

The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.

This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.

The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.