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By examining types of literacies taught by public libraries and the modes through which these programs were offered, this study aims to explore how public libraries might integrate data literacy training for the general public into existing library educational programs.

This study examined programs offered in 30 US public libraries during 2019 and 2020 to better understand types of literacy education announced to the public through library website listings and Facebook Events pages.

While public libraries offered educational programs in literacy areas ranging from basic reading and writing to technology, vocational skills, health literacy and more, data literacy training was not widely offered. However, this study identified many already-existing programs highly compatible for integrating with data literacy training.

This study offered new insights into both the literacies taught in public library programs as well as ways for public libraries to integrate data literacy training into existing educational programming, in order to better provide data literacy education for the general public.

The aim of the article is to develop a framework for assessment for contemporary commercial music (CCM) in a music department that is still organised in a traditional way. Assessment of CCM in tertiary institutions and external graded music exams were described and analysed, followed by the presentation of an assessment framework.

This research design is a content analysis. Data was collected through purposive sampling of primary sources of CCM syllabi. These syllabi are used descriptively and analytically for comparison.

The major findings from the content analysis are presented as an assessment framework for CCM. The assessment framework has implications for teaching and learning vocal CCM at both undergraduate and postgraduate in higher education.

The study focused on the development of assessment criteria for CCM, in a music department where no degree specialisation for CCM exists, and CCM is manifested in the principal instrument only. The framework for assessment may be useful to practitioners and academics who are attempting to introduce CCM in classically oriented music departments and where external constraints prohibit the introduction of a degree specialising in CCM.

This article presents an authentic assessment framework for CCM, that considers its style and performance practices.

Drawing from emotional geography framework promoted by Hargreaves (2000), our research sought to depict the emotional geography of two faculty members who engaged in a virtual teacher professional development (VTPD) sessions during the COVID-19 pandemic. This study aimed to focus on capturing participants’ emotional closeness or distance while they were engaging in VTPD.

This study employed narrative inquiry by exploring three-dimensional narrative inquiry space: temporality, personal-social interaction and place (Clandinin and Connelly, 2000). Following this step, the participants were interviewed online through Zoom meetings and WhatsApp to capture critical incidents of their emotional experience. All collected data were transcribed, and some data from Bahasa Indonesia were translated into English. Member checking was also done several times to ensure the accuracy of the data as well as to avoid misinterpretation. The data were analysed inductively to generate coding categories using systemic functional linguistics (SFL) language appraisal (Martin and White, 2007) and emotional geography parameter (Hargreaves, 2001b).

The findings of the study revealed that both participants experienced greater positive feeling than negative ones. The participants experienced positive feelings such as seriousness, happiness, successfulness and satisfaction. They also experienced negative feelings such as insecurity, unhappiness, dissatisfaction and impatience. Such positive and negative feelings create closeness and distance among participants, mentor and workshop organiser. This study indicates that maintaining positive feelings is a passport to succeed in VTPD.

The study has two limitations. First, its findings cannot be overgeneralised since the analysis was restricted to data gathered from a small number of participants. Second, the scope of investigation was limited in virtual situations.

The present study empirically showed that faculty members need to engage in constructing or maintaining positive emotional bond with the mentor and other participants and create conducive situations to understand their own and others’ emotions (Mayer, 2011). Practically, a mentor in VTPD may ask faculty members to voice and share their emotional experience as an evaluation tool to make VTPD programmes more successful. Future participants can benefit from these findings by engaging in emotional understanding and building a conducive situation during VTPD to develop their academic competence, agency and identity.

While previous research into VTPD in the context of higher education mainly focused on designs, attention to pedagogy of online teacher learning environments, trends toward innovation in teacher collaboration and communities of practice in online settings, the present study specifically looked into how participants emotionally engaged in VTPD, which is inevitably linked to physical, moral, sociocultural, professional and political geographies.

In this study, the authors propose a confirmatory factor analysis (CFA) to create a tenable measurement model and identify the factors that have the potential to enhance awareness of pro-environmental measures. The successful implementation of demand response (DR) programs and their required infrastructure is significant for moving towards green energy communities and a better environment for living. Not only can renewable energy capacities contribute to this desideratum, but also electricity consumers who, until the last decade, have played a passive role.

To answer these questions, a complex data set of 243 post-trial questions created by the Irish CER are analyzed using first-order and hierarchical CFA models with several SAS procedures (PROC CALIS, MIANALYZE). The questionnaire was launched to over 3,000 electricity consumers from Ireland that were participants to a trial program after the installation of smart metering systems and implementation of DR programs.

The effect of five latent factors – positive attitude, negative attitude, perceived impact of own actions, price- and incentive-DR programs – is measured. With a bi-factor CFA measurement model, the authors assess that they significantly influence the electricity consumers' awareness.

However, these findings have to be backed up by relevant information and simulations showing consumers benefits in exchange to their efforts. They have research implications on the design of the business models and DR programs pointing out the importance of benefits and fairness of value sharing mechanisms within energy communities.

Thus, the electricity consumers may change their consumption behavior as they positively perceive the implementation of DR programs.

This paper fulfills an identified need to study post-trial questionnaire and reveal latent factors that make electricity consumer change their behavior.

This study aims to delve into the perspectives of educators on integrating ChatGPT, an AI language model into management education. In the current research, educators were asked to talk as widely as possible about the perceived benefits, limitations of ChatGPT in management education and strategies to improve ChatGPT for management education. Also, shedding light on what motivates or inhibits them to use ChatGPT in management education in the Indian context.

Interpretative phenomenological analysis commonly uses purposive sampling. In this research, the purpose is to delve into educators’ perspectives on ChatGPT in management education. The data was collected from the universities offering management education in Uttarakhand, India. The final sample size for the study was constrained to 57 educators, reflecting the point of theoretical saturation in data collection.

The present study involved educators discussing the various advantages of using ChatGPT in the context of management education. When educators were interviewed, their responses were categorized into nine distinct sub-themes related to the benefits of ChatGPT in management education. Similarly, when educators were asked to provide their insights on the limitations of using ChatGPT in management education, their responses were grouped into six sub-themes that emerged during the interviews. Furthermore, in the process of interviewing educators about potential strategies to enhance ChatGPT for management education, their feedback was organized into seven sub-themes, reflecting the various approaches suggested by the educators.

In the qualitative study, perceptions and experiences of educators at a certain period are captured. It would be necessary to conduct longitudinal research to comprehend how perceptions and experiences might change over time. The study’s exclusive focus on management education may not adequately reflect the experiences and viewpoints of educators in another discipline. The findings may not be generalizable and applicable to other educational disciplines.

The research has helped in identifying the strengths and limitations of ChatGPT as perceived by educators for management education. Understanding educators’ perceptions and experiences with ChatGPT provided valuable insight into how the tool is being used in real-world educational settings. These insights can guide higher education institutions, policymakers and ChatGPT service providers in refining and improving the ChatGPT tool to better align with the specific needs of management educators.

Amid the rising interest in ChatGPT’s educational applications, a research gap exists in exploring educators’ perspectives on AI tools like ChatGPT. While some studies have addressed its role in fields like medical, engineering, legal education and natural sciences, the context of management education remains underexplored. This study focuses on educators’ experiences with ChatGPT in transforming management education, aiming to reveal its benefits, limitations and factors influencing adoption. As research in this area is limited, educators’ insights can guide higher education institutions, ChatGPT providers and policymakers in effectively implementing ChatGPT in Indian management education.

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

This study explores whether and how four main factors of short-branded video content (content matching, information relevance, storytelling and emotionality) facilitate consumer engagement (likes, comments and shares), as well as the moderating effect of the release time (morning, afternoon and evening) in such relationships.

This study uses Python to write programs to crawl relevant data information, such as consumer engagement and short video release time. It combines coding methods to empirically analyze the impact of short-branded video content characteristics on consumer engagement. A total of 10,240 Weibo short videos (total duration: 238.645 h) from 122 well-known brands are utilized as research objects.

Empirical results show that the content characteristics of short videos significantly affected consumer engagement. Furthermore, the release time of videos significantly moderated the relationship between the emotionality of short videos and consumer engagement. Content released in the morning enhanced the positive impact of warmth, excitement and joy on consumer engagement, compared to that released in the afternoon.

The findings provide new insights for the dissemination of products and brand culture through short videos. The authors suggest that enterprises that use brand videos consider content matching, information relevance, storytelling and emotionality in their design.

From a broader perspective, this study constructs a new method for comprehensively evaluating short-branded video content, based on four dimensions (content matching, information relevance, storytelling and emotionality) and explores the value of these dimensions for creating social media marketing success, such as via consumer engagement.

Continuous professional development (CPD) plays a crucial role in the library workplace by equipping library and information science (LIS) professionals with the skills and technical knowledge to bridge the theory-praxis gap, assuring that library practitioners and their organisations continue to innovate and stay successful and tailored to the requirement of their users. The aim of the study was to investigate the channels of CPD for LIS professionals working in the university libraries of Northern India. Also, it explored the motivations and barriers related to the LIS professionals’ CPD.

The study adopted a quantitative research design followed by the survey research method. The study population included LIS professionals working in the university libraries of Northern India. Data was gathered from 177 respondents via the census sampling method.

LIS professionals engaged in CPD activities through multiple channels. Intrinsic factors primarily governed the motivation for CPD. Similarly, time and finance were significant challenges for LIS CPD. Lastly, most libraries were headed by in-charge librarians (non-professionals). De-professionalisation, or the deliberate de-skilling of professional roles, threatens the basis upon which librarians establish their credibility as professionals.

To the best of the authors’ knowledge, the study is the first of its kind from the perspective of a developing country, adding to the CPD literature in academic libraries.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

This study investigates the user’s perspective regarding opportunities, challenges and implications related to artificial intelligence technologies such as ChatGPT in several contexts, such as education and healthcare. In detail, the aim was to understand user’ perceptions of the relationship between ChatGPT and sustainability and their views about how companies can contribute to achieving well-being.

To reach the research aim, the study adopts an explorative inductive design, carried out through a qualitative approach and grounded in 34 in-depth consumer interviews.

The interviewees perceive ChatGPT as a helpful tool that simplifies activities and reduces time and human errors. However, risks associated with using this tool have been identified, such as the reliability of the information provided, the need for more privacy for the requested personal data and the lack of empathy and human interaction. AI tools can support businesses and governments in creating and promoting sustainable strategies and reducing waste. Furthermore, the study shows how these tools can improve the community's well-being by disseminating knowledge and information, supporting workers and generating less stress.

The paper describes the users' perspective on the opportunities of ChatGPT in promoting sustainable development and well-being, providing suggestions for future research.