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The aim of this study was to determine data literacy (DL) training needs of researchers at South African public universities. The outcome of this study would assist librarians and researchers in developing a DL training programme which addressed identified needs.

A survey research method was used to gather data from researchers at these universities by convenience. Online questionnaires were distributed to public universities through library directors for further distribution to researchers.

The results indicate low levels of DL training at the respondent South African public universities with most researchers indicating that they had not received any formal training on DL. A few researchers indicated that they would welcome DL training.

This study was exploratory in nature and data was received from eight universities, which is not representative of all the 26 public universities in South Africa. Nonetheless, the low DL confirmed by the majority in the realised sample is indicative of the need to further investigate the subject.

Librarians and research support personnel should collaborate on the development of DL training courses, workshops and materials used by researchers at institutions of higher learning to enhance DLs on campus.

This study may be novel in South Africa in investigating the DL training needs of researchers at several universities and contributes to the growing body of literature on research data management

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

Considering the relevance of understanding what influences environmentally sustainable consumer choices, the present study aims to examine and synthesize the key determinants factors from literature and outline a new conceptual framework for explaining green purchasing behaviors (GPBs).

A bibliometric analysis was conducted on 161 articles extracted from Web of Science and Scopus databases, which were systematically evaluated and reviewed, and represent the current GPB knowledge base. Content analysis, science mapping and bibliometric analysis techniques were applied to uncover the major theories and constructs from the state-of-the-art.

The evolving debate between altruistic and self-interest consumer motivations reveals challenges for rational-based theories, as most empirical applications are not focused on buying behaviors, but instead either on pro-environmental (non-buying) activities or on buying intentions. From the subset of leading contributions and emerging topics, nine thematic clusters are unveiled in this investigation, which were combined to create the new PSICHE framework with the purpose of predicting GPB: (P)roduct-related factors, (S)ocial influences, (I)ndividual factors, (C)oncerns about the environment, (H)abits and (E)motions.

By uncovering the multiple intervening factors in GPB decision processes, this study will assist practitioners and academics to move forward on how to foster more sustainable consumer behaviors.

The present study provides readers a summary of an unprecedentedly broad collection of papers, from which the key themes are categorized, the domain's intellectual structure is captured and an actionable framework for enhancing the understanding GPB is proposed. Four new thrust areas and a set of future research questions are included.

This study aims to analyze the impact of Artificial Intelligence (AI) and Machine Learning (ML) on heritage conservation and preservation, and to identify relevant future research trends, by applying scientometrics.

A total of 1,646 articles, published between 1985 and 2021, concerning research on the application of ML and AI in cultural heritage were collected from the Scopus database and analyzed using bibliometric methodologies.

The findings of this study have shown that although there is a very important increase in academic literature in relation to AI and ML, publications that specifically deal with these issues in relation to cultural heritage and its conservation and preservation are significantly limited.

This study enriches the academic outline by highlighting the limited literature in this context and therefore the need to advance the study of AI and ML as key elements that support heritage researchers and practitioners in conservation and preservation work.

The key aim of this study is to highlight current financial technology (FinTech) trends by conducting a bibliometric review of literature derived from the Scopus database.

A bibliometric analysis was conducted on articles gathered from the Scopus database. Microsoft Excel was used to perform the frequency analysis, VOSviewer for visualising the data, and Harzing’s Publish or Perish for the metrics citation.

According to this investigation, research into FinTech has been consistently increasing since 2008. The results indicate that the most active publisher of FinTech literature is Bina Nusantara University in Indonesia. In terms of country of publication, China is identified as the most active. The most cited author is Buckley, R.P., with Rabbani, M.R., having the most publications. It was also identified that FinTech researches come under three primary domains namely business management, computer science and economics.

The primary limitation of this current study is that it only relied on one data source, i.e. Scopus. Implications wise, researchers and practitioners can gain a deeper understanding of FinTech from this study, which also describes the trend in related publications on the concept. Future studies could significantly benefit from the findings of the present paper.

The outcomes of this study can assist researchers in better comprehending and summarising the key drivers of FinTech. In addition, the findings can help new researchers identify the starting point for their research on FinTech.

As far as the authors are aware, this is the first study that reviews FinTech publications derived from Scopus from 2008 to 2022. Hence, it is a pioneering study into FinTech bibliometric analysis, providing an understanding of the structural knowledge by reviewing the timeline of academic progression in FinTech.

This study operationalizes risks in stakeholder dialog (SD). It conceptualizes SD as co-produced organizational discourse and examines the capacities of organizers' and stakeholders' practices to create a shared understanding of an organization’s risks to their mutual benefit. The meetings and online forum of a German public service media (PSM) organization were used as a case study.

The authors applied corpus-driven linguistic discourse analysis (topic modeling) to analyze citizens' (n = 2,452) forum posts (n = 14,744). Conversation analysis was used to examine video-recorded online meetings.

Organizers suspended actors' reciprocity in meetings. In the forums, topics emerged autonomously. Citizens' articulation of their identities was more diverse than the categories the organizer provided, and organizers did not respond to the autonomous emergence of contextualizations of citizens' perceptions of PSM performance in relation to their identities. The results suggest that risks arise from interactionally achieved occasions that prevent reasoned agreement and from actors' practices, which constituted autonomous discursive formations of topics and identities in the forums.

This study disentangles actors' practices, mutuality orientation and risk enactment during SD. It advances the methodological knowledge of strategic communication research on SD, utilizing social constructivist research methods to examine the contingencies of organization-stakeholder interaction in SD.

We conceptualized the novel phenomenon of COVID-induced virtual teams and its implications and provided researchers with the required information on how to conduct a phenomenon-based study for conceptualizing novel phenomena in relevant ways.

This article stems from phenomenon-based and, thus, theory-building and grounded qualitative research in the German industrial sector. We conducted 47 problem-centered interviews in two phases (February–July 2021 and February–July 2022) to understand how team members and team leaders experienced COVID-induced virtual teamwork and its subsequent developments.

Empirically, we found COVID-induced virtual teams to be characterized by a high relevance of shaping positive team dynamics via steering internal moderators; crisis is a novel external moderator and transformation becomes the key output factor to be leveraged. Work-from-home leads to specific configuration needs and interrelations between work-from-home and on-site introduce additional dynamics. Methodologically, the phenomenon-based approach is found to be highly suitable for studying the effects of such novel phenomena.

This article is explorative. Thus, we advocate further research on related novel phenomena, such as post-COVID-hybrid and work-from-home teams. A model of how to encourage positive dynamics in post-COVID-hybrid teams is developed and lays the groundwork for further studies on post-COVID teamwork. Concerning methodology, researchers are provided with information on how to conduct phenomenon-based research on novel phenomena, such as the COVID-induced virtual teams that we studied.

Companies receive advice on how to encourage positive dynamics in post-COVID teamwork, e.g. on identifying best practices and resilient individuals.

In a country such as Germany that faces labor shortages, our insights might facilitate better labor-market integration for those with care-work obligations and international workers.

We offer a first conceptualization of a relevant novel phenomenon, namely COVID-induced virtual teams. We exemplify the phenomenon-based approach as a suitable methodology that serves to build relevant theory using active categorization.

The paper aims to examine Indonesian accounting students’ intention to become sustainable accountants (ISAs) using a modified theory of reasoning action model.

Primary data were collected from 239 respondents from five reputable universities in Semarang, Indonesia, using a structured questionnaire. A random sampling technique was employed and used in selecting respondents. The data were then analyzed using smart PLS (version 3.2.9) to obtain the final results.

The results show university sustainability (US) and attitudes toward sustainability (ATS) affect students' intentions to become ISAs. Knowledge has no direct correlation with students' intention to become ISAs. Path analysis shows a significant correlation between US and students' knowledge, attitudes and intentions regarding sustainability.

This is different from previous studies, which only focused on factors influencing students' intentions to pay attention to sustainability. This study focuses on prospective accountants because, in the future, they will be the technical executors of reporting using path analysis. This study further analyzes the relationship between existing antecedent variables. The results show that sustainability at the university is a variable that can influence all other variables.

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.

The purpose of this study is to investigate artificial intelligence (AI) as enabler of future library services, with consideration to how prepared are librarians in African university libraries.

This study applied the interpretive content/document analysis of literature harvested from different databases of Scopus and Web of Science. AI could be used to perform daily routines in circulation, serial, reference and selective dissemination of information among others. It could also be applied to the provision of innovative services of recognition of library activities such as answering research quarries, cataloguing and classification of library materials and management of library system software of different databases within the library systems.

It could be deduced from the study that AI would continue to serve as a panacea to future library services irrespective of its geographical context. Due to the evolving nature of knowledge growth, AI having its roots in the field of engineering has been found useful to support future library services. The support accrued from library service delivery in the library profession has made librarians continue to interact with other intelligent machines that can demonstrate human behaviour even though they are not real human beings. The behaviour of machines and AI where human beings play a significant role has brought many renovations in the management of complex tasks of processing, communication, knowledge representation, decision making and suggestions, on potentials of diverse work operations.

The understanding that the present paper portrays in the context of future library services is that there is no way the AI could function without a human interaction perspective when drawing an analogy from computer science, information science and information systems fields of study.

The interest of users across their background would be strengthen if AI advances transformed the handling complex tasks of processing, communication, knowledge representation, decision-making and giving suggestions, among other things. The possibilities of diverse work operations from empirical evidence of studies consulted in recent times gave the authors the impetus to consider AI as the enabler of future library services.

The increasing demands from library patrons have prompted librarians to adapt their methods of delivering services. These emerging technologies have also brought about shifts in approaches to teaching and learning. Consequently, the recent surge in digital technology-driven service innovations has ushered in a fresh paradigm for education and research. In response to these changes, librarians are actively seeking novel and innovative technologies to enhance user experiences within their libraries. They serve as catalysts for introducing modern and advanced technologies, consistently adapting to contemporary tools that enhance their offerings.