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The purpose of this study is to examine various data sources to identify trends and themes in humanitarian supply chain management (HSCM) in the digital age.

In this study, various data sources such as published literature and social media content from Twitter, LinkedIn, blogs and forums are used to identify trending topics and themes on HSCM using topic modelling.

The study examined 33 published literature and more than 94,000 documents, including tweets and expert opinions, and identified eight themes related to HSCM in the digital age namely “Digital technology enabled global partnerships”, “Digital tech enabled sustainability”, “Digital tech enabled risk reduction for climate changes and uncertainties”, “Digital tech enabled preparedness, response and resilience”, “Digital tech enabled health system enhancement”, “Digital tech enabled food system enhancement”, “Digital tech enabled ethical process and systems” and “Digital tech enabled humanitarian logistics”. The study also proposed a framework of drivers, processes and impacts for each theme and directions for future research.

Previous research has predominantly relied on published literature to identify emerging themes and trends on a particular topic. This study is unique because it examines the ability of social media sources such as blogs, websites, forums and published literature to reveal evolving patterns and trends in HSCM in the digital age.

The main purpose of this paper is to examine the adoption of big data analytics (BDA) in the Tanzania banking industry by investigating the influence of technological, environmental and organizational (TOE) factors while exploring the moderating role of perceived risk (PR).

The study employed a qualitative research design, and the research instrument was developed using per-defined measurement items adopted from prior studies; the items were slightly adjusted to fit the current context. The questionnaires were distributed to top and middle managers in selected banks in Tanzania using the snowball sampling technique. Out of 360 received responses, 302 were considered complete and valid for data analysis. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the developed conceptual framework.

Top management support and financial resources emerged as influential organizational factors, as did competition intensity for the environmental factors. Notably, bank size and perceived trends showed no significant impacts on BDA adoption. The study's novelty lies in revealing PR as a moderating factor, weakening the link between technological readiness, perceived usefulness and the intent to adopt BDA.

This study extends literature by extending the TOE model, through examining the moderating roles of PR on technological factors. Furthermore, the study provides useful managerial support for the adoption of BDA in banking in emerging economies.

Effective use of data across public health organisations (PHOs) is essential for the provision of health services. While health technology and data use in clinical practice have been investigated, interactions with data in non-clinical practice have been largely neglected. The purpose of this paper is to consider what constitutes data, and how people in non-clinical roles in a PHO interact with data in their practice.

This mixed methods study involved a qualitative exploration of how employees of a large PHO interact with data in their non-clinical work roles. A quantitative survey was administered to complement insights gained through qualitative investigation.

Organisational boundaries emerged as a defining issue in interactions with data. The results explain how data work happens through observing, spanning and shifting of boundaries. The paper identifies five key issues that shape data work in relation to boundaries. Boundary objects and processes are considered, as well as the roles of boundary spanners and shifters.

The study was conducted in a large Australian PHO, which is not completely representative of the unique contexts of similar organisations. The study has implications for research in information and organisational studies, opening fields of inquiry for further investigation.

Effective systems-wide data use can improve health service efficiencies and outcomes. There are also implications for the provision of services by other health and public sectors.

The study contributes to closing a significant research gap in understanding interactions with data in the workplace, particularly in non-clinical roles in health. Research analysis connects concepts of knowledge boundaries, boundary spanning and boundary objects with insights into information behaviours in the health workplace. Boundary processes emerge as an important concept to understand interactions with data. The result is a novel typology of interactions with data in relation to organisational boundaries.

This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

It has been demonstrated that AI-powered, data-driven tools’ usage is not universal, but deeply linked to socio-cultural contexts. The purpose of this paper is to display the need of adopting situated lenses, relating to specific personal and professional learning about data protection and privacy.

The authors introduce the results of a case study based on a large educational intervention at a fully online university. The views of the participants from degrees representing different knowledge areas and contexts of technology adoption (work, education and leisure) were explored after engaging in the analysis of the terms and conditions of use about privacy and data usage. After consultation, 27 course instructors (CIs) integrated the activity and worked with 823 students (702 of whom were complete and correct for analytical purposes).

The results of this study indicated that the intervention increased privacy-conscious online behaviour among most participants. Results were more contradictory when looking at the tools’ daily usage, with overall positive considerations around the tools being mostly needed or “indispensable”.

Though appliable only to the authors’ case study and not generalisable, the authors’ results show both the complexity of privacy views and the presence of forms of renunciation in the trade-off between data protection and the need of using a specific software into a personal and professional context.

This study provides an example of teaching and learning activities that supports the development of data literacy, with a focus on data privacy. Therefore, beyond the research findings, any educator can build over the authors’ proposal to produce materials and interventions aimed at developing awareness on data privacy issues.

Developing awareness, understanding and skills relating to data privacy is crucial to live in a society where digital technologies are used in any area of our personal and professional life. Well-informed citizens will be able to obscure, resist or claim for their rights whenever a violation of their privacy takes place. Also, they will be able to support (through adoption) better quality apps and platforms, instead of passively accepting what is evident or easy to use.

The authors specifically spot how students and educators, as part of a specific learning and cultural ecosystem, need tailored opportunities to keep on reflecting on their degrees of freedom and their possibilities to act regarding evolving data systems and their alternatives.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

The purpose of this study is to explore the interplay among self-organization, networks and sustainable innovations within microfinance institutions (MFIs) and to examine the extent to which organizational resilience plays a significant role in shaping these dynamics as a mediator.

This paper adopted a cross-sectional research design combined with analytical and descriptive approach to collect the data. Smart partial least squares structural equation modeling (PLS-SEM) was used to construct the measurement model and structural equation model to test the mediating effect under this study.

The results revealed that organizational resilience is a significant mediator in the relationship between self-organization, networks and sustainable innovations among microfinance institutions in Uganda.

The data for this study were collected only from microfinance institutions in Uganda. Future studies may collect data from other formal financial institutions like commercial banks and credit institutions to test the mediating effect of organizational resilience. More still, the study adopted only a single approach of using a questionnaire. However, future research through interviews may be desirable. Likewise this study was cross-sectional in nature. Therefore, a longitudinal study may be useful in future while investigating the mediating role of organizational resilience traversing over a long time frame.

A possible implication is that microfinance institutions which desire to have sustainable innovative solutions for their business operations in disruptive circumstances may need to scrutinize their capacity to be resilient and self-organize.

Microfinance institutions play a great role to the underserved clients. Thus, for each to re-organize to be able to provide services that meet users’ needs, without physical products so as to ensure long-term financial and social welfare combined with the ability to bounce back and adapt in times of economic downturn to avoid mission adrift.

While most studies have been carried out on organizational resilience, this paper takes center stage and is the first to test the mediating role of organizational resilience in the relationship between self-organization, networks and sustainable innovations, especially in microfinance institutions in Uganda. This paper generates strong evidence and contributes to the powerful influence of organizational resilience in enhancing the level of sustainable innovations based on self-organization and networks.

The conventional project delivery (CPD) approach has been reported in the literature as the most widely used project delivery method in the construction industry globally compared to other delivery methods. However, researchers and practitioners have argued that the approach, specifically during the production of design documentation under the CPD, lacks certain capabilities that ensure quality and enhance project delivery. Therefore, this study aims to use the Ghanaian construction industry to identify the capabilities required of the CPD in practice, particularly during the production of design documentation.

The study design follows a pragmatist philosophy and uses mixed methods based on a deductive approach. Data collection involved a questionnaire survey, followed by semi-structured interviews. Quantitative data analysis used descriptive and inferential statistics, whereas qualitative data analysis used content analysis with the assistance of IBM SPSS and QSR Nvivo 12 Pro.

Findings indicate that there should be incentives for producing good design documentation quality; mandatory coordination of design documentation; improving collaboration among designers; and allowing contractors to make input during the design stage.

The results indicate the need for the identified capabilities to be introduced in the CPD approach to improve design documentation quality.

This study offers a significant insight into the specific capabilities that are required of the CPD approach in practice particularly, in the production of design documentation

M-wallets have emerged as one of the most important financial innovations of the 21st century, enabling users to carry digital cash by securely storing payment methods on their mobile devices. However, the continued use of m-wallets varies among people for several reasons. This study used the technology continuation theory (TCT), gamification and trust factors to examine the variables affecting consumers' intentions to continue using mobile wallets.

The SmartPLS partial least squares software was used to analyze data from 431 m-wallet users in Vietnam using the structural equation modeling technique.

The data revealed that the research model can predict users' intentions to continue using mobile wallets. TCT constructs demonstrated strong exploratory power in explaining consumer satisfaction and attitudes towards m-wallets. Furthermore, the study confirmed the direct effect of the perceived effectiveness of gamification on perceived ease of use and attitude, as well as its indirect effect on consumers' continued use intentions of mobile wallets via attitude. In addition, the trust negatively influenced consumers' intentions to continue using m-wallets.

The findings of this study can help researchers, practitioners and policymakers improve m-wallet design, development and adoption, as well as advance financial technology and define the future of digital payments in terms of consumer attraction, engagement and financial inclusion.

Based on TCT theory, this study enriches m-wallet research by examining two important factors, gamification and trust, and thus provides insights into how to improve consumers’ intentions to continue using m-wallets in developing countries. This study offers timely insights into theory and practice regarding these factors. It therefore paves the way for researchers and practitioners to learn how easy, enjoyable and secure the end-user experience should be to keep users engaged with m-wallets.