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The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

This paper aims to further our understanding of policy–practice gaps in organizations from an organizational learning perspective. The authors conceptualize and analyze policy–practice gaps in terms of what they label the dual challenge of organizational learning, i.e. the organizational tasks of both adapting ongoing practices to prescribed policy demands and adapting the policy itself to the needs of practice. Specifically, the authors address how this dual challenge can be understood in terms of organizational learning and how an organization can be managed to successfully resolve the dual learning challenge and, thereby, bridge policy–practice gaps in organizations.

This paper draws on existing literature to explore the gap between policy and practice. Through a synthesis of theories and an illustrative practical example, this paper highlights key conceptual underpinnings.

In the analysis of the dual challenge of organizational learning, this study provides a conceptual framework that emphasizes the important role of tensions and contradictions between policy and practice and their role as drivers of organizational learning. To bridge policy–practice gaps in organizations, this paper proposes five key principles that aim to resolve the dual challenge and accommodate both deployment and discovery in organizations.

Because this is a conceptual study, empirical research is called for to explore further and test the findings and conclusions of the study. Several avenues of possible future research are proposed.

This paper primarily contributes by introducing and elaborating on a conceptual framework that offers novel perspectives on the dual challenges of facilitating both discovery and deployment processes within organizations.

This paper aims to provide a comprehensive overview of the challenges and employee development strategies for executives and managers when managing flexible work systems.

The paper takes an employee development perspective to discuss management strategies of flexible work systems. Research findings on the effects of work flexibility through flexible work systems, the challenges and the development strategies that executives and managers can use were reviewed from multi-level perspectives.

The flexible work system is the new normal in the workplace. Lack of social and face-to-face interactions reduces employees’ social learning, jeopardizing managerial justice and weakening the culture. To remain competitive and retain talented employees, executives should reexamine their current employee development strategies and implement new strategies that fit the characteristics of flexible work systems.

To the best of the authors’ knowledge, this is the first comprehensive review of employee development strategies for flexible working arrangements. The paper provides practical guidelines and insights for executives and leaders managing employees under various flexible work systems.

This study aimed to examine the role of big data analytics capabilities (BDAC) in fostering organizational innovation capabilities and, consequently, in achieving economic, social and environmental sustainability.

Through the lens of dynamic capability theory, this study surveyed 115 hotels using purposive sampling to gain in-depth insights regarding the factors affecting organizational sustainability in the hospitality industry. The data analysis was conducted using partial least squares-structural equation modeling (PLS-SEM).

The findings reported a substantial impact of seven core dimensions (i.e. technology, data, basic resources, technological skills, managerial skills, organizational learning and data-driven culture) in building BDAC among hotels. Moreover, BDAC was also revealed to significantly influence innovation capabilities, positively impacting all three sorts of sustainability performance. Innovation capability also mediated the relationship between BDAC and all sustainability factors.

The findings will assist policymakers and practitioners in developing effective initiatives to enhance the adoption and implementation of data science and technologies, substantially contributing to the “National IR 4.0 Policy” and “Malaysia Digital Economy Blueprint” and achieving sustainable development goals (SDGs).

The originality of this study is established by investigating the interplay between BDAC, innovation capability and sustainability performance, particularly in the context of the hotel industry, whereas the existing studies focus on exploring the advantages of BDA.

We aimed to identify factors that influence student engagement in distance learning.

The research involved a group of 671 students from economic and technical higher education institutions in Poland. We collected the data with the CAWI technique and an original survey. Next, we processed the data using principal component analysis and then used the extracted components as predictors in the induced smoothing LASSO regression model.

The components of the students’ attitude toward remote classes learning conditions are: satisfaction with teachers’ approach, attitude to distance learning, the system of students’ values and motivation, IT infrastructure of the university, building a network of contacts and communication skills. The final model consisted of seven statistically significant variables, encompassing the student’s sex, level of studies and the first five extracted PCs. Student’s system of values and motivation as well as attitude toward distance learning, were those variables that had the biggest influence on student engagement.

The research result suggests that in addition to students’ system of values and motivation and their attitude toward distance learning, the satisfaction level of teachers’ attitude is one of the three most important factors that influence student engagement during the distance learning process.

The main value of this article is the statistical model of student engagement during distance learning. The article fills the research gap in identifying and evaluating the impact of various factors determining student engagement in the distance learning process.

The purpose of this paper is to examine the contribution of big data in the transforming process of an IT firm to a learning organization.

The authors adopted a qualitative research approach to define and interpret the ideas and experiences of the IT firms’ employees and to present them to the readers directly. For this purpose, they followed a single-case study design. They researched on a small and medium enterprise operating in the IT sector in Düzce province, Turkey. This paper used a semi-structured interview and document analysis as data collecting methods. In all, eight interviews were conducted with employees. Brochures and website of the organization were used as data sources for the document analysis.

As a result of in-depth interviews and document analysis, the authors formed five main themes that describe perception of big data and learning organization concepts, methods and practices adopted in transforming process, usage areas of big data in organization and how the sample organization uses big data as a learning organization. The findings of this paper show that the sample organization is a learning IT firm that has used big data in transforming to learning organization and in maintaining the learning culture.

The findings contribute to literature as it is one of the first studies that examine the influence of big data on the transformation process of an IT firm to a learning organization. The findings reveal that IT firms benefit from the solutions of big data while learning. However, as the design of the research is single-case study, the findings may be specific to the sample organization. Future studies are required that examine the subject in different samples and by different research designs.

In literature, research on how IT firms’ managers and employees use big data in organizational learning process is limited. The authors expect that this paper will shed light on future research that examines the effect of big data on the learning process of the organization.

This study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making (DM) processes within organisations.

The study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and assess their impact on DM processes.

The findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation and global governance and regulation are found to be less central to the DM process.

This research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM within organisations. By providing insights and recommendations for researchers, managers and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies whilst mitigating their associated risks.

This study investigated the attitudes and concerns of Saudi higher educational institution (HEI) academics about privacy and security in online teaching during the COVID-19 pandemic.

Online Questionnaire questionnaire was designed to explore Saudi HEI academic’s attitudes and concerns about privacy and security issues in online teaching. The questionnaire asked about attitudes and concerns held before the pandemic and since the pandemic. The questionnaire included four sections. At the beginning of the questionnaire, participants were asked what the phrase “online privacy and security” meant to them, to gain an initial understanding of what it meant to academics. A definition for what we intended for the survey was then provided: “that a person’s data, including their identity, is not accessible to anyone other than themselves and others whom they have authorised and that their computing devices work properly and are free from unauthorised interference” (based on my reading of a range of sources, e.g. Schatz et al., 2017; Steinberg, 2019; NCS; Windley, 2005). This was to ensure that participants did understand what I was asking about in subsequent sections.

This study investigated the attitudes and concerns of Saudi HEI academics about privacy and security in online teaching during the COVID-19 pandemic. The findings provide several key insights: Key aspects of online privacy and security for Saudi HEI academics: Saudi HEI academic’s notion of online privacy and security is about the protection of personal data, preventing unauthorized access to data and ensuring the confidentiality and integrity of data. This underscores the significance of robust measures to safeguard sensitive information in online teaching, but also the need to make academics aware of the other aspects of online privacy and security. Potential to improve policies and training about online privacy and security in Saudi HEIs: Although many participants were aware of the online privacy and security policies of their HEI, only a small percentage had received training in this area. Thus, there is a need to improve the development and dissemination of policies and to provide academics with appropriate training in this area and encourage them to take available training. Use of videoconferencing and chat technologies and cultural sensitivities: The study highlighted moderate levels of concern among Saudi HEI academics regarding the use of videoconferencing and online chat technologies, and their concerns about cultural factors around the use of these technologies. This emphasizes the need for online teaching and the growing use of technologies in such teaching to respect cultural norms and preferences, highlighting the importance of fostering a culturally sensitive approach to technology deployment and use. Surprising low webcam use: An unexpected finding is the low use of webcams by both academics and students during online teaching sessions, prompting a need for a deeper understanding of the dynamics surrounding webcam engagement in such sessions. This calls for a reevaluation of the effectiveness of webcam use in the teaching process and underscores the importance of exploring methods for enhancing engagement and interaction in online teaching. In summary, this paper investigated the attitudes and concerns about privacy and security in the online teaching of Saudi HEI academics during the coronavirus pandemic. The study reveals areas where further research and policy development can enhance the online teaching experience. As the education landscape continues to evolve, institutions must remain proactive in addressing the concerns of their academics while fostering a culturally sensitive approach to technology deployment.

One limitation of this study is the relatively small qualitative data sample, despite the adequate size of the sample including 36 academics from various Saudi Arabian HEIs for quantitative analysis. It was necessary to make the most of the open-ended questions optional – participants did not have to answer about concerns if they did not want to, as we did not want to make the questionnaire too long and onerous to complete. Consequently, the number of academics responding to the open-ended questions was limited, emphasizing the need for additional data and alternative research methods to further these issues. The study was focused on investigating the concerns of HEI Saudi academics, recognizing that the attitudes and concerns of academics in other countries may differ. Furthermore, the research also includes an exploration of the changes in academic attitudes and concerns before and since the COVID-19 pandemic, which will be the subject of further data analysis.

This research delves into Saudi HEI academics' perceptions and concerns regarding privacy and security in online education during the COVID-19 Pandemic. Notably, it highlights the moderate priority placed on online privacy and security, the unexpectedly low usage of webcams and the potential for enhancing policies and training. The study emphasizes the necessity for comprehensive measures to protect sensitive data and the importance of tailored policies for educators. It also underscores the need for a more nuanced understanding of webcam usage dynamics, offering valuable insights for institutions aiming to improve online education and address educators' concerns amidst evolving educational landscapes.

The purpose of this article is to identify how the organisations are able to improve their business value through acquisition of business analytics capabilities and by improving their performance.

With the help of literature survey, along with standard resource-based view framework, a conceptual model has been developed. These have been statistically tested by collecting the data using the survey questionnaire from 306 selected respondents from various service sector- and product-based organisations in India. To analyse the data we have used partial least square–based structural equation modelling.

The study highlights that by the help of data acquisition and tool acquisition as two vital components the acquisition of business analytics capabilities could improve the business value of the organisation by strengthening its organisational performance. The findings of this research also indicated that acquisition of business analytics capabilities has a significant influence on an organisation's business process performance and business decision, which in turn significantly influences organisational performance. And organisational performance eventually positively influences its business value. The model was found to provide an explanative power of 71%.

The proposed research model can provide effective recommendations to the management of the organisations to realise the importance of acquisition of effective business analytics capabilities to eventually improve the business value of the organisation.

No specific studies, as yet, have analysed the effects of acquisition of business analytics capabilities for improving organisational performance mediated through business process performance and business decision. Therefore, this research has explored the distinctive effort to empirically validate this understanding.

Organisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.

The study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.

This study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.

This study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.

By considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.

This study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.