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This paper engages Olaf Zawacki-Richter and Insung Jung in a frank and penetrating conversation that seeks to ground, frame, and problematise research in the field conceptualised as “open, distance and digital education” (ODDE). Taking as starting point the recent publication of the landmark Handbook of Open, Distance, and Digital Education (2022), it segues into a broad critique of the shortcomings of ODDE research, the importance of knowledge production on the meso- and macro-levels, the autonomy of ODDE as a field coming into its own, the place of postfoundationalism in ODDE discourse, and related topics that are pivotal in ODDE today.

The semi-structured interview was employed as the primary qualitative research method.

The research imperative of the relatively young but complex field of ODDE today is not the incessant reiteration of the same but rather a strategic reorientation that, first, circumvents the well-documented yet too-often-overlooked shortcomings of ODDE research and, second, promotes transboundary collaborations with the potential for system-wide impact.

This novel interview-based critique of ODDE research demonstrates that extending the scholarly discourse beyond the conventional report format is a productive method for enriching conversations on ODDE and vitalising the field itself.

The purpose of this paper is to study the fatigue crack growth (FCG) behaviour of the steel and weldments of a railway bridge.

Tests were carried out on compact tension (CT) specimens using the thickness (B=32 mm) of a structural detail. The test matrix included three R values and three material conditions: base material (BM), heat affected zone (HAZ) and weld metal (WM). An evaluation of opening load behavior was carried out. The full field measurement of the residual stress perpendicular to the crack plane was performed using the contour technique. A simplified finite element analysis supported the interpretation of the results. Scanning electron microscopy (SEM) observation of the fracture surface of BM and HAZ specimens was carried out.

Extensive crack closure effects were found in the welded specimens. Important through‐the‐thickness variation of residual stress was found using the contour technique. The residual stress fields of HAZ and WM specimens led to slowing down the FCG rate in the initial stages of crack propagation and to uncommon fracture surfaces. When the opening load effect was taken into consideration it was found that the da/dN vs ΔK of the different types of specimens are approximately identical. The ratio (striation spacing)/(da/dN) decreases up to approximately unity as a/W increases. In the specimens analyzed, FCG rates below approximately 2E‐7 to 3E‐7 m/cycle are associated with approximately constant striation spacing values, which could be considered a conservative upper bound of the real crack growth rate.

Fatigue crack growth behavior of thick welded steel CT specimens was analyzed on the basis of tests including full field residual stress measurements, crack closure behavior and striation spacing, allowing for the simultaneous consideration of all those aspects. It is shown that the striations spacing provides no more than a conservative upper bond of the real crack propagation rate.

The purpose of this study is to review the use of partial least squares-structural equation modeling (PLS-SEM) in the field of hospitality and tourism and thereby to assess whether the PLS-SEM-based papers followed the recommended application guidelines and to investigate whether a comparison of journal types (hospitality vs tourism) and journal qualities (top-tier vs other leading) reveal significant differences in PLS-SEM use.

A total of 206 PLS-SEM based papers published between 2000 and April 2017 in the 19 SSCI-indexed hospitality and tourism journals were critically analyzed using a wide range of guidelines for the following aspects of PLS-SEM: the rationale of using the method, the data characteristics, the model characteristics, the model assessment and reporting the technical issues.

The results reveal that some aspects of PLS-SEM are correctly applied by researchers, but there are still some misapplications, especially regarding data characteristics, formative measurement model evaluation and structural model assessment. Furthermore, few significant differences were found on the use of PLS-SEM between the two fields (hospitality and tourism) and between the journal tiers (top-tier and other leading).

To enhance the quality of research in hospitality and tourism, the present study provides recommendations for improving the future use of PLS-SEM.

The present study fills a sizeable gap in hospitality and tourism literature and extends the previous assessments on the use of PLS-SEM by providing a wider perspective on the issue (i.e. includes both hospitality and tourism journals rather than the previous reviews that focus on either tourism or hospitality), using a larger sample size of 206 empirical studies, investigating the issue over a longer time period (from 2000 to April, 2017, including the in-press articles), extending the scope of criteria (guidelines) used in the review and comparing the PLS-SEM use between the two allied fields (hospitality and tourism) and between the journal tiers (top-tier and other leading).

In many scientific and engineering fields, large‐scale heat transfer problems with temperature‐dependent pore‐fluid densities are commonly encountered. For example, heat transfer from the mantle into the upper crust of the Earth is a typical problem of them. The main purpose of this paper is to develop and present a new combined methodology to solve large‐scale heat transfer problems with temperature‐dependent pore‐fluid densities in the lithosphere and crust scales.

The theoretical approach is used to determine the thickness and the related thermal boundary conditions of the continental crust on the lithospheric scale, so that some important information can be provided accurately for establishing a numerical model of the crustal scale. The numerical approach is then used to simulate the detailed structures and complicated geometries of the continental crust on the crustal scale. The main advantage in using the proposed combination method of the theoretical and numerical approaches is that if the thermal distribution in the crust is of the primary interest, the use of a reasonable numerical model on the crustal scale can result in a significant reduction in computer efforts.

From the ore body formation and mineralization points of view, the present analytical and numerical solutions have demonstrated that the conductive‐and‐advective lithosphere with variable pore‐fluid density is the most favorite lithosphere because it may result in the thinnest lithosphere so that the temperature at the near surface of the crust can be hot enough to generate the shallow ore deposits there. The upward throughflow (i.e. mantle mass flux) can have a significant effect on the thermal structure within the lithosphere. In addition, the emplacement of hot materials from the mantle may further reduce the thickness of the lithosphere.

The present analytical solutions can be used to: validate numerical methods for solving large‐scale heat transfer problems; provide correct thermal boundary conditions for numerically solving ore body formation and mineralization problems on the crustal scale; and investigate the fundamental issues related to thermal distributions within the lithosphere. The proposed finite element analysis can be effectively used to consider the geometrical and material complexities of large‐scale heat transfer problems with temperature‐dependent fluid densities.

The antecedents and typical stages of development of corporate social responsibility (CSR) programs in a given organization or type of organization have been of minor interest in CSR research. Contrary to that the chapter argues that CSR communication strategies need to take the genesis and drivers of CSR institutionalization into account.

The chapter develops a complex set of interrelated drivers for CSR institutionalization from a literature review – among them leadership styles and management fashion. The chapter further discusses the influence of leadership styles and management fashions on CSR institutionalization and focuses on the diffusion of management concepts along a management fashion cycle. It then refers to executive trainers as the key facilitator and promoter of new business concepts and presents data from a first online-survey among German speaking management trainers.

The chapter clears manager’s role in institutionalization of CSR by contextualizing their behavior in a portfolio of performance indicators. From a management fashion perspective the various forms of explicit and implicit CSR are linked to management styles.

The chapter lays ground for further research of CSR institutionalization and integration into business strategy by providing a conceptualization of CSR drivers and settings that relate to a given organization. As such it is designed as groundwork for a yet to develop CSR scorecard.

The connection between organizational type, organizational environment, leadership behavior, and the chosen CSR approach of a corporation is usually overseen. The chapter aims to uncover this connection.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

The purpose of this paper is to develop a methodology for in situ stress intensity factor (SIF) determination that can be used for the analysis of cracked structures. The technique is based on digital image correlation (DIC) combined with an overdetermined algorithm.

The linear overdeterministic algorithm for calculating the SIF based on stress values around the crack tip is applied to a strain field obtained by DIC.

As long as the image quality is sufficiently high, a good accuracy can be obtained for the measured SIF. The crack tip can be automatically detected based on the same strain field. The use of the strain field instead of the displacement field, eliminates problems related to the rigid body motion of the analysed structure.

In future works, based on the applied techniques, the SIF of complex cracked plane stress structures can be accurately determined in real engineering applications.

The paper demonstrates application of known techniques, refined for other applications, also the use of stress field for SIF overdeterministic calculations.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.