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The principal aim of this research is to acquire a deeper understanding of the opinion held by the training and development (T&D) professionals, regarding the use of artificial intelligence (AI) technology in the area of T&D. Particularly in response to the evolving needs of learners, the research aims to ascertain T&D professionals' perspective on the efficiency of AI in fostering T&D, while understanding the constraints and limitations associated with this technology.

The study is based on qualitative data. With the help of semi-structured interviews, qualitative data has been collected from 21 T&D professionals. Experts working with multinational corporations (MNCs) are selected as a study sample using a convenient sampling technique. Qualitative data were analysed using thematic analysis. Conclusions were drawn based on the results of thematic analysis.

The findings of the study have revealed a notable and rapid evolution in the requirements of learners, particularly during and post-COVID-19 period. AI-based technology has emerged as a significant contributor, offering learners distinct personalised experiences and enhanced convenience. However, the implementation of AI in training remains in its early stages and has not reached widespread adoption. The findings of the study also highlighted various challenges and limitations, while using AI-based technology for training. It has been found that AI complements rather than replaces the role of a physical trainer.

The originality of this study lies in the application of AI-based training for professional learners, from the perspective of the T&D practitioners working with MNCs in Maharashtra, India. Numerous studies that have recently been published, emphasise the areas in which AI technology can transform the T&D industry. Yet, there are currently very less studies that have attempted to understand the evolving needs of learners and support of AI-based training for the same, from the perspective of the T&D professionals working in Maharashtra, India.

The a priori identification and development of army personnel competencies are necessary to enable effective and efficient responses to rapidly changing climate conditions. Accordingly, this study aims to identify the performance requirements of a military flood responder and the competencies (knowledge, skills and abilities) required to perform it.

Using an abductive approach, the authors conducted both secondary and primary research to generate a validated framework of performance criteria and competencies for army personnel responding to floods. This literature review integrated both the peer-reviewed academic literature and public sector grey literature. Using the critical incident technique, the authors then conducted semi-structured interviews with 15 members of the Canadian Armed Forces (CAF) who had previously been tasked with flood response operations. Participants were asked about the tasks required while conducting flood response operations. Interview transcripts were then content analysed to identify themes regarding those tasks, and the competencies needed to perform those tasks were then extracted and contrasted with the literature review findings. Inter-rater reliability for the analysis was established via iterative discussion between the two co-authors.

The primary data reinforced and expanded the list of performance expectations that the authors deductively identified from the integrated literature review, adding granularity to each. It also identified competencies (including both hard and soft skills) and highlighted previously neglected contextual antecedents of military flood response effectiveness.

though knowledge saturation was achieved from the 15 interviews conducted, further research with larger samples could more deeply ground the evidence discovered in this study. Nevertheless, the competencies identified in this paper could serve as a starting guide to staffing and/or training interventions targeted at improving these competencies for personnel responding to flood scenarios.

The theoretical findings also have immediate practical relevance to training for flood response operations. In particular, the subtle challenges in competency crossover from military operations to flood response operations may facilitate not only more efficient, targeted training (that could improve the effectiveness of army personnel involved in humanitarian roles), but could be applied to the selection of army personnel as well. This study may also help provincial/municipal operators and emergency planners by better communicating the strengths and limitations of army personnel in addressing civilian military cooperation for humanitarian operations. Thus, the findings of this research study represent an important first step in prompting attention to the strategic human resource planning studies required to make all responders more efficient and effective in their respective division of labour within the humanitarian domain.

Peering a little beyond these research findings, human-induced climate change is expected to continue increasing the frequency of such events (IPCC, 2021), and a timely, national force is likely to be increasingly required for Canadians impacted by major disasters stemming from natural hazards when local resources become overwhelmed. Yet, there is some concern from the CAF that increasing responsiveness to disaster operations will affect their military readiness (Leuprecht and Kasurak, 2020). One can indeed envision a paradox whereby the CAF is both a “force of last resort” while increasingly becoming a “first choice for domestic disaster and emergency assistance”. The practical implications from this research also suggest that military personnel, while fully capable of successfully conducting flood response operations, may become overburdened and less able to adopt yet greater capacity and training for other additional humanitarian work. Nevertheless, the competencies highlighted by participants can help inform the next flood response operation in Canada.

Most literature in the field of emergency response focuses on cooperation between civilian and military resources and other strategic-level themes. The findings address critical granularity missing at the operational and tactical levels of humanitarian assistance and disaster relief research. The authors also draw implications beyond the military context, including for local/regional governmental players (operators and emergency planners) as well as for volunteers in flood response roles.

To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.

This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.

Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.

This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This study examines the effects of an inclusive psychological climate on leader inclusion, workgroup inclusion, and employee outcomes (trust in organization and organizational identification). Leader inclusion and workgroup inclusion are explored as both direct and serial mediators in the psychological climate to outcome relationships.

Data from 336 employees in 55 teams were collected at two time points from an educational media company in China.

Results from multi-level modeling suggest that, for employees, the inclusive psychological climate to trust relationship has both direct and indirect effects, including a serially occurring indirect effect through leader inclusion and workgroup inclusion. For the inclusive psychological climate to organizational identification relationship, there were only indirect effects, including a serially occurring indirect effect through both leader inclusion and workgroup inclusion.

These results suggest the value of an inclusive psychological climate for setting the stage for more localized inclusion experiences through the leader and the workgroup. These inclusionary work environments promote social exchange as shown by employer trust and social identification with the organization.

This study examines the combined and serial effects of an inclusive psychological climate, leader inclusion, and workgroup inclusion on outcomes that represent a deep connection with the organization (organizational trust and organizational identification).

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

The study investigated the impact of perceived corporate social responsibility (CSR) on employees' turnover intention in professional team sports organizations, focusing on employee identification and co-production’s role in this context.

Data were collected from 225 employees in professional team sports organizations, with analysis conducted via partial least squares structural equation modeling (PLS-SEM).

The results indicated that perceived CSR negatively impacted turnover intention. Employee identification mediated the relationship between perceived CSR and turnover intention. Moreover, co-production moderated the relationship between perceived CSR and employee identification, affecting the mediating role of employee identification between perceived CSR and turnover intention.

Prioritizing CSR offers benefits beyond improving an organization’s public image. It also plays a crucial role in enhancing internal organizational dynamics. Specifically, it helps to increase employee identification with the company, reduce turnover intentions, and promote co-production. These outcomes, when combined, lead to the development of a stronger, more cohesive, and resilient organization.

This study provides empirical evidence of the influence of perceived CSR on employee identification and behavior within professional team sports organizations. It underscores the importance of enhancing employee identification to reduce turnover intention.

This study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.

The methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.

Findings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.

The practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.

The originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.

The purpose of this study is to gain a better understanding of the conditions and motivations that influence teachers to adopt innovations.

Using Diffusion of Innovation theory (Rogers, 2003) and Self-Determination theory (Ryan and Deci, 2017), data from two surveys (n = 568; n = 108) and qualitative follow-up interviews of Early Adopter teachers (n = 16) were triangulated to discern relationships among their identification as Early Adopters, satisfaction of their basic psychological needs (BPN) and their implementation of an educational innovation.

Early Adopters had a positive and statistically significant relationship with the implementation of the innovation. Satisfaction of teachers’ BPN had the largest impact on innovation.

The findings are preliminary and based on a small sample size of teachers. Reliability of the measure of BPN was not as high as the standard, but it did have the largest impact on implementation. Additional studies should explore the connections among Early Adopter teacher motivation, leadership and the satisfaction of their BPN.

School leaders should leverage the influence of Early Adopters to support innovation, and they should provide additional time, training and resources to supports teachers’ BPN.

This study examines how to identify and support Early Adopter teachers as enablers of change within schools. We know of no other studies that have used both Diffusion of Innovation theory and Self-Determination theory to understand the motivations of Early Adopter teachers.

This study examines innovation configurations (i.e. sets of product/service, social and business model innovations) and configuration linkages (i.e. factors that help to combine innovations) across six organizations as contingent upon organizational structure.

Using semi-structured interviews and available public information, qualitative data were collected and examined using content analysis to characterize innovation configurations and linkages in three local/private organizations and three foreign-led/public-private partnerships in Repiblik Ayiti (Haiti).

Organizations tend to combine product/service, social, and business model innovations simultaneously in locally founded private organizations and sequentially in foreign-based public-private partnerships. Linkages for simultaneous combination include limited external support, determined autonomy and shifting from a “beneficiary mindset,” and financial need identification. Sequential combination linkages include social need identification, community connections and flexibility.

The generalizability of our findings for this qualitative study is subject to additional quantitative studies to empirically test the suggested factors and to examine other health care organizations and countries.

Locally led private organizations in low- and middle-income settings may benefit from considering how their innovations are in service to one another as they may have limited resources. Foreign based public-private partnerships may benefit from pacing their efforts alongside a broader set of stakeholders and ecosystem partners.

This study is the first, to our knowledge, to examine how organizations combine sets of innovations, i.e. innovation configurations, in a healthcare setting and the first of any setting to examine innovation configuration linkages.