Search results

This study aims to examine the scope and contribution of Forensic Clinical Psychology (FCP) advice from the National Crime Agency (NCA) to criminal investigations in the UK to address the gap in current knowledge and research.

The 36 FCP reports reviewed were written between 2017 and 2021. They were analysed using Toulmin’s (1958) application of pertinent arguments to the evaluation process. The potential utility of the reports was analysed in terms of the advice provided.

Most of the reports involved murder and equivocal death. The reports focused primarily on understanding the offender’s psychopathology, actions, motivation and risk to self and others using a practitioner model of case study methodology. Out of the 539 claims, grounds were provided for 99% of the claims, 91% had designated modality, 62% of the claims were potentially verifiable and 57% of the claims were supported by a warrant and/or backing. Most of the reports provided either moderate or high insight into the offence/offender (92%) and potential for new leads (64%).

The advice provided relied heavily on extensive forensic clinical and investigative experience of offenders, guided by theory and research and was often performed under considerable time pressure. Flexibility, impartiality, rigour and resilience are essential prerequisites for this type of work.

To the best of the authors’ knowledge, this study is the first to systematically evaluate forensic clinical psychology reports from the NCA. It shows the pragmatic, dynamic and varied nature of FCP contributions to investigations and its potential utility.

Artificial Intelligence-based smart farming technologies have brought impressive changes in farming. This paper aims at exploring the farmers’ intention to adopt smart farming technologies (SFT). Also, the authors intend to know how far the belief of farmers on land as God influences their decision to adopt SFT.

The data were gathered from 500 farmers chosen purposively. A well-crafted survey instrument was employed to amass data from farmers for measuring their adoption of SFT. As the authors sought to measure the farmers’ behavioural intention (BI) towards the adoption of SFT, the technology acceptance model developed by Davis (1989) came in handy, including perceived usefulness (PU), perceived ease of use (PEU) and BI. The authors have adopted this model as it was considered a superior model. The items on the attitude of confidence (AC) were adapted from Adrian et al. (2005). Survey instruments of Thompson and Higgins (1991) and Compeau and Higgins (1995) were also referred to finalize the statements relating to attitude towards use. Moreover, the authors developed items relating to the perceived belief of land as God based on frequent interaction with the farmers.

The study results divulged that attitude to use (AU) is directly influenced by the rural farmers’ PU, PEU and AC. Similarly, this investigation has observed behaviour intention directly influenced by the AU of farmers. It is observed that AU was the most influential variable, which ultimately influenced the BI to use SFT.

This study has an important limitation in the form of representing only the culture, belief and value system of farmers in India.

The outcome of this study will facilitate the policymakers to draw suitable policy measures keeping the sensitivities of the farmers in mind in their technology adoption drive. The agricultural officers can encourage farmers to take logical decisions by supplying adequate information in a time-bound manner. Marketers can make suitable adjustments in their sales and promotion activities that focus on farmers.

The belief of farmers on land as God has a small yet unmissable influence on farmers’ AU and BI in their technology adoption decision. Based on the above evidence, the authors recommend that marketers fine-tune their product design, product packaging and promotional activities keeping the belief and sensitivities of farmers at the core of their marketing campaign.

This article provides original insights by demonstrating the positive influence of PU, PEU and AC on technology adoption by farmers. This research is the first of a kind linking the belief of farmers on land as God with smart farming technology adoption in farming.

This paper aims to present a comprehensive overview of the emergence and significance of digital payments, focusing on their impact on competitiveness and the need for policy interventions. In addition, it explores the design of policies that promote the adoption of digital payments, highlighting the benefits they offer to providers and users.

The paper examines the technological advances that have driven the growth of digital payment systems. It identifies key requirements for successful adoption and discusses the associated risks, along with potential strategies to mitigate these risks.

The findings emphasize the importance of responsible implementation and safeguarding the well-being of end users to fully realize the benefits of digital payment adoption. Understanding the inherent risks and establishing effective risk mitigation mechanisms are crucial. This necessitates the development of appropriate infrastructure to support the provision of digital payment services.

More research is needed to gain deeper insights into how emerging global trends in financial technology should be analyzed and understood by policymakers, service providers and users.

The findings of this study can guide policymakers, private sector managers and consumers in comprehending the effects of emerging digitalization trends and determining their adoption responses accordingly.

This paper stands out as one of the few research contributions that provide comprehensive and actionable policy recommendations to facilitate a smooth transition to a digital payments ecosystem that benefits all stakeholders.

Educational design patterns offer practical strategies that can be shared and adapted to address problems in teaching and learning. This article explores how educational design patterns for connected learning at scale at an Australian university may be adapted to a Vietnamese higher education context.

12 educational design patterns that address the challenges of active learning and large teaching team management are discussed. The authors then critically reflect on their cross-cultural adaptation for the higher education context, from an Australian to a Vietnamese university.

Transitioning from passive to active learning strategies and effectively leading large teaching teams present similar challenges across our contexts. Educational design patterns, when dynamically adapted, may assist educators to teach skills that are critical for work and the future. Higher education institutions globally could enhance their practices by incorporating international best practice approaches to educational design.

The Connected Learning at Scale (CLaS) educational design patterns explored in this article offer solution-oriented strategies that promote a more active learning experience. This paper identifies adaptations for educators, especially those in Vietnamese higher education that respect traditional structures, cultural nuances and resource limitations in implementation.

Whilst educational design patterns are well-researched in the Western contexts, few studies analyse design patterns in an Asian, and in particular the Vietnamese context. More research is needed in the cross-cultural adaptation of educational design patterns that joins practice and theory.

The health of an economy is heavily dependent on the productivity of the economy's major industries including construction. While most macro-measures of productivity in the USA construction industry indicate a decline, corresponding studies at the individual task level indicate an increase in productivity. Therefore, this paper aims to identify areas where productivity challenges exist and thus provide recommendations for improvement in the construction industry.

A model that relates the way construction projects are executed with the sources of data that inform productivity analyses is developed and presented. This effort/value-flow model informs the data analysis that is performed to determine productivity trends for management and field labor. Further analysis for field labor productivity using field data and management productivity was separately conducted. Management productivity was particularly difficult to gauge, resulting in the use of surrogate measures.

It was observed that while both field labor and management productivities at the industry level have been decreasing, the decrease in management productivity was five times that of field labor productivity. A similar trend was observed for management productivity at the project level.

The primary contribution of this paper to the body of knowledge and industry is the introduction of a holistic analysis of USA construction productivity. Recommendations to improve management productivity include the use of technology, especially project management software.

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.

The authors passively apply a researcher profile pitch (RPP) template tool in accounting and across a range of Business School disciplines.

The authors document a diversity of worked examples of the RPP. Using an auto-ethnographic research design, each showcased researcher reflects on the exercise, highlighting nuanced perspectives drawn from their experience. Collectively, these examples and associated independent narratives allow the authors to identify common themes that provide informative insights to potential users.

First, the RPP tool is helpful for accounting scholars to portray their essential research stream. Moreover, the tool proved universally meaningful and applicable irrespective of research discipline or research experience. Second, it offers a distinct advantage over existing popular research profile platforms, because it demands a focused “less”, that delivers a meaningful “more”. Further, the conciseness of the RPP design makes it readily amenable to iteration and dynamism. Third, the authors have identified specific situations of added value, e.g. initiating research collaborations and academic job market preparation.

The RPP tool can provide the basis for developing a scalable interactive researcher exchange platform.

The authors argue that the RPP tool potentially adds meaningful incremental value relative to existing popular platforms for gaining researcher visibility. This additional value derives from the systematic RPP format, combined with the benefit of easy familiarity and strong emphasis on succinctness. Additionally, the authors argue that the RPP adds a depth of nuanced novel information often not contained in other platforms, e.g. around the dimensions of “data” and “tools”. Further, the RPP gives the researcher a “personality”, most notably through the dimensions of “contribution” and “other considerations”.

Properly planned road illumination systems are collectively a public wealth and the commissioning of such systems may require extensive planning, simulation and testing. The purpose of this simulative work is to offer a simple approach to facilitate luminance-based road lighting calculations that can be easier to comprehend and apply to practical designing problems when compared to complex multi-objective algorithms and other convoluted simulative techniques.

Road illumination systems were photometrically simulated with a created model in a validated software platform for specified system design configurations involving high-pressure sodium (HPS) and light-emitting diode (LED) luminaires. Multiple regression analyses were conducted with the simulatively obtained data set to propound a linear model of estimating average luminance, overall uniformity of luminance and energy efficiency of lighting installations, and the simulatively obtained data set was used to explore luminaire power–road surface average luminance characteristics for common geometric design configurations involving HPS and LED luminaires, and four categories of road surfaces.

The six linear equations of the propounded linear model were found to be well-fitted with their corresponding observation sets. Moreover, it was found that the luminaire power–road surface average luminance characteristics were well-fitted with linear trendlines and the increment in road surface average luminance level per watt increment of luminaire power was marginally higher for LEDs.

This neoteric approach of estimating road surface luminance parameters and energy efficiency of lighting installations, and the compendia of luminaire power–road surface average luminance characteristics offer new insights that can prove to be very useful for practical purposes.

This study aims to examine the influence of big data analytics (BDA) on environmental performance (ENP) in the post-COVID-19 context in Vietnam, as a developing country. In which, this study considers environmental process integration in accounting reports as a mediator variable. Furthermore, digital learning orientation (DLO) and environmental strategy (ES) are proposed as the moderator variables for relationships in the proposed model.

Data was collected by survey method via email with convenient sampling method. In total, 611 emails, including the survey, were sent to executive managers of Vietnamese manufacturing companies listed on stock exchanges. The final sample of 419 responses was used for analysis.

By using the partial least squares structural equation modeling, this study’s results elucidate that BDA positively affects ENP. Moreover, DLO positively moderates the nexus between BDA and environmental process integration in accounting reports, while ES plays a positive moderating role on the nexus between environmental process integration and ENP.

In terms of managerial implications, this paper mentions pretty attractive features of using modern technique and ENP. This research emphasizes the key role of the BDA for both reporting and accounting performance (e.g. environmental process integration and ENP) of the company. Thus, managers should examine implementing BDA when necessary to make accounting reports more transparent and modern, thereby enhancing the organization's ENP. Particularly, managers should focus on improving the organization's ENP indicators.

This study complements the ENP literature by showing a positive effect of BDA and environmental process integration on ENP. Additionally, this study’s results determine the efficacy of DLO and ES as well as their regulatory roles. Finally, this study was conducted to supplement empirical evidence on ENP in the post-COVID-19 context in developing countries, specifically Vietnam.

Since the idea of “Unethical Pro-organisational Behaviour” (UPB) was introduced in 2010, a substantial corpus of empirical research has contributed to its expanding, contemporary knowledge. This includes research studies on how leadership exerts an influence on UPB. This paper aims to consolidate the current understanding of organisational leadership’s impact on employee UPB and offer future research agendas.

A systematic literature review (SLR) using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines was adopted for the study. Literature that satisfied the search conditions was examined. The factors determining leadership’s influence on UPB were studied, and the findings were thematically synthesised.

Leader behaviour plays a large part in influencing UPB in organisations. Leader-member exchange and organisational belonging create favourable circumstances for UPB in organisations. UPB is moderated by the employee’s personal moral orientation.

UPB is unethical behaviour that benefits the organisation and is likely to be rewarded. However, there is a cost that other stakeholders pay. UPB has been researched since 2010, as well as the role of leaders in perpetuating UPB. However, there has not been an SLR of this study. This paper seeks to capture the essence of the research so far and pave a path for future research on the subject. These insights would prove valuable to management practitioners and academic experts.