Search results

This study examines the effect of delegation of decision rights, moral justification (MJ), and ethical climate (EC) on managers’ misreporting in the financial services sector. We employed an online research panel called Qualtrics, to collect data based on a sample of 127 middle-level managers from various US financial services firms. We find that MJ mediates the relation between delegation and misreporting, suggesting delegation of decision rights increases employees’ misreporting indirectly by increasing MJ. We also find that EC significantly moderates the relationship between MJ and misreporting. Furthermore, our test of the moderated-mediation effect reveals that the indirect effect of the delegation of decision rights on misreporting through MJ is stronger when there is a higher level of instrumental climate (IC) and a lower level of principle climate (PC).

This paper examines the role played by business cluster ecosystems and intellectual capital (IC) in achieving high-growth firm (HGF) status.

We draw our insights from the knowledge-based perspective and economic geography as a theoretical lens, which combined offer a more unifying understanding of how business cluster ecosystems and IC foster high growth entrepreneurship.

Drawing on a sample of 11,360 German incorporated firms across 80 clusters, we find that cluster ecosystems play a significant role in supporting firms to become HGFs. More specifically, being located in business clusters increases the likelihood of becoming HGFs by 2.2% to 4.49%. We also find that clusters with more productive firms in the ecosystems provide favorable conditions for member firms to achieve HGF status, while the impact of other cluster-specific conditions (high-tech cluster membership and multinational enterprise share in clusters) is less clear. Additional insights suggest that firm IC (investments in intangible assets) enables firms to achieve high growth status.

The findings of this paper hold theoretical and managerial relevance and shed more light on the impact of cluster-specific factors in the ecosystems and firm IC in achieving high growth entrepreneurship.

This paper is among the first of its kind to bring together three distinct literatures (HGFs, business clusters and IC) and utilize insights from each to derive a conceptual framework that links them in explaining high-growth entrepreneurship.

Hyperinflation is a rare form of macroeconomic crisis that often results from extreme political events, such as revolution or regime change. The 1989–1990 Argentine hyperinflation is puzzling because it occurred in the absence of such an event. Moreover, conventional fiscal mechanisms linking political processes to hyperinflation do not sufficiently explain the Argentine case. Previous theories emphasizing distributional conflict and institutional weakness contain key elements of an explanation of the Argentine hyperinflation but do not capture the range of mechanisms that produced extreme financial instability. This chapter offers an elite theory approach that subsumes elements of these approaches within a broader theory of elite fragmentation, competition, and conflict. Elite fragmentation inhibits collective action in both economic and state elites, resulting in deficits in policymaking capacity. Fragmentation among state policy elites leads to policy volatility and incoherence, while fragmentation among politically mobilized economic elites results in elite stalemates constraining the options of policy elites. These policymaking patterns lead to prolonged delays in the adjustment of unsustainable organizational structures, resulting in explosive forms of crises.

This paper aims to explore the barriers and facilitators to psychosocial well-being among ethnic Nepali Bhutanese American older adults from the perspective of health care and service providers working with this population. Specifically, the authors aimed to understand health-care and service providers’ perceptions of the psychosocial well-being in this community and appropriate interventions.

Qualitative research methods were used to collect and analyze data in collaboration with a community-based organization. A total of ten participants were interviewed. Interviews were conducted in either English or Nepali, based on participant preference. An inductive thematic analysis approach was used to analyze the data.

Three major themes were generated from the analysis. The first two were in reference to perceived barriers to mental well-being among Bhutanese American older adults: isolation that older adults faced in the USA and shifting responsibilities and lifestyles that arose from the cultural and structural barriers in the USA. Throughout these themes, there was an understanding that acculturation threatened families’ connections to each other and impacted older adults’ connections with younger generations. The third theme, a perceived facilitator of well-being, was the power of storytelling to counteract feelings of isolation and disempowerment caused by shifting lifestyles felt by older adults, especially amid community events.

Bhutanese American older adults, many of whom have limited English proficiency, face numerous challenges, psychosocial stressors and factors contributing to well-being. Care for this population should prioritize dignity, empowerment and the incorporation of strengths within their narratives. Interventions and services tailored for older Bhutanese American adults need to be adapted to integrate multiple care systems.

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.

Network configurations have been proposed as an efficient form of organisation and a promising area of research; however, a lack of conceptual clarity can be noted. The purpose of this review is to allow for a broad appreciation of network configurations and provide guidance for future studies of the concept.

A systematic literature review was conducted based on the PRISMA method; Scopus, Web of Science, PubMed and the Cochrane Library were searched for conference proceedings and journal articles describing organisational networks to integrate resources aimed at care delivery. Around 80 articles were included in the final review and analysed thematically and by use of bibliographic coupling.

The last decades have seen an increase in the frequency of articles describing networks for healthcare delivery. The most common contexts are care for multiple and/or long-term conditions. Three clusters of articles were found, corresponding to different conceptualisations of networks in healthcare: efficiency-enhancing cooperation, efficiency-enhancing integration and involvement for cocreation.

To increase conceptual clarity and allow the research on network configurations in healthcare to produce meta-learnings and guidance to practice, scholars are advised to provide ample descriptions of studied networks and relate them to established network classifications.

The current review has only included articles including networks as a key concept, which provides a focused overview of the use of network configurations but limits the insights into similar approaches not described explicitly as networks.

COVID-19 has caused unprecedented disruption to health systems. There is much to be gained by capturing what was learned from changes and adaptations made by health services and systems. The Ministry of Health in New South Wales (NSW), Australia, sought to prioritise health services research (HSR) to address critical issues arising from the COVID-19 pandemic. We tested a priority setting methodology to create priorities for a specific funding opportunity and to extract generalisable lessons.

A virtual roundtable meeting of key stakeholders was held in June 2020. We used a modified Nominal Group Technique (NGT) for priority setting, with potential items (n = 35) grouped under headings. Data was analysed through a reflective deliberative process.

We engaged 89 senior policy makers, health service executives, clinicians and researchers in the roundtable. The NGT proved an efficient method with participants reaching consensus on eight priorities. Findings included strong support for learning from the rapid response to COVID-19 and addressing needs of vulnerable populations and the health workforce. Opinions differed about strategic areas investment and where learnings should be via internal evaluation rather than funded research. Three of the eight recommended priorities were included in the funding opportunity.

Coronavirus disease 2019 (COVID-19) required unprecedented change and adaptations within health systems, and rapid, applied health services research can help to create, understand and (where relevant) sustain change beyond the immediate impact of the pandemic. While final decisions may be dependent on a wider range of considerations by government, stakeholder enthusiasm for engagement in priority setting exercises may be dampened if they do not perceive their application in decision-making.

A modified nominal group technique can be used to set research priorities in constrained conditions by engaging large numbers of stakeholders in rankings and then using an online delivery of a roundtable and to reach consensus on priorities in real time. Recommended priorities for health services research can be readily generated through rapid engagement but does not guarantee their application.

Australia’s swift response to COVID-19 pandemic in 2020 was perceived as a relative success due to the rapid public health and policy response and a relatively low number of cases. This response was underpinned by systematic knowledge mobilisation including support for targeted and prioritised health services research to fill knowledge gaps.

Setting priority processes can provide rich, engaged input to support government funding decisions about HSR. A wide range of dynamic and iterative processes influence decision-making in a rapidly evolving situation in the health system response to COVID-19. It is crucial to consider how major investment decisions will support a value-based healthcare system.

This article aims to contribute to the accounting knowledge literature by presenting the framework of shadow accounting functions (SAF) and investigating the effect of stakeholders’ pressure anomie (SPA) on it.

This study adopted a mixed, both inductive and deductive approach to develop an integrated framework, validate its practicability and verify its effectiveness in selected manufacturing firms listed on the Tehran Stock Exchange. In developing the framework and implementation procedure, the study employed an exploratory data collection (qualitative) approach to review the phenomenon of shadow accounting functions. Then, in the second phase, this study tested the research hypothesis through a partial least squares process. The population of the study is made up of all financial managers and heads of the accounting departments of capital market companies in Iran. Presently, there are 185 companies (TSE). From this, a sample of 100 companies was selected, which are all on the Tehran Stock Exchange.

Based on the mixed method of this study, the result in the qualitative part provides the shadow accounting framework of the existence of three categories; there are six components and 37 themes during 12 interviews. In the quantitative section, it was also determined that social pressure anomie has a positive and significant effect on shadow accounting functions.

So far, it is rare to find preceding studies that proposed, validated and practically tested an integrated shadow accounting framework within the context of manufacturing industries. Thus, the authors understand that this is the very first research focused on the development of the framework for manufacturing industries to be competitive and could help managers, institutions, practitioners and academicians in the perception of social values expected by stakeholders.

This paper aims to initiate new avenues of research by examining optimal intellectual capital (IC) inputs, introducing three theories into the discussion: diminishing returns to scale, transaction costs economics and efficiency wage theory. In the second part, it advocates for demonstrating the existence of such non-optimality through empirical tests.

This paper is divided into two parts. The first part provides a theoretical justification for the necessity of observing nonlinear relationships between IC inputs and firm performance. In the empirical section, the research design follows a four-step process, each progressively building on insights gained from the preceding phase: (1) establishing a baseline linear regression model; (2) introducing the logarithm of the IC inputs; (3) incorporating the square terms of the IC inputs and (4) investigating the phenomena of over- and under-input in IC.

The background theories and the obtained results highlight the necessity for firms to adopt a strategic approach to IC, acknowledging the diverse effects of IC components on different outcomes. They emphasize the nonlinear nature of IC returns, underscoring the importance of investing up to an optimal level to maximize benefits.

The study’s discovery of optimal levels for the components of IC highlights the importance for practitioners to identify and invest up to these optimal levels. This ensures that IC initiatives are strategically aligned to maximize their positive impact on firm performance.

The integration of theories such as diminishing returns to scale, transaction costs economics and efficiency wage theory, alongside traditional frameworks like the resource-based view, the theory of dynamic capabilities and the knowledge-based theory of the firm, opens up new avenues for research on IC. The proposed methodology and measures – from financial reports – provide opportunities for replicating this type of study.

This paper presents an original approach for learning models, partially known, of particular interest when performing source identification or structural health monitoring. The proposed procedures employ some amount of knowledge on the system under scrutiny as well as a limited amount of data efficiently assimilated.

Two different formulations are explored. The first, based on the use of informed neural networks, leverages data collected at specific locations and times to determine the unknown source term of a parabolic partial differential equation. The second procedure, more challenging, involves learning the unknown model from a single measured field history, enabling the localization of a region where material properties differ.

Both procedures assume some kind of sparsity, either in the source distribution or in the region where physical properties differ. This paper proposed two different neural approaches able to learn models in order to perform efficient inverse analyses.

Two original methodologies are explored to identify hidden property that can be recovered with the right usage of data. Both methodologies are based on neural network architecture.