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The purpose of this paper is to report on the dynamics of “identity leadership” with a quality improvement project undertaken by an International Medical Graduate (IMG) from Sri Lanka, on a two year Medical Training Initiative (MTI) placement in the National Health Service (NHS) [Academy of Medical Royal Colleges (AoMRC), 2017]. A combined MTI rotation with an integrated Fellowship in Quality Improvement (Subedi et al., 2019) provided the driver to implement the HEART score (HS) in an NHS Emergency Department (ED) in the UK. The project was undertaken across ED, Acute Medicine and Cardiology at the hospital, with stakeholders emphasizing different and conflicting priorities to improve the pathway for chest pain patients.

A social identity approach to leadership provided a framework to understand the insider/outsider approach to leadership which helped RH to negotiate and navigate the conflicting priorities from each departments’ perspective. A staff survey tool was undertaken to identify reasons for the lack of implementation of a clinical protocol for chest pain patients, specifically with reference to the use of the HS. A consensus was reached to develop and implement the pathway for multi-disciplinary use of the HS and a quality improvement methodology (with the use of plan do study act (PDSA) cycles) was used over a period of nine months.

The results demonstrated significant improvements in the reduction (60%) of waiting time by chronic chest pain patients in the ED. The use of the HS as a stratified risk assessment tool resulted in a more efficient and safe way to manage patients. There are specific leadership challenges faced by an MTI doctor when they arrive in the NHS, as the MTI doctor is considered an outsider to the NHS, with reduced influence. Drawing upon the Social Identity Theory of Leadership, NHS Trusts can introduce inclusion strategies to enable greater alignment in social identity with doctors from overseas.

More than one third of doctors (40%) in the English NHS are IMGs and identify as black and minority ethnic (GMC, 2019a) a trend that sees no sign of abating as the NHS continues its international medical workforce recruitment strategy for its survival (NHS England, 2019; Beech et al., 2019). IMGs can provide significant value to improving the NHS using skills developed from their own health-care system. This paper recommends a need for reciprocal learning from low to medium income countries by UK doctors to encourage the development of an inclusive global medical social identity.

This quality improvement research combined with identity leadership provides new insights into how overseas doctors can successfully lead sustainable improvement across different departments within one hospital in the NHS.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

This research interrogates how the construction of narratives and accounting forecasts contributes to managing the emotional state of actors involved in reporting meetings by promoting discourses of hope in their organization's future, mitigating their anxiety. This study shows how narratives are built from multiple antenarratives and accounting forecasts, which restore and strengthen organizational actors' commitment to their organizations. This study contributes to a better understanding of the role played by narratives and accounting documents in mitigating organizational members' anxiety.

Over eight months, an interventionist research design method gave one of the authors the opportunity to record discussions held during reporting meetings in a business incubator. These recordings captured the production of narratives and forecasts in these meetings.

This study shows how the production of multiple antenarratives and accounting forecasts helps organizational actors who attend reporting meetings mitigate the anxiety triggered by disappointing performance figures and restore collective discourses full of hope for the organization's future. This case highlights how personal antenarratives and successive versions of accounting forecasts contribute to restoring a collective commitment to a failing organization.

This study refines current understanding of the under-explored links between accounting forecasts, narratives and anxiety management. The study provides insight into how accounting practices contribute to the production of narratives that successfully restore organizational members' commitment to working for a failing organization. The study also exemplifies the original insights gained from interventionist research protocols.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

The current estimation practice in construction projects greatly needs upgrading, as there has been no improvement in the cost overrun issue over the past 70 years. The purpose of this research was to develop a new multiple regression analysis (MRA)-based model to forecast the final cost of road projects at the pre-design stage using data from 43 projects in New Zealand (NZ).

The research used the case study of 43 completed road projects in NZ. Document analysis was conducted to collect data, and statistical tests were used for model development and analysis.

Eight models were developed, and all models achieved the required F statistics and met the regression assumptions. The models’ mean absolute percentage error (MAPE) was between 21.25% and 22.77%. The model with the lowest MAPE comprised the road length and width, number of bridges, pavement area, cut and fill area, preliminary cost and cost indices change.

The model is based on road projects in NZ. However, it was designed to be able to adapt to other contexts. The findings suggest that the model can be used to improve traditional conceptual estimating methods. Past project data is often stored by the project team but rarely used for analysing and forecasting purposes. This research emphasises that past data can be effectively used to predict the project cost at the pre-design stage with limited information.

No research was conducted to adopt cost modelling techniques into the conceptual estimation practice in the NZ construction industry.

In response to growing concerns over the negative consequences of Internet addiction on adolescents’ mental health, coupled with conflicting results in this literature stream, this meta-analysis sought to (1) examine the association between Internet addiction and depressive symptoms in adolescents, (2) examine the moderating role of Internet freedom across countries, and (3) examine the mediating role of excessive daytime sleepiness.

In total, 52 studies were analyzed using robust variance estimation and meta-analytic structural equation modeling.

There was a significant and moderate association between Internet addiction and depressive symptoms. Furthermore, Internet freedom did not explain heterogeneity in this literature stream before and after controlling for study quality and the percentage of female participants. In support of the displacement hypothesis, this study found that Internet addiction contributes to depressive symptoms through excessive daytime sleepiness (proportion mediated = 17.48%). As the evidence suggests, excessive daytime sleepiness displaces a host of activities beneficial for maintaining mental health. The results were subjected to a battery of robustness checks and the conclusions remain unchanged.

The results underscore the negative consequences of Internet addiction in adolescents. Addressing this issue would involve interventions that promote sleep hygiene and greater offline engagement with peers to alleviate depressive symptoms.

This study utilizes robust meta-analytic techniques to provide the most comprehensive examination of the association between Internet addiction and depressive symptoms in adolescents. The implications intersect with the shared interests of social scientists, health practitioners, and policy makers.

The implementation of income-generating still faces problems, such as the lack of well-established internal control and differences in implementation in each unit. This study aims to analyze internal controls, financial viability (FV) and leadership qualities (LQ) in the implementation of income-generating in Indonesian higher education.

This study is quantitative and uses a causal approach. The population of this research is the unit leader and the person in charge of the activity that generates income, with a total sample of 111 people. The sampling technique used is simple random sampling. Data were analyzed using moderation regression analysis (MRA) with the WrapPLS (partial least square) analysis tool.

The results indicate that internal control and FV significantly affect the management of income-generating. The existence of LQ as a moderating variable can moderate and weaken the influence of internal controls and FV on the management of income-generating. In this finding, the unit leader and the person in charge of activities that generate income in higher education need to improve managerial skills, including ethics, uphold integrity, clear vision, quick adaption, honestly and trust so that the management of income-generating can achieve higher education goals more effectively and efficiently.

This research shows that universities need to create a good environment to build an ecosystem that can improve the management. The university encourages the good management by strengthening the leadership. However, the research has a limitation: the study was only conducted in one state university.

The implementation of income generation in the public financial management system of legal entity universities requires accountability for sources of income so that internal controls and the role of finance are needed to ensure the continuity of universities.

Hazel Kyrk, one of the first women economists at the Economic Department of the University of Chicago and author of A Theory of Consumption (1923), conducted groundbreaking research for the Bureau of Home Economics of the US Department of Agriculture and the Bureau of Labor Statistics. Kyrk made a considerable contribution to the development of standards for a “decent living,” the Consumer Price Index, and the conceptualization of what would later turn into the definition of the poverty line. This chapter evaluates Kyrk’s use of eugenic notions of gender and race that were widely used in Kyrk’s day. This chapter shows that eugenic reasoning impacts Kyrk’s theoretical work only superficially but does structure her research on consumption standards through her focus on the white middle-class family as the unit of analysis for consumer behavior. This chapter also makes clear that the American Institutionalist approach to consumer behavior, rather than marginalized and side-tracked due to a lack of theoretical progress, was relegated to the margins of economics science together with the research of women economists into Home Economics departments and policy research at government institutions.

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

This study examined subjective numeracy and its relationship with accounting judgements on probability issues.

A subjective numeracy scale (SNS) questionnaire was distributed to 231 accounting students to measure self-evaluated numeracy. Modified Bayesian reasoning tasks were applied in an accounting-related probability estimation, manipulating presentation formats.

The study revealed a positive relationship between self-evaluated numeracy and performance in accounting probability estimation. The findings suggest that switching the format of probability expressions from percentages to frequencies can improve the performance of participants with low self-evaluated numeracy.

Adding objective numeracy measurements could enhance results. Future numeracy research could add objective numeracy items and assess whether this influences participants' self-perceived numeracy. Based on this sample population of accounting students, the findings may not apply to large populations of accounting-information users.

Investors' ability to exercise sound judgement depends on the accuracy of their probability estimations. Manipulating the format of probability expressions can improve probability estimation performance in investors with low self-evaluated numeracy.

This study identified a significant performance gap among participants in performing accounting probability estimations: those with high self-evaluated numeracy performed better than those with low self-evaluated numeracy. The authors also explored a method other than additional training to improve participants' performance on probability estimation tasks and discovered that frequency formats enhanced the performance of participants with low self-evaluated numeracy.