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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 study aims to understand the impact of leadership styles on the sustainability of organizational energy, using the mediator role of organizational ambidexterity in family firms in Malaysia. To this end, dual-stage Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) were adopted to determine the leadership style of family firms in Malaysia.

An exploratory design (i.e. questionnaire) was used to collect data from 528 workers in the family firms in Malaysia.

According to the results, leadership styles and long-term organizational energy have a positive and significant relationship. Furthermore, organizational ambidexterity mediates the relationship between leadership styles and organizational energy sustainability. On the other hand, based on nonlinear and compensatory relationships, the ANN method predicted a bureaucratic leadership style typical in Malaysian family businesses. The results of this study indicate transformational, transactional and bureaucratic leadership styles affect sustainable organizational energy. Besides, organizational ambidexterity fully mediates the relationship between leadership styles and sustainable organizational energy. On the other hand, the results of non-compensatory relationships revealed organizational ambidexterity is the most determinant of sustainable organizational energy, followed by bureaucratic leadership. As a result, leadership styles encourage human resources to perform tasks with energy and vitality. In family businesses, bureaucratic leadership increases job immersion and positive motivations toward work challenges.

From a practitioner's perspective, leaders and practitioners must encourage creativity and idea generation to give members sufficient strength to work and focus on goals that support building sustainable organizational energy. A family business is a type of capitalism that significantly impacts employees. The family-owned businesses surveyed by first-generation families lack subsidiaries and are ingrained in a paternalistic culture that offers employees greater security at a lower wage. Although there are few details, the study sample size is small and has limitations. This study suggests that understanding the leadership styles on sustainable organizational energy and using the mediator role of organizational ambidexterity in the family business has immense value. Characteristics such as transformational, transactional and bureaucratic leadership styles have a significant role in sustainable organizational energy. Also, organizational ambidexterity is the mediator for the relationship between leadership styles and sustainable organizational energy.

This study sheds light on the effect of leadership styles on sustainable organizational energy through organizational ambidexterity in family firms. In this context, the novelty of this study includes two perceptions. The first explored the impact of exploration and exploitation on sustainable organizational energy. The second investigates linear and nonlinear relationships to predict sustainable organizational energy determinants.

This paper aims to use artificial neural network (ANN) methods to predict stock price crashes in the Chinese equity market.

Three ANN models are developed and compared with the logistic regression model.

Results from this study conclude that the ANN approaches outperform the traditional logistic regression model, with fewer hidden layers in the ANN model having superior performance compared to the ANNs with multiple hidden layers. Results from the ANN approach also reveal that foreign institutional ownership, financial leverage, weekly average return and market-to-book ratio are the important variables when predicting stock price crashes, consistent with results from the traditional logistic model.

First, the ANN framework has been used in this study to forecast the stock price crashes and compared to the traditional logistic model in the world’s largest emerging market China. Second, the receiver operating characteristics curves and the area under the ROC curve have been used to evaluate the forecasting performance between the ANNs and the traditional approaches, in addition to some traditional performance evaluation methods.

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.

This study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.

A data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.

Among five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.

Addressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

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.

Australian agriculture is facing increasingly uncertain weather patterns which is impacting financial performance, exacerbated by worsening terms of trade and a decline in commodity prices. Increasing the resilience and adaptive capacity of the primary production sector is of key importance. Governments and farmer groups often depict technology adoption as the salvation of farming, frequently ignoring the importance of decision-making processes and soft information skills and needs. The purpose of this study is to explore farmer decision-making and resilience and, in doing so, address ongoing challenges with soft information, including the inaccessibility of accounting data and a lack of awareness of its formal role in strategic decisions.

Drawing on a strategic choice perspective, we explore the links between farmer characteristics, attitudes, technology orientation, decision-making and financial performance to investigate how accounting data and tools could better support growers’ adaptive capacity. Detailed on-farm interviews were conducted with 25 grape growers across the Riverland in South Australia, with information thematically and descriptively analysed.

Results show that farmers with low operating profit margins spend double the time making decisions and struggle with minimising variable costs, especially water costs. Lower profit growers were also less likely to perceive climate change as a threat and demonstrated lower resilience.

The results highlight the potential for accountants to make more use of data-driven technological advances and for this information to be used to enhance on-farm strategic decision-making and support innovative business models. Simply packaged biophysical and financial data could also support strategic decisions and adaptation of farmers struggling to make a profit.

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.

The emergence of networks within education has been driven by a number of factors, including: the complex nature of the issues facing education, which are typically too great for single schools to tackle by themselves; changes to educational governance structures, which involve the hollowing out of the middle tier and the introduction of new approaches with an individualized focus; in addition is the increased emphasis on education systems that are “self-improving and school-led”. Within this context, the realization of teacher and school improvement actively emerges from establishing cultures of enquiry and learning, both within and across schools. Since not every teacher in a school can collaboratively learn with every other teacher in a network, the most efficient formation of networks will comprise small numbers of teachers learning on behalf of others.

Within this context, Professional Learning Networks (PLNs) are defined as any group who engage in collaborative learning with others outside of their everyday community of practice; with the ultimate aim of PLN activity being to improve outcomes for children. Research suggests that the use of PLNs can be effective in supporting school improvement. In addition, PLNs are an effective way to enable schools to collaborate to improve educational provision in disadvantaged areas. Nonetheless harnessing the benefits of PLNs is not without challenge. In response, this paper explores the notion of PLNs in detail; it also sheds light on the key factors and conditions that need to be present if PLNs are to lead to sustained improvements in teaching and learning. In particular, the paper explores the role of school leaders in creating meaningful two-way links between PLNs and their schools, in order to ensure that both teachers and students benefit from the collaborative learning activity that PLNs foster. The paper concludes by suggesting possible future research in this area.