Studies have concluded that men tend to have higher self‐confidence than women and that this affects their entrepreneurial intentions. However, little is known about how…
Abstract
Purpose
Studies have concluded that men tend to have higher self‐confidence than women and that this affects their entrepreneurial intentions. However, little is known about how self‐confidence affects entrepreneurs in their start‐up decision, and even less is understood about how it affects entrepreneurs' decisions and actions in their ongoing business. The purpose of this paper is to meet these two objectives by using a gender comparative approach.
Design/methodology/approach
A total of 50 entrepreneurs (25 women and 25 men) in New Zealand were interviewed in a semi‐structured format.
Findings
Women exhibit a lack of self‐confidence in their own abilities as entrepreneurs compared to men. This finding parallels results of prior research. Once in an established business, women relate to entrepreneurship less than men and do not feel comfortable calling themselves entrepreneurs. For some women, entrepreneurial self‐confidence grew over their time in business. For other women, it appears to continue to act as a constraint – affecting their ability to access finance and curtailing their growth aspirations.
Research limitations/implications
In total, 50 entrepreneurs were studied, and further research could be done to understand the impact of self‐confidence for larger samples of entrepreneurs.
Originality/value
The qualitative nature of the study contributes to the limited understanding of how entrepreneurial self‐confidence affects both the start‐up decision and sustained entrepreneurship, but more research required. A key outcome of this paper is that it provides directions for further research to more fully understand this phenomenon. It also presents a number of policy suggestions.
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Offers a model for the institutional evaluation of pilot programmesof General National Vocational Qualifications (GNVQs). Trialled ina‐large FE college in Manchester, the model is…
Abstract
Offers a model for the institutional evaluation of pilot programmes of General National Vocational Qualifications (GNVQs). Trialled in a‐large FE college in Manchester, the model is general enough to be applicable to any institution offering GNVQ programmes post‐16. It focuses on evaluation from the perspective of management (of the college), client (students, HE, schools, employers) and curriculum. Offers general guidelines on an evaluation strategy, schedule and methodology.
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Proposes that the practice of consulting for organizational changeoften does not change the organization fundamentally. Suggests that realtransformation occurs at deeper levels of…
Abstract
Proposes that the practice of consulting for organizational change often does not change the organization fundamentally. Suggests that real transformation occurs at deeper levels of an organizational system. Presents a more whole and integrated methodology more likely to help organizational systems to develop the capacity to reconfigure and respond to environmental fluctuations and changing strategies.
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Ronald H. Humphrey, Neal M. Ashkanasy and Ashlea C. Troth
Purpose: This introduction sets the stage for the book theme, “Emotions and Negativity,” by reviewing the early work on negative emotions and by discussing the impact of the COVID…
Abstract
Purpose: This introduction sets the stage for the book theme, “Emotions and Negativity,” by reviewing the early work on negative emotions and by discussing the impact of the COVID pandemic on people’s moods and emotions. It discusses how most of the chapters in this book were first presented as conference papers at the Twelfth International Conference on Emotions and Worklife (“Emonet XII”). It then highlights the key contributions from each of the chapters. Study Design/Methodology/Approach: This gives an overview of the organizational structure of the book and explains the four major parts of the book. It then relates each chapter to the theme of each part and discusses the key contributions of each chapter. Findings: The introduction concludes by observing that the chapters offer a variety of practical solutions to negative emotions that should be of use to both practitioners and academicians. Originality/Value: The chapters investigate underresearched topics, and thus make original and important new contributions. Although underresearched, the topics they explore have a major impact on people’s lives. Thus, these chapters add considerable value to the field.
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Lei Zhang, Fengchun Tian, Xiongwei Peng, Xin Yin, Guorui Li and Lijun Dang
The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low-cost electronic noses (e-noses) with metal oxide…
Abstract
Purpose
The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low-cost electronic noses (e-noses) with metal oxide semiconductor sensors in indoor air contaminant monitoring and overcome the potential sensor drift.
Design/methodology/approach
In the quantification model, a piecewise linearly weighted artificial neural network ensemble model (PLWE-ANN) with an embedded self-calibration module based on a threshold network is studied.
Findings
The nonlinear estimation problem of sensor array-based e-noses can be effectively transformed into a piecewise linear estimation through linear weighted neural networks ensemble activated by a threshold network.
Originality/value
In this paper, a number of experimental results have been presented, and it also demonstrates that the proposed model has very good accuracy and robustness in real-time indoor monitoring of formaldehyde.
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Suhang Yang, Tangrui Chen and Zhifeng Xu
Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of…
Abstract
Purpose
Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.
Design/methodology/approach
This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.
Findings
The combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.
Originality/value
ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.
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Brendan Thomas O'Connell, Paul De Lange, Ann Martin-Sardesai and Gloria Agyemang
The purpose of this paper is to examine prominent issues and knowledge contributions from research exploring measurement and assessment of accounting research, impact and…
Abstract
Purpose
The purpose of this paper is to examine prominent issues and knowledge contributions from research exploring measurement and assessment of accounting research, impact and engagement. This paper also provides an overview of the other papers presented in this AAAJ Special Issue and draws from their findings to scope out future impactful research opportunities in this area.
Design/methodology/approach
Consists of a review and examination of the prior literature and the other papers published in this AAAJ Special Issue.
Findings
The paper identifies and summarises three key research themes in the extant literature: research productivity of accounting academics; the rise of the “Corporate University” and commodification of research; and, the benefits and limitations of Research Assessment Exercises. It draws upon work within these research themes to set out four broad areas for future impactful research.
Research limitations/implications
The value of this paper rests with collating and synthesising several important research themes on the nature and impact of measurement and assessment of accounting research, impact and engagement, and in prompting future extensions of this work through setting out areas for further innovative research in the area.
Practical implications
The research examined in this paper and the future research avenues proposed are highly relevant to university academics, administrators and regulators/policymakers. They also offer important insights into matters of accounting measurement, accountability, and control more generally.
Originality/value
This paper adds to vibrant existing streams of research in the area by bringing together authors from different areas of accounting research for this AAAJ Special Issue. In scoping out an agenda for impactful research in the nature and impact of measurement and assessment of accounting research, impact and engagement, this paper also draws attention to underexplored issues pertaining to areas such as the “lived experience” of academics in the corporatised university and envisioning what a future “optimal” system of measurement and assessment of research quality might look like?
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Maede Mohseni and Saeed Khodaygan
This paper aims to improve the manufacturability of additive manufacturing (AM) for topology-optimized (TO) structures. Enhancement of manufacturability focuses on modifying…
Abstract
Purpose
This paper aims to improve the manufacturability of additive manufacturing (AM) for topology-optimized (TO) structures. Enhancement of manufacturability focuses on modifying geometric constraints and classifying the building orientation (BO) of AM parts to reduce stresses and support structures (SSs). To this end, artificial intelligence (AI) networks are being developed to automate design for additive manufacturing (DfAM).
Design/methodology/approach
This study considers three geometric constraints for their correction by convolutional autoencoders (CAEs) and transfer learning (TL). Furthermore, BOs of AM parts are classified using generative adversarial (GAN) and classification networks to reduce the SS. To verify the results, finite element analysis (FEA) is performed to compare the stresses of modified components with the original ones. Moreover, one sample is produced by the laser-based powder bed fusion (LB-PBF) in the BO predicted by the AI to observe its SSs.
Findings
CAE and TL resulted in promoting the manufacturability of TO components. FEA demonstrated that enhancing manufacturability leads to a 50% reduction in stresses. Additionally, training GAN and pre-training the ResNet-18 resulted in 80%, 95% and 96% accuracy for training, validation and testing. The production of a sample with LB-PBF demonstrated that the predicted BO by ResNet-18 does not require SSs.
Originality/value
This paper provides an automatic platform for DfAM of TO parts. Consequently, complex TO parts can be designed most feasibly and manufactured by AM technologies with minimal material usage, residual stresses and distortions.
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Ann Martin-Sardesai and James Guthrie
The purpose of this paper is to examine the perceptions of academic human capital (HC) towards a university’s research performance measurement system (PMS), in response to a…
Abstract
Purpose
The purpose of this paper is to examine the perceptions of academic human capital (HC) towards a university’s research performance measurement system (PMS), in response to a national research assessment exercise (RAE).
Design/methodology/approach
This paper draws on a subset of the data from a large mixed method case study research project about the impact of ERA on an Australian public sector university.
Findings
The findings reveal that the research PMSs were designed, implemented and used as a tool to measure and manage the research performance of HC within the university. The case study university performed well in the RAE. However, this also led to several unintended consequences in the form of fear and anxiety, gaming and strategic initiatives, a focus on quantity and not the quality of research, and increased workload, which led to a loss in the stock of HC.
Practical implications
This empirical evidence can inform governments and policy makers of the unintended consequences of government research evaluations on academic HC. University managers could improve the design of HC management systems by not only measuring academic HC performance, but also providing training and resources to enhance, support and maintain the overall well-being of academics.
Originality/value
This study provides insights regarding the connection between a university’s PMS and academic HC and contributes to the academic literature on intellectual capital and PMSs.
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Sonali Shankar, P. Vigneswara Ilavarasan, Sushil Punia and Surya Prakash Singh
Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it…
Abstract
Purpose
Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods.
Design/methodology/approach
In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty.
Findings
The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods.
Originality/value
The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.