30793 research outputs found
Sort by
Confronting social dominance ideology: how professional women manage career stereotypes in male-dominated occupations
Get PDFPurpose:
Using social dominance theory as a conceptual lens, this study explores how female managers and professionals strive to defy the perceived career stereotypes in traditionally male-dominated occupations.
Design/methodology/approach:
The dataset comprises 30 interviews with female bank managers and senior engineers in Nigeria – a non-Western location and work group – a sample that is considered under-researched.
Findings:
The qualitative analysis identifies how the interviewed women adopted three strategies in managing gender and career stereotypes, with some expressing concerns of experiencing emotional dissonance as they contend with occupational segregation based on gender.
Research limitations/implications:
The extent to which the findings can be generalised may be constrained by the study’s limited sample size. Nevertheless, the findings shed light on the underlying importance of disclosing how working women exert themselves in navigating the social dominance ideology in Nigeria that is notable for extreme gender role differentiation. This often results in an intensification of the efforts made by female professionals in confronting the endemic nature of male chauvinism in Nigerian organisations.
Originality/value:
Research on gender and career constraints has, in the main, restricted our understanding of the barriers that Nigerian women face in their careers as a result of the masculine hegemony perpetuated by social dominance. The present study aims to challenge, however, proponents of social dominance by unveiling the mitigating strategies that women living in an inegalitarian society adopt to confront occupational male-group ascendency
Hearing faculty members’ voice: a gendered view on knowledge sharing
Get PDFPurpose:
The effects of gendered views on employee voice are of great importance for knowledge sharing within public universities. Yet, they are still neglected by current human resource management and entrepreneurship literature. While strengthening themselves by reinforcing the strengths and opportunities, public universities can generate entrepreneurial opportunities through various knowledge-sharing mechanisms, including social networks and employee voice. This became a crucial lever for public universities to leverage competitive advantages and to support entrepreneurial activities through network knowledge-based sharing. For this purpose, this study aims to examine the various aspects of entrepreneurship via the voice of employees, emphasizing the moderating effect of gender and the mediating role of social networks on the link between employee voice and the entrepreneurial atmosphere of universities.
Design/methodology/approach:
The authors collected survey data from a cross-sectional sample of 335 employees engaged in entrepreneurship activities within public universities in an emerging economy context and analyzed the data using partial least squares structural equation modeling (PLS-SEM) with the Smart-PLS software.
Findings:
The PLS-SEM analysis found that different dimensions of the university entrepreneurial climate (communication, knowledge sharing and innovative climate) positively impact members’ voices within public universities. This effect is amplified by social networks, which are crucial for spreading knowledge among faculty, thereby fostering a more open and collaborative academic environment.
Research limitations/implications:
When acting, the university top management team should encourage the generation and dissemination of entrepreneurial ideas to nurture a dynamic entrepreneurial atmosphere and social involvement, ultimately supporting sustainable competitive advantages through a culture of strategic knowledge sharing. The results have practical implications for university managers, entrepreneurship education actors, administrators, policymakers and entrepreneurial ecosystem actors, by demonstrating how social networks can amplify the dissemination of ideas and entrepreneurial spirit.
Originality/value:
This research explores how entrepreneurship and social networks can help faculty members have a stronger influence in academic settings. It also fills in the gaps in knowledge about how human resource management and entrepreneurship can work together to create a more communicative and innovative academic environment. Additionally, this study brings new ideas to existing literature by looking at how gender differences can affect employee voice, particularly emphasizing the importance of women in leadership roles at universities. This study is also the first to delve into how entrepreneurship and social networks, along with gender perceptions, play a role in shaping the voice of employees in a public university.There is no funding associated with the work featured in this article
Solar power forecasting by machine learning
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe inherent uncertainty in photovoltaic (PV) power generation remains a significant challenge to the seamless integration of solar energy into modern power systems. This study addresses this issue by employing advanced machine learning (ML) techniques, with a particular focus on Long Short-Term Memory (LSTM) networks—a class of recurrent neural networks (RNNs) to improve the forecasting accuracy of solar power output. The methodology combines deep learning models with Maximum Power Point (MPP) tracking to enhance both predictive performance and operational efficiency of PV systems.
The research integrates time-series data collected from real-world PV installations, capturing key variables such as solar irradiance, temperature, voltage, and current. The LSTM architecture is trained to model the temporal dependencies inherent in these sequences, allowing for accurate forecasting of solar power production. An ensemble LSTM approach is implemented to further enhance the robustness of predictions and reduce mean squared error (MSE). Moreover, the integration of MPP data enables real-time adaptation to changing environmental conditions, thereby improving energy capture efficiency and enabling early detection of system faults.
Complementary to the LSTM framework, traditional time-series models such as SARIMA and ARIMA are also applied to analyze the temporal variation in solar power production. These statistical models provide valuable baseline comparisons and offer additional insights into the volatility of PV output, which is known to cause operational issues such as frequency instability, dispatch challenges, and voltage/current surges within the grid.
The research demonstrates a hybrid methodology that leverages deep learning and statistical modeling, coupled with MPP analysis, to enhance the reliability, efficiency, and fault tolerance of solar energy systems paving the way for more stable and scalable integration of PV power into the energy mix
Evaluation of Ghana's National Health Insurance Scheme (NHIS)'s mobile renewal service intervention
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonBackground: Taking advantage of technological advancement, the National Health Insurance
Authority (NHIA) of Ghana launched the Mobile Renewal Service (MRS) in December 2018 to
allow the National Health Insurance Scheme (NHIS) subscribers to renew their membership on
mobile money platforms in Ghana. The MRS intervention offers a simple, quick, and cost-effective
method for NHIS Subscribers to renew their membership annually without physically visiting an
NHIS district office. However, there has yet to be any rigorous evaluation of the effectiveness of
this intervention. Also, there is limited understanding of the barriers and facilitators influencing
the adoption of the intervention from the perspective of NHIS subscribers and the intervention
implementers in West Africa. Therefore, this thesis seeks to contribute knowledge on the
effectiveness of MRS in Ghana and provide empirical evidence on the barriers and facilitators
influencing the adoption of MRS. This contribution could offer evidence-informed policies that
could have significant public health implications by ensuring timely insurance renewals and
continuous insurance coverage, which could potentially prevent mortalities and morbidities due to
health accessibility interruptions.
Methods: The thesis adopted multiple approaches, including a literature review, interrupted time
series analysis and a Delphi study. Twenty-five studies in West Africa were reviewed to
understand how they approached this research topic, identify knowledge gaps on mHealth around
the evaluation of mobile renewal service, establish what is known about the topic, and provide the
research questions and methodological directions for this thesis. After that, an Interrupted Time
Series Analysis (ITSA), using an Ordinary Least Squares (OLS) regression model, was fitted to
examine and evaluate the effectiveness of MRS using secondary data from the NHIS’s
membership database. Afterwards, stakeholders (NHIS subscribers and policy implementers) were
engaged through online survey platforms to explore their perspectives on barriers to MRS adoption
and interventions to tackle the identified barriers. Structural equation modelling, Delphi and
thematic analyses were then conducted to ascertain the barriers and facilitators affecting the
implementation of MRS and the need for more evidence on the critical drivers of adherence to
MRS.
Results: The literature review identified research scarcity on the evaluation of mHealth utilisation
and a lack of evidence on the critical drivers of adherence to MRS adoption from the perspective of users and policy implementers. The ITSA showed that the MRS intervention significantly
increased NHIS subscription renewal compared to the conventional/manual renewals (Coefficient
= 6.06; p<0.05), resulting in a statistically significant decline in manual renewals over a 60-month
period (p<0.05). On facilitators of MRS usage, the subscribers highlighted factors, such as time
and travel cost saving, convenience and comfort of renewal, as the key drivers of MRS adoption
while the implementers. These findings were corroborated by the policy implementers as they also
indicated that ease of use and operational convenience, perceived usefulness, and affordability are
the critical enablers of MRS adoption. However, there was divergence in their perspectives on the
barriers to MRS adoption. For example, while the policy implementers indicated that illiteracy,
poverty and resistance to change as the key barriers to MRS adoption, the subscribers mentioned
peer pressure and community endorsement, and the platform’s engagement appeal as the key
challenges to adopting MRS. Notwithstanding, they both agreed that network connectivity is a
significant barrier to using the MRS intervention.
Conclusion: The MRS intervention has significantly increased NHIS renewals, translating into an
uninterrupted access to healthcare through health insurance. To upscale the potential of the MRS
intervention, the NHIA could consider addressing key barriers, such as network connectivity, to
ensure the continuous uptake of the intervention to enhance healthcare accessibility in Ghana
Application of deconvolutional networks for feature interpretability in epilepsy detection
Get PDFData availability statement:
The original contributions presented in this study are included in this article/Supplementary material, further inquiries can be directed to the corresponding authors.Supplementary material:
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2024.1539580/full#supplementary-material .Generative AI Statement:
The authors declare that no Generative AI was used in the creation of this manuscript.Introduction: Scalp electroencephalography (EEG) is commonly used to assist in epilepsy detection. Even automated detection algorithms are already available to assist clinicians in reviewing EEG data, many algorithms used for seizure detection in epilepsy fail to account for the contributions of different channels. The Fully Convolutional Network (FCN) can provide the model’s interpretability but has not been applied in seizure detection.
Methods: To address these challenges, a novel convolutional neural network (CNN) model, combining SE (Squeeze-and-Excitation) modules, was proposed on top of the FCN. The epilepsy detection performance for patient-independent was evaluated on the CHB-MIT dataset. Then, the SE module was removed from the model and integrated the model with Inception, ResNet, and CBAM modules separately.
Results: The method showed superior advancement, stability, and reliability compared to the other three methods. The method demonstrated a G-Mean of 82.7% for sensitivity (SEN) and specificity (SPE) on the CHB-MIT dataset. In addition, The contributions of each channel to the seizure detection task have also been quantified, which led us to find that the FZ, CZ, PZ, FT9, FT10, and T8 brain regions have a more pronounced impact on epileptic seizures.
Discussion: This article presents a novel algorithm for epilepsy detection that accurately identifies seizures in different patients and enhances the model’s interpretability.This work was supported by the Key Specialized Research and Development Breakthrough of Henan Province (Grant No. 232102210030 to YZ); Foundation of State Key Laboratory of Ultrasound in Medicine and Engineering (Grant No. 2022KFKT004 to QL); and National Natural Science Foundation of China (Grant No. 62071323 to AY)
Conceptualising personalised pricing under article 102 TFEU
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe rapid development of algorithms and big data has revolutionized the digital economy, introducing novel challenges for European Union (EU) competition law. Personalised pricing, an advanced technique of price discrimination enabled by extensive data collection and advanced algorithms, allows undertakings to tailor prices according to individual consumers' willingness to pay. While this practice can enhance market efficiency and consumer welfare, it raises significant concerns regarding unfair pricing and market distortions. This research investigates how personalised pricing exacerbates the anti-competitive effects of predatory and excessive pricing, despite not necessitating a new regulation. It demonstrates that personalised pricing transforms predatory pricing into a streamlined, single-stage process and complicates excessive pricing dynamics by enabling precise exploitation of consumer data. This practice not only creates significant barriers to market entry but also disrupts market fairness. Focusing on cases involving undertakings such as Uber, Tinder, and Wish, this study examines how personalised pricing operates as both an exclusionary and exploitative mechanism under Article 102 TFEU. It argues that rather than being treated as a new type of violation under Article 102 TFEU, personalised pricing should be evaluated within the established frameworks of predatory and excessive pricing. The thesis shows that existing legal mechanisms, particularly Article 9 of Regulation 1/2003, are sufficiently equipped to address the adverse effects of personalised pricing while preserving its potential benefits. By integrating economic and legal analyses, this study offers key insights for policymakers and regulatory authorities, emphasizing the need to balance innovation with fair competition to ensure that personalised pricing practices contribute positively to market dynamics and consumer welfare. Ultimately, this thesis advances our understanding of personalised pricing within the scope of EU competition law, offering practical recommendations to address its complexities and promote a fairer digital marketplace.Turkish Ministry of Educatio
Exceptions There Are That Are Not the Case
Get PDFThere is something essential we need to know of power that is visible only when power makes certain exceptions. Power, we are arguing, is fundamentally without content. This occluded piece of information about power is partially illuminated at every exception to a rule but appears to only be fully visible to thought when a state of exception is declared by someone in power. This seems to be the crucial point of the theories of the exception elaborated by Giorgio Agamben, Carl Schmitt and Walter Benjamin. Schmitt because the sovereign decision is content indifferent. Benjamin because it is only if you remove referential content from the terms exception and rule that you could mistake the two words for the same thing. Agamben because according to his theory of signatures, the law appears as ultimately contentless. Through a close engagement with the theories of these three authors, this article suggest that an exception is not some statement or ruling which stands outside the rule, but is the process wherein the interior of the rule, its actual rulings, is either negated or suspended. Is this what the legal exception is, the indifferentiation of law’s specific contents?Arts & Humanities Research Council, grant: Covid-19 Compliance and Culture: Saving lives by improving compliance
Process-figurational sociology and gender relations in sport management
No full textIn this chapter we outline how the principles of process-figurational sociology can facilitate a critical and theoretical approach to understanding gender relations in the diverse field of sport management. We highlight that while process-figurational sociology has been applied in areas of study related to sport management including sport policy and practice, organisation and structure of sport, sport tourism and marketing, none of these studies offer a direct and detailed analysis of gender relations and sport management. While there is existing research on gender, business and management taking a process-figurational theoretical approach this does not include sport, and the extensive research on gender and sport management does not draw on process-figurational sociology. In response to this we set out in this chapter how a process-figurational approach can provide a set of theoretical and conceptual resources for advancing knowledge about gender relations in sport management that may provide further critical scrutiny of and knowledge about issues of gender inequality and discrimination in the field
Big data and competition law: Improving competition regulation outcomes of the big data analytics market
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe increased adoption of Big Data Analytics Software across the economy has led to the
cross-sectoral dominance of early adopters of the software, including companies such as
Google, Apple, Meta, Microsoft and Amazon- the ‘GAMMA’ Group- leading to the creation
of new specialised Market Growth strategies being developed by the group. Data-Led
acquisitions of the GAMMA Group into non-digital market sectors have resulted in the
GAMMA Group being able to enter new markets and quickly garner a dominant market
position at entry, by virtue of the value gained though acquiring unique data sets, a valuable
asset class unable to be exploited by companies that do not adopt Big Data Analytics
Software.
These specialised forms of Market Share Growth Strategies, have allowed for the GAMMA
Group to rapidly grow across multiple sectors, entrenching their dominance across the wider
economy, which has resulted in negative impacts across the markets they inhabit, including
having undue influence on the market, harming the overall competitiveness of the market and
conferring harms upon the consumer without significant challenge from Competition
Regulators.
The Thesis’ primary aim is to find a suitable development of UK Merger Control and wider
UK Competition Law to improve the competitive outcomes of the Big Data Analytics
Market, improving the competitiveness of the Market for consumers, new market entrants
and increasing innovation. The Research will employ interdisciplinary research using
multiple fields of research to determine create the most effective advancements including the
proposed creation of a specialised market regulator for a newly defined market the ‘Big Data
Analytics Market’. Providing a unique and novel advancement to the UK Competition Law
Framework in addressing the inability to effectively regulate Big Data Analytics Software, in
a manner that can provide the UK an internationally competitive advantage by increasing the
competitiveness of the UK Market, and increasing the desirability of the UK Market for the
GAMMA Group and other such companies using Big Data Analytics Market
Exploring perceived risk and digital ethics affecting consumer experiences and decision-making in smart retailing
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonSmart technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and virtual reality (VR), are transforming the retail industry by reshaping consumer interactions and enabling personalised, enhanced shopping experiences. These advancements, while revolutionising operational efficiency and consumer engagement, also raise critical challenges related to ethics, privacy, and perceived risks, significantly influencing consumer trust, satisfaction, and behaviour. Despite the increasing integration of smart technologies in retail, limited research has explored how constructs such as perceived risk, trust, and digital ethics collectively impact consumer behaviour. This study addresses this gap by introducing and empirically validating the constructs of "smart consumer experience," "smart satisfaction," and "digital well-being," offering a comprehensive framework to understand consumer engagement in smart retailing.
This research employs a quantitative approach, collecting survey data from over 500 respondents to examine consumer interactions across pre-purchase, purchase, and post-purchase stages. Using structural equation modelling (SEM), this study investigates the relationships among perceived privacy concerns, fairness, risk, trust, satisfaction, purchasing behaviour, e-loyalty, and digital well-being. Findings reveal that perceived fairness and privacy concerns significantly influence trust, which mediates their impact on smart satisfaction and e-loyalty. Notably, while smart satisfaction enhances consumer engagement and e-loyalty; however, its direct effect on purchasing behaviour remains complex and requires further exploration. This study highlights the reciprocal relationships among trust, smart satisfaction, and digital well-being, underscoring their collective importance in shaping positive consumer experiences. By addressing key barriers such as trust, perceived risk, and ethical concerns, this research contributes to the growing discourse on digital transformation in retail. The findings provide actionable strategies for stakeholders, including enhancing transparency in data usage, integrating fairness into algorithmic processes, and designing consumer-centric technologies to promote digital well-being. These insights are critical for retailers, policymakers, and technologists striving to optimise consumer engagement while fostering ethical accountability in smart retailing.
In addition to the retail sector, the implications of this research extend to other industries, such as healthcare, education, and finance, where smart technologies are increasingly being adopted. This study provides a robust empirical foundation for understanding how ethical considerations and digital transformation intersect to shape consumer behaviour, trust, and satisfaction. By bridging critical gaps in the literature and offering practical guidance, this research advances academic knowledge and equips stakeholders to navigate the complexities of ethical and sustainable technology adoption in an increasingly digital world
Data sources:
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇