The role of business manager attitudes and perceptions in driving climate change risk action in the agricultural sector in Uganda

Much is already known about climate change risk mitigation and adaptation globally. However, much needs to be done to make this knowledge cascaded down to a business manager in the agricultural sector in Uganda. This study aimed to understand the role of business manager perceptions and attitudes in influencing climate change risk action in business organizations in the agricultural sector in Uganda with its particular climatic, social and economic circumstances. An assessment was made of whether and how the climate change risk perceptions of business managers from 16 companies engaged in downstream agricultural processing differ from 15 managers engaged in commercial agricultural production in Uganda. The study utilized a phenomenological approach using comparative case study method. The respondents were selected purposively from managed agriculture processor and producer companies. It is believed that the study of perceptions and beliefs involves uncovering tacit knowledge, knowledge in the minds of managers which cannot easily be articulated and documented. The study therefore made use of George Kelly’s Personal Construct theory and its repertory grid analysis technique for data collection, a very useful tool for making tacit knowledge explicit. The study examined nine risks as elements for the repertory grid exploring how business managers perceive there risks and how such perceptions influence their climate change risk action in the agriculture sector in Uganda. The study also intended to identify if there are variations in climate change risk perception between the agriculture producers and processors in Uganda. The personal constructs generated from respondents during the grid interviews are the units of analysis. The results were analyzed using Content analysis, and Honey’s data analysis procedures. The results indicate that as long as business managers perceive climate change risks to have an effect on their business continuity or survival, their production capacities, their profitability, their marketing decisions, affect their cost of production, influence their investment decisions, there are available response options, and consider that they have the capacity to manage those risks, they will take immediate action to put in place strategies to respond to those climate change risks. There is no appreciable variation in climate change risk perception between producers and processors. The study results provide policy makers an opportunity to understand what concerns business owners along the agriculture value chain for them to respond to climate change risks and also informs business owners the areas of key concern that they have to reflect on as they consider climate change risk strategies

Understanding CO2 flow measurement for carbon capture and storage (CCS) transport applications

Carbon Capture and Storage (CCS) is a decarbonization solution, particularly suited to industries with hard-to-abate emissions such as cement, iron & steel, and fertilizer production. However, as a prerequisite for commercialisation of CCS, accurate measurement is required for quantifying CO2 streams across the CCS value chain and to comply with a range of environmental legislation and regulations. Unlike other industrial process fluids such as water, oil, and natural gas, it is still unclear whether current commercially available metering technologies can meet the requisite accuracy levels, specifically the ±2.5% accuracy recommended within the EU/UK European Trading Scheme for CO2 mass transfer. Therefore, this research is aimed towards gaining a comprehensive understanding of flow measurement of CO2 under relevant CCS transport conditions. This understanding is crucial for examining the capabilities of both Coriolis and orifice meters under more realistic CCS transport conditions, specifically assessing whether these CCS metering technologies meet the MRR Tier 4 MPE requirement. The experimental study predominantly focuses on evaluating the performance of two distinct designs of Coriolis meters and an orifice meter, across gas, liquid, and supercritical conditions, using both pure CO2 and CO2-rich mixture samples. In order to understand the influence of non-condensable gas impurities in CCS flow operations, a review of relevant thermodynamic modelling equations was conducted. These models play a relevant role in predicting the optimal transport conditions for the CO2-rich mixtures. Moreover, a dedicated laboratory-scale gravimetric flow facility was designed for conducting CO2 flow measurement tests. Using this facility, flow measurement tests were conducted to evaluate the performance of the selected meters under gas, liquid, and supercritical flow conditions. Additional tests were conducted to assess the performance of one of the Coriolis meters with light energy carrier gases (hydrogen-methane blend). The findings from these flow experiments indicate that the non-condensable impurities, such as N2, H2, O2, Ar, and CH4 have a relatively minor impact on Coriolis meters, with maximum mean absolute errors of 0.51%, 0.26%, and 0.56% observed in gas, liquid, and supercritical CO2 flow conditions, respectively. However, the impact of these impurities, which is often associated with an increase in the compressibility of the fluid and reduction in density or homogeneity of the fluid, tends to become apparent with different Coriolis designs or quality of flow operation (flow rates and regions). In the case of the test orifice meters, impurities also have a less noticeable impact during gaseous flow conditions, with the highest recorded mean absolute error reaching approximately 1%. However, the impact of these impurities becomes more noticeable in liquid and supercritical flow conditions, resulting in maximum mean absolute errors of 2.84% and 11.14%, respectively. It is worth noting that although impurities seem to have a more pronounced effect in these dense phases (high density liquid and supercritical phases), a substantial component of these errors can be attributed to uncertainty in the density measurements. These results conclude that Coriolis metering technology as a robust choice for CCS metering, underscoring its suitability for accurate measurements in single phase CO2 transport conditions, as well as in handling other relevant low-carbon fluids. Meanwhile, the performance of orifice meters in gaseous flow conditions emphasizes their effectiveness and potential applicability in repurposed gas pipeline infrastructures for CCS transport applications. The overall outcome of this study helps contribute towards understanding flow measurement capabilities of specific commercially available CCS metering technologies. The assessment of these meters offers crucial insights and measurement data to understand how well some existing flow metering technologies, currently employed in the oil and gas industry, can be adapted for CCS transport metering applications. The study also helps understand the impacts of non-condensable gas impurities in CCS flow operations, showing how well these impacts can be handled to improve flow activities

Fast hyperparameter optimisation of graph neural network for molecular property prediction

In the evolving domain of graph neural networks, there is a growing effort focused on predicting molecular properties. However, a noticeable gap persists, as much of the research overlooks the comprehensive exploration of hyperparameters—a crucial aspect for achieving positive outcomes in graph neural network applications. This underscores the vital role of hyperparameter optimisation, despite the challenge posed by resource-intensive procedures. To address this gap and overcome the challenge, our study achieves significant advancements. Firstly, we summarise graph neural networks for molecular property prediction into a structured framework, systematically identifying key hyperparameters for optimisation. Secondly, we introduce an innovative hierarchical evaluation strategy embedded in a genetic algorithm named HESGA, expediting optimisation by early elimination of unpromising solutions. This approach demonstrates improved efficiency and cost-effectiveness compared to traditional Bayesian optimisation. Thirdly, we propose the implementation of a binary tree to model the hyperparameter space, further enhancing HESGA’s effectiveness. Lastly, guided by empirical insights, we present a hybrid evaluation strategy that surpasses advanced optimisation methods, demonstrating reduced computational costs and accelerated optimisation. Overall, our research not only addresses the challenge of elevated computational expenses in hyperparameter optimisation but also enhances graph neural network performance, effectively bridging the research gap in hyperparameter optimisation for graph neural networks in the context of predicting molecular properties

Knowledge-related processes critical to the enabling of systematic software asset reuse in a global IT company

This research presents a case study on the knowledge management processes critical for achieving systematic software asset reuse in a global IT company. Reusing a software asset and its related artefacts at selected other business clients drives innovation, increases efficiency and can generate several million dollars in revenue from just one reuse. To date, known software asset reuse success is limited. Despite practical relevance, research has stagnated by mainly investigating technical reuse aspects. This thesis addresses three gaps in the literature by looking from a business perspective at the intellectual capital required for software asset reuse, presenting five real-life software asset reuses and detailing the knowledge management processes towards systematic software asset reuse. The answers to the research questions advance the academic literature in the fields of intellectual capital, circular economy and knowledge. Theoretical implications are: The intellectual capital required for systematic software asset reuse is a particular software asset, the reusable software asset, further defined here. The circular economy is enriched by adding a two-step distribution task to the reuse process. The thesis refines knowledge management concerning intangible reuse. A new finding is that software asset reuse requires a proactive decision to anticipate a scarcity of knowledge in space or time, which has been identified as the software asset reuse trigger. Reuse sets in before the life end of the software asset is reached. It creates parallel software instances via abstraction, repurposing and adaptation. These boost the asset lifetime as they are logically linked. They represent tailored solutions for a heterogeneous client base and, therefore, target business-to-business niche markets. This thesis makes an original contribution to knowledge by identifying that collaborative sharing of the change required for one client with existing reusers leads to improved software quality, surpassing that of other software constructs. Further, it claims that reusable software assets target a parallel market to software products. This research significantly contributes to practice: First, by demonstrating that reuse is only feasible if the software asset can be adapted. Second, one reason for being of some reusable software assets is to stop the flood of less funded individual software trying to serve the same need. Third, a managerial guide provides advice on building reuse capabilities in the IT organisation to support the change that drives software asset reuse

Strategic drivers for market penetration in Zambia’s insurance industry

This study investigated the key strategic drivers for market penetration, explicitly focusing on Zambia’s insurance sector. Insurance penetration in Zambia is still relatively low compared to the African average and those in developed markets. Several strategic factors are hypothesised to influence this lower penetration. However, some studies on strategic management suggest a positive correlation between effective strategy formulation and implementation and market penetration. There is no documented evidence suggesting this theory has been critically investigated in Zambia’s insurance sector. Thus, this study sought to examine this theory regarding the Zambian insurance sector and understand the strategic factors that impact insurance uptake. Specific strategic management factors explored were leadership competency score, organisational structure, and culture, including the effect of innovation and technology and government policies on other variables and market penetration. The research followed a positivist paradigm employing a quantitative mode of inquiry with a cross-sectional survey design, where a sample of respondents from 30 insurance firms was used for primary data collection. Structural Equation Modelling (SEM) using Smart Partial Least Square (Smart PLS4) and Statistical Package for Social Sciences (SPSS) software facilitated data analysis. The study findings suggest that organisational culture exerts a positive and statistically significant impact on market penetration. However, leadership competency and organisational structure within insurance firms exhibit a counterintuitive impact, as they were found to have a statistically insignificant effect on market penetration. Further, the research uncovered the nuanced interplay of experience as an additional determinant of market penetration in the Zambian insurance landscape. The findings underscore the significance of strategic management in influencing market penetration and contribute to the literature by adding the Zambian insurance industry perspective. These findings contribute to an enhanced understanding of the Zambian insurance sector and hold relevance for a broader spectrum of industries. The insights from this study put forth a practical model to guide effective strategy formulation and implementation, fostering sustainable market penetration. The study provides a valuable resource for insurance industry practitioners, policymakers, and academics seeking to navigate Zambia's intricate landscape of market penetration and strategic development. The findings encourage a nuanced perspective on strategy and market dynamics, offering a foundation for future research and industry enhancement

An investigation into the social, political and economic barriers to the adoption of a Mass Rapid Transit system in Malta. A study based on the Extended Theory of Planned Behavior and Behavioural Economics

Congestion in Malta is difficult to alleviate. In a country which has a land mass of just 316km2 , in 2020, Malta had a population of 519,562, with 263,352 licensed drivers, 422,576 vehicles on the road, increasing at 65 every day, and with a road network of 2,450km, making this island the fifth most densely populated nation and the fifth most dense transport network in the world. The Government of Malta has finally proposed to build an underground Mass Rapid Transit (MRT) system, a METRO that is estimated to cost €6.2b, take 20 years to complete and cover 25 stations. For such a system to succeed, it is vital that a significant number of Maltese licensed drivers opt to switch and use this new public transport. This study adopted a post-positivist quantitative approach, employed a convenience, non-representative stratified sample, through both a pilot and a main study, employing second generation statistical methods, specifically Partial Least Squares Structural Equation Modelling (PLS-SEM), to test the validity, reliability and predictability of a proposed Extended Theory of Planned Behavior (ETPB) model, that combined the Theory of Planned Behavior (TPB) and Behavioural Economics (BE). The final resulting model was a combination of these two theories, as well as the inclusion of the UK Department for Transport (DfT, 2011) intervention ladder approach. This allowed the application of both soft-behavioural methods and hard-policy regulations to overcome a number of social, economic and political barriers, identified in this study that can inhibit adoption and use of the MRT by Maltese licensed drivers. The final model passes all reliability, validity and predictability tests, showing distinct path coefficients between the unobservable endogenous latent variables and the exogenous observable variables. The study findings confirmed that TPB and BE are coherent interwoven behavioural models, especially within the social and subjective norms. This academic study is a first for Malta for a transportation study, which also adopts PLS-SEM as a data analysis process. The study findings identified 12 key social, economic and political barriers that would deter the adoption and use of MRT by Maltese licensed drivers and offers 20 recommendations to ensure that prior and leading to the introduction to MRT, their intent to switch and use an MRT, becomes a behavioural reality

Integration of Industry 4.0 with Lean Management in large German manufacturing firms - a dynamic capabilities perspective

This research is concerned with how large German manufacturing firms can realise the integration of Lean Management and Industry 4.0. This is relevant for companies that are engaged in a semi-matured Lean transformation and have not yet realised all key principles but, at the same time, are confronted with a fourth industrial revolution. Building on mature but primarily separate research streams of Lean and Industry 4.0, this study employs an exploratory sequential mixed-methods design to derive a framework for integrating these important themes of Operations Management and to support firms that are unwilling to approach a sequential integration of Lean or Industry 4.0 first, as typically advised by previous research and seeking advice on concurrent integrations. Based on a review of existing academic literature, the main themes of Operations Management as the setting for this research, Dynamic Capabilities as the theoretical lens, and Lean and Industry 4.0 as the subject focus are synthesised, research questions derived and a conceptual framework formulated. The research design utilised an exploratory qualitative strand, which derived major integration themes and 201 potential modes of action through a Thematic Analysis of 16 semi-structured interviews with German subject matter experts. The subsequent quantitative strand evaluated and prioritised six dimensions and 43 potential modes of action through a triangulating exploratory survey with 256 subject matter experts from Germany. Finally, the validating strand utilised a Delphi study with 15 subject matter experts. It derived a refined and validated framework consisting of 50 items organised in the six dimensions of ‘initiating’, ‘sensing’, ‘seizing’, ‘transforming’, ‘resources’, and ‘capabilities’ to execute an integration of Lean and Industry 4.0. Consequently, the findings are influenced by the geographical focus, firm size, theoretical lens of Dynamic Capabilities, and methodological design, which opens up exciting possibilities for future research contributions. This research contributes practically to the field of Operations Management by proposing executable modes of action and a concurrent pathway as an alternative for firms intending to integrate Lean and Industry 4.0. It theoretically contributes to advancing Dynamic Capabilities theory by proposing a novel dimension derived from an application and concretisation in a new research area

Soundscape, engagement and spatial planning : an exploration of perceived control, annoyance, indirect health outcomes and wellbeing in the context of UK aviation expansion projects

The sound environment directly affects human health and wellbeing. Essential to soundscape design, management and implementation in spatial planning are people’s perceptual responses to the existing and/or imagined sound environment. Internationally standardised soundscape practice places stakeholders as co-specifiers of projects from the planning inception stage, but crucially challenging to assessing/predicting stakeholders’ response to sound is the impact of non-acoustic factors, accounting for at least one-third of the human response to sound in context. The non-acoustic factor of ‘perceived control’ critically influences person-environment spacetime interaction/s, making it essential to physical and mental health, while perception of engagement in spatial planning substantially impacts stakeholders’ perceived control. This qualitative study explores perceived control and engagement in the context of spatial planning for UK aviation activities. Constructivist grounded theory methodology was used while data collection comprised of a series of in-depth semi-structured interviews, focus groups, stakeholder engagement activities and autoethnographic observations. Three project outputs were delivered. First, the emergent Grounded Theory from the data conceptualising a trauma informed response to contextually salient and relevant non-acoustic factors and the impact on perceived control and engagement. Next, a Conceptual Framework applying the Grounded Theory to environmental impact assessment. Third, recommendations and implications of the outputs to inform salutogenic spatial planning were considered for aviation and for large and small infrastructure projects. This project builds on existing soundscape, transportation noise and health research adding a novel applied Grounded Theory to the corpus. Finally, a dimensional evolvement of stress-related noise annoyance theory is posited regarding perceived control and the impact on wellbeing.Engineering and Physical Sciences Research Council (EPSRC) grant EP/R003467/

Multi-frequency bandwidth Empirical Market Factors in regularised covariance regression

We survey and test non-constructive basis decomposition algorithms capable of analysing the structures present in financial security times series data. We name these implicit financial structures Empirical Market Factors (EMFs) as a homage to Huang et al. (1998) and Empirical Mode Decomposition (EMD) upon which part of this work is based. The EMF covariates are isolated via implicit factor extraction (IFE) which is a decomposition algorithm or feature engineering technique. `Implicit' is used to differentiate these covariates from explicit (easily observable or contructable) covariates such as the return of a market portfolio, ratios of market capitalisations, and book-to-market ratios such as in Fama and French (1993). The forthcoming investment period's covariance structure is forecast using these estimated EMFs in a regularised covariance regression (RCR) framework from Hoff and Niu (2012) to which we made very modest extensions. We present a real-world case study in which we test our method in forecasting the covariance of the potential investments before we weight the portfolio accordingly. The strategies assessed are also restricted to Long/Short Equity (LSE) and Risk Premia Parity (RPP) weighting strategies in which there are cumulative weight shorting restrictions (speci cally the 130/30 strategy) as opposed to restrictions on the individual weights - this mimics real-world shorting limitations. All these techniques and technologies (IFE, RCR, RPP, and LSE) are combined to construct risk-conscious leveraged RPP portfolios using EMFs in a lagged RCR framework

The customer journey in the German automotive industry : a model of practice to identify and implement new touchpoints

Driven by evolving customer preferences, technological advancements, environmental requirements to reduce CO-2 emissions and other external factors such as the recent COVID-19 pandemic, the automotive industry is undergoing significant changes in customer buying behaviour. Over the past decade, researchers and practitioners have been debating these developments including the understanding, improvement, and management of customer journeys. Traditional touchpoints are deemed less relevant, while new touchpoints emerge rapidly due to innovative technologies, novel market entrants, and new marketing approaches. However, selecting and developing the most valuable touchpoints is challenging from a company’s perspective. Research has focused on customer journey management but lacks practical guidance for organisations to pinpoint and implement new touchpoints effectively. This gap is being addressed using mixed-methods research to explore customer journey management in the context of a Japanese automotive brand operating in Germany. The customer perspective is investigated by directly exchanging with customers of the brand using questionnaires and focus group interviews. Firstly, the customer journey in the awareness, research and purchase phase is analysed based on questionnaires, taking online and offline touchpoints from the brand, its dealer network and third parties into account. Insights are generated to identify different behaviour patterns between groups of customers, which are evaluated and assessed using hypothesis testing. Secondly, focus group interviews are conducted in which the findings from the questionnaires are confirmed, and additional qualitative insights are generated. By generating and analysing this customer feedback, a deeper understanding of customer behaviour and touchpoint usage in the automotive industry and beyond is generated, and a customer journey model is proposed. Moreover, a model of practice is developed to identify upcoming touchpoints and evaluate their potential future benefits from a company’s standpoint. This eight-step model is intended to serve as a guiding framework for practitioners, enabling them to capitalise on customer interactions by strategically investing in the most relevant and valuable touchpoints, including new ones. Practically, being able to identify new and existing touchpoints, as well as critical success factors, offers opportunities to implement and enhance the touchpoints of the organisation. Investing in these touchpoints can improve customer experiences and increase customer satisfaction and loyalty. Understanding customer behaviour throughout the automotive purchase process enables businesses to tailor marketing efforts, product offerings, and customer interactions according to customer needs and expectations