Supply Chain Management Practices in Construction and Inter-organisational trust Dynamics

A thesis submittedThe poor trust culture in the construction sector is often considered an inhibiting factor to collaboration success in the United Kingdom (UK) despite reform efforts. Numerous reform initiatives tend to have focused on improvements in client and main contractor aspects of construction supply chain relationships, prompting claims that failure to integrate subcontractors, suppliers and consultants into collaborative arrangements remains a major shortcoming. Main contractor and subcontractor relationships therefore continue to be typified by such problems as late payments, charging fees to tender for work, award of contracts based on cheapest price rather than best value, negative margins and demand of retrospective discounts and cash rebates; all of which negatively impact on trust. Some main contractor organisations however, continue to embed supply chain management practices as a strategy for levering value from subcontractors. Such collaborative practices and their implications for inter-organisational trust development, and indeed overall project outcomes, have nonetheless received limited attention in construction management research, raising significant questions on the empirical basis for their implementation. This research was thus undertaken to investigate strategic supply chain management practices adopted by UK main contractors and its implications for inter-organisational trust development during projects. The study adopts a multiple case study design so as to unravel complex subtleties of inter-organisational trust development in the main contractors’ supply chain during projects. With four purposefully selected UK main contractor organisations that had implemented strategic supply chain management, data was gathered through a supply chain workshop, semi-structured interviews, passive observations and documentary analysis. From analysis of the data, it was revealed that strategic supply chain management practices of the main contractors were instrumental for trust manifestation across cognition, system and relational based dimensions. These practices served as constitutive elements of face-to-face interactions through which inter-organisational trust developed, whilst providing the institutional framework to which respective supply chain parties directed their psychological expectations. These findings highlight the importance of maintaining a core of subcontractors from which the main contractor can leverage long-term value irrespective of economic climate. This can be achieved by adequately prioritizing relationally trusted subcontractors for sensitive and high risk work packages whilst ensuring that strategic supply chain management principles can be used to engender impersonal (cognition and system-based) trust dimensions amongst other subcontractors used on a project. Accordingly, a supply chain management oriented framework for engendering inter-organisational trust during projects has been developed based on the study findings and evaluated through semi-structured interviews with selected target participants. This framework does not only provide a systematic and coherent approach for implementing or benchmarking strategic supply chain management in a main contractor’s organisation, but can also be used to prioritize and promote different trust dimensions and their associated behavioural consequences on projects, depending on perceived work package risks

Use of electronic resources by postgraduate students in University of Cape Coast

The study investigates the use of electronic resources by postgraduate students in University of Cape Coast (UCC). It specifically targets first year postgraduate students of UCC. Awareness, usage, training, and access were explored. A survey method was employed and a structured questionnaire was utilized to solicit data. The findings revealed that, though students are aware of electronic resources, they do not fully utilize them to support their academic pursuit due to poor level of information literacy skills. However, few students had not participated in all information literacy skills training organized by the library. Results from the study showed that, significant number of postgraduate students do access electronic resources when on campus and mostly use electronic devices such as laptops, ipad, desktop computers, and mobile phones. The findings indicated that students use the electronic resources to complete assignments, write project work, to update lessons note, for research, and update themselves on new information in their fields of study. It was recommended that a structured curriculum should therefore be established as part of postgraduate students’ normal lecture periods where time is allocated on their time table for electronic resource training, and if possible, credited to their academic performance ratings or grading

Are tier 1 contractors making their money out of wasteful procurement arrangements?

The UK Government challenged construction to achieve 50 faster delivery and a 33 reduction of clients' capital costs by 2025 – prevailing business models won't meet these targets. Eliminating waste from construction design and delivery as advocated by lean ideals is therefore a necessary step towards these goals. However, waste understood simply as the improvement of current processes rather than fundamental system redesign will not be enough. Obtaining a better understanding and conceptualisation of waste in construction is therefore becoming more crucial. One aspect of this is to challenge the apparent coherence of prevailing procurement practices generated by the institutional, organisational, and commercial environments that surround the design and delivery of construction projects. This paper contributes to this by examining Tier 1 contractors and presents examples of practices that open debate on how to challenge prevailing procurement models for construction. Through literature review and interviews, the study discusses the factors influencing the 'Principal-Agent' relationship demonstrating how procurement arrangements often mirror institutional forces. These forces do not necessarily guarantee better value services, they are more likely to serve the interests of large industry players with the bargaining power to create new rules (North, 1994). A radically different delivery model, where the client intends to eliminate the management fees and confrontational behaviours of their Tier 1 contractors is described

Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery

Depleting lake ice is a climate change indicator, just like sea-level rise or glacial retreat. Monitoring Lake Ice Phenology (LIP) is useful because long-term freezing and thawing patterns serve as sentinels to understand regional and global climate change. We report a study for the Oberengadin region of Switzerland, where several small- and medium-sized mountain lakes are located. We observe the LIP events, such as freeze-up, break-up and ice cover duration, across two decades (2000–2020) from optical satellite images. We analyse the time series of MODIS imagery by estimating spatially resolved maps of lake ice for these Alpine lakes with supervised machine learning. To train the classifier we rely on reference data annotated manually based on webcam images. From the ice maps, we derive long-term LIP trends. Since the webcam data are only available for two winters, we cross-check our results against the operational MODIS and VIIRS snow products. We find a change in complete freeze duration of −0.76 and −0.89 days per annum for lakes Sils and Silvaplana, respectively. Furthermore, we observe plausible correlations of the LIP trends with climate data measured at nearby meteorological stations. We notice that mean winter air temperature has a negative correlation with the freeze duration and break-up events and a positive correlation with the freeze-up events. Additionally, we observe a strong negative correlation of sunshine during the winter months with the freeze duration and break-up events

Credit Risk Management as a Good Measure of Financial Performance of Banks in Ghana

Banks are exposed to several forms of risks that affect their performance. The main objective of banking management is to maximize wealth. In efforts to realize this goal managers and shareholders should evaluate the cash flows and risks to direct its financial resources in different areas of use. This paper aims to investigate the effect of credit risk management (CRM) on financial performance (FP) of banks in Ghana. The indicators used in the study are CRM, bank credit (BC), liquidity risk (LR) and capital risk (CR) are regressed on FP. The CADF and CIPS panel unit root tests report that, the variables are non-stationary at their levels but become stationary at their first difference. The Westerlund-Edgerton panel bootstrap cointegration test show that, the variables are cointegrated and hence possess a structural long-run relationship. Also the Granger causality through the ARDL model show; (1) A two-way causality between bank credit and FP in the long-period and short-period; (2) A positive and significant one-way cause running from liquidity to FP, a one-way causality between capital risk and FP, lastly one-way causality in the long-period for LR and bank credit are evidenced; (3) The ARDL framework is evidenced to be very significantly effective to the application of Granger causativeness test. Keywords: bank credit; credit risk management; financial performance; liquidity risk. DOI: 10.7176/JESD/11-4-04 Publication date: February 29th 2020

Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring

Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and Synthetic Aperture Radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra MODIS, and Suomi-NPP VIIRS. The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding. I.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of < 1.5 days. The network produces spatially explicit lake ice maps with pixel-wise accuracies > 91% (respectively, mIoU scores > 60%) and generalises well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement

Do environmental regulations and technological innovation enhance environmental well‐being in sub‐Saharan Africa?

We investigate the regulation–technology–environment nexus in sub‐Saharan Africa (SSA), one of the world's most rapidly growing regions. Using a comprehensive panel dataset consisting of 32 countries from 2000 to 2022, we find that stronger environmental regulations and technological innovation enhance environmental well‐being. Moreover, we identify that stronger environmental regulations positively affect pro‐environment innovation. Finally, we present clear evidence for a dynamic and nonlinear regulation–technology–environment relationship, ruling out one‐size‐fits‐all policy approaches to environmental well‐being. Our results remain robust to different estimators, measurements, and sample selections

Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery

Depleting lake ice is a climate change indicator, just like sea-level rise or glacial retreat. Monitoring Lake Ice Phenology (LIP) is useful because long-term freezing and thawing patterns serve as sentinels to understand regional and global climate change. We report a study for the Oberengadin region of Switzerland, where several small- and medium-sized mountain lakes are located. We observe the LIP events, such as freeze-up, break-up and ice cover duration, across two decades (2000-2020) from optical satellite images. We analyse the time series of MODIS imagery by estimating spatially resolved maps of lake ice for these Alpine lakes with supervised machine learning. To train the classifier we rely on reference data annotated manually based on webcam images. From the ice maps, we derive long-term LIP trends. Since the webcam data are only available for two winters, we cross-check our results against the operational MODIS and VIIRS snow products. We find a change in complete freeze duration of -0.76 and -0.89 days per annum for lakes Sils and Silvaplana, respectively. Furthermore, we observe plausible correlations of the LIP trends with climate data measured at nearby meteorological stations. We notice that mean winter air temperature has a negative correlation with the freeze duration and break-up events and a positive correlation with the freeze-up events. Additionally, we observe a strong negative correlation of sunshine during the winter months with the freeze duration and break-up events.Comment: accepted for PFG Journal of Photogrammetry, Remote Sensing and Geoinformation Scienc

Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring

Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and synthetic aperture radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra moderate resolution imaging spectroradiometer (MODIS), and Suomi-NPP visible infrared imaging radiometer suite (VIIRS). The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding, i.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of 91% [respectively, mean per-class Intersection-over-Union (mIoU) scores >60%] and generalizes well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement