Stakeholder management in public sector infrastructure projects

The underlying risks and complexities in government infrastructure projects have increased the importance of external stakeholder management in contemporary project management. In developing countries, it is also important for policymaking and planning of infrastructure programs due to the varying nature of stakeholders and their expectations from the government. Few studies have looked at how external stakeholders are involved in public infrastructure projects and how they work together to achieve common project goals by overcoming communication and decision-making barriers. The internal stakeholders and project managers also need to properly liaise with the external stakeholders without compromising the project goals. Thus, there is a need to strategize the stakeholder management process to improve public sector infrastructure projects, especially from a developing nation's perspective. Therefore, the scope of this research has evaluated the prevalence of external stakeholder management in public sector infrastructure construction projects in Pakistan by developing and validating its five core dimensions. Among the constructs were identification and classification, communication, engagement, empowerment, and risk control. Besides this, twenty-seven sub-variables of stakeholder management have also been identified in the context of public sector projects. The results of the factor loading show that "Risk Control" is the most contributing dimension of stakeholder management, and "Empowerment" is the least concern in the current practices. The study emphasizes the importance of establishing a systematic and comprehensive framework for empowering external stakeholders, which will strengthen and improve performance and project outcomes. This study reveals insights that will assist project organizations in integrating external stakeholders into their government-sponsored projects with their effective empowerment and sufficient engagement

Phytoremediation of potentially toxic elements from contaminated saline soils using Salvadora persica L.: seasonal evaluation

Plants in coastal ecosystems are primarily known as natural sinks of trace metals and their importance for phytoremediation is well established. Salvadora persica L., a medicinally important woody crop of marginal coasts, was evaluated for the accumulation of metal pollutants (viz. Fe, Mn, Cu, Pb, Zn, and Cr) from three coastal areas of Karachi on a seasonal basis. Korangi creek, being the most polluted site, had higher heavy metals (HM’s) in soil (Fe up to 17,389, Mn: 268, Zn: 105, Cu: 23, Pb: 64.7 and Cr up to 35.9 mg kg−1) and S. persica accumulated most of the metals with >1 TF (translocation factor), yet none of them exceeded standard permissible ranges except for Pb (up to 3.1 in roots and 3.37 mg kg−1 in leaves with TF = 11.7). Seasonal data suggested that higher salinity in Clifton and Korangi creeks during pre- and post-monsoon summers resulted in lower leaf water (ΨWo) and osmotic potential at full turgor (ΨSo) and bulk elasticity (ε), higher leaf Na+ and Pb but lower extractable concentrations of other toxic metals (Cr, Cu, and Zn) in S. persica. Variation in metal accumulation may be linked to metal speciation via specific transporters and leaf water relation dynamics. Our results suggested that S. persica could be grown on Zn, Cr and Cu polluted soils but not on Pb affected soils as its leaves accumulated higher concentrations than the proposed limits.Higher Education Commission, Islamabad | Ref. 6592/Sindh/NRPU/R&D/HEC/201

A smart warehouse framework, architecture and system aspects under industry 4.0: A bibliometric networks visualisation and analysis

This study contributes to a better understanding of smart warehouse systems and design in a few ways. (1) warehouse underperformance reasons; (2) smart warehouse enablers in Industry 4.0; and (3) the construction of a smart warehouse design from the viewpoint of recommended framework, architecture and system aspects. This study presents a smart warehouse framework, architecture, and system aspects in Industry 4.0. Manufacturing firms require well-designed smart warehouse technology and ecosystem to monitor and control inventory capacity for cyber-physical production environments. However, only a few studies exist to guide the industry on smart warehouse design. The industry remains skeptical of leveraging technology to compete in this digitalisation era. Symptom versus problem and bibliometric networks visualisation and analysis is deployed to observe the thematic patterns. The findings show that inventory mismanagement and communication hurdles between firms and suppliers often cause warehouse underperformance. The insights are useful in extending the literature and designing smart warehouses for creating business competitiveness

Eco-innovation impacts on recycled product performance and competitiveness: Malaysian automotive industry

This study aims to develop a theoretical circular economy model that examines the impact of eco-innovation practices on recycled product performance and competitiveness in Malaysian automotive industry. The supply of recycled materials has been recognized as one of the critical components to produce brand new automotive products. This empirical study was conducted among automotive firms and groups consisting first, second, and third-tier suppliers. The data were analysed using structural equation modeling. The results support the importance of innovation and cost efficiency from the perspective of resource-based view and the ability of a firm to manage resources while practicing circular economy initiatives, whilst instrumental to the success of recycled product performance and competitiveness. The degree of competitiveness that a firm experiences through new product development is targeted by integrating the theory of resource-based view, eco-innovation, and circular economy. The findings reveal that eco-innovation practices with circular economy principles assist business competitiveness in the automotive industry

Application of algal nanotechnology for leather wastewater treatment and heavy metal removal efficiency

Wastewater from tanneries may ruin agricultural fields by polluting them with trace metals. The synthesis of nanoparticles (NPs) from algal sources and their application could help in decreasing hazardous materials, for environmental safety. The potential of zinc oxide nanoparticles made from Oedogonium sp. was evaluated for removal of heavy metals from leather industrial wastewater. Synthesized algal nanoparticles (0 (control), 0.1, 0.5, and 1 mg) were applied to treat wastewater by using different concentrations of leather industrial effluents (0%, 5%, 10%, 15%, and 100%) for 15, 30, and 45 d. The wastewater collected was dark brown to black in color with very high pH (8.21), EC (23.08 μs/cm), and TDS, (11.54 mg/L), while the chloride content was 6750 mg/L. The values of biological oxygen demand (BOD) and chemical oxygen demand (COD) ranged between 420 mg/L and 1123 mg/L in the current study. Prior to the application of nanoparticles, Cr (310.1), Cd (210.5), and Pb (75.5 mg/L) contents were higher in the leather effluents. The removal efficiency of TDS, chlorides, Cr, Cd, and Pb was improved by 46.5%, 43.5%, 54%, 57.6%, and 59.3%, respectively, following treatment with 1 mg of nanoparticles after 45 d. Our results suggested that the green synthesis of ZnO nanoparticles is a useful and ecofriendly biotechnological tool for treating tannery effluents, before they are discharged into water bodies, thus making the soil environment clean.Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia | Ref. PNURSP2022R7

A Comparative Study of Single and Multi-Stage Forecasting Algorithms for the Prediction of Electricity Consumption Using a UK-National Health Service (NHS) Hospital Dataset

Accurately looking into the future was a significantly major challenge prior to the era of big data, but with rapid advancements in the Internet of Things (IoT), Artificial Intelligence (AI), and the data availability around us, this has become relatively easier. Nevertheless, in order to ensure high-accuracy forecasting, it is crucial to consider suitable algorithms and the impact of the extracted features. This paper presents a framework to evaluate a total of nine forecasting algorithms categorised into single and multistage models, constructed from the Prophet, Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and the Least Absolute Shrinkage and Selection Operator (LASSO) approaches, applied to an electricity demand dataset from an NHS hospital. The aim is to see such techniques widely used in accurately predicting energy consumption, limiting the negative impacts of future waste on energy, and making a contribution towards the 2050 net zero carbon target. The proposed method accounts for patterns in demand and temperature to accurately forecast consumption. The Coefficient of Determination (R 2 ), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were used to evaluate the algorithms’ performance. The results show the superiority of the Long Short-Term Memory (LSTM) model and the multistage Facebook Prophet model, with R 2 values of 87.20% and 68.06%, respectivel

Circular economy-based reverse logistics: dynamic interplay between sustainable resource commitment and financial performance

Purpose: The study aims to propose a circular economy-based reverse logistics (CERL) that emphasises the mediation effect of reverse logistics (RL) on sustainable resource commitment and financial performance. Design/methodology/approach:The structural equation modelling (SEM) approach has been applied to analyse the data acquired through the survey method that included 113 vendors of automotive supplies of the 1st and 2nd levels. Findings:The results confirm that CERL acts as an essential intervening entity between resources and financial performance. The findings of the study have provided research and development (R&D) opportunities for the industries to find alternative revenue streams and generate profit from resource investment whilst upholding environmental standards through reverse logistic practices. Practical implications: Reverse logistic practices are the key components of a circular business model and a sustainable supply chain. The manufacturing companies need to explore critical enablers that can contribute to business productivity and financial growth. Originality/value: The study has validated a CERL model that portrays the circular economy's resilient relationship with RL practices

The nexus of information sharing, technology capability and inventory efficiency

Purpose: This paper aims to examine the effect of inventory information sharing on inventory efficiency and its intervening effect of information technology (IT) capability in manufacturing firms. Design/methodology/approach; Stratified random sampling and filter questions selected targeted respondents, and an online survey collected 124 completed questionnaires from Malaysian manufacturing firms. partial least squares structural equation modeling (PLS-SEM) examined the structural model and hypothesis statement. An analysis of importance-performance map analysis (IPMA) test identified the relative importance drivers of inventory efficiency. Findings: The findings showed that enhanced IT capabilities in manufacturing firms mediate a positive relationship between inventory sharing and inventory efficiency. Research limitations/implications: This study portrays the relationship between inventory level, demand and information sharing. The research was carried out only within Malaysian manufacturing firms. Practical implications: These findings will enable the management of manufacturing firms to design and visualise their inventory levels and share best practices across supply chain networks to achieve effective and optimised inventory planning. Social implications: This study illustrates an intervention model that offers a direct and indirect impact of IT capabilities that allow scholars to close inventories productivity gaps in research. Originality/value: This paper extends the limited literature on the sharing of inventory information and inventory productivity, notably from a strategic management perspective. The findings help scholars clearly understand the information systems capability and its mediating impact on information sharing and inventory efficiency’s relationship in the manufacturing sector. Moreover, demand information sharing affected the dynamic supply chain

Rehabilitation of reinforced concrete deep beams by near-surface-mounted steel reinforcement

This article describes an experimental investigation of the behavior of reinforced concrete deep beams repaired by near-surface-mounted (NSM) steel bars. The first beam was loaded under a two-point load up to failure, and the other six deep beams were loaded to 0.4 and 0.75 of ultimate load. Then, they were repaired by NSM steel bars. The bar orientation and angle were the main variables in these beams. The primary goal of this study is to determine whether it is possible to restore the reinforced concrete deep beam with shear reinforcement to its full load-carrying capacity by NSM steel bars as the method of repair. All deep beams were tested with a shear span-depth ratio of 0.8. The test findings showed that the NSM steel repair bars were very effective in restoring the loaded deep beams’ full capacity. Moreover, NSM steel bars enhanced the original deep beams’ strength capacity from 4.16 to 19.44%. The ultimate load, mechanisms of failure, load–crack width distribution, and load–deflection profile are tracked as results