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Several waste bin sanitation initiatives have been introduced in Ghana to address the surge in indiscriminate solid waste disposal in households. What is not known are the behavior factors that determine the acceptability and use of waste bins. This study aimed to identify the determinants of waste bin acceptability and use in Ghana using the theory of planned behavior (TPB).

Data on waste bin acceptability and usage were collected from 881 households in the Volta and Oti regions of Ghana. The data were analyzed using the partial least squares-structural equation modeling technique in SmartPLS 3 software.

The coefficient of determination (R-squared value) of the original TPB and the extended model explained 39.9 and 44.7% of the variance in waste bin acceptability and use intentions, respectively. The results revealed that attitudes (ß = 0.114, t = 3.322, p < 0.001), subjective norms (ß = 0.306, t = 6.979, p < 0.001) and perceived moral obligation (ß = 0.352, t = 8.062, p < 0.001) significantly predicted household waste bin acceptability and use behavior intentions, but perceived behavioral control (ß = −0.003, t = 0.064, p < 0.949) did not influence behavior intentions significantly.

The study provides valuable insights into the behavioral factors to be prioritized by waste management service providers to improve household waste bin acceptability and usage.

This is one of Ghana's first studies investigating the behavioral determinants of waste bin acceptability and usage.

This paper aims to investigate the factors influencing trade artisans’ choice of skills selection as apprentice’s trainee in the Ghanaian construction sector and to identify and address the challenges associated with traditional apprenticeship. Trade artisans with technical know-how in construction and general workplace skills from the traditional apprenticeship training (TAT) in the area of construction were selected from selected sites and training centers.

This paper adopted the purposive sampling technique with the aim of gathering knowledge from individuals with expertise in the research area, particularly trade craftsmen who have been trained through the TAT system and are directly involved in construction-related works. Partial least square structure equation modelling (PLS-SEM) analytical approach and principal component analysis were used to reduce the dimensionality of the data set and preserve as much information as possible.

Three major components, namely, personal and social interest, job assessment and stability and family and faith were identified as the variables that influence an artisan's choice of a skill trade. These influenced the choice of apprenticeship training by young trainees in choosing apprenticeship as a mode of training. Personal interest, living situation of artisans and parents’ educational attainment are the most influencing factors that determine artisans’ choice of selected trades. Moreover, the study also shed light on the challenges inherent in traditional apprenticeship systems, such as the lack of formal technical education, limited access to modern technology and information and poor working conditions.

The study underscores the imperative for stakeholders to enhance apprenticeship programmes within the construction sector. This involves providing more stable job opportunities, improving working conditions and offering access to modern technology and information. Such enhancements not only attract more young individuals to apprenticeship training but also ensure the sustainability and relevance of the workforce in meeting industry demands.

The study finally developed a model that could be used as a foundation for future PLS-SEM evaluation and identified the factors that influence the selection of apprenticeship training by trade artisans.

The purpose of this paper is to develop a familiarity analysis of resistive-capacitive (RC) network active circuit operating with unfamiliar low-pass (LP) type negative group delay (NGD) behavior. The design method of NGD circuit is validated by simulation with commercial tool and experimental measurement.

The present research work methodology is structured in three main parts. The familiarity theory of RC-network LP-NGD circuit is developed. The LP-NGD circuit parameters are expressed in function of the targeted time-advance. Then, the feasibility study is based on the theory, simulation and measurement result comparisons.

The RC-network based LP-NGD proof of concept is validated with −1 and −0.5 ms targeted time-advances after design, simulation, test and characterized. The LP-NGD circuit unity gain prototype presents NGD cut-off frequencies of about 269 and 569 Hz for the targeted time-advances, −1 and −0.5 ms, respectively. Bi-exponential and arbitrary waveform signals were tested to verify the targeted time-advance.

The performance of the unfamiliar LP-NGD topology developed in the present study is limited by the parasitic elements of constituting lumped components.

The NGD circuit enables to naturally reduce the undesired delay effect from the electronic and communication systems. The NGD circuit can be exploited to reduce the delay induced by electronic devices and system.

As social impacts of the NGD circuit application, the NGD function is one of prominent solutions to improve the technology performances of future electronic device in term of communication aspect and the transportation system.

The originality of the paper concerns the theoretical approach of the RC-network parameters in function of the targeted time-advance and the input signal bandwidth. In addition, the experimental results are also particularly original.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

The purpose of this study is to examine the effect of perceived value of recommended product on consumer’s e-loyalty, based on the proposition of expectation confirmation theory. Vendors’ reputation is tested as the mediator in the perceived value of recommended product and e-loyalty relationship, whereas shopping enjoyment is predicted as the moderator that conditions the perceived value of recommended product and e-loyalty relationship through vendors reputation.

Data were collected via an online survey platform and through a QR code. Partial least squares analysis, confirmatory factor analysis and structural equation modeling were used to verify the research proposed model.

The findings revealed that the perceived value of recommended product had a significant positive effect on E-loyalty; in addition, the perceived value of the recommended product and e-loyalty link was partly explained by e-shopper’s confidence in vendor reputation. Therefore, the study established that the direct and indirect relationship between the perceived value of the recommended product and e-loyalty was sensitive and profound to shopping enjoyment.

This study has established that the perceived value of a recommended product can result in consumer loyalty. This has successively provided the e-shop manager and other stakeholders with novel perspectives about why it is necessary to understand consumers’ pre- and postacquisition behavior before recommending certain products to the consumer.

To examine how shareholding affects optimal profits, R&D innovation, NEV market scale and social welfare in two supply chain models with partial and cross ownership patterns.

The gradual retreat of government subsidies has directly weakened the financial support available to the stakeholders of new energy vehicles (NEVs). In this context, upstream and downstream enterprises of NEV are constantly seeking new business models of cooperation to achieve possible win-wins. NEV supply chain shareholding is an emerging new practice for such explorations. However, its performance in the NEV supply chain is seldom investigated. In this paper, we employ a Stackelberg game model to investigate how partial and cross-ownership affect the optimal decisions in a NEV supply chain.

Results showed that: (1) Compared with the unilateral shareholding model, the battery supplier will benefit from cross-ownership in the supply chain, while the NEV manufacturer will not necessarily benefit from it. At the same time, cross-ownership will bring the greatest incentive for battery R&D (2) Supply chain downstream competition will not necessarily lead to the improvement of the total consumption of NEVs or the level of battery design. Pareto improvement can be brought only when one of the manufacturers holds less than a certain equity threshold. In addition, downstream competition will also not necessarily bring more benefits to the battery supplier.

At present, NEV supply chain management has attracted widespread attention from scholars from all walks of life. Previous studies have been carried out that covers topics such as pricing strategies and optimal profits and the role of NEV in the sustainable development of the automotive industry supply chain, or disparate impacts of government subsidies and carbon emission regulation on supply chain members. However, as far as the authors know, compared with the new emerging NEV corporate practice, the shareholding phenomenon between upstream and downstream in the supply chain of NEV has not been studied in the existing studies.

Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

This paper explores the relationship between board interlocks and firm outcomes by reviewing the most recent peer-reviewed articles examining this research theme.

A systematic and bibliometric methodology of assessing 369 peer-reviewed articles from the Web of Science (WoS) database was applied. The study also leverages key R-packages litsearchr and Bibliometrix software to enhance the descriptive and thematic literature analysis to identify gaps and opportunities for new research.

This study confirms a rapid increase in articles on this thematic area, over the last decade, with increasing collaboration occurring among researchers in the United States, Europe, China, South Korea and India. Four core research clusters are identified. The first and largest cluster links interlocked directors to issues related to corporate governance and firm outcomes. The second cluster links social network theory, interlocking directorates and firm outcomes. Smaller emerging research clusters include topics related to ownership structure, board size, political connectedness and impacts on firm outcomes. The final cluster examines the influence of board interlocks on market value and firm innovation.

Interlocked directors can have both positive and negative impacts on a wide variety of firm outcomes. This study places great interest in the selection of new directors, ensuring that the selection has aligned with the needs and interests of the company and disclosures of potential competing interests are declared and considered. Equally important are the governance practices used to monitor directors' behavior and to protect the interest of shareholders and the firm. This is particularly relevant in the internal appointment of interlocked directors to critical positions, such as audit committees or instances where interlocked directors may simultaneously hold CEO or executive leadership positions in other companies.

This paper examines the board interlocks literature related to firm outcomes. Additionally, this review identifies several topics and disciplines which, if pursued, could enrich the literature and promise new avenues for future research.

Against the background of the I50 paradigm, this paper queries in what ways blockchain and blockchain-based applications deployed in the smart city context facilitate the integration of the I50 paradigm in smart urban contexts.

A mixed methods approach is applied. First, by means of desk research and thematic literature review, a conceptual model integrating the I50 paradigm, smart city and blockchain-based solutions is built. Second, science mapping bibliometric analysis (SciMat) based on keywords’ co-occurrence is applied to a sample of 491 research articles to identify key domains of blockchain-based applications’ use in smart city. Third, a semi-systematic literature review complements insights gained through SciMat. Fourth, the findings are interpreted through the precepts of the conceptual model devised earlier.

The key blockchain-based applications in smart cities pertain to two domains, i.e. the foundational, service facilitation-oriented domain, including security (and safety), networks, computing, resource management and the service delivery-oriented domain, including mobility, energy and healthcare. Blockchain serves as the key building block for applications developed to deliver functions specific to each of the thus identified domains. A substantial layering of blockchain-based tools and applications is necessary to advance from the less to the more complex functional domains of the smart city.

At the conceptual level, the intricacies of the (making of the) I50 paradigm are discussed and a case for I50 – smart city – blockchain nexus is made. Easton’s input–output model as well as constructivism is referenced. At the empirical level, the key major domains of blockchain-based applications are discussed; those that bear the prospect of integrating the I50 paradigm in the smart city are highlighted. At the methodological level, a strategic move is made aimed at restoring the literature review’s role as subservient to the key line of exploration, to justify and ultimately support it, rather than to showcase the literature review as the ultimate purpose for itself.