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In literature, it is recognized that there is no universal set of critical success factors (CSFs) applicable to all projects. The goal of this research is to validate a theoretical model which considers that CSFs’ influence on project success (PS) is configurational, that CSFs combine to influence PS.

The authors proposed a theoretical framework which operationalizes CSFs considering contingency and institutional theories' terms, as external contingencies, organizational resources and project strategies, which influence PS. The framework is validated through a qualitative approach on 18 social projects implemented by nongovernmental organizations (NGOs). Based on the conducted semistructured interviews with NGO managers or project managers, 91 instances when CSFs combine to influence PS were identified.

The dominant path reveals the combination of CSFs in terms of strategies adopted to face contingencies (70 instances), another as resources which moderate managers' strategies (14 instances), and in seven instances positive contingencies and resources combine and influence the PS. The results reveal that CSFs combine in reactive and dynamic ways to influence PS.

The research contributes to the vast literature on projects' success by adopting a different perspective. Configurational theory explains project management and projects' complexity better than the traditional approaches, which have a rather correlational perspective.

Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.

This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.

The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.

This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

This paper aims to quantify the loss (or leakage) of organic cattle to conventional value chains in Ireland and assess its economic and environmental impacts.

The paper adopts a Bio-economy Input-Output (BIO) model, a quantitative economic model representing the interdependencies between different sectors of the economy, to assess the economic and environmental impacts of organic leakage in the Irish beef sector.

The study reveals that 17% of organic cattle aged under 1 year old leave the organic value chain, leaking to the conventional market as a result of imbalances in the development of the beef value chain. The economic cost of this organic leakage is 5.66 million euros. Leakage also has environmental effects because of changes in lifecycle methane and nitrogen emissions based on longer finishing times on organic farms and chemical fertilisers applied on conventional farms. The organic leakage results in a reduction of 82 tons of methane emission and 52 additional tons of nitrogen emission, which leads to 11,484 tons of net global warming potential (GWP) for a 100-year time horizon.

Because of data availability, the research focussed on the baseline year 2015, which had national data available for disaggregation in Ireland. Therefore, researchers are encouraged to assess the economic and environmental impacts when more recent data are available and to analyse the change in the impacts over the years.

This study contributes to the discussion on organic conversion and provides valuable insights for stakeholders, especially policymakers, for the design of future organic schemes.

This is the first paper to assess organic leakage in the beef sector.

In recent times, reverse logistics (RL) is gaining significant traction in various automobile industries to recapture returned vehicles’ value. A good RL program can lower manufacturing costs, establish a green supply chain, enhance customer satisfaction and provide a competitive advantage. However, reducing disruptions and increasing operational efficiency in the automobile RL requires implementing innovative technology to improve information flow and security. Thus, this manuscript aims to examine the hurdles in automobile RL activities and how they can be effectively tackled by blockchain technology (BCT). Merging BCT and RL provides the entire automobile industry a chance to generate value for its consumers through effective vehicle return policies, manufacturing cost reduction, maintenance records tracking, administration of vehicle information and a clear payment record of insurance contracts.

This research is presented in three stages to accomplish the task. First, previous literature and experts' opinions are examined to highlight certain factors that are an aggravation to BCT implementation. Next, this study proposed an interval-valued intuitionistic fuzzy set (IVIFS) – decision-making trial and evaluation laboratory (DEMATEL) with Choquet integral framework for computing and analyzing the comparative results of factor interrelationships. Finally, the causal outline diagrams are plotted to determine the influence of factors on one another for BCT implementation in automobile RL.

This study has categorized the barriers to BCT implementation into five major factors – operational and strategical, technical, knowledge and behavioral, financial and infrastructural, and government rules and regulations. The results revealed that disreputable technology, low-bearing capacity of IT systems and operational inefficiency are the most significant factors to be dealt with by automobile industry professionals for finer and enhanced RL processes utilizing BCT. The most noticeable advantage of BCT is its enormous amount of data, permitting automobile RL to develop client experience through real-time data insights.

This study reveals several factors that are hindering the implementation of BCT in RL activities of the automobile industry. The results can assist experts and policymakers improve their existing decision-making systems while making an effort to implement BCT into the automobile industry's RL activities.

Although there are several studies on the benefits of BCT in RL and the adoption of BCT in the automobile industry, individually, none have explicated the use of BCT in automobile RL. This is also the first kind of study that has used IVIFS-DEMATEL with the Choquet integral framework for computing and analyzing the comparative results of factor interrelationships hindering BCT implementation in automobile RL activities.

This study aims to provide an understanding of how directors perceive the relationship between board independence and corporate social responsibility (CSR) performance which has remained under-researched.

The qualitative data were collected through semi-structured interviews of 19 directors from 14 organisations operating in Australia. Data were analysed following the six-phase process of thematic analysis.

The findings indicate that independent directors contribute to board CSR decisions in two major ways: they bring an outsider view to the board, and they monitor managers in taking decisions that consider the interests of the broader stakeholder groups.

The in-depth analysis of director independence and CSR highlights the structural and behavioural aspects of director independence and CSR playing out in board rooms. Propositions are offered which can be tested to advance the research in this arena.

The findings suggest that efforts are required at organisational policy level to ensure the effectiveness of director independence for CSR.

This study provides insights into the “black box” of boardroom dynamics highlighting important contextual factors influencing director independence and CSR decisions previously under-explored.

Due to the influence of complex and intersecting factors, self-governed public open spaces (POSs) (managed by local communities) are subject to collective action dilemmas such as tragedy of the commons (overexploitation), free-riding, underinvestment and mismanagement. This review paper adopts a multi-dimensional and multi-tier social-ecological system (SES) framework proposed by McGinnis and Ostrom, drawing on collective action theory to explore the key institutional-social-ecological factors that impact POS self-governance.

In this paper, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was utilized to systematically screen and review the relevant literature for the period from 2000 to 2023 in three databases: Web of Science, Scopus and Google Scholar. A total of 57 papers were chosen for in-depth analysis.

The literature review identified and categorized several variables associated with the self-organizing system of POS; consequently, an SES-based POS management framework was developed for the first time, consisting of 114 institutional-social-ecological sub-variables from different dimensions and three levels. Compared to ecological factors, among others, governance organizations, property-rights systems, socioeconomic attributes and actors' knowledge of SES have been commonly and primarily studied.

There is still room for the refinement of the conceptual SES-based POS collective action framework over the time (by adding in new factors), and indefinitely empirical research validating those identified factors is also worth to be undertaken, particularly testing how SES factors and interaction variables affect the POS quality (collective action).

The findings of this study can provide local policy insights and POS management strategies based on the identification of specific SES factors for relevant managers. Moreover, this research makes significant theoretical contributions to the integration of the SES framework and collective action theory with POS governance studies.

The purpose of this paper is to report on the use and content of written guidance produced by mental health services in England and Wales describing hospital leave for informally admitted patients.

Guidance on leave was requested from National Health Service (NHS) mental health trusts in England and health boards in Wales (n = 61) using a Freedom of Information submission. Data were analysed using content analysis.

In total, 32 organisations had a leave policy for informal patients. Policies varied considerably in content and quality. The content of policies was not supported by research evidence. Organisations appeared to have developed their policies by either adapting or copying the guidance on section 17 leave outlined in the Mental Health Act Codes of Practice for England and Wales (Department of Health, 2016; Welsh Government, 2016). Definitions of important terms, for example, leave and hospital premises, were either absent or poorly defined. Finally, some organisations appeared to be operating pseudo-legal coercive contracts to prevent informal patients from leaving hospital wards.

Research should be undertaken to explore the impact of local policies on the informal patient’s right to life and liberty.

All NHS organisations need to develop an evidence-based policy to facilitate the informal patient’s right to take leave. A set of national standards that organisations are required to comply with would help to standardise the content of leave policies.

To the best of the author’s knowledge, this is the first study to examine the use and content of local policies describing how informal patients can take leave from hospital.

The main purpose of the present work is to introduce two new Schiff bases as corrosion inhibitors (CIs) for carbon steel (CS). The anti-corrosion performance of these Schiff bases having N and S heteroatoms in their structures was investigated and compared in 2 M HCl electrolyte. The inhibitory activity of these Schiff bases was also assessed.

Common electrochemical assays like potentiodynamic polarization and electrochemical impedance measurements were used to evaluate the ability of compounds in reduction of the rate of corrosion. Quantum chemical calculations (QCCs) were also used to examine the corrosion inhibitive and the process related to the electrical and structural characteristics of the molecules acting as CIs.

The electrochemical measurements indicate that both Schiff bases acted as the efficient CIs of CS in 2 M HCl electrolyte. The adsorption of the Schiff base on the surface of the CS caused the corrosion to be inhibited. The change of Gibbs energies indicated that both physical and chemical interactions are involved in the adsorption of NNS and SNS on CS surfaces. The predicted QCCs of the CIs neutral and positively charged versions were well-aligned with those obtained by electrochemical experiments.

Using electrochemical experiments and quantum chemical modelings, two new Schiff bases, N-2-((2-nitrophenyl)thio)phenyl)-1-(pyrrole-2-yl)methanimine (NNS) and N-2-((2-nitrophenyl)thio)phenyl)-1-(thiophen-2-yl)methanimine (SNS), were evaluated as anti-corrosion agents for CS in 2 M HCl electrolyte. The DFT calculations were considered to compute the quantum chemical parameters of the inhibitors.

This study aims to verify the influence of stakeholders on Disruptive Innovation Disclosure (DID) by using company size as a control variable. DID is measured using the DID index. The authors use panel data regression with the period 2011–2020. Observations were made on 198 companies throughout the year in companies around the world. This study proves that shareholders, customers, suppliers, and company size are dimensions that affect DID. This situation shows that these dimensions have the power to control DID. The average company in the world has provided information about disruptive innovation. The scope of this research is limited to countries that have a visualization network of disruptive innovation collaboration in as many as 15 countries. The value of this study is to portray DID in countries that have disruptive innovation collaborative visualization networks.

In response to mounting global concerns about climate change and scarcity of natural resources, manufacturers have been pressured to develop strategies and enhance their sustainability performance. The integration of sustainable lean manufacturing (SLM) during value chain processes could balance environmental, social and economic concerns into their decision-making, which not only ensures responsible practices but also drives efficiency and success. This paper aims to identify, measure and prioritize metrics to develop a performance measurement system that assesses the multi-dimensional performance of SLM.

Strategic decision-making has some conflicting criteria and objectives to be considered simultaneously. The Multi-Criteria Decision Making provides a foundation for selecting, sorting and prioritizing these strategies with the determination of drivers and indicator weight.

The performance model enables the decision-makers to consistently evaluate the level of sustainability through a multidimensional framework, which could support the assessment of the existing sustainability of a manufacturing process and analyze opportunities for improvement. This study divided the performance into five drivers: Quality, Operational, Finance, Environment, Safety and People and selected 17 KPIs for assessing the multi-dimensional performance of SLM organizations. The research results revealed an organization's perspective transition from strategies focused on operational and economic performance to a more sustainable ideal with greater importance for social and environmental directions.

This framework will be facilitated by the selection of the most significant drivers and the development of strategic plans for the successful adoption of sustainable manufacturing. The practices support implementation, pursue competitive advantages and sustain manufacturing, meeting strategic requirements of suitable and lean performance. With the limited resources of the organizations, the framework proposed will guide the priorities and actions to be taken toward the SLM.