A study on the assessment of risk management performance in maritime start-ups: evidence from China

Due to the COVID-19 pandemic, the international shipping market has been highly volatile, posing a serious threat to the survival and development of many maritime start-ups. With the development of the digital economy, digital transformation is affecting the evolution and upgrading of many traditional enterprises, including maritime enterprises. In the post-COVID-19 era, start-up small and medium-sized enterprises will need to consider the importance of enterprise risk management to achieve transformation and upgrading. The purpose of this study is to provide guidance for the establishment and upgrading of risk management systems for start-ups based on the identification of risk management strategies of maritime enterprises and the evaluation of their performance. The fuzzy analytic hierarchy process and importance-performance analysis methods were used to rank the operational risk, financial risk, market risk, innovation risk, and disaster risk according to sub-items and screen out the risk management schemes for priority improvements. Through empirical research, it was found that the financial risk and market risk response schemes have the lowest performance and need to be prioritised for improvement. This study argues that start-ups can appropriately challenge their risk management strategies to meet potential risk management needs based on their own circumstances.

Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53

Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO2 emissions and non-CO2 impacts, such as NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NOx emitted, or per flown kilometre.In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NOx-induced ozone and H2O effects are well represented by the ACCF model output. The NOx-induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NOx-induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NOx-induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO2 contribution observed in this study is low compared with the non-CO2 effects, which requires further diagnosis.

Port strategy and performance: empirical evidence from major container ports and implications for role of data analytics

Container ports operate in more challenging and volatile environments at present times. Events such as US-China trade tensions and the COVID-19 pandemic severely affect numerous container ports at various levels. Strategies pursued by container ports are key to port development and management amidst these challenges. Drawing on configuration theory, this research employs Fuzzy-set Qualitative Comparative Analysis to investigate the relation between port strategies and container throughput. The research contributes to the literature by proposing an approach to account for complexity of the port sector and offers insights into strategies adopted by major container ports. The research further identifies 10 port strategies and proposed indicators that can represent the essence of these strategies. Being able to represent strategies in a quantitative format is important for strategy analysis and performance evaluation. Results reveal that major container ports employ a combination of strategies which address both the supply and demand-side aspects of the port business. Growing digitalization and digitization coupled with advancements in information capture, diagnostics capabilities and predictive abilities means a greater role for data analytics to influence container port strategy and performance. Implications for port managers, policy makers and researchers from the perspective of port policy and management are proposed.

Control System for Indoor Safety Measures Using a Faster R-CNN Architecture

This paper presents a control system for indoor safety measures using a Faster R-CNN (Region-based Convolutional Neural Network) architecture. The proposed system aims to ensure the safety of occupants in indoor environments by detecting and recognizing potential safety hazards in real time, such as capacity control, social distancing, or mask use. Using deep learning techniques, the system detects these situations to be controlled, notifying the person in charge of the company if any of these are violated. The proposed system was tested in a real teaching environment at Rey Juan Carlos University, using Raspberry Pi 4 as a hardware platform together with an Intel Neural Stick board and a pair of PiCamera RGB (Red Green Blue) cameras to capture images of the environment and a Faster R-CNN architecture to detect and classify objects within the images. To evaluate the performance of the system, a dataset of indoor images was collected and annotated for object detection and classification. The system was trained using this dataset, and its performance was evaluated based on precision, recall, and F1 score. The results show that the proposed system achieved a high level of accuracy in detecting and classifying potential safety hazards in indoor environments. The proposed system includes an efficiently implemented software infrastructure to be launched on a low-cost hardware platform, which is affordable for any company, regardless of size or revenue, and it has the potential to be integrated into existing safety systems in indoor environments such as hospitals, warehouses, and factories, to provide real-time monitoring and alerts for safety hazards. Future work will focus on enhancing the system's robustness and scalability to larger indoor environments with more complex safety hazards.

Key performance measurement capabilities for managing distributed teams

Distributed teams are a reality for several companies nowadays, many authors covered their benefits and problems, and the rate of adoption of such team's structure by companies is growing fast. Since these teams are more present in companies, a performance measurement system must get adapted to fulfill the gap of not having a vast theory about the subject. To fill that gap, this paper brings results from previous steps in the research (Systematic Literature Review and Qualitative analysis of the data). It presents to a group of experts to reach a consensus on which capabilities are essential to managing/developing distributed teams' performance. The experts were exposed to the information following a Delphi Panel format and provided output that reached consensus and refined the list. The experts indicated that a group of six capabilities (engagement, development of a culture of performance measurement, organizational learning, alignment between planning and execution, accurate information and consistency) are essential to have their performance measurement system working correctly and reaching all functions. The work also identified the success factors for virtual teams, providing directions for the adoption and the monitoring of this kind of team that gained importance during the COVID-19 pandemic.

Implementation of Image Quality Improvement Methods and Lung Segmentation on Chest X-Ray Images Using U-Net Architectural Modifications

COVID-19 is an infectious disease that causes acute respiratory distress syndrome due to the SARS-CoV-2 virus. Rapid and accurate screening and early diagnosis of patients play an essential role in controlling outbreaks and reducing the spread of this disease. This disease can be diagnosed by manually reading CXR images, but it is time-consuming and prone to errors. For this reason, this research proposes an automatic medical image segmentation system using a combination of U-Net architecture with Batch Normalization to obtain more accurate and fast results. The method used in this study consists of pre-processing using the CLAHE method and morphology opening, CXR image segmentation using a combination of U-Net-4 Convolution Block architecture with Batch Normalization, then evaluated using performance measures such as accuracy, sensitivity, specificity, F1-score, and IoU. The results showed that the U-Net architecture modified with Batch Normalization had successfully segmented CXR images, as seen from all performance measurement values above 94%.

Systematic Literature Review of Performance Measurement Systems in Health Sector of Pakistan

Building The research study is primarily focused on identifying the parameters of Performance Measurement System within the healthcare sectors of Pakistan. The main purpose is to identify the efficacy of different Performance Measurement Systems within Pakistan, and its impacts on performance of physicians. Considering the current performance and situation in healthcare sector of Pakistan, it has been analyzed that the country has come a long way towards progress, however there is still a major lacking of proper standards and guidelines which must be followed in all the healthcare institutions. The problem statement emphasizes over the need of PMS in the healthcare institutions, with the help of which the improvements and efficacy in performance of the healthcare professionals can be determined. The research objective designed for this study is identify the impact of Performance Measurement Systems on the improvisations in current practices, on patient satisfaction and recovery, changes in patterns of mortality rates and budgetary control within the country for healthcare sectors. In order to conduct this research study, the type of research method which has been mainly opted is qualitative analysis involving the write up of a Systematic Literature Review. This review has been designed on the basis of PRISMA method, and proper skimming of research articles have been performed accordingly. 22 articles have been taken for further investigation, published after the year of 2010. The indicators which have been focused on in this study include Patient Satisfaction, Mortality, Survival rates and Cost Allocation to healthcare sectors of the country. Based on the findings of number of research articles, it has been identified that Patient Satisfaction and Cost Allocation have not been improved via Performance Management System. However, during the COVID-19 pandemic, the mortality and survival rates in the public and private sectors of the country were controlled due to constant supervision by governmental agencies and the use of an effective and efficient Performance Measurement method for staff members in the healthcare industry.

Post COVID-19's opportunities for customer-centric green supply chain management and customers' resilience, the moderate effect of corporate social responsibility

PurposeThis research aims to profoundly investigate the post-COVID-19's opportunities for customer-centric green supply chain management (GSCM) and perceived customer resilience by studying the correlation between fear-uncertainty of COVID-19, customer-centric GSCM, and the perceived customers' resilience. Moreover, to examine how the perceived corporate social responsibility (CSR) activities moderates the relationship among the variables.Design/methodology/approachIn this study partial least squares structural equation modeling (PLS-SEM) was adopted on a sample of 298 managers and customers in the Egyptian small and medium enterprises (SMEs) market for data analysis and hypotheses testing.FindingsPreliminary results indicate that the fear-uncertainty of COVID-19 positively affects customer-centric GSCM. Also, external CSR moderates the association between fear-uncertainty towards COVID-19 and customer-centric GSCM. However, internal CSR does not moderate this relationship. Customer-centric GSCM has a significant positive impact on the perceived environmental and social resilience. However, it has an insignificant effect on the perceived financial resilience. Also, customer-centric GSCM has a significant mediation outcome on the relation between fear-uncertainty of COVID-19 and the perceived environmental and social resilience. However, this relation is insignificant regarding the perceived financial resilience.Practical implicationsManagers could develop a consistent strategy for applying CSR practices, providing clear information and focusing on their procedures to meet their customer needs during COVID-19. Governments and managers should develop a consistent strategy to apply customer-oriented green practices to achieve customers' resilience, especially during the pandemic.Originality/valueBased on the "social-cognitive,” "stakeholder” and "consumer culture” theories, this study shed light on the optimistic side of the COVID-19 pandemic, as it also brings the concepts of social responsibility, resilience and green practices back into the light, which helps in solving customers' issues and help to achieve their resilience.

Clinical efficacy of hydroxychloroquine in patients with covid-19 pneumonia who require oxygen: observational comparative study using routine care data

ObjectiveTo assess the effectiveness of hydroxychloroquine in patients admitted to hospital with coronavirus disease 2019 (covid-19) pneumonia who require oxygen.DesignComparative observational study using data collected from routine care.SettingFour French tertiary care centres providing care to patients with covid-19 pneumonia between 12 March and 31 March 2020.Participants181 patients aged 18-80 years with documented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia who required oxygen but not intensive care.InterventionsHydroxychloroquine at a dose of 600 mg/day within 48 hours of admission to hospital (treatment group) versus standard care without hydroxychloroquine (control group).Main outcome measuresThe primary outcome was survival without transfer to the intensive care unit at day 21. Secondary outcomes were overall survival, survival without acute respiratory distress syndrome, weaning from oxygen, and discharge from hospital to home or rehabilitation (all at day 21). Analyses were adjusted for confounding factors by inverse probability of treatment weighting.ResultsIn the main analysis, 84 patients who received hydroxychloroquine within 48 hours of admission to hospital (treatment group) were compared with 89 patients who did not receive hydroxychloroquine (control group). Eight additional patients received hydroxychloroquine more than 48 hours after admission. In the weighted analyses, the survival rate without transfer to the intensive care unit at day 21 was 76% in the treatment group and 75% in the control group (weighted hazard ratio 0.9, 95% confidence interval 0.4 to 2.1). Overall survival at day 21 was 89% in the treatment group and 91% in the control group (1.2, 0.4 to 3.3). Survival without acute respiratory distress syndrome at day 21 was 69% in the treatment group compared with 74% in the control group (1.3, 0.7 to 2.6). At day 21, 82% of patients in the treatment group had been weaned from oxygen compared with 76% in the control group (weighted risk ratio 1.1, 95% confidence interval 0.9 to 1.3). Eight patients in the treatment group (10%) experienced electrocardiographic modifications that required discontinuation of treatment.ConclusionsHydroxychloroquine has received worldwide attention as a potential treatment for covid-19 because of positive results from small studies. However, the results of this study do not support its use in patients admitted to hospital with covid-19 who require oxygen.

Assistance tools for the evaluation of machine learning algorithm performance: The decision tree-based tools developed by the Medical Imaging and Data Resource Center (MIDRC) Technology Development Project (TDP) 3c effort

MIDRC was created to facilitate machine learning research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the COVID-19 pandemic and beyond. The purpose of the Technology Development Project (TDP) 3c is to create resources to assist researchers in evaluating the performance of their machine learning algorithms. An interactive decision tree has been developed, organized by the type of task that the machine learning algorithm is being trained to perform. The user can select information such as: (a) the type of task, (b) the nature of the reference standard, and (c) the type of the algorithm output. Based on the user responses, they can obtain recommendations regarding appropriate performance evaluation approaches and metrics, including literature references, short video tutorials, and links to available software. Five tasks have been identified for the decision tree: (a) classification, (b) detection/localization, (c) segmentation, (d) time-to-event analysis, and (e) estimation. As an example, the classification branch of the decision tree includes binary and multi-class classification tasks and provides suggestions for methods and metrics as well as software recommendations, and literature references for situations where the algorithm produces either binary or non-binary (e.g., continuous) output and for reference standards with negligible or non-negligible variability and unreliability. The decision tree has been made publicly available on the MIDRC website to assist researchers in conducting task-specific performance evaluations, including classification, detection/localization, segmentation, estimation, and time-to-event tasks. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Analyzing the effects of COVID-19 pandemic on supplier performance evaluation factors via fuzzy cognitive map with a hybrid learning system

PurposeThe COVID-19 pandemic has proven that how supply chain management (SCM) can become a crucial process for sustainability of the world's production/service. The global supply chain crisis during pandemic has affected most of the sectors. Home and personal care products manufacturers are among them. In this study (1) the problems at SCM of personal and home care products manufacturers during pandemic are discussed with the help of medium-size manufacturer and (2) the factors affecting suppliers' performance for the relevant sector during COVID-19 are analyzed comprehensively.Design/methodology/approachThe importance of the factors is evaluated using fuzzy cognitive maps that can help to reveal hidden casual relationships with the help of expert knowledge. In order to eliminate subjectivity due to usage of expert knowledge, the maps are trained with a hybrid learning approach that consists of Non-linear Learning and Extended Great Deluge Algorithms to increase robustness of the analysis.FindingsThe findings of the study indicate that the factors such as general quality level of products/services, compliance to delivery time, communication skills and total production capacity of suppliers have been crucial factors during pandemic.Originality/valueWhile the implementation of the hybrid learning approach on supply chain can fill the gap in the relevant literature, the promising results of the study can prove the convenience of the methodology to model the of complex systems like supply chain processes.

Relocation trends determined by increasing risks in Eastern Europe: An ANP-TOPSIS approach

BACKGROUND: The COVID-19 pandemic has changed companies' perspective on relocation and brought uncertainty into people's lives. Uncertainty, a decisive factor in today's global environment, requires new research about human resources and companies. The COVID-19 pandemic has caused an unexpected need for change within organizations, especially in terms of human resources management, creating a complex and challenging environment that interferes with business continuity, forcing employees to cope with this challenging situation. OBJECTIVE: The article aims identifying the changes generated by the COVID 19 pandemic in the relocation of businesses from the European Union, assessing the relocation trends of companies around the world in European countries in this volatile macroeconomic environment. The sustainability of companies, the way they can overcome the crises generated by the pandemic depend mainly on economic, social, financial, political factors and human resource involved in the relocation process. Studying the influence of the pandemic on relocation decision contributes to better management of crises in the future and to reducing risks. METHODS: The study proposes an integrated ANP-TOPSIS (Analytic Network Process-Technique for Order of Preference by Similarity to Ideal Solution) for ordering preference according to the ideal solution framework. Priority should be given to solutions that consider the interactions between factors involved in decision-making. The proposed model will increase the efficiency of the transfer decision-making process and help managers choose solutions effectively based on their importance and impact on the company and the human resources involved. RESULTS: The synthesis of the indicators and methods used, in addition to the factors that affect relocation, complements the specialized literature. The results showed a shift in business relocation options from east to west, demonstrating the current trend in the relocation issue associated with the COVID-19 virus. Eastern European countries are no longer as attractive for companies relocating compared to the pre-pandemic period. The countries with more stable economies, characterized by lower risks, seem to become more attractive to companies that relocate their facilities. CONCLUSIONS: The strategic positioning of the firm, its growth or adaptation to the present environment, and its geographic focus are fundamental components of a company's migration. The selection of an ideal site is a research problem;not only to find a place where firms will have access to qualified human resources, to lower their costs, to be close to raw materials or the market, but also to prevent associated relocation hazards. According to research, today's reality necessitates a risk-focused strategy.

Performance Evaluation of Tourism and Hospitality Companies in the Indian Stock Market, A Post-Covid-19 perspective

The Covid-19 pandemic affected the tourism industry's supply chain and reflected its performance and financial market. This paper aims to evaluate the performance of selected tourism-related companies listed in the Indian stock market. This study evaluates the performance of companies share prices and their business performance in post covid perspective. No studies have been conducted before on the performance evaluation of tourism-related companies listed in the Indian Stock Market from a post covid perspective. Fundamental data analysis for the reports from 2018 to 2022 and the share price charts from 2019 to 2022 was undertaken by twenty-five companies in four categorised sectors: Travel Agencies, hotels and resorts, Airlines, and Amusement parks. This study unveils that companies are underperforming in post covid and at the same time, they performed well in the share market after a negative correction due to covid-19. Airline companies are the most affected and least performed in the stock market by their share price growth. The study result helps investors and people interested in the share market assess the influence of a pandemic situation and to help in decision-making related to investment in the tourism and hospitality industry.

Eco-Efficiency and Its Evolutionary Change under Regulatory Constraints: A Case Study of Chinese Transportation Industry

The transportation industry is characterized as a capital-intensive industry that plays a crucial role in economic and social development, and the rapid expansion of this industry has led to serious environmental problems, which makes the eco-efficiency analysis of the transportation industry an important issue. Previous research paid little attention to the regulatory scenarios and suffered from the incomparability problem, hence this paper aims to reasonably estimate the eco-efficiency and identify its evolutionary characteristics. We measure the eco-efficiency and the corresponding global Malmquist–Luenberger productivity index using a modified model of the data envelopment analysis framework, in which different regulatory constraints are incorporated. Based on the empirical study on the transportation industry of thirty provinces in China, we find that the eco-efficiency of Chinese transportation industry experienced a slight increase during 2015–2016, a sharp decline during 2016–2017, and a continuous rise since year 2017. The Middle Yangtze River area was the best performer among the eight regions in terms of eco-efficiency, while the Southwest area was placed last. The global Malmquist–Luenberger productivity index showed an earlier increase and later decrease trend, which was quite consistent with the reality of the variation of inputs and outputs and the emergence of COVID-19. Moreover, the best practice gap change was found to be the main driven force of productivity. The empirical results verify the practicability of our measurement models and the conclusions can be adopted in guiding the formulation of corresponding policies and regulations.

Development of a novel gold immunochromatographic double-antibody sandwich assay for detection of SARS-CoV-2 antigen

Objective To develop a novel gold immunochromatographic double antibody sandwich assay for the detection of SARS-CoV-2 antigen, and to evaluate the performance of major reagents. Methods Potassium carbonate, large colloidal gold and SARS-CoV-2 antibody were used to prepare colloidal gold antibody markers, SARS-CoV-2 antibody concentration was optimized to prepare the binding pad, SARS-CoV-2 antibody and goat anti-mouse IgG were coated on nitrocellulose membrane as detection line and quality control line, according to the process requirements to assembly the assay. The minimum detection limit, cross-reactivity, accelerated stability test and clinical evaluation of the antigen detection reagent were determined. Results The minimum detection limit of SARS-CoV-2 inactivated virus was 3. 3×10~2 TCID50/ml, and no cross-reaction was found in the samples containing 10 common pathogens. The results of 37 °C high temperature accelerated test for 28 d showed high stability of the reagent. The sensitivity, specificity and total coincidence rate were 92. 00%, 100. 00% and 98. 67% and the Kappa value of concordance test was 0. 939, P＜0. 01. Conclusion The developed antigen detection assay has high sensitivity and specificity, which is also simple to operate in a short time. It can be used as a rapid detection method for large-scale screening of novel coronavirus.

A proposed circular-SCOR model for supply chain performance measurement in manufacturing industry during COVID-19

PurposeCOVID-19 is a global event affecting supply chain operations and human health. With COVID-19, many issues in business models, business processes and supply chains, especially in the manufacturing industry, have had to change. The ability to analyze supply chain performances and ensure circularity in supply chains has become one of the factors whose importance has increased rapidly with COVID-19. Therefore, it aims to determine which supply chain performance criteria come to the fore for the company under consideration to accelerate the transformation into high performance and circularity in supply chains.Design/methodology/approachIn this study, a new circular-SCOR model is proposed, and 17 supply chain performance measurement criteria are prioritized for a manufacturing company in the context of circular economy principles during COVID-19 by using stepwise weight assessment ratio analysis and analytical hierarchy process method, separately.FindingsAs a result, for both methods, in the case study discussed, the demand fulfillment rate is determined as the most prominent criterion in line with the circular economy principles in the COVID-19 period in manufacturing supply chains.Originality/valueIt is expected that this study will contribute to managers and policy makers as it addresses the "new normal” that started after COVID-19 and the criteria to be considered in supply chain performance measurement and emphasizes the need to adopt circular supply chains, especially in manufacturing industries.

Computational Intelligence and Soft Computing Paradigm for Cheating Detection in Online Examinations

Covid-19 has been a life-changer in the sphere of online education. With complete lockdown in various countries, there has been a tumultuous increase in the need for providing online education, and hence, it has become mandatory for examiners to ensure that a fair methodology is followed for evaluation, and academic integrity is met. A plethora of literature is available related to methods to mitigate cheating during online examinations. A systematic literature review (SLR) has been followed in our article which aims at introducing the research gap in terms of the usage of soft computing techniques to combat cheating during online examinations. We have also presented state-of-the-art methods followed, which are capable of mitigating online cheating, namely, face recognition, face expression recognition, head posture analysis, eye gaze tracking, network data traffic analysis, and detection of IP spoofing. A discussion on improvement of existing online cheating detection systems has also been presented.

The role of Industry 4.0 technologies on performance measurement systems of supply chains during global pandemics: an interval-valued intuitionistic hesitant fuzzy approach

PurposeThis paper aims to investigate Supply Chain (SC) Performance Measurement Systems (PMSs) (SCPMSs) that are suitable and applicable to evaluate SC performance during unexpected events such as global pandemics. Furthermore, the contribution of Industry 4.0 Disruptive Technologies (IDTs) to implement SCPMSs during such Black Swan events is investigated in this study.Design/methodology/approachThe research methodology is based upon a novel qualitative and quantitative mixed-method. A Systematic Literature Review (SLR) was initially employed to identify two complete lists of SCPMSs and IDTs. Then, a novel Interval-Valued Intuitionistic Hesitant-Fuzzy (IVIHF)-Delphi method was firstly developed in this paper to screen the extracted SCPMSs. Afterward, the Propriety, Economic, Acceptable, Resource, Legal (PEARL) indicator of the Hanlon method was innovatively applied to prioritize the identified IDTs for each finalized SCPMS.FindingsTwo high-score SCPMSs including the SC operations reference (SCOR) model and sustainable SCPMS were recommended to improve measuring the performance of the pharmaceutical SC of emerging economies such as Iran in which the societal, biological and economic issues were undeniable, particularly during unexpected events. Employing nine IDTs such as simulation, big data analytics, cloud technologies, etc., would facilitate implementing sustainable SCPMS from distinct perspectives.Originality/valueThis is one of the first papers to provide in-depth insights into determining the priority of contribution of IDTs in applying different SCPMSs during global pandemics. Proposing a novel multi-layer mixed-methodology involving SLR, IVIHF-Delphi, and the PEARL indicator of the Hanlon method is another originality offered by this paper.

Performance of Equity Investments in Sustainable Environmental Markets

Despite a significant increase in global clean energy investments, as part of the decarbonization process, it remains insufficient to meet the demand for energy services in a sustainable manner. This study investigates the performance of sustainable energy equity investments, with focus on environmental markets, using monthly equity index data from 31 August 2009 to 30 December 2022. The main contributions of our study are (i) assessment of the performance of trading strategies based on the trend, momentum, and volatility of Environmental Opportunities (EO) and Environmental Technologies (ET) equity indices;and (ii) comparison of the performance of sustainable equity index investments to fossil fuel-based and major global equity indices. Market performance evaluation based on technical analysis tools such as the Relative Strength Index (RSI), Moving Averages, and Average True Range (ATR) is captured through the Sharpe and the Sharpe per trade. The analysis is divided according to regional, sector, and global EO indices, fossil fuel-based indices, and the key global stock market indices. Our findings reveal that a momentum-based strategy performed best for the MSCI Global Alternative Energy index with the highest excess return per unit of risk, followed by the fossil fuel-based indices. A trend-based strategy worked best for the MSCI Global Alternative Energy and EO 100 indices. The use of volatility-based information yielded the highest Sharpe ratio for EO Europe, followed by the Oil and Gas Exploration and Production industry, and MSCI Global Alternative Energy. We further find that a trader relying on a system which simultaneously provides momentum, trend, or volatility information would yield positive returns only for the MSCI Global Alternative Energy, the S&P Oil and Exploration and Production industry, NYSE Arca Oil, and FTSE 100 indices. Overall, despite the superior performance of the MSCI Global Alternative Energy index when using momentum and trend strategies, most region and sector EOs performed poorly compared to fossil fuel-based indices. The results suggest that the existing crude oil prices continue to allow fossil fuel-based equity investments to outperform most environmentally sustainable equity investments. These findings support that sustainable investments, on average, have yet to demonstrate consistent superior performance over non-renewable energy investments which demonstrates the need for continued, rigorous, and accommodating regulatory policy actions from government bodies in order to reorient significant capital flows towards sustainable equity investments.