ESG (ÇEVRESEL, SOSYAL VE KURUMSAL YÖNETÍM) SKORUNUN FÍRMA PERFORMANSINA ETKÍSÍ: TÜRK BANKACILIK SEKTÖRÜ ÖRNEĞÍ

ESG skorları, firmaların çevresel, sosyal ve kurumsal yönetim alanlarındaki yatırım ve faaliyetleri ile ilgili performansını ortaya koyan bir ölçüttür. Son yıllarda firmaların paydaşlardan gelen talepleri dikkate alarak çeşitli raporlama modelleri geliştirmesi sonucunda ortaya çıkan bu kavram, tüm paydaşların finansal kararları üzerinde daha fazla belirleyici olmaya başlamıştır. Bu çalışmanın amacı Türkiye'de faaliyet gösteren mevduat bankalarında ESG skorlarının finansal performans üzerindeki etkisini araştırmaktır. Çalışma 2010-2020 dönemini kapsamaktadır. PCSE ve FGLS panel veri tahmincileri kullanarak yapılan analizlerde, bankaların toplam ESG, sosyal (SPS) ve kurumsal yönetim (GPS) skorlarının muhasebe ve piyasa temelli performans göstergelerini (ROA ve Tobin Q) pozitif yönde etkilediği tespit edilmiştir. Diğer taraftan çevresel (EPS) skorunun her iki performans göstergesi üzerinde de istatistiksel olarak anlamlı bir etkiye sahip olmadığı görülmüştür. Ayrıca sonuçlar, Covid-19 pandemisinin bankaların ROA ve Tobin Q ile ölçülen performanslarında azalışa yol açtığını göstermektedir. Analiz bulguları firma performansını artırmada finansal olmayan raporlamaya ve ESG faaliyetlerine daha fazla önem verilmesi gerektiğini işaret etmektedir.Alternate :ESG scores are a measure that reveals the performance of companies regarding their investments and activities in the fields of environmental, social, and corporate governance. In recent years, this concept, which emerged because of companies developing reporting models by considering the demands from stakeholders, has begun to become more decisive on the financial decisions of all stakeholders. The aim of this study is to investigate the effect of ESG scores on financial performance of Turkish commercial banks in the period of 2010-2020. In the analyzes performed using the PCSE and FGLS panel data estimators, it has been concluded that the total ESG, social (SPS) and corporate governance (GPS) scores of the banks positively affect the accounting and market-based performance indicators (ROA and Tobin's Q). On the other hand, it was seen that the environmental (EPS) score did not have a statistically significant effect on both performance indicators. In addition, the results show that the Covid-19 pandemic has led to a decrease in banks' performance as measured by ROA and Tobin's Q. Analysis outcomes indicate that giving more importance to non-financial reporting and ESG activities will contribute to enhancing firm performance.

Innovations Through Virtual Reality Simulation

The need to augment standardized learner outcomes related to performance and clinical competency led to creating curricular elements that would provide instruction and assessment from multiple perspectives. The COVID-19 pandemic brought about needs for re-imagination of standardized simulated clinical experiences given the need for increased distance-learning and asynchronous formats. Our goal was to identify activities that would engage pre-clinical simulation through asynchronous virtual reality (VR) case scenarios. The intent was to provide additional resources whereby competencies could be more defined through performance metrics and standardized assessments additive to our established simulation-based curriculum throughout all curricular phases. Student reflection and metacognition identified gaps to guide future performance improvement through the VR activities. Learner outcomes encompassing history-taking, physical assessment, evidence-based clinical reasoning, and medical decision-making guided the instructional objectives. The composite data showed progressive improvements over five scenarios delivered in our second-year clinical medicine curriculum.

SMEs Managers' Perceptions of MCS: A Mixed Methods Approach

The goals of this study are to explore the use of the Management Control Systems (MCS) by SMEs' managers at the country level in order to identify the importance given to financial and nonfinancial measures, as well as key performance indicators. In this study, we use the behavioral accounting lens and adopt mixed methods approach to study the use of the MCS in Portuguese small to medium enterprises (SMEs): a correlational and a configurational analysis. Data was collected from a cross-sectional survey of 414 top managers of Portuguese SMEs across several industries. The results show that managers' perceptions of the importance given to financial measures is positively and significantly related to the importance given to several nonfinancial measures. We take an original approach by addressing the managers' perceptions to contribute to the understanding of Portuguese SMEs' use of tools for strategy implementation: the use of different MCS. Additionally, the study discovers alternative configurations of individual and organizational conditions that lead to the managers' perception of the importance given to financial and nonfinancial measures. This paper offers support for SMEs based on controlling strategy implementation by using MCS. The study's limitations regard a relatively low response rate to the questionnaire (4.56%), which may be justified because data was collected during the COVID-19 pandemic. We offer alternative configurations that generate the perception of managers about the importance of using financial and nonfinancial measures. Our results enlighten the use of such tools in support of strategic accomplishment.

Bank-specific performance indicators, macroeconomic variables and capital adequacy of Nigerian banking industry

[...]we canvass those Nigerian banks should reduce dividend payouts and increase retained profits as a buffer against exposed risks. To ensure the healthiness of banks in the banking industry as well as facilitate international transaction, the central bank of ten countries (Belgium, Canada, France, Germany, Italy, Japan, the Netherlands, Sweden, the UK and the US) formed the committee of banking supervision in 1988 (the Basel Committee on Banking Supervision). Since the formation of this committee, it has undergone at least three stages called the Basel I, Basel II and Basel III. Premised on shock to the economy brought on by the coronavirus pandemic, with economic growth in 2020 expected to contract by as much as 4.4 percent to 8.94 percent, a drop in oil receipt and a devalued Naira in the range of 380-450 to US dollar, the capital adequacy of banks could be severely threatened, (Egba, 2020). [...]scholars have extensively shown that bank specific performance indicators and macroeconomic factors affected capital adequacy ratio. [...]this paper examined the effect of banks specific-performance indicators and macroeconomic factors on bank financing which is the minimum funds required for their short-term obligation or capital adequacy ratio.

Strategic Analysis of Financial and Operational Data During a Global Economic Crisis: Lessons from the COVID-19 Pandemic

Assessing the operational efficiency and financial health of a company, from a Strategic Management perspective, is a major challenge even during normal times. The economic crisis and its associated uncertainties, caused by the Covid-19 pandemic, have made these challenges even more daunting for business students, faculty, and professional analysts alike. In this paper, we first review the broad impact that the pandemic has inflicted on the industrial and economic climate globally. We then take an in-depth look at two representative industries and analyze how they have been impacted by the crisis. Specifically, we look at the airlines industry and the restaurants and food delivery industry, by reviewing and interpreting a select set of financial and operational metrics pre-crisis, during a crisis, and the post / recovery stages of a crisis. We look at performance indicators in these two industries and identify which metrics might provide us with the sharpest insights on the future performance of companies in these industries. We also provide more general guidelines for analysis of companies across other industries, their inter-connectedness, and the ecosystems that they operate in.

Guest editorial: KPIs to help develop the next normal in construction practice, teaching-learning and research

[...]this special issue contributes to priming the construction industry for the next normal by re-examining the emerging needs for reengineering or developing novel and more relevant key performance indicators (KPIs) to better measure the performance of construction projects, online teaching-learning and research following vast digital and other transformations triggered, if not accelerated by the COVID-19 pandemic. [...]virtual FAT (vFAT) became a popular substitute for physical FAT. The paper showed construction digitisation such as VR, augmented reality (AR) and building information modelling (BIM) is highly cooperative as it can easily be made available for online learning. [...]the findings support construction educators to use online-based VR learning to promote efficient teaching of design buildability to students. The research papers cover findings related to a wide range of countries such as India, Malaysia, New Zealand, South Africa, Sri Lanka and USA, and the authors of the papers also represent several different institutions within or across countries. [...]this special issue provides a snapshot of various KPIs and metrics proposed for the next normal in construction, considering different contextual factors experienced by various different geographical regions across the world.

A review of methodologies for performance evaluation of automated construction processes

Purpose>The aim of this paper is to synthesize knowledge related to performance evaluation of automated construction processes during the planning and execution phases through a theme-based literature classification. The primary research question that is addressed is “How to quantify the performance improvement in automated construction processes?”Design/methodology/approach>A systematic literature review of papers on automated construction was conducted involving three stages-planning, conducting and reporting. In the planning stage, the purpose of the review is established through key research questions. Then, a four-step process is employed consisting of identification, screening, shortlisting and inclusion of papers. For reporting, observations were critically analysed and categorized according to themes.Findings>The primary conclusion from this study is that the effectiveness of construction processes can only be benchmarked using realistic simulations. Simulations help to pinpoint the root causes of success or failure of projects that are either already completed or under execution. In automated construction, there are many complex interactions between humans and machines;therefore, detailed simulation models are needed for accurate predictions. One key requirement for simulation is the calibration of the models using real data from construction sites.Research limitations/implications>This study is based on a review of 169 papers from a database of peer-reviewed journals, within a time span of 50 years.Originality/value>Gap in research in the area of performance evaluation of automated construction is brought out. The importance of simulation models calibrated with on-site data within a methodology for performance evaluation is highlighted.

Enhanced K-Means ++ Using Probability Theory For Efficient Prediction Of Air Pollution

Increasing concentrations of air pollutants is a global concern as it is a major underlying cause for other serious issues like premature deaths, global warming, increased susceptibility to heart diseases, lung disorders and skin disorders. Exposure to particulate pollutants increases vulnerability to Covid-19 and risk of succumbing to the virus. Air pollution analysis is a widely undertaken study by government officials and research scholars. K-means is a frequently used algorithm to understand the condition of the atmosphere from massive sensor generated data. The algorithm however comes with its drawbacks. Random initialization of the initial centroids can lead to bad clustering, an alternative, K-means++ does away with this, however, takes more execution time and iterations which is not ideal. We propose an advanced K-means++ initialization algorithm which incorporates an oversampling factor for smarter initialization of centroids using probability theory and weight assignment. We also propose a probability based convergence algorithm as opposed to the regular convergence algorithm to smartly select a portion of the data points to recompute the centroids. This will ensure a faster formation of final clusters. Real time Bengaluru, India air pollution data is scraped, pre-processed and clustered using the proposed technique. All the variants of K-means under study are compared over parameters of execution time, iterations and performance metrics. This work is also extended to tackle future air data points using a fast ensemble model. The solution proposed is better in terms of being reliable, fast and helps with better clustering, which leads to better air quality analysis, which leads to better air quality prediction, which leads to taking apt precautions to mitigate and regulate the air pollution.

Directions of Stimulation of the Development of Tourism Infrastructure by Attraction of Investments

This article notes that the uncertainty of the consequences of the pandemic has shown the need for the development of domestic tourism, the creation of modern health resort and tourism services and improving the quality of tourism infrastructure by attracting investments for maintenance and provision of transport and the development of tourism products and services, the introduction of environmental protection measures. Using the given statistical data on the development of the tourism and hospitality industry of the Republic of Kazakhstan, the authors analyzed the influence of some factors influencing the development of the tourism industry. The indicators of the structure of the population's expenditures on paid services, the distribution of resident visitors by purpose of travel at places of accommodation for 2021, the number of visitors served at places of accommodation for tourists in general in Kazakhstan, the main indicators of financial and economic activities in the field of tourism for 2016-2021 are given, performance indicators of tourist infrastructure facilities for the analyzed period. Proposals are given for the transformation of the most significant tourist sites included in the Touristification Map of Kazakhstan into one tourist cluster with a unique dominant experimental structure with transfer to foreign tourism markets in the future.

Sustainable Assessment in Supply Chain and Infrastructure Management

Assessing sustainability in supply chain and infrastructure management is important for any organization in the competitive business environment or public domain. Public buildings such as higher education institutions are responsible for a substantial portion of energy consumption and anthropogenic greenhouse gas (GHG) emissions. Roukouni et al. (contribution twelve) developed truck platooning and multi-sided digital platforms games for barge transportation, both improving the sustainability of hinterland transportation. Besides these studies, Özdemir et al. (contribution thirteen) assessed the efficiency of the operations strategy matrix in the healthcare system amid COVID-19.

ANALYSIS OF THE CORRELATIONS BETWEEN THE DEGREE OF INDEBTEDNESS AND THE PERFORMANCE OF THE ECONOMIC ENTITIES FROM EMERGING ECONOMY

The purpose of this research is to assess the performance of the economic entities that are part of the BRICS economies (Brazil, Russia, India, China, South Africa). Thus, the following objectives have been set to achieve the intended purpose: O1 - analysis and evaluation of the economic performance that were reported by entities in the emerging BRICS economies;O2 - identification of the correlations between the performance indicators that were reported by entities from emerging BRICS economies (Return on Assets;level of indebtedness;equity ratio;Earnings Before Interest, Taxes, Depreciation, and Amortization growth). For these objectives to be achieved, we have collected and analyzed the financial data from the reports of 50 companies that are listed on a regulated market in Brazil, Russia, India, China and South Africa. This research focuses on assessing the effects of the financial report and of the level of indebtedness on the performance of the entities from emerging BRICS economies. Research is relevant to current and potential investors interested in emerging BRICS economies, as well as for other categories of stakeholders.

Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology

Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning approaches have been used to predict toxicity-related biological activities using chemical structure descriptors. However, toxicity-related proteomic features have not been fully investigated. In this study, we construct a computational pipeline using machine learning models for predicting the most important protein features responsible for the toxicity of compounds taken from the Tox21 dataset that is implemented within the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) therapeutic discovery platform. Tox21 is a highly imbalanced dataset consisting of twelve in vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For the machine learning model, we employed a random forest with the combination of Synthetic Minority Oversampling Technique (SMOTE) and the Edited Nearest Neighbor (ENN) method (SMOTE+ENN), which is a resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR) and the mitochondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUCROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were analyzed for enrichment to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidates significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong for twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.

Towards reimagined technical assistance: the current policy options and opportunities for change: [version 2;peer review: 1 approved, 1 approved with reservations]

Technical assistance has been at the heart of development assistance provided to country governments by donor agencies over the past several decades. The current debates on reimagining technical assistance focus on the existing challenges of the different types of technical assistance and the (re)construction of an ideal model for delivering this type of support, with little discussion about the dilemmas involved in making day-to-day decisions and trade-offs in implementation. This article presents technical assistance as a policy option for governments and details the existing models of delivering technical assistance, their limitations, and the required enabling conditions. The models presented focus on the type of role for the technical advisers- as doers (performing government functions), partners (working with the government to perform a specific role) and facilitators (enabling and facilitating change programmes to address wicked problems). Finally, the paper provides a practical account of the implications of the programme design and suggests potential opportunities for change particularly in the context of COVID-19 pandemic. It complements an open letter on the practical account of the current challenges in the design and implementation of technical assistance programmes.

Tackling the gender biases in higher education careers in Pakistan: potential online opportunities post COVID-19: [version 1;peer review: 2 approved]

Driven to improve the quality of higher education as an engine of growth and socio-economic development within Pakistan for 20 years, the Higher Education Commission (HEC) in Pakistan has focused on linking academics and professional services staff with their counterparts in various countries, including the UK, US, and Australia. In collaboration with the British Council, the PAK-UK initiative has been launched to offer deeper linkages between the academics and universities in the UK and Pakistan. This paper presents statistical analysis of data collected in a British Council project highlighting the gender inequalities of the current HEC strategy. The results suggest the potential for online opportunities to help close and amend this gender gap and improve higher education in Pakistan, and the PAK-UK initiative’s role in contributing more broadly to the United Nations Sustainable Development Goals.

TASK PERFORMANCE AND JOB SATISFACTION UNDER THE EFFECT OF REMOTE WORKING: CALL CENTER EVIDENCE

The COVID-19 pandemic affected the entire world and caused radical changes in conducting business. During the pandemic, some companies adopted hybrid working, and some requested their employees to work entirely from their homes or workplace. This study is performed to determine the differences in the perceived task performance of employees working from home compared to those operating from their workplace and the possible mediating effect of job satisfaction due to such distinction. The study sample is limited to one call center to prevent the interference of other organizational variables such as management style, human resources practices, organizational culture, and to only call center agents to prevent any interference of the position-related factors. All the call center agents (n=421) participated in this study. The data obtained was examined by a path analysis with the Structural Equation Modeling (SEM). The results show that the employees who work from home full-time or on certain days of the week have better general task performance perception compared to those who work only from the workplace. The work location has not been found to affect job satisfaction significantly.

Corporate Resilience and Performance in Food Industry SMEs During COVID-19

The small and medium entrepreneurs' and managers' main interest is to avoid the problems resulting from the economic and social crisis during COVID-19. One way to prevent these problems is to increase the organizations' resilience to achieve optimal business performance. Resilience helps with business continuity and performance, simultaneously assisting social, economic, and cultural policymakers and planners in their tasks. This study investigates the impact of corporate resilience dimensions on performance in the food SMEs industry in Iran during the COVID-19 era. After reviewing the literature on organizational resilience (planned and adaptive) based on Porter's value chain, the effects of five dimensions (human resources, marketing, finance, supply chain management, and services) on corporate performance were assessed. Ninety-five questionnaires were collected from food industry SMEs using the simple random sampling method. SPSS 26 and SMART PLS 2.0 were used to analyze the data. The results of data analysis with Partial Least Squares (PLS) showed that all dimensions had a positive effect on corporate performance because all the items had high t coefficients (t > 1.96). With GOF, the overall validation of the model is high. Appropriate changes in Porter's value chain components (Human Resources, Marketing, Finance, Supply Chain Management, and Services) under the COVID-19 influence have a positive effect on performance.

SUSTAINABLE TRANSFORMATION OF ROMANIAN COMPANIES THROUGH INDUSTRY 4.0, GREEN PRODUCTION AND ENVIRONMENT COMMITMENT

The main aim of this research article is to develop an econometric model in order to establish the influence of green performance on digitization, green production and environment commitment. The data was collected through a questionnaire- based survey on companies' representatives. The analysis was made using the Partial Least Square - Structural Equation Modelling (PLS-SeM) with the statistical software SmartPLS. The results of the research confirm the three hypotheses. Thus, green performance of Romanian companies has a positive impact on green production, digitization and environment commitment. The novelty consists in the interconnected analysis of the four variables (green performance, digitization, green production and environment commitment), the research highlighting valuable results that can be used by the companies to improve their green performance, using green production and digitization. The paper offers a picture of the sustainable transformation of Romanian companies based on the industry 4.0, green production and environment commitment, highlighting the interdependence of the analysed variables. The research is helpful for companies that want to be more responsible towards the environment and the community.

Measuring the Supply Chain Performance of the Floricultural Sector Using the SCOR Model and a Multicriteria Decision-Making Method

This study aims to highlight the usefulness of studying the performance of supply chains (SC) at the sectoral level in greater detail through the combination of a disaggregated supply chain operations reference (SCOR) model, with a multicriteria decision-making approach, specifically using an AHP, to adjust the analysis to the particularities of the sector under study by stakeholders’ judgements. The methodology was applied to the Ecuadorian flower industry, and the data for the analysis was from a survey of a group of companies that represent this sector. In addition, a focus group of SC experts weighted the model constructs as part of the analytic hierarchy process (AHP), and then the performance level for each construct was determined. According to the results methodologies, this model allows the classification of companies by their performance, as well as the performance of the aggregate sector. The processes that Ecuadorian flower companies need to improve on are planning, procurement, and manufacturing. The study’s main contribution is developing a general framework for measuring the overall performance of SCs and how the results are obtained. This tool could help managers, consultants, industries, and governments to assess the performance of SCs, as well as improving SC management in order to increase the sector’s competitiveness in the international market.

Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning

Malware is a key component of cyber-crime, and its analysis is the first line of defence against cyber-attack. This study proposes two new malware classification frameworks: Deep Feature Space-based Malware classification (DFS-MC) and Deep Boosted Feature Space-based Malware classification (DBFS-MC). In the proposed DFS-MC framework, deep features are generated from the customized CNN architectures and are fed to a support vector machine (SVM) algorithm for malware classification, while, in the DBFS-MC framework, the discrimination power is enhanced by first combining deep feature spaces of two customized CNN architectures to achieve boosted feature spaces. Further, the detection of exceptional malware is performed by providing the deep boosted feature space to SVM. The performance of the proposed malware classification frameworks is evaluated on the MalImg malware dataset using the hold-out cross-validation technique. Malware variants like Autorun.K, Swizzor.gen!I, Wintrim.BX and Yuner.A is hard to be correctly classified due to their minor inter-class differences in their features. The proposed DBFS-MC improved performance for these difficult to discriminate malware classes using the idea of feature boosting generated through customized CNNs. The proposed classification framework DBFS-MC showed good results in term of accuracy: 98.61%, F-score: 0.96, precision: 0.96, and recall: 0.96 on stringent test data, using 40% unseen data.

Applying Clustering Methods to Develop an Optimal Storage Location Planning-Based Consolidated Picking Methodology for Driving the Smart Manufacturing of Wireless Modules

Picking operations is the most time-consuming and laborious warehousing activity. Managers have been seeking smart manufacturing methods to increase picking efficiency. Because storage location planning profoundly affects the efficiency of picking operations, this study uses clustering methods to propose an optimal storage location planning-based consolidated picking methodology for driving the smart manufacturing of wireless modules. Firstly, based on the requirements of components derived by the customer orders, this research analyzes the storage space demands for these components. Next, this research uses the data of the received dates and the pick-up dates for these components to calculate the average duration of stay (DoS) values. Using the DoS values and the storage space demands, this paper executes the analysis of optimal storage location planning to decide the optimal storage location of each component. In accordance with the optimal storage location, this research can evaluate the similarity among the picking lists and then separately applies hierarchical clustering and K-means clustering to formulate the optimal consolidated picking strategy. Finally, the proposed method was verified by using the real case of company H. The result shows that the travel time and the distance for the picking operation can be diminished drastically.