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Yayın How do human resources practices affect the performance of the employees in Syria? International Committee of the Red Cross (ICRC)(IGI Global, 2024-03-05) Soykut Sarıca, Yeşim Pınar; Kekhia, BahjatThe purpose of this research is to analyses how HRM practices affect worker output in a high-pressure setting like the International Committee of the Red Cross (ICRC) in Syria. It contributes to literature by expanding the understanding of HRM. It provides useful insights and ideas for improving the efficiency of HRM procedures in businesses. This study highlighted the need for the ICRC to prioritize and invest in strong human resource management practices. Primary data was collected from ICRC employees through survey questionnaire. Findings show that HRM practices have a significant direct impact on the motivation and performance of ICRC employees. Meanwhile, psychological safety mediates the relationship between employee's motivation and employee's performance. On the other hand, employee's motivation partially mediates the relationship between HR Practices and employee's performance at ICRC.Yayın The impact of expectations on the co-integration relationship between the stock and REIT markets(Inderscience Publishers, 2022-06-29) Ümit, Erol; Yüksel, Aydın; Yüksel, Aslı; Öztürk, HakkıThis paper examines if expectations have a significant impact on the co-integration relationship between stock and real estate investment trust markets. We use two widely followed expectation indicators which are the US yield spread and the expected US stock market volatility (VIX) to test this hypothesis. The US yield spread is decomposed into two components which are the expected short-term interest rate (EF) and a variable term premium (TP) using Hamilton-Kim algorithm. A dataset covering ten developed markets is used. Using co-integration score analysis our findings indicate that expected US short-term interest rates and expected US stock market volatility have a statistically significant and positive impact on the global co-integrations of different countries. This effect is especially valid in the post-global financial crisis period. The expectation-based indicators EF and VIX, however, do not seem to have a significant impact on co-integration at regional and local levels.Yayın Gap between mobile and online advergames: the possible effects of the optimal gaming experience-flow(IGI Global, 2022-10-07) Ozansoy Çadırcı, Tuğçe; Sağkaya Güngör, AyşegülMobile and online advergames are likely to influence brand associations differently. Regardless of the advergame environment, successful games are capable of taking the player into the flow state. How the experience of flow influences the outcomes of the advergames in different environments is a new and an important subject for the advertisers. In order to understand the outcomes (i.e., brand recall and brand attitude) of the advergames in different mediums (online vs. mobile) with the flow introduced, a lab experiment was conducted. Results of the experiment yielded that brand recall and brand attitude were different in different environments. When the interaction of skill and challenge was introduced to the study, however, hypotheses were partially supported. Furthermore, arousal resulted in better brand recall and more positive brand attitudes in the mobile environment. Lastly, time distortion caused no difference in brand attitude, while supporting mobile in brand recall.Yayın Hukukçu bilirkişi atanması yasağı ve bu yasağın iptalinin reddine ilişkin anayasa mahkemesi kararının değerlendirilmesi(Legal Yayıncılık San. ve Tic. Ltd. Şti., 2022) Çelik, AydınYeni düzenlemelerle, yeni bir bilirkişilik sistemi benimsenmiş, hu-kukçu bilirkişinin atanması yasaklanmıştır. Bazı mahkemelerce; yasağın iptali için Anayasa Mahkemesine başvurulmuştur. Anayasa Mahkemesi tarafından genel bilgi veya tecrübeyle ya da hâkimlik mesleğinin gerek-tirdiği hukuki bilgiyle çözümlenmesi mümkün olan konularda bilirkişiye başvurulamayacağı gerekçeleriyle yapılan başvuru reddedilmiştir. Ancak fiili gerçeklikle uyuşmayan ve ihtiyaca cevap vermeyen bu yasağın daha önceki düzenlemeler gibi uygulama olanağı yoktur.Yayın Anonim ortaklığın haklı nedenle feshini sınırlandıran hususlar(Legal Yayıncılık San. ve Tic. Ltd. Şti., 2021) Çelik, AydınAnonim şirkette azınlığa haklı nedenle fesih hakkı tanınmış olması, bu hakkın her zaman ve kolayca kullanılabileceği anlamına gelmez. Haklı nedenle şirketin feshini isteme imkanı, olağanüstü nitelikte ve istisnai durumlarda kullanılabilecek bir hak olarak tanınmıştır. Şirketin haklı sebeple feshine karar verilmesi, sadece şirketin feshine yol açabilecek bir takım sebeplerin varlığı ve soruna bir başka yolla çözüm bulunamaması halinde söz konusu olabilir. Anonim ortaklığın feshi talebinde haklı neden olabilecek durumlar, yasadaki şartlar, anonim ortaklığın niteliği ve anonim ortaklıkta tanınan bireysel ve azınlık hakları nedeniyle sınırlıdır.Yayın An analysis of risk transfer and trust nexus in international trade with reference to Turkish data(IGI Global, 2022-08-05) Şen Taşbaşı, Aslı; Soykut Sarıca, Yeşim Pınar; Yüksel, Ahmet HakanInternational trade introduces a range of risks, which causes uncertainty over the timing of delivery and payment between exporters and importers. This chapter is a first attempt in dissecting Turkey's trade data in terms of risk allocation and trust between the parties involved. Breaking down Turkish export and import data for the years 2000 to 2018 according to methods of payment and use of currencies, the chapter first finds the risk is distributed unevenly between the exporter and the importer. Then findings are evaluated to open a new avenue of future research, constructed on the inquiry whether emerging economies like Turkey can establish trust in their trade with developed economies by using blockchain technology.Yayın The transition of the pharmaceutical sector marketing activities: traditional marketing to digital marketing after the pandemic period and the results(IGI Global, 2022) Soykut Sarıca, Yeşim PınarThe aim of the study is to understand the change in marketing practices in the pharmaceutical industry in human health during the pandemic process and to reveal the projections for post-marketing strategies in Turkey. With analyzes such as the speed of the industry’s adaptation to change, the flexibility of digitalization, the suitability of the existing structure, and the number of employees, future projections have been analyzed on the basis of the evaluations of pharmaceutical sector managers. The environment of the sector that has developed as a result of digitalization has been examined with its positive and negative aspects in terms of employment and employee competencies. As a result, all stakeholders in the pharmaceutical industry are not fully prepared for this process, adapting more slowly than expected in terms of adaptation time. As a living process, the preferences and methods need continuous and regular updating, but it has revealed the fact that digitalization will be passed as a partial and complete final result in its forward-looking predictions.Yayın Performance of airlines: a comparative analysis for the COVID-19 era(IGI Global, 2022-04) Teker, Dilek; Teker, Suat; Kurnaz, Salim; Argın, EmrahThis chapter investigates the financial performance of airline companies and proposes a harmonic index to state a performance ranking for the COVID-19 era covering the years 2018, 2019, and 2020. All data required for this study were obtained from the Thomson-Reuters database. A total number of 111 airlines are reached and listed by total assets. The 20 biggest airlines by total assets in 2020 are chosen for this study. A harmonic index is constructed by using performance indicators for profitability, liquidity, and efficiency. Then, the biggest 20 airlines are ranked by the harmonic index values for the COVID-19 era. The results revealed that North America and European-based airlines performed very badly in 2020 compared to pre-COVID years while Far East-based airlines were able to manage the pandemic year much better.Yayın Artificial Intelligence in Economic Modeling and Forecasting(S & B World Foundation, 2025) Kaytaz, Mehmet; Özmucur, Süleyman; Yürükoğlu, TanjuArtificial Intelligence (AI) originated from multiple disciplines, with Alan Turing’s work on the Turing Machine laying its foundation. Early developments such as neural networks and the Turing Test marked the beginning of AI’s evolution through cycles of enthusiasm and setbacks. Recent breakthroughs in big data, GPU computing, and deep learning have made AI a part of daily life, from healthcare and translation to robotics and gaming. However, its rapid expansion raises serious concerns regarding autonomous weapons, surveillance, labor displacement, and the existential threat posed by unsupervised superintelligent systems. Reflecting this surge, AI and machine learning publications have skyrocketed since 2019, with most research emerging only in the past five years and spanning diverse fields beyond computer science. In parallel, machine learning techniques have increasingly influenced modern economics, building on econometric tools such as regression, principal components, and ARIMA models. Concepts such as supervised learning and methods like logistic regression, LASSO, and neural networks have bridged the gap between traditional econometrics and data science, enhancing predictive accuracy and flexibility. Lawrence Klein’s Current Quarter Model (CQM), which leverages high-frequency indicators and bridge equations to nowcast GDP, exemplifies this integration. His approach, now echoed in MIDAS regressions and global modeling efforts like Project LINK, remains vital. The COVID-19 shock underscored the need for adaptive, interdisciplinary forecasting frameworks that incorporate health, behavioral, and environmental variables in an interconnected world. The use of AI, specifically machine learning (ML), in official statistics is very recent compared to other disciplines and areas. This may seem contradictory to the objectives of official statistics. At the same time, the digital revolution led to an abundance of all types of data and the demand for data has considerably increased. Several reasons may be specified for this delay. One reason may be the structure of official statistical organizations and the role of statisticians in these organizations. The breakthroughs in AI technology and the use of satellite imagery have disrupted the way official statisticians collect, process, and analyze data. There is skepticism among some official statisticians about employing new technological developments. Official statisticians are reluctant to work with data that does not rely on probability samples and legacy methods. Concerns about quality, ethics, and privacy are the major factors contributing to this unwillingness. There is also an insufficiency of both financial and human resources. Over the last seven years, the efforts of the United Nations Economic Commission for Europe (UNECE) within the framework of modernizing official statistics, along with its Machine Learning Group, have played a significant role in many national statistical offices adopting and applying machine learning methods. In two years, it grew from 120 statisticians from 23 countries to more than 400 statisticians from 35 countries. Currently, many NSOs and international organizations are involved in developing applications of ML in various areas of data collection. In various areas of data collection, many NSOs and international organizations are also involved in developing applications of ML. The IMF has developed the PortWatch Platform, utilizing satellite-based vessel data to provide real-time indicators of port and trade activity. Statistics Colombia is predicting poverty rates using daytime and nighttime satellite imagery. Statistics Indonesia has a similar project. Statistics Netherlands is using webscraping to identify different types of companies. The U.S. Census Bureau and the Bureau of Transportation Statistics (BTS) jointly produce the Commodity Flow Survey. They reduced manual workload by using machine learning methods. Federal Statistical Office of Switzerland developed StatBot, a chatbot for sharing statistical information, soon to provide services in three different languages. The Swedish Land Registry (SLR) is the government agency with the mission of securing the ownership of real estate and making geodata available for the society. SLR uses handwritten text recognition together with neural networks to get information from documents going back to 1850s. The Australian Bureau of Statistics is undertaking a comprehensive review of the Australian and New Zealand Standard Classification of Occupations using large language models. Statistics Canada also has explored the use of large language models to automate and enhance statistical report generation, aiming to improve efficiency and reduce manual workloads. The experience of statistical offices showed that machine learning has proven to contribute to producing data that is more relevant, with better quality, in a faster or more cost-efficient manner, without any significant reduction to any of these dimensions. Machine learning is advantageous particularly in processes that are labor intensive, repetitive and stable, such as in classification and coding. Another lesson from the activities of the Machine Learning group is that sharing and collaboration within and between statistical organizations are also essential to advance the use of machine learning based on lessons learned on where it adds value, where it shows promise and where it offers less value. Artificial intelligence (AI) is reshaping economic policymaking by enabling more dynamic, data-driven analysis and forecasting. Unlike traditional models, AI systems, especially those utilizing machine learning, can adapt to changing conditions and extract insights from massive datasets. Central banks and institutions, such as the IMF and World Bank, now utilize AI for inflation tracking, labor analysis, and risk forecasting, while natural language processing aids in interpreting media and public sentiment. AI enhances forecasting accuracy for key indicators, such as GDP and inflation, by continuously updating projections. Deep learning and reinforcement learning further enhance real-time decision-making in an increasingly volatile global economy. AI is also transforming fiscal policy, trade, and regulation. Governments use predictive analytics for tax reform, compliance, and investment planning, while AI models assess trade shocks and climate risks. However, the rapid adoption of AI raises concerns about bias, transparency, and inequality, particularly in developing countries that lack data infrastructure and expertise. Ensuring AI systems are ethical, auditable, and inclusive is essential. Ultimately, AI's societal impact will hinge not just on innovation but on building governance frameworks that safeguard human rights and promote equitable outcomes. The global approach to AI regulation is fragmented. The EU leads with comprehensive laws, while countries like the U.S. favor sector-specific guidelines, and China pursues centralized, state-aligned control. International bodies such as the OECD promote ethical principles, but challenges remain, including cross-border enforcement, rapid innovation, and definitional ambiguity. To govern AI ethically, regulations must embed transparency, explainability, and oversight from the start. Independent audits, impact assessments, and robust privacy protections are crucial, especially in sensitive sectors such as healthcare and justice. Public trust depends on democratic participation and the inclusion of marginalized voices in shaping AI governance. Human-centered AI (HCAI) presents an alternative vision, one that supports rather than replaces human decision-making, and promotes usability, accountability, and equity. In fields such as education and healthcare, HCAI can enhance services while upholding ethical standards. However, AI’s labor market effects are concerning, as automation threatens jobs and exacerbates inequality. Without deliberate policies, such as reskilling and fair labor protections, especially in the Global South, AI could deepen global divides. Yet with inclusive governance, AI has the potential to reduce poverty, empower workers, and create a more equitable digital economy. The labor market implications of AI are profound. Cognitive automation threatens both lowskill and middle-income jobs while concentrating wealth among those who own the technology. These risks are widening income inequality and weakening social cohesion. Scholars such as Daron Acemoglu warn of "excessive automation" that replaces workers rather than empowering them, while others like Erik Brynjolfsson advocate for worker-augmenting AI and institutional reform to ensure inclusive innovation. Global disparities are stark—developed nations invest in reskilling and infrastructure, while developing economies face job displacement without adequate digital capacity. AI can be a force for upward mobility or social fragmentation, depending on how societies manage the transition. The impact of AI on poverty will depend heavily on policy choices. While it has already enabled life-saving advances in agriculture, healthcare, education, and microfinance in countries like Kenya, Colombia, and India, it also risks excluding low-income workers through automation and exploitative digital labor models. The rise of precarious gig work, digital piecework, and content moderation in the Global South underscores the need for inclusive labor protections, fair compensation, and recognition of data as a form of labor. Without intervention, the benefits of AI will continue to deepen global inequalities. With deliberate governance, however, AI can help build a fairer, more resilient, and more equitable digital economy.Yayın Global Inflation(S & B World Foundation, 2023-06) Kaytaz, Mehmet; Özmucur, Süleyman; Yürükoğlu, TanjuGlobalization led to the integration of more economies and more labor sources into the global economy. Thus, the availability of cheap labor helped accelerate economic growth and increase trade, particularly the expansion of global value chains. And the end of the cold war after the break of the Soviet Union also contributed to reducing inflationary pressures on a global scale. The global inflation rate tended to increase starting in 2019, and with COVID-19, it began to rise steadily. The pandemic caused further disruptions in economic activity and created supply chain issues, leading to higher commodity and consumer prices. Moreover, governments trying to cope with the pandemic had significant increases in government expenditures and Money supply. The average inflation rate reached the Great Recession levels in the middle months of 2020, while the median rate reached that level in October 2022. With output and employment, inflation is one of the most important macroeconomic variables. An inflation rate higher than moderate levels or a deflationary development creates instability in the economy. The instability leads to volatility in economic activities and economic inefficiencies. The result is lower rates of growth and social problems. Workers and pensioners whose wage rates are fixed for a period suffer the most because of inflation. To tame inflation, to reduce it to moderate rates requires reducing output. The cost of this is borne again by lowerincome groups. The Phillips curve has been at the center of the debates on inflation, economic growth, and monetary policy for over sixty years. It has been criticized and changed a lot from its original version of 1958. However, its modeling of the relationship between economic activity and inflation made it a useful and essential tool for policymakers. There are several measures of inflation, measured as the percentage change in prices over time. The most common measure is the Consumer Price Index (CPI), which reflects the changes in prices paid by consumers. The consumer should be a typical consumer. It requires a representative household and thousands of prices for the goods and services that comprise a typical consumer’s budget. Although gathering all needed data may be much simpler compared to earlier years, there may be issues with new commodities introduced and a pandemic where conducting the same surveys with the same accuracy may not be possible. Commodity prices, determined primarily by supply and demand, are influenced by various factors, such as speculators, producers' cartels, force majeure events, and disruptions in the supply chain, like the COVID-19 pandemic. Commodity price cycles have occurred throughout history, with increasing frequencies over time. The unprecedented swings in commodity prices during and after the pandemic had a significant impact on inflation dynamics worldwide. Energy prices, particularly natural gas, and hydrocarbon-based fertilizer prices, experienced the highest increases since 2018, affecting agricultural production costs and global food prices. The pass-through of commodity prices to domestic prices varies depending on the commodity type, inflation regime, exchange rates, and the level of competition in the market. Energy prices reached record highs due to geopolitical tensions, while food prices remained elevated, causing concerns about food insecurity. Metals and minerals experienced a rebound in demand, driven by the recovery of the global economy, but prices have since declined. The empirical studies show that supply chain disruptions, particularly shipping costs, influence import price inflation and domestic prices. These effects change from industry to industry and from economy to economy. Economies more integrated into the global economy are more affected than those less integrated. Island economies are affected more. In general, those economies with a strong central bank are less affected. These results suggest that the study of inflation and inflation policies should take globalization into account. Supply chains, especially global value chains, play an important role in forming inflationary expectations, the price formation behavior of firms, and the labor force. The Phillips curve would perform better with the inclusion of global variables into the model. A disequilibrium in demand and supply of goods & services is reflected in prices. A demand exceeding supply results in increases in prices. The percentage change in the general price level is the rate of inflation. This imbalance between supply and demand may start in the product market, as realized shortages during the pandemic, or it may originate in other markets and affect the product markets. The pandemic had a profound negative effect and created imbalances in labor markets, financial markets, government budget and foreign markets which led to more inflation. Some of these negative effects subsided, but most of them still linger and continue to have adverse effects on all the economies in the world. A very coordinated effort by world leaders and policy makers is the first step that is necessary to combat inflation and other issues that the world faces. Dealing with inflation, domestic or pass-thru global, requires the effective use of the combination of macroeconomic policy tools in a coordinated and judicious manner. Monetary policy, carried out by central banks, and fiscal policy, determined by the executive and legislative bodies of governments, played crucial roles in addressing the pandemic's economic and social effects. Monetary policy aimed to maintain macroeconomic stability, while fiscal policy interventions were targeted at specific problems arising from government-imposed restrictions. Monetary policy instruments such as interest rates, money supply management, inflation targeting, and expectations management were used by central banks worldwide. Interest rate reductions, quantitative easing, liquidity provisions, forward guidance, currency swaps, and targeted lending programs were common measures implemented during the pandemic. Quantitative easing proved to be a powerful tool, involving purchasing financial assets from the market to inject liquidity, lower borrowing costs, and stimulate lending and spending. However, the effectiveness of quantitative easing in stimulating inflation depends on various factors and the state of the economy. Following the formal ending of quantitative easing in major economies, monetary expansion started to slow down and even reverse in some cases in 2022. Policy rates were raised to manage the inflationary pressures. Fiscal policies adopted during the pandemic were a major inflationary shock, supported by the financial system and accommodated by monetary policies. These policies included direct income support, business support and stimulus packages, healthcare and vaccination spending, job protection and retraining programs, infrastructure investment, tax relief and deferrals, debt relief and financial sector support, and enhancements to social welfare programs. The extent to which these fiscal policy instruments were used varied across countries. Targeted interventions were found to have a lower inflationary effect compared to broad-based support. Providing targeted support to households through cash transfers was highlighted as the most cost-effective way to alleviate the burden on vulnerable families. In terms of the relationship between budget surplus and inflation, historical analyses show an inverse correlation in most periods, except during times of oil price hikes. Additionally, the relationship between the rate of inflation and changes in the assets of central banks showed long lags in their impact. The indicators suggest that the current inflationary process is not ending soon, although there is a decline in the global inflation rate. The effect of inflationary shock caused by fiscal policies adopted and implemented during the pandemic is continuing. The supply chain disruptions got weaker; however, the impact of supply shocks is longer lasting than expected. A significant problem is the labor market imbalance leading to sectoral price surges.
