Search results

The purpose of this paper is to demonstrate the use of two general purpose decision‐making techniques in selecting the most appropriate maintenance strategy for organizations with critical production requirements.

The Analytical Hierarchical Process (AHP) and the Analytical Network Process (ANP) are used for the selection of the most appropriate maintenance strategy in a local newspaper printing facility in Turkey.

The two methods were shown to be effective in choosing a strategy for maintaining the printing machines. The two methods resulted in almost the same results. Both methods take into account the specific requirements of the organization through its own available expertise.

The techniques demonstrated in this paper can be used by all types of organizations for selecting and adopting maintenance strategies that have higher impact on maintenance performance and hence overall business productivity. The two methods are explained in a step‐by‐step approach for easier adaptation by practitioners in all types of organizations.

The value of the paper is in applying AHP and ANP decision‐making methodologies in maintenance strategy selection. These two methods are not very common in the area of maintenance, and hence add to the pool of techniques utilized in selecting maintenance strategies.

Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring an easier way of handling the hard work of intermittent demand forecasting by using commonly used Excel spreadsheet and also performing parameter optimization.

Smoothing parameters of the forecasting methods are optimized dynamically by Excel Solver in order to achieve the best performance. Application is done on real data of Turkish Airlines’ spare parts comprising 262 weekly periods from January 2009 to December 2013. The data set are composed of 500 stock-keeping units, so there are 131,000 data points in total.

From the results of implementation, it is shown that using the optimum parameter values yields better performance for each of the methods.

Although it is an intensive study, this research has some limitations. Since only real data are considered, this research is limited to the aviation industry.

This study guides market players by explaining the features of intermittent demand. With the help of the study, decision makers dealing with intermittent demand are capable of applying specialized intermittent demand forecasting methods.

The study brings simplicity to intermittent demand forecasting work by using commonly used spreadsheet software. The study is valuable for giving insights to market players dealing with items having intermittent demand characteristics, and it is one of the first study which is optimizing the smoothing parameters of the forecasting methods by using spreadsheet in the area of intermittent demand forecasting.

The metaverse offers great potential for creating a new educational environment with unique experiences. Currently, it has been integrated into many stages of education, including classroom study aids, clinical skill interaction and image training simulators, thanks to a new generation of Internet applications. This paper aims to provide a comprehensive systematic review using bibliometric analysis on the metaverse in health education and analyze the trends and patterns of research output within the field.

The paper conducts bibliometric analysis and follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a rigorous and transparent review process. Specifically, this article identifies research questions, develops a data-collection strategy and establishes a screening approach that includes determining relevant keywords and applying inclusion and exclusion criteria.

A bibliometric analysis is conducted comprising 231 studies from 145 scientific journals to assess the trends, patterns and collaboration networks in research on the use of metaverse technology in health education. This paper provides insights into the research themes, publication trends and countries leading in this field, which can guide future research in this field.

The use of metaverse technology in health education has gained momentum in recent years. Despite this interest, comprehensive studies to review and analyze the existing literature on this topic systematically are lacking. In response, this paper provides a systematic review that explores the potential role of the metaverse in health education. By considering the current research, key trends, research hotspots and opportunities for future investigations are identified. The findings not only shed light on the current state of research but also offer guidance for advancing this exciting field.

This study aims to discuss the transformational effect of the smart governance concept, which is one of the complementary elements of the smart city concept and to explain the change in governance structures according to the developments in information and communication technology.

In this study, the case study as one of the qualitative research methods is preferred, and smart city models of Barcelona, Amsterdam, Kocaeli and Ankara are examined.

In the research, scientific studies in the academic literature were evaluated according to the content analysis, and as a result of this analysis, the cities examined were grouped as “beginner,” “medium” and “advanced.” In the group, the characteristics of smart cities and the services they offer were taken into account. In this context, smart governance methods and their transformational effects are analyzed.

The most important contribution of this study to the literature is to identify the important characteristics of developed and successful smart city initiatives and to encourage their application to other developing world cities as a best practices model.

The purpose of this paper is to manage the demand uncertainty considered as lower and upper levels for a medium-scale industrial distribution planning problem in a biobjective inventory routing problem (IRP). In order to achieve this, the grey system theory is applied since no statistical distribution from the past data and incomplete information.

This study is investigated with optimizing the distribution plan, which involves 30 customers of 12 periods in a manufacturing company under demand uncertainty that is considered as lower and upper levels for a biobjective IRP with using grey demand parameters as a grey integer programming model. In the data set, there are also some missing demand values for the customers. So, the seven different grey models are developed to eliminat the effects on demand uncertainties in computational analysis using a piece of developed software considering the logistical performance indicators such as total deliveries, total cost, the total number of tours, distribution capacity, average remaining capacity and solution time.

Results show that comparing the grey models, the cost per unit and the maximum number of vehicle parameters are also calculated as the new key performance indicator, and then results were ranked and evaluated in detail. Another important finding is the demand uncertainties can be managed with a new approach via logistics performance indicators using alternative solutions.

The results enable logistics managers to understand the importance of demand uncertainties if more reliable decisions are wanted to make with obtaining a proper distribution plan for effective use of their expectations about the success factors in logistics management.

The study is the first in terms of the application of grey models in a biobjective IRP with using interval grey demand data. Successful implementation of the grey approaches allows obtaining a more reliable distribution plan. In addition, this paper also offers a new key performance indicator for the final decision.

Previous researches have proven that customer satisfaction and loyalty are affected by complicated relationships and are challenging to European customer satisfaction index (ECSI) model. Existing approaches mostly limit their hypotheses to linear relationships, which hinder much information that would lead to better modeling and understanding the relationship between customer satisfaction and loyalty. The purpose of this paper is to reveal potential nonlinear and interaction effects that might be embedded in antecedents of ECSI by exemplifying it in Turkish telecommunications sector.

This papar has justified the validity and reliability of the ECSI model implementation in Turk Telekom Company. The path models are tested via conventional structural equation modeling (SEM) and using a novel method, i.e. universal structure modeling with Bayesian neural networks.

The findings of this study reveal that quality has the most important impact on customer satisfaction. The next important construct was found to be the company image. The relationship between customer expectation and customer satisfaction was revealed to be insignificant. This study reveals the fact that while using the ECSI model more attention must be paid to the consideration of potential nonlinear relationships that might be available among model constructs.

This research presents uniqueness in that it reveals significant nonlinear relationships between the model constructs of the ECSI model. Previous studies have identified purely linear relationships, which may not hold true in reality. However, in this study it is revealed that improving one determinant of customer satisfaction may not be as worthy as it is assumed to be in theory, which refers to a nonlinear relationship.

Introduction – The banking sector is one of the most important building blocks of the financial system. A failure in the banking sector can cause serious problems in a country’s economy. In order for countries to achieve economic growth and development goals, the banking sector, which affects all sectors significantly, needs to be strong. Countries with a robust and reliable banking system have a high credit rating. As a result of this high credit rating, the interest of foreign capital in the country increases. Thus, the credit volume of banks expands and loans are provided at a more appropriate rate for investments. In this respect, the performance and profitability of banks are important. The CAMELS performance model is a valuation system used to determine the general status of banks. The CAMELS model consists of six components. According to this, C represents capital adequacy; A, asset quality; M, management adequacy; E, earnings; L, liquidity; and S, sensitivity to market risks.

Purpose – The purpose of this study is to demonstrate the effect of the CAMLS variables on the variable E.

Methodology – In the implementation part of the study, the data of 11 banks in the BIST Bank Index between 2004 and 2018 were used. In the analysis part of the study, a panel data analysis method was used.

Findings – The capital adequacy (C), management adequacy (M) and liquidity (L) variables were effective on profitability. This study revealed the importance of the capital, management and liquidity variables, which are internal factors, in increasing the profitability of banks.

This paper aims to determine the knowledge and attitudes of the physicians regarding human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS), to emphasize that these patients exist and they will exist in the future and to raise awareness so as to prevent that their rights to treatment are revoked.

The survey was conducted via a link sent through an online system. Random physicians from 81 cities of the country were invited to the survey. The survey has 41 questions regarding knowledge and attitudes in total, including epidemiological information such as age, gender and title.

A total of 3,107 physicians has voluntarily participated in the study. In total, 2,195 (70.7%) are internal physicians and 912 (29.3%) are surgical physicians among the participant physicians. In total, 1,452 (46.7%) of the participants are specialist physicians, 608 (19.6%) of the participants are practising physician and the rest of it is physician assistants, academicians and dentists, respectively.

In this study, it has been found out that the physicians have a lack of knowledge on HIV/AIDS and they adopt a discriminatory attitude towards HIV-positive persons. HIV-positive patients who are exposed to discrimination and scared of being uncovered refrain from applying to hospitals for treatment, which puts public health into jeopardy due to the high viral load and these patients are faced with difficulties in coping with both medical and emotional load of the disease.

Purpose: Investors and portfolio managers can earn profitably when they correctly predict when stock prices will go up or down. For this reason, it is crucial to know the effect levels of the factors that affect stock prices. In addition to macroeconomic factors, the psychological behavior of investors also affects stock prices. Therefore, the study aims to reveal the different sensitivity levels of the stock index against macroeconomic and psychological factors.

Design/Methodology/Approach: In this study, dollar rate (USD), euro rate (EURO), time deposit interest rate (IR), gold price (GOLD), industrial production index (IPI), and consumer price index (CPI) (inflation (INF)) were used as macroeconomic factors, while Consumer Confidence Index (CCI) and VIX Fear Index (VIX) were used as psychological factors. In addition, the BIST-100 index, which is listed in Borsa Istanbul, was used as the stock index. The sensitivity of the stock index to macroeconomic and psychological factors was investigated using the Multivariate Adaptive Regression Spline (MARS) method using data from January 2012 to October 2020.

Findings: In the analyses performed using the MARS method, the coefficients of INF, USD, EURO, IR, CCI, and VIX Index were found to be statistically significant and effective on the stock index. Among these variables, INF has the highest effect on stocks. It is followed by USD, IR, EURO, CCI, and VIX. GOLD and IPI variables did not show statistical significance in the model. The most important difference of the MARS model from other regressions is that each factor’s effect on the stock index is analyzed by separating it according to the value of the factor. According to the results obtained from the MARS model: (1) it has been determined that USD, EURO, IR, and CPI have both positive and negative effects on the stock market index and (2) CCI and VIX have been found to have negative effects on stocks. These results provide essential information about how investors who plan to invest in the stock index should take into consideration different macroeconomic and psychological values.

Originality/value: This study contributes to the literature as it is one of the first studies to examine the effects of factors affecting the stock index by decomposing it according to the values it takes. Also, this study provides additional information by listing the factors affecting the stock index in order of importance. These results will help investors, portfolio managers, company executives, and policy-makers understand the stock markets.