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The purpose of this paper is to better understand knowledge management (KM) methods that can be carried out to determine the critical processes in that KM can provide important benefits.

In order to achieve this objective, a literature review was developed and a case study was applied in a building materials company in Turkey.

The results reflect that critical processes for KM can be determined in relation to four criteria. These are: value added, decision support, information‐material intensity, and information amount.

The paper points out how KM methods can be implemented in organizations effectively. This article provides a frame which explains how knowledge‐based process analyses can be applied.

Despite the intense social media (SoMe) campaigns promoting organ donation, the direct impact on registration and transplantation rates remains unclear among Sunni Muslims who constitute a significant portion of Muslim population. Given the observed tendency to avoid engaging with SoMe content focused on organ donation promotion, this study aims to comprehend the reasons for reluctance among Sunni Muslims.

A qualitative approach comprising focus group and individual interviews was conducted among community manager of SoMe campaigns interested in promoting organ donation, Sunni Muslims subscribed on those SoMe platforms and certain members of the medical staff involved in transplant operations.

The results indicate that reluctance toward SoMe campaigns about organ donation is justified because of the creation of irrelevant content that does not align with the sociocultural characteristics of the majority Sunni Muslims who are the intended audience. Additional discussions are required concerning religious beliefs, the culture of altruism and the credibility of SoMe appeals.

This research could serve as a foundation upon which social organizations and associations, focused on public health promotion through SoMe, can build specific content designs tailored for Sunni Muslims.

The distinctive aspect of this research is founded upon the diverse perspectives of various stakeholders, which have the potential to impact the registration of Muslim users on SoMe as organ donors.

The crude oil market plays a key role in addressing the issue of energy economics. This paper aims to detect the causality relationship between the crude oil market and economy based on the financial system.

This paper used the static and dynamic Hatemi-J Bootstrap Toda–Yamamoto and Diebold–Yilmaz connectedness index. The Hatemi-J Bootstrap Toda-Yamamoto approach allows researchers to use nonstationary data and that method is robust to nonnormal distribution and heteroscedasticity. The Diebold–Yilmaz connectedness index model provides researchers to detect the power of connectedness besides linkage direction. The analyzed period is the span from January 3, 2005 to October 3, 2022.

The results show bidirectional causality in the full sample but unidirectional causality before and after the 2008 financial crisis. During the 2008 financial crisis period and the COVID-19 period, there was a bidirectional and unidirectional causality, respectively. The connectedness approach indicates that the crude oil market affects financial stress through investors’ risk preferences.

The Diebold–Yilmaz spillover index model is based on vector autoregression methods with a stationarity precondition. However, some of the five dimensions that constitute the financial stress index (FSI) are nonstationary in level. Therefore, the authors takes the first difference of the nonstationary data.

The linkage between the crude oil market and the FSI provides useful information for investors and policymakers. For instance, this paper indicates that an investor wanted to forecast future value of the crude oil (financial stress) should consider the current and past values of financial stress (crude oil). Moreover, policymaker should consider the crude oil market (FSI) to make a policy proposal for financial system (crude oil market).

Recently, indicators of economic activity levels (economic policy uncertainty, implied volatility index) have begun to be considered to analyze the relationship between energy and the economy but very little is known in the literature about the leading and lagging roles of data in subsample periods and the linkage channel. The other originality of this research is using the new econometric approaches.

Human resources can differentiate firms from their competitors or directly affect the success or failure of firms. As in many sectors, there is a need for qualified employees in the logistics sector, which is of great importance for the national economies. Qualified employees in this sector contribute to the success of the companies and the development of the industry. In this study, it is aimed to reveal the qualifications and characteristics of the labour force needed by logistics companies. It was also aimed to show the impact of COVID-19 on logistics job ads. For this purpose, a total of 1,410 job vacancy postings (before COVID-19) and a total of 1,700 job vacancy postings (during COVID-19) were searched on the kariyer.net website with the word “logistics” and analysed by content analysis method. As a result, it was found that the most advertised province was Istanbul in both periods, the most looked up experience requirement in the candidates was 1-5 years in both periods, the opportunities provided to the candidates (transportation, food and beverage, career, social activity) were not mentioned much in both periods. This study reveals the status of logistics job postings in the period before COVID-19 and during COVID-19. It was also aimed to show the impact of COVID-19 on logistics job ads. We investigated whether the logistics employee demand has changed and whether the pandemic is affecting workforce characteristics. This is the first empirical analysis of the impact of COVID-19 on logistics vacancy postings.

This paper aims to investigate how predictions of football league standings and efficiency measures of teams, obtained through frontier estimation technique, evolve compared to actual results.

The study is based on data from the Turkish first division football league. Historical data for five seasons, from 2011 to 2016, are used to compare weekly estimates to de facto results. Data envelopment analysis efficiency measures are used to estimate team performances. After each week, a data envelopment analysis is run using available data until then, and final team standings are estimated via computed efficiencies. Estimations are improved by using a data envelopment analysis model that incorporates expert knowledge about football.

Results indicate that deductions can be made about the league’s future progress. Model incorporating expert knowledge tends to estimate the performance better. Although the prediction accuracy starts out low in early stages, it improves as the season advances. Scatter of individual teams’ performances show fluxional behaviour, which attracts studying the impact of uncontrollable factors such as refereeing.

While all previous studies focus on season performance, this study handles the problem as a combination of weekly performance and how it converges to reality. By tracking weekly performance, managers get a chance to confront their weak performance indicators and achieve higher ranking by improving on these inefficiencies.

This study aims to examine the links between oil price shocks and Gulf Cooperation Council (GCC) stock markets from February 2004 to December 2019. Knowledge of such links is important to both investors and policymakers in understanding the transmission of shocks across markets.

The authors use the Ready (2018) oil price decomposition method and the quantile regression approach to conduct the analysis.

Initial results show a positive oil price change increases stock returns, while greater volatility decreases returns. The oil shock decomposition results reveal a significant positive impact of supply-side shocks on stocks. This contrasts with the literature that argues demand-side shocks are more important. While factors such as liquidity and the lack of hedging instruments can increase the vulnerability of GCC equities to oil price shocks, the result reflects the unique economic structure of the GCC bloc, notably, marked by dependency on oil revenues. In analysing quantile-based results, oil supply shocks mainly exhibit lower-tail dependence, while the authors do uncover some evidence of demand-side shocks affecting mid and upper-tail dependence.

Acknowledging the presence of endogeneity in the relation between oil and economic activity, to the best of the authors’ knowledge, this study is the first to combine the oil price decompositions of Ready (2018) with a quantile regression framework in the GCC context. The results reveal notable difference to those previously reported in the literature.

The purpose of this paper is to propose a scenario-based grey methodology using clustering and optimizing with imprecise and uncertain body size data in an emergency assembly point area to assign the people on a campus to reach the emergency assembly points under uncertain disaster times.

Grey clustering and a new grey p-median linear programming model are developed to determine which units to assign to the pre-determined assembly points for a main campus in case of a disaster. The models have two scenarios: 70 and 100% occurrence capacities of administrative and academic personnel and students.

In this study, the academic and administrative units have been assigned to determine five different emergency assembly points on the main campus by using the numbers of the academic and administrative personnel and student and distances of the units to the assembly point areas of each other. The alternative solutions are obtained effectively by evaluating capacity utilization rates in the scenarios.

It is often unclear when disasters can occur and therefore, a preliminary preparation time must be required to minimize the risk. In the case of natural, man-made (unnatural) or technological disasters, the people are required to defend themselves and move away from the disaster area as soon as possible in a proper direction. The proposed assignment model yields a final solution that effectively eliminates uncertainty regarding the selection of emergency assembly points for administrative and academic staff as well as students, in the event of disasters.

Grey clustering suggests an assignment plan and concurrently, an investigation is underway utilizing the grey p-median linear programming model. This investigation aims to optimize various scenarios and body sizes concerning emergency assembly areas. All campus users who are present at the disaster in units of the campus are getting uncertainty about which emergency assembly point to use, and with this study, the vital risks aim to be ultimately reduced with reasonable plans.

Supplier selection is a complex multiple criteria decision (MCDM) problem which directly depends on decision makers’ choice. Some decisions are getting involved with linguistic variables and they are not mathematically operable. To solve a typical decision problem through MCDM techniques, a number or a numerical interval should be defined. The purpose of this paper is to focus on that numerical interval and in a case of supplier selection, the aim is to close the decisions to the real number that the decision maker mentions and this number is in a numerical interval.

The proposed method deals with grey relational analysis (GRA) and develops it by applying triangular fuzzy numbers. The grey numbers have two defined bounds; the proposed method defines two fuzzy bounds for each grey attribute. In the proposed method, the fuzzy membership function has been employed for each bounds of grey attribute to make them to fuzzy bounds with two undefined bounds. Also to make comparison, with employing of TOPSIS technique, both of the grey fuzzy combination decision matrix and the original grey decision matrix are obtained.

The results indicate that, except to the ideal solutions, the grey relation coefficient for each alternative is too close to each other. Indeed, they are too close to zero. Applying the proposed method in problem of supplier selection shows the difference between two selected supplier in proposed method and the original grey method.

As mentioned heretofore this paper aims to make decision makers’s decision more accurate and actually there is no other researches which used this combination method.

This paper aims to examine the impact of the Greek economic crisis on the sustainability of the Turkish economy.

A generalized autoregressive conditional heteroskedasticity (GARCH) model is used over the Thomson Reuter’s Turkey Index for the period of May 1999 to August 2018 using monthly data. The control variables introduced in the proposed model are the S&P 500 of the US stock market and crude oil prices which are used to isolate more general systemic factors.

The structural analysis of volatility with the EGARCH model has shown that current volatility is more influenced by past volatility than by previous month shocks.

The results can be exploited by investors, portfolio managers and policy makers in their decision-making process.

It is a first-time effort that examines the impact of the Greek economic crisis on the sustainability of the Turkish economy. The developed methodology can be used by investors, portfolio managers and policy makers in their decision-making process.