The main purpose of this study is to investigate CSR activities of national, foreign and private banks listed in Borsa İstanbul (BIST) 30 Index in Turkey and reveal the differences between these banks in terms of the fulfilment of their social responsibilities. Within this scope; the concept of CSR, CSR definitions, the main reasons of CSR activities and four dimensions of CSR constitute the conceptual framework of the study. On the other hand; shareholder theory, stakeholder theory and institutional theory establish the theoretical framework of this study. In parallel with the study purpose, CSR practises of three different banks listed in BIST 30 Index as of the second quarter of year 2016 were analyzed. The relevant data were acquired from archival sources such as corporate governance compliance reports, annual sustainability reports and official company websites. The qualitative research was considered to be useful method to clarify the differences in social responsibility practises. For this reason, content analysis as a qualitative research method was used in order to investigate CSR activities of banks. Research findings reveal that there are not significant differences between Isbank (private bank) and VakıfBank (national bank) with regard to the fulfilment of their social responsibilities. On the other side, Garanti Bank (foreign bank) implements more CSR practises in comparison with other banks. This dissimilarity is also observed among CSR headings. All in all, a great majority of CSR activities are carried out about environmental conservation and stakeholders.

The spatial heterogeneity in hydrologic simulations is a key difference between lumped and distributed models. Not all distributed models benefit from pedo-transfer functions based on soil properties and crop-vegetation dynamics. Mostly coarse scale meteorological forcing is used to estimate water balance at the catchment outlet only. Mesoscale hydrologic model (mHM) is one of the rare models that incorporates remote sensing data i.e. leaf area index (LAI) and aspect to improve actual evapotranspiration (AET) simulations and water balance together. The user can select either LAI or aspect to scale PET. However, herein we introduced a new weighting parameter “alphax” that allows user to incorporate both LAI and aspect together for PET scaling. With this mHM code enhancement, the modeler has an also option of using raw PET with no scaling. In this study, streamflow, and AET are simulated using the mesoscale Hydrological Model (mHM) in Main (Germany) basin for the period of 2002-2014. The additional value of PET scaling with LAI and aspect for model performance is investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) AET and LAI products. From 69 mHM parameters, 26 parameters are selected for calibration using Optimization Software Toolkit (OSTRICH). For calibration and evaluation, KGE metric is used for water balance and SPAEF metric is used for evaluating spatial patterns of AET. Our results show that AET performance of the mHM is highest when using both LAI and aspect indicating that LAI and aspect contain valuable spatial heterogeneity information from topography and canopy (e.g., forests, grasslands, and croplands) that should be preserved during modeling. The additional “alphax” parameter makes the model physically more flexible and robust as the model can decide the weights according to the study domain.

The spatial heterogeneity in hydrologic simulations is a key difference between lumped and distributed models. Not all distributed models benefit from pedo-transfer functions based on the soil properties and crop-vegetation dynamics. Mostly coarse-scale meteorological forcing is used to estimate only the water balance at the catchment outlet. The mesoscale Hydrologic Model (mHM) is one of the rare models that incorporate remote sensing data, i.e., leaf area index (LAI) and aspect, to improve the actual evapotranspiration (AET) simulations and water balance together. The user can select either LAI or aspect to scale PET. However, herein we introduce a new weight parameter, “alphax”, that allows the user to incorporate both LAI and aspect together for potential evapotranspiration (PET) scaling. With the mHM code enhancement, the modeler also has the option of using raw PET with no scaling. In this study, streamflow and AET are simulated using the mHM in The Main Basin (Germany) for the period of 2002–2014. The additional value of PET scaling with LAI and aspect for model performance is investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) AET and LAI products. From 69 mHM parameters, 26 parameters are selected for calibration using the Optimization Software Toolkit (OSTRICH). For calibration and evaluation, the KGE metric is used for water balance, and the SPAEF metric is used for evaluating spatial patterns of AET. Our results show that the AET performance of the mHM is highest when using both LAI and aspect indicating that LAI and aspect contain valuable spatial heterogeneity information from topography and canopy (e.g., forests, grasslands, and croplands) that should be preserved during modeling. This is key for agronomic studies like crop yield estimations and irrigation water use. The additional “alphax” parameter makes the model physically more flexible and robust as the model can decide the weights according to the study domain.

Recommendation has become an inseparable component of many software applications, such as e-commerce, social media and gaming platforms. Particularly in collaborative filtering-based recommendation solutions, the preferences of other users are considered heavily. At this point, trust among the users comes into the scene as an important concept to improve the recommendation performance. Trust describes the nature and the strength of ties between individuals and hence provides useful information to improve the recommendation accuracy, particularly against data sparsity and cold start problems. The Trust notion helps alleviate the effect of these problems by providing additional reliable relationships between the users. However, trust information, specifically explicit trust, is not straightforward to collect and is only scarcely available. Therefore, implicit trust models have been proposed to fill in the gap. The literature includes a variety of studies proposing the use of trust for recommendation. In this work, two specific sub-problems are elaborated on: the relationship between explicit and implicit trust scores, and the construction of a machine learning model for explicit trust. For the first sub-problem, an implicit trust model is devised and the compatibility of implicit trust scores with explicit scores is analyzed. For the second sub-problem, two different explicit trust models are proposed: Explicit trust modeling through users’ rating behavior and explicit trust modeling as a link prediction problem. The performances of the prediction models are analyzed on a set of benchmark data sets. It is observed that explicit and implicit trust models have different natures, and are to be used in a complementary way for recommendation. Another important result is that the accuracy of the machine learning models for explicit trust is promising and depends on the availability of data.

Are groups and teams the same thing? What turns a group into a team? What differentiates a team from a group? These are all critical questions and understanding of them is crucial to understanding of what makes an effective team. The main purpose of this study is to examine effectiveness of groups and teams on organizational performance within a business environment. Within this scope; definitions of groups and teams, similarities and differences between groups and teams, effectiveness of team performance and effectiveness of groups are investigated in order to clarify whether groups and teams are the same. This study reveals that the effectiveness of a group or a team is dependent on which market sector the organization is in and the organizational objectives. As a result, groups and teams are very much different. However they have the ability to produce the same outcome which is successful organizational performance.