The Effect of Organizatonal Learning on Organizational Commitment in Accommodation Sector

The research was done with the purpose of identifying the relationship between two conceptual structures which were organizational learning and organizational commitment. Main hypothesis was identified as “There is a positive correlation between employees’ organizational learning inclination and organizational commitment.” In order to identify the relation between two conceptual structures, Likert type scale was used by using the literature. Stated likert type scales were applied for 279 people including managers’ and employees’ and statistical analysis on gathered datas from 260 questionnaires was done. In the research, both the correlation between organizational learning and sub-dimensions of organizational commitment which classified as emotional, normative, continuance commitment and the correlation between employees’ age, level of education, position and working period variables and organizational commitment were examined. Regression analysis was used in order to identify the correlation between organizational learning and organizational commitment which was the main hypothesis. Other hypotheses were tested by one way analysis of variance. It was found that that there was a positive correlation between organizational learning and organizational commitment. Result of the analysis indicates that in order to increase organizational learning inclination of managers and employees for developing their organizational commitment, applications for organizational learning should be given importance in companies

The Moderator Effect of the Multinationality Factor on the Relationships Between Organizational Structure and Managerial Competency

The purpose of this study is to reveal the relationship between the organizational structure perceptions and the managerial competence perceptions of individuals working as managers in national and multinational companies in Turkey and to investigate whether these perceptions are affected by “the multi-nationality” factor. In this context, the relationship between the estimation variable "Organizational Structure" and the outcome variable "Managerial Competence" is researched; additionally, the effects of both the estimation variable and the organizational and individual demographic variables on the outcome variable are analyzed. The main research question is whether the managers’ perception of organizational structure affects the perception of managerial competence. The main research hypothesis is that the perception of organizational structure has a positive effect on the perception of managerial competence. The measurement tools developed by the researcher were applied to entry-, middle- and senior-level managers. The research data were obtained from 330 managers working in national companies and 270 managers working in multinational companies operating in Turkey. The results of this research indicated that there was a statistically significant relationship between the estimation variable and the outcome variable and that the perception of organizational structure affects the perception of managerial competence (R2 = 0.530, p <0.05). It was observed that the organizational structure factor explained 53% of the managerial competence factor. In addition, when organizational demographic variables such as “the number of personnel working in the department” and “the model of organization” and individual demographic variables such as “management level” and “total work experience” are considered as factors in the analyses, it was observed that the R2 parameter showing a relationship between the estimation variable and the outcome variable has increased to 62%. Other individual and demographic variables did not contribute to the model, and as a result, their effects are concluded to be either equal or constant. The moderator effect of the "multi-nationality" factor was seen to be not statistically significant, and the moderator effect was not found (R2 = 0.001, p = 0.253> 0.05)

The Effect of Decision Making Competence on Managerial Performance

The performance of the manager, is partly related with decision-making competence. Making decisions properly at the right time and in the best period has the potential to increase the overall success of the manager. Decision-making which means comprehending, thinking, evalua-ting the alternatives and choosing one of the alternatives is a factor that affects manager’s performance directly and provides a competitive advantage for organisations. In this research, the relationships between decision-making competence and managerial performance were discus-sed. The main thesis of this research is that the managers who have high decision-making competence will have high managerial performance. The research was carried out with a population of 424 managers, subordinates, executives and customers/farmers. The evaluation of mana-gerial performance was conducted by taking the factors of subordinates, executives and customers into consideration. The research scales compiled from the literature review and measurement tools developed by the researcher were used in the research. The test of hypothesis was examined by the method of linear regression analysis. The results of this research provided that there was a statistically significant relationship between the decision-making competence of the managers and managerial performance. However, this preliminary study needs to be tested in other businesses and sectors because the data of this study were gathered from a single institution of business

Solar Tracking System based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Fotovoltaik panellerin güç toplama verimliliğini artırmak için genellikle güneş takip sistemleri (GTS) ile entegre edilmelidir. Bu çalışmada, uyarlamalı sinirsel bulanık çıkarım uygulaması ile GTS sunulmuştur. GTS, zenit ve azimut açılarını kontrol eden iki motora sahip çift eksenli olarak tasarlanmıştır. Bu motorların hızının kontrol edilmesi için ANFIS’in tasarlanmasından sonra bulanık mantık kontrolörünün giriş-çıkış ilişkisini öğrenmek için yapay sinir ağı eğitilmiştir. Pozisyon hatası ve hatanın değişimi modellerin girişi olarak alınmıştır. Motora uygulanan gerilim modellerin çıkışı olarak alınmıştır. ANFIS modelde, deneysel verilerden doğrudan üretilen kurallar kümesine sahip yapay sinir ağının öğrenme yeteneği ile bulanık çıkarım modeli birleştirilir. Sonuç olarak, elde edilen sonuçlar GTS için amaçlanan kontrol yaklaşımının doğru cevap ve takip etme etkinliğini doğrular.Solar tracking systems (STS) should usually be integrated with photovoltaic (PV) panel so that the photovoltaic panels can increase power collection efficiency. In this paper, STS with implementation of adaptive neuro-fuzzy inference system (ANFIS) is presented. STS designed as dual axis has two motors that control azimuth angle and zenith angle. After designing an ANFIS for controlling these motors' speed, a Neural Network is trained to learn the input–output relationship of fuzzy logic controller. Position error and error variation were taken as model’s inputs. Applied voltage to the motor was taken as model's output. The ANFIS model is combined modeling function of fuzzy inference with the learning ability of artificial neural network that has set of rules generated directly from the experimental data. Finally, the obtained results confirm the tracking efficiency and correct response of the proposed control approach for STS

Toward a functional reference model for master data quality management

The quality of master data has become an issue of increasing prominence in companies. One reason for that is the growing number of regulatory and legal provisions companies need to comply with. Another reason is the growing importance of information systems supporting decision-making, requiring master data that is up-to-date, accurate and complete. While improving and maintaining master data quality is an organizational task that cannot be encountered by simply implementing a suitable software system, system support is mandatory in order to be able to meet challenges efficiently and make for good results. This paper describes the design process toward a functional reference model for master data quality management (MDQM). The model design process spanned several iterations comprising multiple design and evaluation cycles, including the model's application in a participative case study at consumer goods manufacturer Beiersdorf. Practitioners may use the reference model as an instrument for the analysis, design and implementation of a company's MDQM system landscape. Moreover, the reference model facilitates evaluation of software systems and supports company-internal and external communication. From a scientific perspective, the reference model is a design artifact; hence it represents a theory for designing information systems in the area of MDQ

Measuring Master Data Quality: Findings from a Case Study

Data quality management plays a critical role in all kinds of organizations. Data is one of the most important criteria for strategic business decisions within organizations and the foundation for the execution of business processes. For the assessment of a company’s data quality, to ensure the process execution and to monitor the effectiveness of data quality initiatives, data quality has to be measured in the same way over a certain period of time. This can be achieved by implementing a measurement system. By now, the implementation of such a system to measure data quality is realized in very few organizations. This paper presents a case study describing the implementation process of a Master Data Quality Cockpit as well as the system used for measuring. The study assesses organizational, process-related, and system level changes as well as success factors necessary to implement such a tool

Fachliches Metadatenmanagement mit einem semantischen Wiki

Zusammenfassung: Um aktuellen Herausforderungen weltweiter Märkte begegnen zu können, brauchen Unternehmen ein einheitliches Verständnis ihrer Geschäftsobjekte. Einerseits sind regionale Spezifika zu unterstützen, um weltweit agieren und z.B. günstige Produktionsstandorte nutzen zu können. Andererseits sind einheitliche Daten-strukturen für unternehmensweite Analysen erforderlich, um z.B. globale Einkaufsstrategien umsetzen und vorteilhafte Einkaufskonditionen aushandeln zu können. Zusätzlich müssen Geschäftsobjekte neue Anforderungen seitens des Markts und regulierender Institutionen möglichst schnell abbilden — und das weltweit und konsistent. Ziel eines effektiven Managements von Geschäftsobjekt-Metadaten ist somit die Bereitstellung aktueller, detaillierter, flexibler und gleichzeitig unternehmensweit konsistenter Metadaten (z.B. technische undfachliche Spezifikationen, anwendungsspezifische Informationen zur korrekten Nutzung). Zur Unterstützung dieser Aufgabe stellt der Beitrag das Konzept eines fachlichen Metadatenkatalogs vor und diskutiert einen Wiki-basierten Prototyp, der gemeinsam mit dem Unternehmen Bayer CropScience realisiert wurde. Die Evaluation des Prototyps zeigt, dass sich insbesondere semantische Wikisgutzur Realisierung eines fachlichen Metadatenkatalogs eigne

A Cybernetic View on Data Quality Management

Corporate data of poor quality can have a negative impact on the performance of business processes and thereby the success of companies. In order to be able to work with data of good quality, data quality requirements must clearly be defined. In doing so, one has to take into account that both the provision of high-quality data and the damage caused by low-quality data brings about considerable costs. As each company’s database is a dynamic system, the paper proposes a cybernetic view on data quality management (DQM). First, the principles of a closed-loop control system are transferred to the field of DQM. After that a meta-model is developed that accounts for the central relations between data quality, business process perfor-mance, and related costs. The meta-model then constitutes the basis of a simulation technique which aims at the explication of assumptions (e.g. on the effect of improving a data architecture) and the support of DQM decision processes

Product data quality in supply chains: the case of Beiersdorf

A number of business requirements (e.g. compliance with regulatory and legal provisions, diffusion of global standards, supply chain integration) are forcing consumer goods manufacturers to increase their efforts to provide product data (e.g. product identifiers, dimensions) at business-to-business interfaces timely and accurately. The quality of such data is a critical success factor for efficient and effective cross-company collaboration. If compliance relevant data (e.g. dangerous goods indicators) is missing or false, consumer goods manufacturers risk being fined and see their company's image damaged. Or if logistics data (e.g. product dimensions, gross weight) is inaccurate or provided not in time, business with key account trading partners is endangered. To be able to manage the risk of business critical data defects, companies must be able to a) identify such data defects, and b) specify and use metrics that allow to monitor the data's quality. As scientific research on both these issues has come up with only few results so far, this case study explores the process of identifying business critical product data defects at German consumer goods manufacturing company Beiersdorf AG. Despite advanced data quality management structures such defects still occur and can result in complaints, service level impairment and avoidable costs. The case study analyzes product data use and maintenance in Beiersdorf's ecosystem, identifies typical product data defects, and proposes a set of data quality metrics for monitoring those defect