Student Thesis Level: Master’s Thesis in Business Administration Corporate Social Responsibility (CSR) networking : A case study on why members join CSR network and why are they sharing their knowledge

Purpose – This research focuses on finding the reasons, why members from different sectors join a cross-sector/multi-stakeholder CSR network and what motivates them to share (or not to share) their knowledge of CSR and their best practices. Design/methodology/approach – Semi-structured interviews were conducted with members of the largest cross-sector CSR network in Sweden. The sample base of 15 people was chosen to be able to represent a wider variety of members from each participating sectors. As well as the CEO of the intermediary organization was interviewed. The interviews were conducted via email and telephone. Findings – The findings include several reasons linked to the business case of CSR such as stakeholder pressure, competitive advantage, legitimacy and reputation as well as new reasons like the importance of CSR, and the access of further knowledge in the field. Further reasons are in line with members wanting to join a network, such as access to contact or having personal contacts. As to why members are sharing their CSR knowledge, the findings indicate to inspire others, to show CSR commitment, to be visible, it leads to business opportunity and the access of others knowledge, and because it was requested. Reasons for not sharing their knowledge would be the lack of opportunity, lack of time and the lack of experience to do so. Originality/value – The research contributes to existing studies, which focused on Corporate Social Responsibility and cross-sector networking as well as to inter-organizational knowledge sharing in the field of CSR.

Brand Awareness among Customers : A case study of ICA-Kvantum

This research paper has been prepared by Bachelor students from Dalarna University in Borlänge. The project is centered on a case study of ICA – Kvantum and its brand awareness among customers. The purpose of this study is to find out that which measures can help ICA-Kvantum to create brand awareness among its current and potential customers by looking in to the importance of information of its offerings and use of effective communication tools to convey this information. Further, to recommend them what they need to do, to increase brand awareness among their customers with the help of managerial implications. The research question was formulated as what actions could be seen effective for ICA-Kvantum to maintain or improve brand awareness among its current and potential customers.The project was created with the help of theoretical concepts of brand awareness, brand loyalty, perceived quality, consumer decision model, integrated marketing communication approach and strategic planning process. These theories were applied in this thesis in order to find out the most effective communication measures to maintain or improve brand awareness among current and potential customers of ICA-Kvantum.The primary and secondary data was collected. Primary data was gathered through the survey among ICA-Kvantum customers in the front of the store in Borlänge. The personal interview with manager was conducted in the office of ICA-Kvantum store located in Borlänge. Secondary data was gathered from textbooks, academic journals, theses and websites.The empirical findings have been presented in detail and then analyzed with the help of theoretical concepts. The analysis and further results from survey and interview focused on importance of information, marketing communication tools, brand awareness and loyalty, perceived quality and implementation of strategic planning process. Moreover, the main weaknesses and strengths of ICA-Kvantun have been evaluated. The conclusion including short summary of analysis and its results have been provided at the end. Each weakness of issues related to brand awareness i.e. importance of information, effectiveness of marketing communication tools and strengths and weaknesses of ICA-Kvantum discussed in the paper, has been pointed out along with solutions and managerial implications.

Barriers to adapt eCommerce by rural microenterprises in Sweden : a case study

Research has shown that small and medium-sized enterprises (SMEs) are rapidly adopting the e-commerce. However, there is nearly no research into how microenterprises are adopting eCommerce. Present paper focus on microenterprise adaption of eCommerce in terms of barriers in relation to already known research on SMEs. A case study, carried out by 12 microenterprises to find out barriers to adapt eCommerce had been done. The empirical results show that the microenterprises share most of the barriers to adapt the eCommerce with studies of SMEs, but also reveal additional factors affecting adaptation option of eCommerce; supplier agreement, communication and customer strategy. Conclusions are that microenterprises need additional support and communication and customer strategy to adapt eCommerce, depending of their requirement and needs of eCommerce.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.