Attempts to adopt agile practices are challenged by outsourcing and distributed ways of working popularized in the world today. This paper focuses on outsourcing vendors and empirically examines the progress of agile and Scrum adoption in such environments through the case of information technology companies in Bosnia and Herzegovina. Qualitative content analysis was employed to analyze data obtained from 142 professionals from 43 companies, whose clients are located around the globe. The empirical results reveal a fairly good level of agile awareness in the tested community, but also confirm the findings of earlier research that obstacles to implementing Scrum in outsourced projects exist, emphasizing issues originating from distant Product Owners, diminished Scrum Master role, and customer influence. Team self-organization is recognized as the best adopted Scrum practice, while the product backlog definition, Scrum events, and the process managed through excessive meetings are among those that still require significant improvements.

This research aims to evaluate JavaScript frontend frameworks by their performance and popularity to provide practitioners with guidance in making informed decisions when choosing frontend frameworks for their projects. For that purpose, three applications with the same features were built using three best-known JavaScript frontend frameworks: Angular, React, and Vue. Comparative analysis was employed to conduct the framework comparison using the content of the three applications. For performance evaluation, tools like PageSpeed Insights, WebPageTest, GTmetrix, Pingdom, Lighthouse, and Google Performance were used. In addition, the research also offers the overall comparison of JavaScript frameworks including documentation quality, ability to develop mobile applications, learning curve, built-in features, etc. The results indicate that while React is accepted as the most popular frontend framework, Vue is the one that offers the best performance. The research also compares JavaScript frameworks by specific features they offer such as state management and analyses the implications of these features to the development process. According to the results, these features have big implications on the development process and they are very important when we are making decisions on which framework we will choose for some project.

This paper aims to show how business intelligence can be applied in the credit card approval process. More specifically, the paper investigates how information like an applicant’s age, credit score, debt, income, and prior default can be used in credit card approval prediction.The dataset used for analysis is a publicly available dataset from the UCI machine learning repository. Logistic regression is used to make a prediction model with a reasonable number of attributes for a comprehensible business model. The Chi-square test of independence is used to test the dependence of credit card approval results with attributes. Research uncovers that prior default is supposed to be the most important attribute in the approval process. Finally, the authors propose several visualizations that could help make smarter decisions with effective credit risk assessment.

This paper demonstrates the application of business intelligence in decision-making in digital advertising through a case study. Data used for analysis was collected during a test phase of an advertising platform. The study analyzes multiple types of traffic, related to countries, browsers, household incomes, and days of a week. Beside tabular reports, the paper presents how to visualize those results using Python libraries to make them more visually appealing. Furthermore, logistic regression was used to build models to detect relationships between the number of impressions and clicks. Finally, the authors propose multiple combinations of data that could be used to create different reports that lead to smarter decision-making and cost-effectiveness.

This paper defines project success factors and aspects that are significant for successful project performance and outcomes, in the context of Information Technology (IT) projects. The list of total 38 factors is obtained through the qualitative content analysis of data collected via survey of 108 IT professionals, through one open-ended question. Detected factors are grouped into five categories: project team, project customer, project planning, project execution and project control. The results extend and support findings of the former quantitative study and the resulting project success model. They emphasize the significance of project team and project control activities for successful project outcomes.

Aim of this paper is to give insight in Covid 19 data and to try to predict whether individual person will recover from this virus. Furthermore, this paper aims to give some answers how information like the country, the age, and the gender of the patient, the number of cases in their country and whether they’re from or have visited Wuhan can be used to make that prediction. Study uses Novel Corona Virus (COVID-19) epidemiological dataset. Logistic regression model and Random Forest algorithm are used in order to make prediction, and the Chi-Square test of independence is used to determine if there is a significant relationship between two nominal (categorical) variables. Paper reveals that recovery/survival is supposed to depend on the age of the patient, gender and country from which patient come. Information whether the patient is from Wuhan or has visited Wuhan does not affect recovery/survival of patient.