Purpose: This research aimed to explore whether different project management approaches (traditional, agile or hybrid) differentiate concerning their impact on project success, taking project success as multidimensional phenomena. In addition to this, the authors wanted to explore if specific project characteristics moderate these effects.Design/methodology/approach: The authors empirically addressed these on a sample of 227 project professionals worldwide. The exploratory factor analysis (EFA) of project success dimensions was done to validate these factors' constitution concerning their manifest variables. The K-means cluster method was used to distinguish respondents' profiles among agile, hybrid and traditional project management approaches. To test the significance among research groups, the research hypotheses were tested with ANOVA tests.Findings: The authors evidenced that the agile approach has a more significant positive impact concerning the two out of five dimensions of project success, under analysis in this research (impact on the team and preparing for the future), over the traditional approach.Practical implications: The research is relevant for project management practitioners to tailor the success-oriented project management approach and for academics to develop project management contingency theory.Originality/value: The authors constructed a research framework to test the impact and effectiveness of different project management approaches (traditional, agile, hybrid) on the dimensions of project success in different contextual conditions (organization industry, project type, novelty, technology, complexity and pace). The paper's main contribution is to expand data on the impact of these approaches on project success and compare them with relevant results and findings of previous research.

Land administration systems are of great importance for a large number of stakeholders. One of the key problems related to land administration systems is the problem of the correctness of their state, meaning that data stored in land administration systems are not in concordance with the actual legal, spatial and topographic situation. The main causes of land administration systems’ incorrect state are data collection and compilation, data processing, and data misuse. In this paper, we discuss the problems of data tampering, the long time needed for registering land administration system’s transactions, and the possibility of double spending, which all can add incorrectness in a land administration system. Our research is based on the hypothesis that these problems may be addressed by means of distributed ledger technology, or to be more precise, by means of blockchain technology. The solution is presented in a form of a smart contract written in Solidity programing language that can cover even those more specific use cases in land administration systems such as sharing of ownership, transferring part of ownership, splitting or merging of real estate, and limiting the possibility of trading a real estate. The proposed smart contract represents an implementation of a programming interface that was created based on both ERC-20 and ERC-721 token standards, to satisfy the specific needs of land administration systems.

Customer churn is a problem virtually all companies face, and the ability to predict it reliably can be a cornerstone for successful retention campaigns. In this study, we propose an approach to customer churn prediction in non-contractual B2B settings that relies exclusively on invoice-level data for feature engineering and uses multi-slicing to maximally utilize available data. We cast churn as a binary classification problem and assess the ability of three established classifiers to predict it when using different churn definitions. We also compare classifier performance when different amounts of historical data are used for feature engineering. The results indicate that robust models for different churn definitions can be derived by using invoice-level data alone and that using more historical data for creating some of the features tends to lead to better performing models for some classifiers. We also confirm that the multi-slicing approach to dataset creation yields better performing models compared to the traditionally used single-slicing approach.

Video quality as perceived by human observers is the ground truth when Video Quality Assessment (VQA) is in question. It is dependent on many variables, one of them being the content of the video that is being evaluated. Despite the evidence that content has an impact on the quality score the sequence receives from human evaluators, currently available VQA databases mostly comprise of sequences which fail to take this into account. In this paper, we aim to identify and analyze differences between human cognitive, affective, and conative responses to a set of videos commonly used for VQA and a set of videos specifically chosen to include video content which might affect the judgment of evaluators when perceived video quality is in question. Our findings indicate that considerable differences exist between the two sets on selected factors, which leads us to conclude that videos starring a different type of content than the currently employed ones might be more appropriate for VQA.

This study investigated the crucial factors for measuring the success of the information system used in the e-learning process, considering the transformations in the work environment. This study was motivated by the changes caused by COVID-19 witnessed after the shift to fully online learning environments supported by e-learning systems, i.e., learning emphasized with information systems. Empirical research was conducted on a sample comprising teaching staff from two European universities: the University of Novi Sad, Faculty of Technical Sciences in Serbia and the Polytechnic Institute of Castelo Branco in Portugal. By synthesizing knowledge from review of the prior literature, supported by the findings of this study, the authors propose an Extended Information System Success Measurement Model—EISSMM. EISSMM underlines the importance of workforce agility, which includes the factors of proactivity, adaptability, and resistance to change, in the information system performance measurement model. The results of our research provide more extensive evidence and findings for scholars and practitioners that could support measuring information system success primarily in e-learning and other various contextual settings, highlighting the importance of people’s responses to work environment changes.