RFOS - Repository of Faculty of Organizational Sciences Univ. of Belgrade
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Organisational Design - Learning from Engagement During High-impact Low-frequency Events
No full textThe purpose of this paper is to provide an insight into the structural, cultural and relationship changes among the organisations engaged during high-impact low-frequency events. Our binary basis variable was calculated from the level of engagement in high-impact low-frequency events throughout the Western Balkans area. Furthermore, the findings presented in this paper are based on empirical data obtained through questionnaires among the firefighters, mountain rescue squads, Red Cross, as well as some military and police personnel. Questionnaire respondents were selected due to their engagement during the high-impact low frequency events in the Western Balkan area. Finally, the data analysis revealed that changes in the structure, culture and organisational relationships significantly differ among organisations related to the level of engagement. It shows us a minor change in organisations that engaged less than 25 per cent of personnel during high-impact low-frequency events. In that regard, the paper adds a new value to the expansion and deepening of the existing opus of the literature in the field of Emergency Management and a unique point to the understanding of organisational learning from emergencies and organisational change
D1.1 Ekonomija deljenja – karakteristike, poslovni modeli, primeri platformi i razvojni izazovi (2022)
No full textОвај извештај представља почетну фазу истраживања у оквиру реализације пројекта PANACEA, која има за циљ да научној, стручној и широј јавности
представи основне појмове, карактеристике, моделе реализације и правце развоја економије дељења
How does genre preference influence the importance of film marketing mix elements: evidence during the COVID-19 pandemics
No full textThis paper aims to propose a conceptual model which will unveil how fans of different film genres observe the importance of elements of marketing mix when consuming products in the film industry during COVID-19 pandemics. To verify the proposed conceptual model, a survey was conducted during the lockdown and the responses of 1606 individuals from Serbia, who declare themselves as film fans, were analyzed using structural equation modelling analysis. The results support the assumption that the respondents who prefer different film genres give different importance to elements of the film marketing mix. Our findings show that based on the genre of the film marketing activities can be tailored so as to improve their effects, especially during the pandemics and post-pandemics period. It is believed that the herein presented research could initiate further research on the issue of modelling marketing activities in the film industry based on consumers' genre preference and behavior
BargCrEx: A System for Bargaining Based Aggregation of Crowd and Expert Opinions in Crowdsourcing
No full textCrowdsourcing and crowd voting systems are being increasingly used in societal, industry, and academic problems (labeling, recommendations, social choice, etc.) due to their possibility to exploit "wisdom of crowd" and obtain good quality solutions, and/or voter satisfaction, with high cost-efficiency. However, the decisions based on crowd vote aggregation do not guarantee high-quality results due to crowd voter data quality. Additionally, such decisions often do not satisfy the majority of voters due to data heterogeneity (multimodal or uniform vote distributions) and/or outliers, which cause traditional aggregation procedures (e.g., central tendency measures) to propose decisions with low voter satisfaction. In this research, we propose a system for the integration of crowd and expert knowledge in a crowdsourcing setting with limited resources. The system addresses the problem of sparse voting data by using machine learning models (matrix factorization and regression) for the estimation of crowd and expert votes/grades. The problem of vote aggregation under multimodal or uniform vote distributions is addressed by the inclusion of expert votes and aggregation of crowd and expert votes based on optimization and bargaining models (Kalai-Smorodinsky and Nash) usually used in game theory. Experimental evaluation on real world and artificial problems showed that the bargaining-based aggregation outperforms the traditional methods in terms of cumulative satisfaction of experts and crowd. Additionally, the machine learning models showed satisfactory predictive performance and enabled cost reduction in the process of vote collection
Cookies Implementation Analysis and the Impact on User Privacy Regarding GDPR and CCPA Regulations
No full textThis paper will mostly focus on the analysis of the implementation of cookies and their impact on the data collected from users. The first part of the paper will describe the basic characteristics and concepts of cookies. Their functionalities, categories and possibilities for creation will be presented, as well as the role of the privacy management software and its importance in cookie processing. The last part of the paper will deal with the impact of cookies on user privacy, with reference to two important regulations related to the protection of user privacy (GDPR and CCPA). The processing refers to the technological goals and challenges that arise from the introduction of data protection principles as well as the possibility of overcoming the gap between GDPR and CCPA requirements and technical capabilities. Finally, a description of the general concept of cookies is provided, with the advantages and disadvantages of their introduction. Comparing the approaches of working with cookies contributes users' insight into their specifications in order to correctly draw conclusions about the implementation of cookies. The authors give proposals and critical opinions on safety and potential directions for future development
Guest editorial introduction to the special section on emerging trends and challenges of machine learning in human-computer interaction
No full textHow Entrepreneurial Education and Environment Affect Entrepreneurial Readiness of STEM and Business Students? A Longitudinal Study
No full textThis paper explores how entrepreneurial education and entrepreneurial environment affect entrepreneurial readiness (ER) of students from Science, Technology, Engineering, Mathematics (STEM), and business, i.e., economics and management (E&M) studies. Moreover, it examines how the combination of the aforementioned factors affect the difference in ER between STEM and E&M students. The evaluation is performed on the sample of 595 university students. The results show that two sources of entrepreneurial learning, entrepreneurial experience in the family environment and entrepreneurial education at university, combined with the field of studies represent significant factors that predetermine students' ER. To be able to reach the highest level of ER, the combination of having entrepreneurial environment and entrepreneurial education is crucial for both E&M and STEM students. However, since E&M students show higher level of ER, the paper emphasises the importance of fostering systemic entrepreneurial education among STEM students
A q-rung orthopair fuzzy combined compromise solution approach for selecting sustainable third-party reverse logistics provider
No full textPurposeReverse logistics (RL) is a type of supply chain management that moves goods from the end customer to the original manufacturer for reuse, remanufacturing and disposal purposes. Owing to growing environmental legislations and the development of new technologies in marketing, RL has attracted more significance among experts and academicians. Outsourcing RL practices to third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies due to complexity of RL operations and the lack of available resource. Current sustainability trends have made 3PRLP assessment and selection process more complex. In order to select the 3PRLP, the existence of several aspects of sustainability motivates the experts to establish a new multi-criteria decision analysis (MCDA) approach.Design/methodology/approachWith the growing complexity and high uncertainty of decision environments, the preference values of 3PRLPs are not always expressed with real numbers. As the generalized version of fuzzy set, intuitionistic fuzzy set and Fermatean fuzzy set, the theory of q-rung orthopair fuzzy set (q-ROFS) is used to permit decision experts (DEs) to their assessments in a larger space and to better cope with uncertain information. Given that the combined compromise solution (CoCoSo) is an innovative MCDA approach with higher degree of stability and reliability than several existing methods.FindingsTo exhibit the potentiality and applicability of the presented framework, a case study of S3PRLPs assessment is taken from q-rung orthopair fuzzy perspective. The assessment process consists of three sustainability aspects namely economic, environment and social dimensions related with a total of 14 criteria. Further, sensitivity and comparative analyses are made to display the solidity and strength of the presented approach. The results of this study approve that the presented methodology is more stable and efficient in comparison with other methods.Originality/valueThus, the objective of the study is to develop a hybrid decision-making methodology by combining CoCoSo method and discrimination measure with q-ROFS for selecting an appropriate sustainable 3PRLP (S3PRLP) candidate under uncertain environment. In the proposed method, a novel procedure is proposed to obtain the weights of DEs within q-ROFS context. To calculate the criteria weights, a new formula is presented based on discrimination measure, which provides more realistic weights. In this respect, a new discrimination measure is proposed for q-ROFSs
Hybrid q-Rung Orthopair Fuzzy Sets Based CoCoSo Model for Floating Offshore Wind Farm Site Selection in Norway
No full textUnlocking offshore wind farms' high energy generation potential requires a comprehensive multi-disciplinary analysis that consists of intensive technical, economic, logistical, and environmental investigations. Offshore wind energy projects have high investment volumes that make it essential to conduct extensive site selection to ensure feasible investment decisions that reduce the potential financial risks. Depending on the scenario and circumstances, a ranking of alternative offshore wind energy projects helps to prioritise the investment decisions. Decision-making algorithms based on expert knowledge can support the prioritisation and thus alleviate the work load for investment decisions in the future. The case study considered here is to find the best site for a floating offshore wind farm in Norway from four pre-selected alternatives: Utsira Nord, Stadthavet, Froyabanken, and Tr AE na Vest. We propose a hybrid decision-making model as a combined compromised solution (CoCoSo) based on the q-rung orthopair fuzzy sets (q-ROFSs) including the weighted q-rung orthopair fuzzy Hamacher average (Wq-ROFHA) and the weighted q-rung orthopair fuzzy Hamacher geometric mean (Wq-ROFHGM) operators. In this model, the q-ROFSs based full consistency method (FUCOM) is introduced as a new methodology to determine the weights of the decision criteria. The results of the proposed model show that the best site among the investigated four alternatives is A1: Utsira Nord. A sensitivity analysis has verified the stability of the proposed decision-making model
Model of an intelligent smart home system based on ambient intelligence and user profiling
No full textThis paper introduces a new approach to building an intelligent smart home. The main goal is to leverage a modern, flexible, smart home by employing concepts and technologies of IoT, ambient intelligence, user profiling, and multimedia. The model combines these elements to develop not only an effective platform but also a rich, personalized and unique experience for the smart home users. By using ambient intelligence to gather and analyze the environmental data and combine them with user profiles, the system finds the right multimedia content adapted to the user's needs, based on their habits, time of the day and the weather. Besides, the system adapts the user's environment, as to make them feel as comfortable as possible, by adjusting the amount of light, movement of the curtains and setting the room temperature. The evaluation has shown the presented model possesses great potential for gaining new knowledge in the area of smart environment and IoT, enhancing everyday life, as well as innovations in various contexts