2021
Authors
Varajão, J; Amaral, A;
Publication
International Journal of Project Management and Productivity Assessment
Abstract
2021
Authors
Martins, P; Rodrigues, P; Martins, C; Barros, T; Duarte, N; Dong, RK; Liao, YY; Comite, U; Yue, XG;
Publication
JOURNAL OF RISK AND FINANCIAL MANAGEMENT
Abstract
This paper aims to (1) compare consumers' preferences between individual products and bundles as well as (2) investigate some of the factors involved in bundle characteristics that may affect consumer's preferences. Those factors are complementarity, price level, and discount level. An online survey developed by means of questionnaires were collected from the Portuguese population. Student's t-tests were used to test the hypothesis formulated and to analyze the consumers' preferences. The findings corroborate that in a scenario where the bundle does not offer any discounts, preference of individual products is higher. When a 20% discount is assigned to bundles, the overall preference for individual products is still superior. By offering a discount level of 45%, the overall preference for bundles becomes higher. The positive effect of complementarity bundles valuation is confirmed. This is the first approach to evaluate the preferences between bundles and individual products in the Portuguese market. The findings contribute to clarify the customer map within a Business Model Canvas. Furthermore, this paper analyzes the bundle complementarity and discount level effects simultaneously.
2021
Authors
Ludviga, I; Niezurawska, J; Duarte, N; Pereira, C; Sluka, I;
Publication
Academy of Management Proceedings
Abstract
2021
Authors
Grzywinska-Rapca, M; Duarte, N; Janusz, M;
Publication
Olsztyn Economic Journal
Abstract
2021
Authors
Kowalewska, G; Markowski, L; Wojarska, M; Duarte, N;
Publication
EUROPEAN RESEARCH STUDIES JOURNAL
Abstract
2021
Authors
Gao, J; Yue, XG; Hao, LL; Crabbe, MJC; Manta, O; Duarte, N;
Publication
INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING
Abstract
The rapid development of Internet technology and information technology is rapidly changing the way people think, recognize, live, work and learn. In the context of Internet + education, the emerging learning form of a cloud classroom has emerged. Cloud classroom refers to the process in which learners use the network as a way to obtain learning objectives and learning resources, communicate with teachers and other learners through the network, and build their own knowledge structure. Because it breaks the boundaries of time and space, it has the characteristics of freedom, high efficiency and extensiveness, and is quickly accepted by learners of different ages and occupations. The traditional cloud classroom teaching mode has no personalized recommendation module and cannot solve an information overload problem. Therefore, this paper proposes a cloud classroom online teaching system under the personalized recommendation system. The system adopts a collaborative filtering recommendation algorithm, which helps to mine the potential preferences of users and thus complete more accurate recommendations. It not only highlights the core position of personalized curriculum recommendation in the field of online education, but also makes the cloud classroom online teaching mode more intelligent and meets the needs of intelligent teaching.
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