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The Estimation of the Optimal Level of Productivity for Sponsors in the Recovery and Enhancement of the Historical-Architectural Heritage

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12253))

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Abstract

The paper explores the theme of sponsorship aimed at enhancing the historical-architectural heritage, analyzing the point of view of companies. Specifically, a static analysis model proposed in a previous paper, whose objective is to establish the optimal amount to invest in sponsorship to maximize business profit, is integrated with an innovative model that allows the company to assess the degree of financial efficiency of the investment, i.e. its productivity \( \Upsilon \). The latter depends on a series of variables that characterize both the monument to be enhanced, the location in which it is located, and the sponsorship strategy adopted by the company. However, in the case of sponsorship of recovery/restoration work, the critical variable that most affects the efficiency of the investment is the number of visitors to the location where the monument is located (direct audience). This is because as the number of visitors increases, the level of exposure to the sponsorship message increases. Therefore, we assume two functional relationships, one linear and the other logarithmic, which correlate financial efficiency to the average number of visitors. In this way, the advantage of the company is twofold. On the one hand, it has the opportunity to maximize profits by investing the optimal amount of sponsorship. On the other hand, it can choose the level of productivity of the investment by deciding to finance the monument of a specific location according to its degree of exposure.

L. Dolores, M. Macchiaroli, G. De Mare, A. Nesticò, G. Maselli and E. M. Gómez—The six authors contributed equally to this work.

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Authors and Affiliations

  1. University of Salerno, Via Giovanni Paolo II, 132, Fisciano, SA, Italy

    Luigi Dolores, Maria Macchiaroli, Gianluigi De Mare, Antonio Nesticò & Gabriella Maselli

  2. Nebrija University, Calle Pirineos, 55, 28040, Madrid, Spain

    Elena Merino Gómez

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  1. Luigi Dolores
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  2. Maria Macchiaroli
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  3. Gianluigi De Mare
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  6. Elena Merino Gómez
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Correspondence to Luigi Dolores .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Dolores, L., Macchiaroli, M., De Mare, G., Nesticò, A., Maselli, G., Gómez, E.M. (2020). The Estimation of the Optimal Level of Productivity for Sponsors in the Recovery and Enhancement of the Historical-Architectural Heritage. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12253. Springer, Cham. https://doi.org/10.1007/978-3-030-58814-4_20

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