Abstract
Paint bases are the essence of the color palette, allowing for the creation of a wide range of tones by combining them in different proportions. In this paper, an Artificial Neural Network is developed incorporating a pre-trained Decoder to predict the proportion of each paint base in an ink mixture in order to achieve the desired color. Color coordinates in the CIELAB space and the final finish are considered as input parameters. The proposed model is compared with commonly used models such as Linear Regression, Random Forest and Artificial Neural Network. It is important to note that the Artificial Neural Network was implemented with the same architecture as the proposed model but without incorporating the pre-trained Decoder. Experimental results demonstrate that the Artificial Neural Network with a pre-trained Decoder consistently outperforms the other models in predicting the proportions of paint bases for color tuning. This model exhibits lower Mean Absolute Error and Root Mean Square Error values across multiple objectives, indicating its superior accuracy in capturing the complexities of color relationships. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract

Abstract
This study intends to understand whether hotels should choose to surprise through a discount or a surprise gift. The experiment consisted in identifying whether there were differences in satisfaction and delight, according to the associated treatment (no surprise, surprise discount, or gift). With this purpose, a fictional hotel website was created for participants to simulate a reservation. Through the analysis of the experiment, the impact of surprise on customer satisfaction was confirmed. It was also found that, in the hospitality industry, a gift has a higher impact on satisfaction than a discount. When analyzing the guest delight, the results differ from what is stipulated in the literature (which points to the significant impact of surprise in this measure). It was concluded that between the two promotion tools, only the gift can significantly increase customer delight. This study demonstrates the importance of understanding the concept of surprise according to different industries. It also points to the importance of identifying the best methods to surprise customers, as different methods may lead to different results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Increasing digitalization has posed new challenges for businesses, and digital coupons are an important means of promoting their sales. However, there are still gaps in the literature on how they are distributed. The objective of this paper is to study whether the distribution of digital coupons through a referral program increases purchase intention and perceived quality towards a product. By conducting an experimental design, the results point out that consumer purchase intention increases if the recommendation is made by someone with a strong relationship and if a digital coupon is offered, and when the tie relationship is weak or no relationship, it does not vary significantly. On the other hand, the results showed that perceived quality does not vary with the offer of a digital coupon, regardless of the strength of the tie between the person who recommended and the consumer. Current research suggests that managers should use this information to design a digital coupon program tailored to the company’s objectives to retain and capture customers. This new approach to digital voucher distribution is one of the first to investigate their distribution, and their simultaneous use with a referral program. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
What is the relationship between data science, statistics, and Civic Statistics? Are they symbiotic, or are they in conflict? A graphic on the homepage of the American Statistical Association (https://www.amstat.org/ASA/about/home.aspx?hkey=6a706b5c-e60b-496b-b0c6-195c953ffdbc) reads BIGTENT statistics+data science, indicating their intended direction of travel—statistics and data science need to live together. Products of data science (including social media) have transformed modern life. We outline the idea of disruptive socio-technical systems (DST)—new social practices that have been made possible by innovative technologies, and which have profound social consequences—and we point to some examples of technologies that are, or have capacity to facilitate DST. Civic Statistics aims to address pressing social issues, and data science has created new concerns and also new approaches to work on social issues. Here, we argue that this should go beyond simply addressing known problems, and should include empowering citizens to engage in discussions about our possible futures, including the regulation of potential and actual DST. These are exciting times; there are new approaches to knowing about and understanding the world, many of them associated with data science, and students need to engage with these important epistemological issues as a key element in Civic Statistics skills. Here, we relate features of data science to features of Civic Statistics, and to dimensions of knowledge relevant to Civic Statistics. From the viewpoint of Civic Statistics, we argue that we have a responsibility to prepare students for their roles as spectators (understanding the nature and potential of data science products in creating DST), and as referees (having a political voice about which DST are acceptable and unacceptable), and as players (engaging with data science for their own and others’ benefit). We elaborate on the skills needed for these roles. We argue that citizens should use ideas and tools from data science to improve their lives and their environments. © Springer Nature Switzerl and AG 2022.