The Internet introduces new business choices for customer interaction. In this article we introduce two claims. Firstly, we will show that the way companies shape their customer interaction, and not their sector or size, determine the market segmentation. Secondly, Internet dynamics and its effect on customer interaction rebalances the companies’ marketing and sales function: the Internet shortens the time window for new market opportunities and makes everyone a salesman. Therefore, traditional marketing activities become more and more part of Sales. Corporate communication and branding become more vital.
Masonry structures represent the highest proportion of building stock worldwide. Currently, the structural condition of such structures is predominantly manually inspected which is a laborious, costly and subjective process. With developments in computer vision, there is an opportunity to use digital images to automate the visual inspection process. The aim of this study is to examine deep learning techniques for crack detection on images from masonry walls. A dataset with photos from masonry structures is produced containing complex backgrounds and various crack types and sizes. Different deep learning networks are considered and by leveraging the effect of transfer learning crack detection on masonry surfaces is performed on patch level with 95.3% accuracy and on pixel level with 79.6% F1 score. This is the first implementation of deep learning for pixel-level crack segmentation on masonry surfaces. Codes, data and networks relevant to the herein study are available in: github.com/dimitrisdais/crack_detection_CNN_masonry.
Purpose: Little is known about how tourists’ eating habits change between everyday life and holidays. This study aims to identify market segments based on changes in food consumption and experiences of a sun-and-sea destination’s local food. The authors evaluate to what extent tourists consume local food and assess the contribution of local food experiences to the tourists’ overall experience. Design/methodology/approach: The target population was all tourists visiting the Algarve in the Summer 2018 and included both domestic and international sun-and-sea tourists. A sample of 378 valid questionnaires was collected. Data analysis included descriptive analysis, statistical tests and cluster analysis. Findings: Cluster analysis identified three segments: non-foodies, selective foodies and local gastronomy foodies. Results indicate that tourists change their eating habits during holidays, eating significantly more seafood and fish and less legumes, meat, fast food and cereals and their derivatives. International and domestic sun-and-sea tourists reported that eating local food contributes significantly to their overall tourism experience. Practical implications: Sun-and-sea destinations should promote the offer of local dishes, especially those that include locally produced fish and seafood, to improve the tourist experience, differentiate the destination and increase sustainability. Originality/value: The authors address three identified research gaps: a posteriori segmentation based on tourists’ food consumption behaviour; measurement of changes in eating practices between home and in a sun-and-sea destination; and assessment of the role of food experiences to overall tourism experience of tourists visiting a sun-and-sea destination.
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