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This paper aims to investigate the brand building behavior in the luxury hotel industry from the perspective of frontline employees. In particular, this study addresses the importance and relevance of supportive leadership, brand building behavior and customers’ perceived brand image in the hotel industry.

The research uses data from four luxury class (4 and 5 star) hotels in the Pearl River Delta of China. Contact with frontline employees yielded employee and customer data, with 243 of 369 employee questionnaires having one or more matches with 1,158 customer questionnaires. Hierarchical linear modeling was adopted to test the research model.

Luxury hotels benefit from managers who provide supportive leadership that encourages employee brand building behavior. In turn, employee brand building behavior influences customers’ positive perception of brand image.

Brand building behavior is a top-to-bottom process. Luxury hotels need to pay attention to internal brand building orientation, while managers should reinforce the organization’s cultural orientation and provide appropriate job skills training to improve employees’ willingness and ability to build the company’s brand.

Findings of this study contribute to the brand management literature and the hotel management literature by addressing important matters affecting the frontline employees’ brand building behavior.

The current technique used to measure construction is the conventional total station method. However, the conventional method is time-consuming and could not be used to create a photo-realistic three-dimensional (3D) model of an object. Furthermore, the Canseleri building is located at a slope. The paper aims to discuss this issue.

The aim of this study is to assess the geometric accuracy of a 3D model using unmanned aerial vehicle (UAV) images. There are two objectives in this study. The first is to construct a 3D model of the Canseleri building using UAV images. The second objective is to validate the 3D model of the Canseleri building based on actual measurements.

The close-range photogrammetry method, using the UAV platform, was able to produce a 3D building model. The results show that the errors between the actual measurement and the generated 3D model were less than 4 cm. The accuracy of the 3D model achieved in this study was about 0.015 m, compared to total station measurements.

Accuracy assessment was done by comparing the estimated measurement of the 3D model with the direct measurement. The differences between the measured values with actual values could be compared. Based on this study, the 3D building model gave a reliable accuracy for specific applications.

Light Detection and Ranging (LiDAR) offers a fast and effective way to acquire DSM and extract ground objects such as building, trees and so on. However, it is difficult to extract sharp and precise building boundary from LiDAR data, because its ground sample distance (GSD) is often worse than that of high resolution image. Recently, fusion of LiDAR and high resolution image becomes a promising approach to extract precise boundary. To find the correct and precise boundary, the aim of this paper is to present a series of novel algorithms to improve the quality.

To find the correct and precise boundary, this paper presents a series of novel algorithms to improve the quality. At first, a progressive algorithm is presented to register LiDAR data and images; second, a modified adaptive TIN algorithm is presented to filter ground point, where a region growth method is applied in the adaptive TIN algorithm; third, a novel criterion based on the density, connectivity and distribution of point cluster is developed to distinguish trees point; fourth, a novel method based on the height difference between neighbor points is employed to extract coarse boundaries; at last, a knowledge based rule is put forward to identify correct building boundary from parallel edges.

Thorough experiments, it is conducted that: the registration results are accurate and reliable; filtered ground points has good quality, without missing or redundancy; all tree clusters bigger than one grid are detected, and points of walls and edges are eliminated with the new criterion; detected edges exactly locate at real building boundaries, and statistics show the detection correctness is 98 percent, and the detection completeness is 95 percent.

All results prove that precise boundary can be extracted with fusion of LiDAR and high resolution image.

Legislative changes have opened up new opportunities for building societies in Britain to operate as retail banks offering a wide range of services. These organisations have operated very successfully in their traditional role for many years and have acquired a favourable generic image. The factors underlying consumer perceptions of the images of building societies are explored and compared with those relating to banks. Findings are presented to suggest that, although the building societies retain a generally favourable image, there are aspects of it that may hinder their successful operation in new markets. There is no evidence of the existence of distinct images for particular societies. Possible marketing implications of the findings are considered.

Automated technologies have been applied to facility management (FM) practices to address labour demands of, and time consumed by, inputting and processing manual data. Less attention has been focussed on automation of visual information, such as images, when improving timely maintenance decisions. This study aims to develop image classification algorithms to improve information flow in the inspection-repair process through building information modelling (BIM).

To improve and automate the inspection-repair process, image classification algorithms were used to connect images with a corresponding image database in a BIM knowledge repository. Quick response (QR) code decoding and Bag of Words were chosen to classify images in the system. Graphical user interfaces (GUIs) were developed to facilitate activity collaboration and communication. A pilot case study in an inspection-repair process was applied to demonstrate the applications of this system.

The system developed in this study associates the inspection-repair process with a digital three-dimensional (3D) model, GUIs, a BIM knowledge repository and image classification algorithms. By implementing the proposed application in a case study, the authors found that improvement of the inspection-repair process and automated image classification with a BIM knowledge repository (such as the one developed in this study) can enhance FM practices by increasing productivity and reducing time and costs associated with ecision-making.

This study introduces an innovative approach that applies image classification and leverages a BIM knowledge repository to enhance the inspection-repair process in FM practice. The system designed provides automated image-classifying data from a smart phone, eliminates time required to input image data manually and improves communication and collaboration between FM personnel for maintenance in the decision-making process.

The purpose of this paper is twofold: first, to propose a methodology to empirically investigate the longitudinal development of social media content concerning buildings characterized by iconic architecture and second, to report on the application of this methodology.

We collected and analyzed empirical data of social media content shared via Instagram between 2011 and 2019 on 16 buildings that can be considered iconic architecture projects. Using an automated pipeline, we collected and processed 264,000 posts and 140,000 images from Instagram for the selected case studies. By studying the posting activity of Instagram users through time series analysis and conducting content analysis of the social media posts by means of both image classification and topic modeling, we report on the development of users’ capturing and reception of the selected case studies on Instagram over time.

First, we identify two distinct time patterns of social media content: instantly popular buildings whose popularity fades over time and buildings that gradually gain popularity over time. Second, we distinguish differences in the content of social media posts: some buildings are primarily covered for their architectural features and others for their cultural function and facilities.

Using empirical investigation of Instagram data on iconic architectural projects, we have identified a correlation: buildings primarily posted for their architecture are generally also the ones to gain instant online popularity that subsequently faded over time. In contrast, buildings primarily posted for their function and facilities slowly gained popularity on the social media platform over time.

The purpose of this paper is to present a method and results of evaluating damaged building extraction using an object recognition task in pre‐ and post‐tsunami event. The advantage of remote sensing and its applications made it possible to extract damaged building images and vulnerability easement of wide urban areas due to natural disasters.

The proposed approach involves several advanced morphological operators, among which are adaptive transforms with varying size, shape and grey level of the structuring elements. IKONOS‐2 satellite images consisting of pre‐ and post‐2004 Indian Ocean Tsunami site of the Kalmunai area on the East coast of Sri Lanka were used. Morphological operation using structural element are applied for segmented images, then extracted remaining building foot print using random forest classification method. This work extended further the road lines extraction using Hough transform.

The result was investigated using geographic information system (GIS) data and global positioning system (GPS) ground survey in the field and it appeared to have high accuracy: the confidence measures produced of a completely destroyed structure give 86 percent by object‐based, respectively, after the tsunami in one segment of Maruthamune GN Division.

This study has also identified significant limitations, due to the resolution and clearness of satellite images and vegetation canopy over the building footprint.

The authors develop an automated method to detect damaged buildings and compare the results with GIS‐based ground survey.

This paper aims to interpret the workers’ perception of combined cycle power plants through visual qualities.

In this qualitative research landscape image, the sketching technique is applied as a data collection method to extract participants’ mental images by asking them to draw sketches. The resulted sketches besides obtained verbal and written data were analyzed and coded in three stages to explain the workers’ perception. Visual qualities were studied as a mean which made it possible to interpret the workers’ perception of their workplace.

Careful analysis of the gathered data and the emerged concepts via open coding identify four axial categories of the concepts forming the workers’ perception of the power plants: “inconsistency with nature,” “emphasis on function and product,” “health and environmental threats” and “interpretation of the built form as a mass instead of space.” These four categories support the core category of the proposed theory which is “perceiving building as the machine.” This phrase explains how workers perceive power plants as machines, not as supportive and lively environments. This is followed by consequences, “precedence of building over human” is prominent among them.

In the relevant body of literature, visual impact and visual perception of conventional thermal power plants are largely missed, as well as visual relation to environment focusing on a single building or groups of adjacent buildings. This paper covers both areas via asking for sketches as a data collection method, in addition, to interview the participants to clarify their mental image of the work environment.

This research distinguishes between abstract brand concepts built through the development of diverse product portfolios (i.e. portfolio abstractness) and those built through establishing human-like images (i.e. image abstractness), and investigates the joint effect of the two types of brand abstractness on building brand equity.

The three studies presented use experimental design with participants in a laboratory setting and members of an online participant panel.

Three studies demonstrate that while building abstractness by expanding a brand’s product portfolio can generate favorable brand evaluations, this positive effect is marginal compared to when the brand is imbued with human-like characteristics. Furthermore, the favorable effects on brand equity because of abstractness associated with a human-like brand image are evident in protection from brand dilution in the face of negative publicity.

The findings suggest that a consideration of different forms of abstractness is key to unlocking the complexities of understanding customer-based brand equity.

This research shows that although building abstractness through a diversified product portfolio or a symbolic, human-like brand image can favorably impact customer-based brand equity (i.e. attitudes and responses to negative publicity), the former strategy has a marginal effect compared to the latter.

This is the first research to conceptualize brand abstractness as stemming from broad portfolios or from human-like brand images. Additionally, it provides a holistic understanding of how these two forms of abstractness jointly influence brand evaluations and responses to negative publicity.