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This study aims to use a bottom-up, inductive approach to derive destination image attributes from large quantities of online consumer narratives and establish a destination classification system based on relationships among attributes and places.

Content and social network analyses were used to explore the consumer image structure for destinations based on online narratives. Cluster analysis was then used to group destinations by attributes, and ANOVA provided comparisons.

Twenty-two attributes were identified and combined into three groups (core, expected, latent). Destinations were classified into three clusters (comprehensive urban, scenic and lifestyle) based on their network centralities. Using data on Chinese tourism, the most mentioned (core) attributes were determined to be landscape, traffic within the destination, food and beverages and resource-based attractions. Social life was meaningful in consumer narratives but often overlooked by researchers.

Destinations should determine into which category they belong and then appeal to the real needs of tourists. Destination management organizations should provide the essential attributes while paying greater attention to highlighting the destinations’ social life atmosphere.

This research produced empirical work on Chinese tourism by combining a bottom-up, inductive research design with big data. It divided the 49 destinations into three categories and established a new system based on rich data to classify travel destinations.

本研究旨在使用自下而上的归纳方法从大量的在线消费者的叙述中总结出目的地形象的属性, 并根据目的地形象的属性和地点之间的关系建立一个目的地分类系统。

首先通过内容分析方法和社会网络分析方法分析在线消费者的叙述数据得出目的地的消费者形象结构, 然后采用聚类分析方法按照属性对目的地形象进行分组, 并通过方差分析进行比较。

结果显示总结出22种属性, 并将其组合为三组(核心、预期和潜在)。目的地根据其网络中心度被分为三个集群(综合城市、风景和生活方式)。最常被提及的(核心)属性是景观、目的地的交通、食品和饮料以及资源型景点。在消费者的叙述数据中表明社会生活是有意义的, 但常常被研究人员忽视。

首先本研究通过将自下而上的归纳研究设计与大数据相结合对中国旅游业进行了实证研究。其次通过将49个旅游目的地分为三类以及基于大数据建立了一个新的旅游目的地分类系统。

旅游目的地应该明确自己属于哪一类目的地类型然后迎合游客的真正需求。DMOs应该提供旅游目的地的基本属性, 注重提升旅游目的地的社会生活氛围。

Se realizó un análisis de contenido en redes sociales para explorar la estructura de la imagen de los destinos por parte de los consumidores basándose en las descripciones en línea. A continuación, se empleó el análisis de clusters para agrupar los destinos por atributos, estableciendo comparaciones mediante el análisis ANOVA.

Los propósitos de esta investigación eran utilizar un enfoque ascendente e inductivo para obtener atributos de imagen de los destinos a partir de grandes cantidades de descripciones de consumidores en línea, y establecer un sistema de clasificación de destinos basado en las relaciones entre atributos y lugares.

Se identificaron 22 atributos que luego se agruparon en tres grupos (principales, esperados, latentes). Los destinos se clasificaron en tres grupos (urbano integral, paisajístico y de estilo de vida) en función de sus centralidades de red. Utilizando datos sobre el turismo chino, se determinó que los atributos (centrales) más mencionados eran el paisaje, el tráfico dentro del destino, la comida y las bebidas, y las atracciones basadas en los recursos. La vida social era importante en los comentarios de los consumidores, pero a menudo los investigadores la pasaban por alto.

Los destinos deberían determinar a qué categoría pertenecen y luego apelar a las necesidades reales de los turistas. Los DMO deberían proporcionar los atributos esenciales prestando mayor atención a resaltar el entorno de vida social de los destinos.

Esta investigación elaboró un trabajo empírico sobre el turismo chino combinando un diseño de investigación inductiva ascendente con big data. Dividió los 49 destinos en tres categorías y estableció un nuevo sistema basado en los grandes datos para clasificar los destinos turísticos.

The purpose of this study is to give an informative map of sustainable architectural education by focusing on publishing trends, prominent publications, prolific contributors, research challenges and future research prospects. As a consequence, an efficient framework for collecting significant knowledge and identifying prevalent topics in sustainable architectural education towards more sustainable environments at the urban and building scales may be provided.

The methodology adopted in this study is based on examining the subjects of many consecutive rounds of the Passive and Low Energy Architecture (PLEA) conference, with an emphasis on the most recent five rounds. The PLEA's official website served as the main source for gathering all proceedings. Earlier rounds from 2016 to 2020, which cover a time span of five years, were utilized to analyze patterns during that time period. The rationale for selecting this time period was the limited availability of data required to observe the trends, area of interest and emerging themes in these fields that could be analyzed qualitatively.

The findings show that the following drive themes emerged around education and research-driven sustainable architecture: emerging design as a core idea; concentration on the way of delivering and developing education; constructing and shaping the building; assessing current practices; acquiring and recruiting knowledge and new concepts; reporting on the current condition; portraying the target audience and ensuring the long-term viability of the architectural educational process. This insight provides academics and professionals a greater grasp of the state of the art, allowing them to direct their research toward developing concerns in education and research-driven sustainable design.

The study results provide an effective framework for collecting relevant content and identifying prominent topics in sustainable, passive and passive low-energy architecture for the creation of more sustainable urban and building environments. Furthermore, this qualitative and exploratory study may serve as a framework for those researching, creating and verifying different research approaches in education and research-driven sustainable architecture.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

The hospitality industry values segmentation and loyalty programs (LPs), but there is limited research on new methods for segmenting loyalty program members, so managers often rely on conventional techniques. This study aims to use big data-driven segmentation methods to cluster customers and provide a new solution for customer segmentation in hotel LPs.

Using the k-means algorithm, this study examined 498,655 profiles of guests enrolled in a multinational hotel chain’s loyalty program. The objective was to cluster guests according to their consumption behavior and monetary value and compare data-driven segments based on brand preferences, demographic data and monetary value with loyalty program tiers.

This study shows that current tier-based LPs lack features to improve customer segmentation, and some high-tier members generate less revenue than low-tier members. Therefore, more attention should be given to truly valuable customers.

Hotels can segment LP members to develop targeted campaigns and uncover new insights. This will help to transform LPs to make them more valuable and profitable and use differentiated rewards and strategies.

As not all guests or hotel brands benefit equally from LPs, additional segmentation is required to suit varying guest behaviors. Hotel managers can use data mining techniques to develop more efficient and valuable LPs with personalized strategies and rewards.

Building defects are becoming recurrent phenomena in most high-rise buildings. However, little research exists on the analysis of defects in high-rise buildings based on data from real-life projects. This study aims to develop dashboards and models for revealing the most common locations of defects, understanding associations among defects and predicting the rectification periods.

In total, 15,484 defect reports comprising qualitative and quantitative data were obtained from a company that provides consulting services for the construction industry in Victoria, Australia. Data mining methods were applied using a wide range of Python libraries including NumPy, Pandas, Natural Language Toolkit, SpaCy and Regular Expression, alongside association rule mining (ARM) and simulations.

Findings reveal that defects in multi-storey buildings often occur on lower levels, rather than on higher levels. Joinery defects were found to be the most recurrent problem on ground floors. The ARM outcomes show that the occurrence of one type of defect can be taken as an indication for the existence of other types of defects. For instance, in laundry, the chance of occurrence of plumbing and joinery defects, where paint defects are observed, is 88%. The stochastic model built for door defects showed that there is a 60% chance that defects on doors can be rectified within 60 days.

The dashboards provide original insight and novel ideas regarding the frequency of defects in various positions in multi-storey buildings. The stochastic models can provide a reliable point of reference for property managers, occupants and sub-contractors for taking measures to avoid reoccurring defects; so too, findings provide estimations of possible rectification periods for various types of defects.

The purpose of the study is to discover a model of knowledge conversion and knowledge transferring/sharing barriers in an entrepreneurial team (ET) working with innovative users at the early and fuzzy front end (FFE) stage of collaborative product design (Co-PD) process.

The exploratory research framework included sampling, data collection and data analysis, through sequential levels of categorizations, undertaken jointly with constant comparative analysis. The sample frame is the pool of ETs/startups from university business accelerators that carry out Co-PD at the FFE stage. The research survey is based on observations of the collaborative ETs activities, questionnaires and in-depth interviews with them. The research was conducted on individual and team levels when Co-PD process and ET activities were in progress.

This study identified specific set of concepts of knowledge conversion and transferring/sharing and their barriers that reflect specificity of Co-PD processes at the FFE stage in collaborative ETs. The discovered conversion process is represented by the socialization, externalization and internalization, three-mode knowledge conversion model. The significance of barriers and frequency of their occurrence were measured in knowledge transferring/sharing in collaborative ETs on individual and team levels.

This study shows novel insights into how knowledge transfers/shares and converts in the context of ET working with innovative users in Co-PD process at the FFE stage.