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Digitalisation, which involves the use of digital technologies in transforming an organisation’s activities, transcends just the acquiring of emerging digital tools. Having the right people to drive the implementation of these technologies and attaining strategic organisational goals is essential. While most studies have focused on the use of emerging technologies in the construction industry, less attention has been given to the ‘people’ dimension. Therefore, this study aims to assess the people-related features needed for construction digitalisation.

The study adopted pragmatic thinking using a mixed-method approach. A Delphi was used to achieve the qualitative aspect of the research, while a questionnaire survey conducted among 222 construction professionals was used to achieve the quantitative aspect. The data gathered were analysed using frequency, percentage, mean item score, Kruskal–Wallis H test, exploratory factor analysis and confirmatory factor analysis.

Based on acceptable reliability, validity and model fit indices, the study found that the people-related factors needed for construction digitalisation can be grouped into technical capability of personnel, attracting and retaining digital talent and organisation’s digital culture.

The findings offer valuable benefits to construction organisations as understanding these identified people features can help lead to better deployment of digital tools and the attainment of the digital transformation.

This study attempts to fill the gap in the shortage of literature exploring the people dimension of construction digitalisation. The study offers an excellent theoretical backdrop for future works on digital talent for construction digitalisation, which has gained less attention in the current construction digitalisation discourse.

This study aims to analyze communication from the perspective of social marketing, positive emotions, and the topics chosen by Spanish tourist destinations to show their destination image. Additionally, this research shows a message classification model, based on the aforementioned characteristics, that has generated a greater impact, offering clarity to tourism managers on the type of content they should publish to achieve greater visibility.

The methodology used in this work combines content analysis and data mining techniques. The classification tree using the chi-square automatic interaction detector (CHAID) algorithm was selected to determine predictors of like behaviour.

The results show that the predictor variables have been emotions, social marketing and topics. Also, the characteristics of the messages most likely to have a high impact are those related to emotions of joy or happiness, their purpose is behavioural, and they talk about rural, cultural issues, special dates, getaways, or highlights of a town or city for something specific.

This study is the first to analyze the content of the tweets shared by destination tourism managers from a social marketing, positive emotions, and sustainability perspective, determining the possible predictors of likes on Twitter. The authors contribute to the literature by deepening the understanding of how social marketing and the positive emotions promoted drive a more significant impact in tourism communication campaigns on social media. The authors provide destination managers with a way better to understand the variables relevant to users in tourism content.

La presente investigación examina la existencia de memoria de largo plazo por medio del cálculo del coeficiente de Hurst y Hurst ajustado, y del análisis de características de estructuras caóticas en la serie del mercado bursátil de Chile, específicamente a través del Índice de Precios Selectivo de Acciones.

Se desarrolló un breve análisis del mercado, según la metodología de Box y Jenkings. La validez de los resultados se realizó por medio de la prueba propuesta por Brock, Dechert y Scheinkman. En segundo lugar, se procedió a analizar la dinámica y patrones del índice y de su rendimiento, para observar si existía evidencia de memoria de largo plazo.

Los resultados demuestran la presencia de esta memoria en el mercado bursátil chileno, determinado a través del índice accionario en dos escalas, diaria y trimestral, lo que además corrobora resultados obtenidos por otros autores, confirmando el uso de la metodología de Rango Re-escaldo para la identificación y determinación de memoria de largo plazo en una serie temporal.

Este estudio permitirá a futuros investigadores realizar análisis similares en otros mercados, aportando un nuevo enfoque al analizar la memoria de la largo plazo y los factores que inciden en ella.

Exponente de Hurst, Índice bursátil, Mercados eficientes, Mercados fractales

Artículo de investigación

This research examined the existence of long-term memory by calculating the coefficient of Hurst and Hurst set, and the analysis of characteristics of chaotic structures in the series of stock market of Chile, specifically through the Selective Price Index Shares.

A brief analysis of the market was developed, according to Box and Jenkins methodology. The validity of the results was performed by means of the test proposed by Brock, Dechert and Scheinkman. Secondly, we proceeded to analyze the dynamics and patterns of the index and its performance, to see if there was evidence of long-term memory.

The results demonstrate the presence of long-term memory in the Chilean stock market, determined by stock index in two scales, daily and quarterly, which also corroborates results obtained by other authors, confirming the use of the methodology Range Re-scalded for the identification and determination of long-term memory in a time series.

This study will allow future researchers to perform similar analyzes in other markets, providing a new approach when analyzing the long-term memory and the factors that affect it.