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Article
Publication date: 20 December 2023

Yafei Feng, Yan Zhang and Lifu Li

The privacy calculus based on a single stakeholder failed to explain users' co-owned information disclosure owing to the uniqueness of co-owned information. Drawing on collective…

Abstract

Purpose

The privacy calculus based on a single stakeholder failed to explain users' co-owned information disclosure owing to the uniqueness of co-owned information. Drawing on collective privacy calculus theory and impression management theory, this study attempts to explore the co-owned information disclosure of social network platform users from a collective perspective rather than an individual perspective.

Design/methodology/approach

Drawing on collective privacy calculus theory and impression management theory, this study explores the co-owned information disclosure of social network platform users from a collective perspective rather than an individual perspective based on a survey of 740 respondents.

Findings

This study finds that self-presentation and others presentation directly positively affect users' co-owned information disclosure. Also, self-presentation, others presentation and relationship presentation indirectly positively affect users' co-owned information disclosure via relationship support. Furthermore, personal privacy concern, others' privacy concern and relationship privacy concern indirectly negatively affect users' co-owned information disclosure via relationship risk.

Originality/value

The findings develop the theory of collective privacy calculus and impression management, which offer insights into the design of the collective privacy protection function of social network platform service providers.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 2 October 2023

Qi Zheng, Chuqing Dong and Yafei Zhang

This study examines how the different attributes of authentic leadership influence trust and employee organization fit and how such influences differ by gender and the level of…

Abstract

Purpose

This study examines how the different attributes of authentic leadership influence trust and employee organization fit and how such influences differ by gender and the level of positions during the COVID-19 pandemic.

Design/methodology/approach

The study employed a survey to examine US employees' perceptions toward different attributes of authentic leadership during the COVID-19 pandemic.

Findings

The study showed that self-awareness, balanced processing and internalized moral perspective positively relate to trust in the employer, mediated through employee–organization fit. However, relational transparency has a backfiring effect, negatively related to trust through the mediation of employee–organization fit. Additionally, this study highlights the differences in gender and level of positions in reactions to authentic leadership.

Originality/value

This study contributes to the understanding of internal public relations in a turbulent crisis time by proposing a mediated model that explains the effects of authentic leadership on employees' trust through their fit with the organization. Additionally, it identified that gender and position level are important factors moderating such effects.

Details

Corporate Communications: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1356-3289

Keywords

Article
Publication date: 26 June 2023

Argaw Gurmu, M. Reza Hosseini, Mehrdad Arashpour and Wellia Lioeng

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…

Abstract

Purpose

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.

Design/methodology/approach

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

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.

Originality/value

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.

Details

Construction Innovation , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1471-4175

Keywords

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