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This paper examines contextual factors that affect the association between board gender diversity and firm performance.

The authors use a global sample of listed firms in the tourism industry in 30 countries from 2015 to 2020.

First, firm performance is positively associated with the proportion of female directors on a board. Second, the positive association between firm performance and the proportion of female directors on the board is higher in (1) countries with stronger shareholder rights, (2) countries with stronger securities law regulation stipulating disclosure of board diversity, (3) countries with stronger economic empowerment of women, and (4) during the COVID-19 crisis. Third, corporate financial distress risk is lower in firms with higher proportion of female directors on the board. Fourth, the negative association between corporate financial distress risk and the proportion of female directors on the board is more pronounced in (1) countries with stronger securities law regulations stipulating disclosure of board gender diversity, (2) countries with stronger economic empowerment of women, and (3) during the COVID-19 crisis.

The results indicate that contextual factors (comprising country-level corporate governance structures, economic empowerment of women and economic crisis) can affect the association between board gender diversity and firm performance.

This study examined the effect of different types of politically connected (PCON) Malaysian firms on analysts' forecast accuracy and dispersion.

The study identified different types of PCON firms according to Wong and Hooy's (2018) classification, which divided political connections into government-linked companies (GLCs), boards of directors, business owners and family members of government leaders. The sample covered the period 2007–2016, for which earnings forecast data were obtained from the Institutional Brokers' Estimate System (IBES) database and financial data were extracted from Thomson Reuters Fundamentals. We deleted any market consensus estimates made by less than three analysts and/or firms with less than three years of analyst forecast information to control for the impact of individual analysts' personal attributes.

The study found that PCON firms were associated with lower analyst forecast accuracy and higher forecast dispersion. The effect was more salient in GLCs than in other PCON firms, either through families, business ties or boards of directors. Further analyses showed that PCON firms—in particular GLCs—were associated with more aggressive reporting of earnings and poorer quality of accruals, hence providing inadequate information for analysts to produce accurate and less dispersed earnings forecasts. The results were robust even after addressing endogeneity issues.

This study found new evidence of the impact of different types of PCON firms in exacerbating information asymmetry, which was not addressed in prior studies.

This study has a significant practical implication for investors that they should be mindful of high information asymmetry in politically connected firms, particularly government-linked companies.

This is the first study to provide evidence of the impact of different types of PCON firms on analysts' earnings forecasts.

Building information modeling (BIM) is recognized as a major innovation in the architecture, engineering, and construction (AEC) industry. Understanding the factors that influence the AEC’s adoption of BIM will benefit the research and practice of BIM. The paper aims to discuss these issues.

This study provides empirical evidence for the accumulated knowledge of BIM adoption by examining the context of Chinese construction industry. Based on the technology-organization-environment (TOE) framework in the innovation diffusion literature, the authors develop a research model that integrates the critical success factors related to the technology of BIM, the construction company and the environment in Chinese construction industry. The authors collected two different data sets from engineering consulting firms and construction firms in China, and conducted rigorous analyses using a sophisticated statistical approach.

The authors found that the relative advantage of BIM was a major factor that enabled BIM adoption, while the complexity of BIM was an inhibiter. In addition, management support was also a significant antecedent of BIM adoption. However, organizational readiness was significant for engineering consulting firms but not for construction firms. Surprisingly, the authors did not find consistent significant impacts of any environmental factors. Last, younger firms were more likely to adopt BIM.

One of the first to apply the TOE framework to integrate three groups of factors that may explain BIM adoption in China. Such a comprehensive framework provides a much broader perspective of BIM adoption to evaluate the impacts of different antecedent factors. The authors conducted an empirical study based on survey data collected from two different types of companies, i.e., engineering consulting firms and construction firms, representing the two parties in the principal-agent relationship of a construction project. One of the first to apply a sophisticated statistical approach, i.e., partial least squares, to analyze the data in the BIM literature.

This study aims to provide empirical evidence to verify the dimensional structure of artificial intelligence (AI) Chatbot quality and examine the impact of these dimensions on consumer satisfaction and brand advocacy among Gen Z in the fast food industry in Egypt.

The empirical data was obtained with an electronic self-administered survey instrument from 397 young consumers who had prior experience using AI Chatbots across multiple fast food brands in Egypt. Structural equation modeling was used to analyze the formulated hypotheses.

The results showed that AI Chatbot quality dimensions, specifically information authenticity and system compliance, significantly enhance young consumers’ satisfaction. In addition, information authenticity of AI Chatbot quality was observed to wield a significant influence on young consumers’ advocacy. In contrast, an insignificant relationship was noticed between satisfaction and advocacy. Moreover, the mediating role of consumer satisfaction was not established.

Given that Gen Z is more technology savvy and computer literate, marketers and practitioners of fast food brands should invest in AI tools to respond to young consumers’ expectations and improve their perception of their services.

This study uses stimulus-organism-response theory to understand the mediating effect of young consumers’ satisfaction in the relationship between AI Chatbot quality and consumer brand advocacy within the fast food industry. Also, it introduced two novel main constructs of AI Chatbot quality, namely, information authenticity and system compliance.

This article aims to compare smart meters' acceptance studies worldwide to consolidate trends and highlight factors that are not a consensus.

This work performs a statistical meta-analysis, using the Hunter–Schmidt method and the UTAUT2 model, of the factors of acceptance of smart meters in the world literature. A meta-regression was also conducted to verify the moderation exercised by gender, level of education and timeline context of the articles.

The main results point to hedonic motivation, performance expectancy and effort expectancy as the leading influencers for smart meter's acceptance. Meta-regression indicates that the influence is more significant among the male gender and that over the years, the social influence must gain weight in the smart meter's acceptance.

Specific strategies are suggested to improve projects for the implementation of smart meters based on the obtained results.

The contribution given by this work is relevant, considering it is the first meta-analysis focused on smart meters' acceptance published in the literature

The Internet of Things (IoT) offers substantial potential for improving efficiency and effectiveness in various applications, notably within the domain of smart construction. Despite its growing adoption within the Architecture, Engineering, and Construction (AEC) industry, its utilization remains limited. Despite efforts made by policymakers, the shift from traditional construction practices to smart construction poses significant challenges. Consequently, this study aims to explore, compare, and prioritize the determinants that impact the acceptance of the IoT among construction practitioners.

Based on the integrated model of Unified Theory of Acceptance and Use of Technology (UTAUT2), Task-Technology Fit (TTF), and perceived risk. A cross-sectional survey was administered to 309 construction practitioners in China, and the collected data were analyzed using structural equation modeling (SEM) to test the proposed hypotheses.

The findings indicate that TTF, performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, and perceived risk exert significant influence on construction practitioners’ intention to adopt IoT. Conversely, social influence and habit exhibit no significant impact. Notably, the results unveil the moderating influence of gender on key relationships – specifically, performance expectancy, hedonic motivation, and habit – in relation to the behavioral intention to adopt IoT among construction practitioners. In general, the model explains 71% of the variance in the behavioral intention to adopt IoT, indicating that the independent constructs influenced 71% of practitioners’ intentions to use IoT.

These findings provide both theoretical support and empirical evidence, offering valuable insights for stakeholders aiming to gain a deeper understanding of the critical factors influencing practitioners’ intention to adopt IoT. This knowledge equips them to formulate programs and strategies for promoting effective IoT implementation within the AEC field.

This study contributes to the existing literature by affirming antecedents and uncovering moderators in IoT adoption. It enhances the existing theoretical frameworks by integrating UTAUT2, TTF, and perceived risk, thereby making a substantial contribution to the advancement of technology adoption research in the AEC sector.

This research aims to explore the factors influencing the adoption intention of eco-friendly smart home appliances among residents in densely populated urban areas of a developing country.

A quantitative research approach was employed to gather data from 348 respondents through purposive sampling. A comparative analysis strategy was then utilized to investigate the adoption of eco-friendly smart home appliances, combining both linear (PLS-SEM) and non-linear (fsQCA) approaches.

The results obtained from PLS-SEM highlight that performance expectancy, facilitating conditions, hedonic motivation, price value, and environmental knowledge significantly influence the adoption intention of eco-friendly smart home appliances. However, the findings suggest that effort expectancy, social influence, and habit are not significantly associated with customers' intention to adopt eco-friendly smart home appliances. On the other hand, the fsQCA results identified eight configurations of antecedents, offering valuable insights into interpreting the complex combined causal relationships among these factors that can generate (each combination) the adoption intention of eco-friendly smart home appliances among densely populated city dwellers.

This study offers crucial marketing insights for various stakeholders, including homeowners, technology developers and manufacturers, smart home service providers, real estate developers, and government entities. The findings provide guidance on how these stakeholders can effectively encourage customers to adopt eco-friendly smart home appliances, aligning with future environmental sustainability demands. The research implications underscore the significance of exploring the antecedents that influence customers' adoption intention of eco-friendly technologies, contributing to the attainment of future sustainability goals.

The environmental sustainability of smart homes, particularly in densely populated city settings in developing countries, has received limited attention in previous studies. Therefore, this study aims to address the pressing issue of global warming and make a meaningful contribution to future sustainability goals related to smart housing technologies. Therefore, this study employs a comprehensive approach, combining both PLS-SEM (linear) and fsQCA (non-linear) techniques to provide a more thorough examination of the factors influencing the adoption of environmentally sustainable smart home appliances.