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Executive turnover has increased in recent years. Most studies of executive turnover focus on CEO turnover and treat each incident of turnover as an isolated event. This research considers both CEO and CFO turnover and investigates whether the frequency of executive turnover has distinct effects on financial reporting quality.

The authors use a sample of firms extracted from Execucomp from the 1992 to 2021 period and examine three important indicators of firm’s accounting information quality: earning persistence; earnings informativeness; and accrual earnings management.

The authors find that higher frequency of executive turnovers in a 5-year period is associated with lower financial reporting quality. Specifically, the authors find that the frequency of executive turnovers is negatively associated with earnings persistence and positively associated with accrual earnings management, especially income-increasing accrual earnings management. Furthermore, the authors find that the frequencies of CEO-only turnover and combined CEO and CFO turnover, but not CFO-only turnover, are negatively associated with earnings informativeness about future cash flows. In addition, the authors find some evidence that promoting executives internally weakens the negative effect of frequent executive turnover on financial reporting quality.

The results suggest that while change is sometimes inevitable, frequent executive changes can create a short-horizon problem and make the realization of adaptation effects of leadership change difficult, and hence hurt company’s performance. The study suggests that organizations, especially corporate board should have robust and effective change management systems and strategies in place, which can help to mitigate the negative effect of frequent executive changes and align firms’ operations with the new leadership’s vision, maintain operational continuity, and employee engagement during periods of transition.

The study contributes to the management turnover literature by examining the impact of executive turnover frequency on firms’ financial reporting quality. While previous studies primarily rely on binary variables to measure management turnover, this study is among the first that focuses on the frequency of executive turnover, thus capturing more nuanced information beyond the scope of a binary variable. This measure allows the authors to focus on the disruptive effect of executive turnover, and hence better disentangles the distinct effects that multiple executive turnovers have on firm performance, which can differ from the effect of individual turnover. This distinction is crucial because the adaptation effect from executive turnover may not have adequate time to materialize within the context of several short executive tenures. The authors provide evidence that the disruptive effect manifests more strongly in firms with a higher rate of executive turnover and such disruption deteriorates firms’ financial reporting quality.

This study examines conditions under which taxes and subsidies designed to mitigate corporate environmental impact positively influence corporate pro-environmental behavior (CPEB) adoption, considering unique dynamics within different industries.

A systematic literature review was conducted on 171 articles. Articles were coded using an inductive grid for comprehensive examination.

Taxes generally positively influence CPEB adoption, but reduced positive or even negative effects can arise. Subsidies, while often facilitating the achievement of environmental goals, variously impact CPEB. Explanations for variations include the level of taxation or subsidy, economic agent affected, subsidy source, nature of subsidy, factors external to tax or subsidy characteristics and conflicting environmental objectives. We suggest research avenues for each aspect, to enhance literature on the influence of tax policies on promoting CPEB.

Beyond general tax-policy considerations, we provide policymakers with recommendations for tax policies designed to promote CPEB.

We examine the distinctive effects of taxes and subsidies on CPEB adoption within diverse industries ((re)manufacturing, agriculture, shipping, automobile, freight transport and power generation). We compare specific effects across industries, and advocate detailed exploration of recurrent elements identified, emphasizing their potential significance in designing taxes and subsidies that promote CPEB.

This study leverages theories of social influence to explore how “likes” for consumption-related content on social media fulfill consumers’ needs for social acceptance, subsequently affecting their repurchase and word-of-mouth (WOM) intentions. It aims to understand the extent to which social media engagements, specifically likes, serve as markers of social validation in the context of consumer behavior.

Our mixed-methods approach incorporated two experiments and an analysis of an archival dataset from a popular Chinese social media platform. This comprehensive methodology allowed for empirical validation of our hypotheses, combining experimental control with real-world relevance to scrutinize the impact of likes on consumption behavior.

The results of Studies 1 and 2 revealed that the number of likes significantly enhanced behavioral intentions regarding repurchase and positive word-of-mouth by fulfilling consumers’ need for social acceptance. Moreover, this effect was observed only under conditions of high (vs low) ideal social self-congruity. Study 3, conducted in a natural social media context, further validated these findings, demonstrating that likes positively influenced behavioral intention, with ideal social self-congruity acting as a boundary condition.

This study provides a novel perspective by directly linking social media likes to consumer behavioral intentions, specifically repurchase and WOM, through the mediating role of social acceptance and the moderating effect of ideal social self-congruity. By focusing on user-generated content (UGC) rather than marketer-generated content (MGC), it addresses a critical gap in the literature, emphasizing how receiving likes on consumer-shared content impacts content creators’ post-purchase behaviors, with practical implications for marketers on leveraging aspirational self-congruity to optimize promotional strategies.

Industry 4.0 in the construction industry (CI), also known as Construction 4.0, transforms construction projects into digital environments, integrating advanced technologies throughout each stage. This study investigated the application, maturity and adoption interest of 14 Industry 4.0-related technologies in the CI (Con4.0-tech) in Australian construction firms.

Using purposive and snowball sampling, data was collected via 19 semi-structured interviews with directors, managers, innovation officers, etc. Data was then content analysed using NVivo software.

The study revealed five key application areas for Con4.0-tech: real-time data capture, digital communication, data analysis, visualisation and off-site construction. While the level of technology use varied, the research suggests a clear shift towards technology-driven practices in Australian construction. An interesting finding is the direct correlation between technology maturity and its use. Construction firms demonstrated a stronger preference for established technologies with proven value. Notably, innovative firms actively explore new technologies, while others wait for broader industry adoption before integrating them.

This research uniquely explores the adoption of Con4.0-tech within Australian construction firms, providing a novel perspective on current industry practices. The empirical evidence offers valuable implications for driving technological advancement and enhancing construction project delivery.

This research aims to investigate the relationship between value orientations, attitude, behavioral intention and usage behavior of electric ride hailing service in Vietnam through the moderating role of social influences.

In total, 552 valid questionnaires were collected using the survey questionnaire to analyze the effects of value orientations on attitude toward electric ride hailing and behavioral intention. Besides, this study investigates the mediating effect of attitude toward electric ride hailing and behavioral intention in the relationship with the behavior usage of electric ride hailing service as well as the moderating effect of social influence.

Three types of value orientations stimulate attitude toward electric ride hailing service and behavioral intention. In addition, attitude toward electric ride hailing service and behavioral intention play the mediating role in the effect of value orientations on behavior usage. Specifically, social influence plays a negative moderating role in the linkage between attitude towards and behavioral intention of electric ride hailing service and between intention and the behavior usage.

While numerous studies have investigated the influence of customers’ value orientations on multiple green purchase behaviors, this study offers new insights into the moderating role of social influence in the linkages between attitude, behavioral intention and usage behavior of electric ride hailing service.

This study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.

The research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.

Validation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.

The main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.