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Given the widespread adoption of empowerment in the workplace, increasing research has investigated the influences of empowering leadership. However, previous research has found confounding effects of it. This study aims to examine how and when empowering leadership exhibits “double-edged sword” effects on followers’ work outcomes.

The authors used a three-wave survey with a final sample of 215 full-time employees to test the research model.

The results indicate that followers’ role-breadth self-efficacy (RBSE) interacted with empowering leadership to predict their hindrance-related stress, subsequently influencing their turnover intention. Specifically, empowering leadership is found to elicit hindrance-related stress among followers with low RBSE. Furthermore, empowering leadership indirectly affects turnover intention by eliciting hindrance-related stress only among followers with low RBSE.

This study broadens the exploration of the “dark side” of empowering leadership, offering a more nuanced explanation of how it can lead to both beneficial and detrimental outcomes. It refines the understanding of empowering leadership’s effectiveness by highlighting the role of followers’ RBSE rather than focusing solely on the degree of empowerment. In addition, by contributing to the stress theory, this research demonstrates how individual differences influence followers’ cognitive appraisal of stress, shaping distinct stress experiences and driving the adoption of varying work-related coping strategies.

This study aims to propose mindfulness as a key factor in merchants’ adoption of mobile payments through the trust and perceived value pathways in an unorganized sector.

Data were collected physically via a structured questionnaire from a random sample of 350 merchants from the unorganized retail sector in India. Partial least squares-based structural equation modeling was used to analyze the data.

Mindfulness plays a key role in the formation of trust and perceived value, which further influences merchants’ intentions to adopt mobile payments. Furthermore, moderating effects of technology anxiety and skepticism were also observed.

To the best of the authors’ knowledge, this study is the first to illustrate mindfulness in merchants of the unorganized sector in adopting mobile payments to influence the adoption intentions of mobile payments in emerging markets.

This paper aims to study whether rural collective construction land marketization (RCCLM) affects pension industry and analyze its impact mechanism.

Based on the theory of planned behavior (TPB) and social cognition theory (SCT), this paper constructs a theoretical framework for the purchase behavior of rural retirement housing with the influence of RCCLM. A mixed-methods investigation combining qualitative and quantitative study is adopted in this paper.

The research results indicate that the purchase intention of rural retirement housing has a significant positive impact on the purchase behavior. However, RCCLM has a significant negative impact on the purchase intention of rural retirement housing. A logical framework of “land system participant behavior” has been constructed from three main bodies: government, developers and elderly urban and rural residents.

This paper provides suggestions for the three entities from the perspectives of macro, medium and micro-level to improve transaction system for rural collective construction land use right (TSRCCLUR), providing references for the collective construction land marketization policy and the development of the pension industry.

This paper deepens the study of behavior intention in planned behavior, enriches TPB model in the study of rural retirement housing security and clarifies the influence mechanism of the rural retirement housing purchase intention with the theoretical and empirical test of the model.

The present research aims to explore the influence of enterprise social media (ESM) features on employee agility through knowledge sharing and hiding. ESM strain was taken as a moderator in the association among knowledge sharing, knowledge hiding and employee agility. Communication visibility theory is used to underpin the hypothesized research model.

Data is collected from 374 Chinese employees working in different companies. A structural equation modeling (SEM) is applied using AMOS software 21.0.

Findings illustrate that ESM features namely analysis, tracking and communication are significantly connected to knowledge sharing and negatively to knowledge hiding. Knowledge sharing is significantly related to employee agility while knowledge hiding is negatively linked to employee agility. Further, ESM strain strengthens the negative connection between knowledge hiding and employee agility. However, ESM strain has an insignificant moderating effect in the connection between knowledge sharing and employee agility.

The current research has practical implications for ESM users and ESM-enabled organizations. For individuals who use ESM to promote knowledge within an organization, this study highlights the important functioning of ESM features that include analysis, tracking and communication. For managers, this study suggests the use of ESM features to promote knowledge sharing and inhibit knowledge hiding.

The current research investigates how ESM features are associated with employee agility in the workplace. This study proposes that knowledge sharing and knowledge hiding are the mediators of the effect path between ESM features and employee agility. Moreover, the present study extends to ESM literature by highlighting the moderating role of ESM strain using communication visibility theory as a theoretical lens. Lastly, this study quantifies the benefits of ESM features to help organizations train agile employees.

This study aims to empirically examine the role of privacy, security, trust and autonomy in Millennials’ continued use of online dating applications. Emerging markets have been famous for their rapid modernization, growth and cultural shifts. Yet, consumer worries about online dating consumption need to be addressed in the literature through a better understanding of their behavioural intentions. This study examines the interplay of the underlying variables by testing a conceptual framework grounded on the theory of reasoned action (TRA), the self-determination theory (SDT) and the information systems continuance theory (ISCT) with regard to an emerging market.

A cross-sectional and quantitative research design is adopted. Using a well-designed structured questionnaire adapted from established scales, data was collected through a survey featuring 332 substantive responses from actively involved Millennial users of online dating apps in India. The data was analysed using confirmatory factor analysis (CFA) and structural equation modelling (SEM) through IBM SPSS AMOS (Analysis of Moment Structures) 24.0 to provide evidence of the reliability and validity of constructs alongside testing the hypothesis in the proposed model. Furthermore, Preacher and Hayes’ (2004) approach is used to explore mediation effects.

The results show that autonomy and trust positively affect the continued use of online dating apps. The study further reveals that autonomous behaviour motivates Millennials to use online dating apps, overriding their concerns for privacy and security. Furthermore, the results indicate that trust fully mediates the relationship between concern for privacy, perceived security and continued usage intention. Therefore, trust affects users’ intentions to continue using online dating apps as it bridges the effect of users’ concern for privacy and the perceived security in the online dating platform, making these dimensions relevant as a whole.

This study provides critical insights into the theory and practice. The findings demonstrate that autonomy is more significant in motivating Millennials to use online dating apps than concerns about privacy and perceptions of security. In addition, trust plays a crucial role in mediating the relationship between these variables. Therefore, app developers, app marketers and app stores can use the findings to motivate Millennials to continue using their platforms by encouraging a sense of freedom and a better inner self, fostering engagement and conveying user trust.

Several studies have investigated the pre-adoption behaviour of users of online dating apps, and little attention has been paid to continuance usage. This study provides a unique theoretical research model and perspective for online dating apps that uncovers the role of concern for privacy, perceived security and trust that affect post-adoption behaviour by integrating the TRA, SDT and ISCT. This integration offers a more futuristic and refined perspective on human behaviour by considering both cognitive and motivational aspects. The study establishes that autonomy outweighs concern for privacy and perceived security.

The construction sector is highly prone to accidents, traditionally assessed using subjective qualitative measurements. To enhance the allocation of risk management resources and identify high-risk projects during pre-construction, an objective and quantitative approach is necessary. This study introduces a three-step clustering methodology to quantitatively evaluate accident risk levels in construction projects.

In the first step, accident and total construction revenue by project were collected to calculate accident probabilities. In the second step, accident probabilities were calculated by project type using the data collected in the first step. After that, benchmark models were suggested using clustering methods to identify high-risk project types for risk management. Before suggesting the benchmark models, an uncertainty analysis was conducted due to the limited amount of data. In the third step, the suggested benchmark models were validated for accuracy.

The results categorized risk levels for fatalities and injuries into four distinct groups. Validation through ordinal logistic regression demonstrated high explanatory power, with fatality risk levels ranging from 79.9 to 100% and injury risk levels from 90.3 to 100%.

This benchmark model facilitates effective comparisons and analyses across various construction sectors and countries, offering a robust quantitative standard for risk management. By identifying high-risk projects such as “Dam,” this methodology enables better resource allocation during the pre-construction phase, thereby improving overall safety management in the construction industry and providing a basis for legislative applications.

This paper aims to propose an active disturbance rejection control (ADRC)-based visual servoing strategy for regulating a wheeled mobile robot from varying initial poses to a desired pose at an exponential rate. It addresses challenges associated with non-holonomic constraints, uncertain depth information and unknown translational parameters in monocular vision systems.

The uncertain depth information in monocular vision and unknown camera-to-robot translational parameters are modeled as internal uncertainties of the visual servo system. An input-state scaling technique is used to decouple the system into two subsystems, controlled by angular and linear velocities, respectively. The angular velocity controller is designed to ensure strict exponential convergence, while the internal parametric and bounded uncertainties of the system are estimated and compensated for by an extended state observer and a switching linear velocity controller.

The separate design of the angular and linear velocity controllers effectively overcomes the non-holonomic constraints of the mobile robot, ensuring robust performance under diverse conditions. Furthermore, the ADRC-based strategy successfully handles uncertain depth information and unknown translational parameters. The convergence of the error system is rigorously proven using Lyapunov theory, and simulation results verify the effectiveness of the proposed scheme.

To the best of the authors’ knowledge, this study introduces, for the first time, a novel approach that combines ADRC with visual servoing for non-holonomic mobile robots. This approach enhances the adaptability and accuracy of the robot’s navigation in environments characterized by unknown system uncertainties. The proposed method demonstrates enhanced practical performance over conventional techniques by effectively managing the inherent uncertainties of the system.

This paper aims to examine the relationship between environmental, social and governance (ESG) performance and text-based corporate innovation based on a sample of India’s ESG-disclosed companies from financial year 2011–2012 to 2021–2022. Further, it endeavors to investigate the moderating role of heightened climate policy uncertainty (CPU) in this relationship.

To verify these hypotheses, the authors first construct a corporate innovation index for India using a sophisticated natural language processing model on each firm-year’s management discussion and analysis reports. Next, the authors use a panel fixed effects model to examine how ESG performance impacts corporate innovation and its moderating and mediating components.

Empirical evidence suggests higher ESG performance bolsters text-based corporate innovation. After addressing endogeneity issues with the system GMM estimator and two-stage least square IV, incorporating additional control variables and using alternative innovation measurement, the baseline results remain unchanged. Next, the authors find this link is mediated by reducing information asymmetry, financial constraints and managerial myopia. The authors also observe that increased CPU favorably moderates the ESG-innovation nexus. Additionally, the heterogeneity research shows that ESG only positively impacts innovation in specific industries and firms in their growth and mature life cycle phases.

The results demonstrate that sustainable and ethical business practices can foster corporate innovation. Thus, this study may provide valuable insight for investors, managers and policymakers.

To the best of the authors’ knowledge, this is the first study to examine the relationship between ESG performance and text-based corporate innovation using a machine learning model.

Augmented Reality (AR) and Virtual Reality (VR) technologies possess the potential to transform the scenario of making real estate investment decisions through the immersive experience they offer. From the literature it was observed that the research in this domain is still emergent and there is a need to identify the latent variables that influence real estate investment decisions. Therefore, by examining the effects of these technologies on investment decision-making, the purpose of the study is to provide valuable insights into how AR and VR could be applied to enhance customers' property buying experiences and assist in their decision-making process.

From an extensive review of the literature four latent variables and their measure were identified, and based on these a survey instrument was developed. The survey was distributed online and received 300 responses from the respondents including home buyers, developers, AEC professionals and real estate agents. To validate the latent variables exploratory factor analysis was used whereas to establish their criticality second-order confirmatory factor analysis was used.

From the results, the four latent constructs were identified based on standard factor loadings (SFL) that is Confident Value Perception (CVP, SFL = 0.70), Innovative Investment Appeal (IIA, SFL = 0.60), Trusted Property Transactions (TPT, SFL = 0.58) and Effortless Property Engagement (EPE, SFL = 0.54), that significantly influence investor decision-making and property purchase experience.

This study contributes to the literature on real estate investment decisions by providing empirical evidence on the role of AR and VR technologies. The identified key variables provided practical guidelines for developers, investors and policymakers in understanding and leveraging the potential of AR and VR technologies in the real estate industry.

This study aims to provide a comprehensive overview of thin-film temperature sensors (TTS), focusing on the interplay between material properties and fabrication techniques. It evaluates the current state of the art, addressing both low- and high-temperature sensors, and explores the potential applications across various fields. The study also identifies challenges and highlights emerging trends that may shape the future of this technology.

This study systematically examines existing literature on TTS, categorizing the materials and fabrication methods used. The study compares the performance metrics of different materials, addresses the challenges encountered in thin-film sensors and reviews the case studies to identify successful applications. Emerging trends and future directions are also analyzed.

This study finds that TTS are integral to various advanced technologies, particularly in high-performance and specialized applications. However, their development is constrained by challenges such as limited operational range, material degradation, fabrication complexities and long-term stability. The integration of nanostructured materials and the advancement of wireless, self-powered and multifunctional sensors are poised to drive significant advancements in this field.

This study offers a unique perspective by bridging the gap between material science and application engineering in TTS. By critically analyzing both established and emerging technologies, the study provides valuable insights into the current state of the field and proposes pathways for future innovation in terms of interdisciplinary approaches. The focus on emerging trends and multifunctional applications sets this review apart from existing literature.