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This research aims to examine the link between distributed leadership and team ambidexterity and the sequential mediation of team climate innovation and knowledge management in this relationship.

This study draws on a sample of 546 knowledge workers involved within 157 service research and development (R&D) teams of knowledge-intensive firms (KIFs) and uses partial least squares-structural equation modelling (PLS-SEM) through SMART PLS 4 to analyse the data collected.

Findings reveal that distributed leadership has a significant direct impact on team ambidexterity. Besides, they indicate that team climate innovation and knowledge management in teams mediate this link. Results also highlight the sequential mediation of team climate innovation and knowledge management in teams, linking distributed leadership to team ambidexterity.

This study explores the relationship between distributed leadership and ambidexterity at the team level and proposes a sequential mediation model linking these variables through team climate innovation and knowledge management in teams. It offers practical insights for KIFs’ managers on the importance of using a distributed leadership approach and building a team climate innovation to motivate R&D teams, encourage dynamic participation in knowledge management practices and cultivate both exploitative and exploratory learning activities.

Body language is an integral part of interpersonal communication and exchange, which can convey rich emotions, intentions and information. However, how anchor’s body language works in live-streaming e-commerce (LSE) has yet to receive adequate attention. Based on dual systems theory of decision-making, this paper aims to explore the impact of anchor’s body language on the performance of LSE from the perspective of customer engagement behavior and to examine the moderating role of anchor’s relational social interaction.

The authors confirmed the theoretical model through empirical analysis of structured data from 1,415 actual livestreaming rooms from Douyin, as well as unstructured data of 418,939 min of video and audio, 1,985,473 words of text and 423,302 keyframe images.

The study found that anchor’s body language has a significant positive effect on the performance of LSE, and customer engagement behavior plays a partially mediating role. The moderating effect suggests that anchor’s relational social interaction and body language have substitution effects in enhancing customer engagement behavior and the performance of LSE, which reveals the substitution relationship between anchor’s verbal and nonverbal interactions in LSE.

This study is one of the earlier literature focusing on anchor’s body language, and the findings provide practical references for enhancing customer engagement behavior and achieving performance growth in LSE.

This study investigates the effect of auditing industry specialization (AIS) on the relative derivatives use for earnings management.

The sample chosen in this study comprises 30,599 firm-year observations of Chinese public companies from 2005 to 2018. The sample is divided into two time periods (2005–2013 and 2014–2018) according to the year when IFRS 9 was implemented (IFRS 9, first discussed by the International Accounting Standards Board in March 2008, is based on an expected credit loss model for determining new and existing expected credit losses on financial assets. The definition was completed in July 2014 and implemented in 2018). AIS was gauged with respect to audit firms and individual auditors, and measured by market share in number and scale of clients. Linear regression is adopted to test hypotheses. Moreover, two-stage least square model (2SLS) is used to eliminate the concern of possible endogeneity.

When gauged with respect to client scale, the scale-based AIS constrained the level of derivatives use for earnings management in the first period (2005–2013) while increased the level in the second period (2014–2018). The findings sustain for the analysis of audit firms and that of individual auditors, and for different definitions of AIS.

The positive AIS-IN relation after the adoption of IFRS 9 implies the sacrifice audit independence. This could be indebted to the government policy that favors local audit firms to be comparable to international Big 4 audit firms, and therefore results in competition among local auditors/audit firms in securing number rather than quality of clients.

The data of AIS in China are collected using a Python web crawler.

This paper aims to propose a new ultrasonic detection method for stainless steel weld defects based on complex synergetic convolutional calculation to solve two problems in the ultrasonic detection of austenitic stainless steel weld defects. These include ignoring the nonlinear information of the imaginary part in the complex domain of the signal and the correlation information between the amplitude of the real part and phase of the imaginary part and subjective dependence of diagnosis model parameters.

An ultrasonic detection method for weld defects based on complex synergetic convolution calculation is proposed in this paper to address the above issues. By mapping low-density, 1D detection samples to a complex domain space with high representation richness, a multi-scale and multilevel complex synergetic convolution calculation model (CSCC) is designed to match and transform samples to mine amplitude changes, phase shifts, weak phase angle changes and amplitude-phase correlation features deeply from the detection signal. This study proposed an Elite Sine-Cosine Sobol Sampling Dung Beetle Optimization Algorithm, and the detection model CSCC achieves global adaptive matching of key hyperparameters in 19 dimensions. Finally, a regulative complex synergetic convolutional calculation model is constructed to achieve high-performance detection of weld defects.

Through experiments on a self-developed Taiyuan intelligent detection and information processing weld defect dataset, the results show that the method achieves a detection accuracy of 92% for five types of weld defects: cracks, porosity, slag inclusion and unfused and unwelded components, which represent an average improvement of 11% relative to comparable models. This method is also validated with the PhysioNet electrocardiogram public dataset, which achieves better detection performance relative to the other models.

This method provides a theoretical basis and technical reference for developing and applying intelligent, efficient and accurate ultrasonic defects detection technology.

The study aims to present a systematic literature review (SLR) to understand the current status of research on social media usage among the bottom of the pyramid (BOP). The purpose of this study is to identify the research gaps in this domain and review future research agendas by using theory, context, characteristics and methods [TCCM] framework.

An SLR, keywords co-occurrence and TCCM analysis were used to analyse and synthesize insights from 44 studies gained from Web of Science and Scopus databases.

The findings suggest that the USA and India are popular contexts for studying BOP. The BOP population uses social media to gain utilitarian, hedonic and social values. Further, social media can help BOP explore “entrepreneurship” opportunities, value co-creation and bring innovations.

This study expands the intellectual boundaries of social media at BOP and suggests multidisciplinary research. Additionally, adopting novel theoretical lenses helped determine social media's impact on BOP.

The carbon complementary supply chain (CCSC) is a collaborative framework that facilitates internal carbon credit trading agreements among supply chain agents in compliance with prevailing carbon regulations. Such agreements are highly beneficial, prompting agents to consider joint investment in emission reduction initiatives. However, capital investments come with inevitable opportunity costs, compelling agents to weigh the potential revenue from collaborative investments against these costs. Thus, this paper mainly explores carbon abatement strategies and operational decisions of the CCSC members and the influence of opportunity costs on the strategic choice of cooperative and noncooperative investment.

The authors propose a novel biform game-based theoretical framework that captures the interplay of pricing competition and investment cooperation among CCSC agents and assesses the impact of opportunity costs on CCSC profits and social welfare. Besides, the authors also compare the biform game-based collaborative scenario (Model B) to the noncooperative investment scenario (Model N) to investigate the conditions under which collaborative investment is most effective.

The biform game-based collaborative investment strategy enhances the economic performance of the traditional energy manufacturer, who bears the risk of opportunity costs, as well as the retailer. Additionally, it incentivizes the renewable energy manufacturer to improve environmental performance through renewable projects.

This research contributes significantly by establishing a theoretical framework that integrates the concepts of opportunity costs and biform game theory, offering new insights into the strategic management of carbon emissions within supply chains.

This study’s aim is to introduce a high-performance sorbent for the removal of both anionic (Congo red; CR) and cationic (methylene blue; MB) dyes from aqueous solutions.

Sodium silicate is adopted as a substrate for GO and AgNPs with positive charge are used as modifiers. The synthesized nanocomposite is characterized by FTIR, FESEM, EDS, BET and XRD techniques. Then, some of the most effective parameters on the removal of CR and MB dyes such as solution pH, sorbent dose, adsorption equilibrium time, primary dye concentration and salt effect are optimized using the spectrophotometry technique.

The authors successfully achieved notable maximum adsorption capacities (Qmax) of CR and MB, which were 41.15 and 37.04 mg g⁻¹, respectively. The required equilibrium times for maximum efficiency of the developed sorbent were 10 and 15 min for CR and MB dyes, respectively. Adsorption equilibrium data present a good correlation with Langmuir isotherm, with a correlation coefficient of R2 = 0.9924 for CR and R2 = 0.9904 for MB, and kinetic studies prove that the dye adsorption process follows pseudo second-order models (CR R2 = 0.9986 and MB R2 = 0.9967).

The results showed that the proposed mechanism for the function of the developed sorbent in dye adsorption was based on physical and multilayer adsorption for both dyes onto the active sites of non-homogeneous sorbent.

The as-prepared nano-adsorbent has a high ability to remove both cationic and anionic dyes; moreover, to the high efficiency of the adsorbent, it has been tried to make its synthesis steps as simple as possible using inexpensive and available materials.

This study aims to optimize control strategies for multi-unmanned aerial vehicle (UAV) systems by integrating differential game theory with sliding mode control and neural networks. This approach addresses challenges in dynamic and uncertain environments, enhancing UAV system coordination, operational stability and precision under varying flight conditions.

The methodology combines sliding mode control, differential game theory and neural network algorithms to devise a robust control framework for multi-UAV systems. Using a nonsingular fast terminal sliding mode observer and Nash equilibrium concepts, the approach counters external disturbances and optimizes UAV interactions for complex task execution.

Simulations demonstrate the effectiveness of the proposed control strategy, showcasing enhanced stability and robustness in managing multi-UAV operations. The integration of neural networks successfully solves high-dimensional Hamilton–Jacobi–Bellman equations, validating the precision and adaptability of the control strategy under simulated external disturbances.

This research introduces a novel control framework for multi-UAV systems that uniquely combines differential game theory, sliding mode control and neural networks. The approach significantly enhances UAV coordination and operational stability in dynamic environments, providing a robust solution to high-dimensional control challenges. The use of neural networks to solve complex Hamilton–Jacobi–Bellman equations for real-time multi-UAV management represents a groundbreaking advancement in autonomous aerial vehicle research.

This study aims to design a high-performance building envelope system with a focus on evaluating energy savings considering the hot climate.

A thermal energy storage approach based on inorganic phase change material (PCM) was adopted to regulate both temperature and humidity conditions when added to building envelopes. The microencapsulation technology was used to form new PCM microcapsules by combining a humidity agent material with thermal conduction accelerating material to encapsulate an inorganic PCM. The relevant parameters of the thermophysical characteristics of the synthesized PCM microcapsules were tested and analyzed. Also, a numerical validation was done for the energy-saving evaluation of the new synthesized PCM microcapsules when used in a building envelope.

The study results showed that the new synthesized PCM microcapsules have high latent heat capacities and enhanced thermal conduction values. The simulation results conducted by the Conduction Finite Difference solution algorithm (CondFD) approved the efficiency of the proposed PCM compositions when added to a building envelope.

Due to applying the PCM to the building envelope composition by the addition instead of the replacement method, the proposed design solutions are not suitable for improving the performance of lightweight construction.

This study provides a promising energy-efficient system for building envelopes. The study originality is represented in adding carbon nanoparticles as a shell material to overcome the low thermal conduction issue of PCMs. In addition to testing the impacts of different doses of the carbon shell material on the thermal performance of inorganic PCM microcapsules.

Human resources analytics (HRA) applications are of theoretical and practical interest to both researchers and practitioners. While organizations have begun to implement HRA applications, there is currently no established approach for measuring their performance. This study aims to address this gap in the literature by proposing a new approach for measuring the performance of HRA applications.

This study proposes a hybrid approach that combines the cumulative belief degree (CBD) and partial least squares structural equation modeling (PLS-SEM) to measure organizational HRA performance.

The performance measurement approach proposed in this study has the capacity to reveal the total HRA performance level of an organization, while also providing the opportunity to measure the performance of the subdimensions that make up HRA. These subdimensions include data sufficiency, technological capability, workforce capability, application level of HRA and organizational climate. This approach has the potential to assist organizations that do not currently utilize HRA in their operations to make an informed decision regarding the implementation of HRA and enables organizations to assess their potential performance if they were to implement HRA.

The proposed approach allows organizations to assess the performance of analytical applications in the human resources (HR) field. This assessment can be carried out at both the pre-implementation and post-implementation stages of HRA applications. Therefore, the approach provides a valuable contribution to organizations, enabling them to enhance their capabilities in this domain. Consequently, the study addresses a significant gap in practical research. Furthermore, in terms of the applicability of the developed HRA performance measurement model to diverse analytical domains, it paves the way for the advancement of other performance measurement studies.

The HRA performance assessment process encompasses multiple interrelated HRA subdimensions and performance indicators that can be measured using different scales. It is therefore essential to implement a flexible methodology that can convert diverse forms of evaluation into a unified scale and integrate them in order to effectively manage the inherent complexities and uncertainties associated with the assessment process. In this regard, the CBD approach proves particularly effective. In the CBD approach, a fuzzy set of linguistic terms is used to convert the performance indicator scores into a common scale and therefore takes into account the uncertainty inherent in the assessment process. In addition, it is also proposed to use the PLS-SEM method to combine CBDs.