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This paper examines the effect of board reforms on managerial risk-taking incentive provision in state-owned enterprises (SOEs) whose managers are undue risk-averse.

We use the staggered implementation of board reforms in Chinese central government-controlled state-owned enterprises (CSOEs) as an exogenous shock to board governance. We collect data on board reforms for a set of pilot CSOEs during the period 2005 to 2020 from the State-owned Assets Supervision and Administration Commission (SASAC) website by hand. We use a generalized difference-in-difference (DID) design to test the effect of staggered board reform adoption on managerial risk-taking incentive provision.

We find a positive relationship between board reforms and risk-taking inventive provision, i.e. pay-performance sensitivity, promotion-performance sensitivity and performance target difficulty. The documented relationship is stronger when the value of risk-taking is higher. We also find that board reforms lead to greater risky but value-enhancing investments and that managerial risk-taking incentive provision acts as an important channel through which board reforms improve value-enhancing risk-taking.

Our findings suggest that board reforms that improve board governance are effective in addressing risk-related agency conflicts in emerging markets. The findings also highlight the importance of managerial risk-taking incentive provision in inducing risky but value-enhancing investments.

The purpose of this paper is to investigate the fault estimation issue of nonlinear dynamical systems via distributed sensor networks. Furthermore, based on the communication topology of sensor networks, the nonfragile design strategy considering the gain fluctuation is also adopted for distributed fault estimators.

By means of intensive dynamical model transformation, sufficient conditions with disturbance attenuation performance are established to design desired fault estimator gains with the help of convex optimization.

A novel distributed fault estimation framework for a class of nonlinear dynamical systems is established over a set of distributed sensor networks, where sampled data of sensor nodes via local information exchanges can be used for more efficiency.

The proposed distributed fault estimator gain fluctuations are taken into account for the nonfragile strategy, such that the distributed fault estimators are more applicable for practical sensor networks implementations. In addition, an illustrative example with simulation results are provided to validate the effectiveness and applicableness of the developed distributed fault estimation technique.

The purpose of this study is to explore the motivational role of feedback information (positive and negative) provided by the firm in the face of participant heterogeneity, in terms of past success experience, under the research setting of crowdsourcing contests.

Taking insights from feedback studies and the dynamics of self-regulation theory, four theoretical hypotheses are proposed. An integrated dataset of 4,880 contest-participant pairs, which is obtained from an online contest platform and a survey, is empirically analyzed.

Empirical analysis shows that both positive feedback and negative feedback are able to stimulate the inner needs of participants. Notably, negative (positive) feedback becomes more (less) effective in intrinsically motivating crowds as they gain more successful experience during contest participation.

This study brings some new knowledge for the intrinsic motivation of crowds by exploring its antecedents, which have been undervalued in extant literature. The motivational role of feedback information is particularly explored.

Although it is commonly agreed that prescriptive analytics can benefit organizations by enabling better decision-making, the deployment of prescriptive analytics tools can be challenging. Previous studies have primarily focused on methodological issues rather than the organizational deployment of analytics. However, successful deployment is key to achieving the intended benefits of prescriptive analytics tools. Therefore, this study aims to identify the enablers of successful deployment of prescriptive analytics.

The authors examine the enablers for the successful deployment of prescriptive analytics through five organizational case studies. To provide a comprehensive view of the deployment process, each case includes interviews with users, managers and top management.

The findings suggest the key enablers for successful analytics deployment are strong leadership and management support, sufficient resources, user participation in development and a common dialogue between users, managers and top management. However, contrary to the existing literature, the authors found little evidence of external pressures to develop and deploy analytics. Importantly, the success of deployment in each case was related to the similarity with which different actors within the organization viewed the deployment process. Furthermore, end users tended to highlight user participation, skills and training, whereas managers and top management placed greater emphasis on the importance of organizational changes.

The results will help practitioners ensure that key enablers are in place to increase the likelihood of the successful deployment of prescriptive analytics.

This paper aims to propose a hybrid force/position controller based on the adaptive variable impedance.

First, the working space is divided into a force control subspace and a position subspace, the force control subspace adopts the position impedance control strategy. At the same time, the contact force model between the robot and the surface is analyzed in this space. Second, based on the traditional position impedance, the model reference adaptive control is introduced to provide an accurate reference position for the impedance controller. Then, the BP neural network is used to adjust the impedance parameters online.

The experimental results show that compared with the traditional PI control method, the proposed method has a higher flexibility, the dynamic response accommodation time is reduced by 7.688 s and the steady-state error is reduced by 30.531%. The overshoot of the contact force between the end of robot and the workpiece is reduced by 34.325% comparing with the fixed impedance control method.

The proposed control method compares with a hybrid force/position based on PI control method and a position fixed impedance control method by simulation and experiment.

The adaptive variable impedance control method improves accuracy of force tracking and solves the problem of the large surfaces with robot grinding often over-polished at the protrusion and under-polished at the concave.

This study aims to determine whether knowledge creation processes (KCPs) – knowledge exchange and knowledge integration affect digital innovation (DI), including information technology (IT)-enabled capabilities (ITECs) as a mediator and absorptive capacity (AC) as a moderator.

With a survey data set of 390 employees from Pakistani software small- and medium-sized enterprises (SMEs), the current study employed Structural Equation Modeling (SEM) using Smart Partial Least Squares to estimate the structural relationships in the conceptual model.

The results confirm that KCPs – knowledge exchange and knowledge integration positively enhance software SME's DI; ITECs play a partial mediating role in the linkage between KCPs and DI; AC positively moderates the relationship between knowledge integration and ITECs, and ITECs and DI, while AC doesn’t moderate the relationship between knowledge exchange and ITECs. The AC positively moderates the mediating role of ITECs amongst KCPs (knowledge exchange and knowledge integration) and DI, respectively.

This research uniquely integrates the knowledge-based view and dynamic capability theory to present a comprehensive framework that explains the interdependencies between knowledge process, ITECs and AC in driving DI. This approach advances the understanding of how software SMEs can strengthen internal knowledge and IT resources to achieve superior innovation outcomes.

The primary objective of this research is to identify and analyze the influence of digital marketing practises on performance of business of manufacturing enterprises in Delhi-NCR. A quantitative and descriptive research design was implemented in this study. 368 participants from various enterprises were chosen. Questionnaires were used to collect data on the benefits and challenges which are perceived of digital marketing, as well as usage and business performance of these enterprises. SPSS v24.0 is utilized to carry out statistical analysis on the collected data. The study discovered that advantages and problems of digital marketing practices influenced the usage of digital marketing. Furthermore, the execution of digital marketing had a prominent influence on sales and total number of customers of these enterprises, and this relationship was moderated by type and size of businesses. It was discovered that using digital marketing strategies increased business profits by raising digital marketing awareness among consumers and various industries. It made digital marketing easier to implement in various businesses. However, research should be conducted for appropriate generalization in a larger scale.

Turnover intention is a critical predictor of an employee’s turnover behaviour. A high level of turnover rate significantly affects the productivity and morale of an enterprise. Previous research has indicated that job satisfaction plays a critical role in influencing an employee's turnover intention, but the underlying factors related to job satisfaction remain under-explored, which impedes the development of effective strategies for reducing turnover intention. In addition, little research examined job satisfaction and turnover intention in the context of the COVID-19 pandemic, specifically in the Chinese construction industry. This study aims to investigate the impact of job satisfaction on turnover intention among professionals in the construction industry.

A questionnaire survey was employed to collect viewpoints from 449 professionals in the Chinese construction industry, followed by descriptive analysis, correlation analysis and structural equation modelling analysis to derive results.

The findings indicate that professionals in the industry generally have a slightly high level of job satisfaction while a slightly low level of turnover intention in the special period of the pandemic outbreak. Leadership and management, training and career development and interpersonal relationships are critical underlying factors leading to their turnover intention. Although demographic factors have no moderating effect between job satisfaction and turnover intention, among them, age, marital status and years of working experience have strongly positive relationships with job satisfaction while significantly negative relationships with turnover intention.

The findings provide valuable insights to fully understand the critical factors leading to turnover intention from the perspective of job satisfaction, which is helpful in developing effective measures to address the turnover problems for enterprises in the Chinese construction industry and those industries with similar characteristics in other regions.

This scoping review article examined research on information behavior in communities over the past two decades (2000–2023). The review aims to uncover the characteristics and types of communities studied, the featured information behaviors, and the research methods employed.

The PRISMA-ScR guidelines were followed to conduct this review. Five databases were selected to search for relevant empirical research. A total of 57 studies met the inclusion criteria for review. Thematic synthesis was used to analyze the multidimensional findings of included studies.

A steady increase in the number of articles is evident in the past two decades. The review suggests that information behavior in community studies involved collaboration from other disciplines, such as public health and business management. More than half of the communities studied are virtual communities (56.1%), followed by communities of identity, professional communities and support communities, communities of interest, geographic communities, and academic communities. There are overlaps among these categories. Information sharing (63.2%) and information seeking (57.9%) were the most studied behavior of communities, followed by information use, information needs, and information judgment. Questionnaires (38.6%) and interviews (35.1%) were the most commonly used data collection techniques in studying information behavior in communities. It is noteworthy that eleven (19.3%) mentioned utilizing community-engaged approaches.

This is the first scoping review to explore the intersecting constructs of community research and information behavior studies. We call for further research to understand the contextual factors that shape the community’s information environments and to increase awareness of the partnership between communities and researchers.

In November 2022, the commercial company, OpenAI, launched ChatGPT. Since then, university students have rapidly become regular users of this artificial intelligence (AI) platform. One reason for this is the powerful capability of this generative AI tool to produce textual content, which in many cases, is almost indistinguishable from human-generated content. Another reason is that ChatGPT easily gives anyone access to knowledge. However, there is a problem as the vast majority of its users have no idea how this AI platform works and thus overlook the importance of thinking critically about information communicated in ChatGPT. While some call for banning this generative AI tool, this study aims to provide evidence that science classrooms can become scenarios where students find explicit, concrete, and realistic opportunities to critically evaluate scientific information generated by ChatGPT.

An intervention study was conducted with 55 students (26 females and 29 males, 17–24 years old) during a university Spanish-English bilingual science course taught within an active learning environment. The data consist of the written critiques of the students about Spanish-English bilingual scientific texts produced by ChatGPT.

Results indicate that the intervention had a positive effect on students’ abilities to construct sound arguments in Spanish and in English while judging the quality of scientific texts produced by this AI bot. Moreover, the findings suggest that the intervention enriched students’ skills to make improvements to texts produced by this generative AI tool.

The evidence provided in this study contributes to the exploration of possibilities to help students become critical users of ChatGPT.