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Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.

Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.

This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.

This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.

This paper aims to investigate and document the effects of copper-clad laminate (CCL) inorganic filler on the hole performance in printed circuit boards drilling process.

Drilling of brittle laminates can result in hole cracking, layer-to-layer delamination and drill-bit wear and tool breakage. Adding large amount of fillers not only shortens the life of the drilling tool but also affects the drilling properties significantly regarding hole quality. This paper introduces the influence of filler content, type, hardness, particle size and the compounding method in the manufacture of the CCL on the drilling performance.

The filler content, filler type, hardness of filler, particle size of filler and the compounding method used for the filler have a great influence on the drilling properties of CCL. The higher the filler content, the larger the particle size and the more the hardness of the filler, the worse the drilling properties. The combination of hard particles like silica with softer particles can improve the drilling performance of CCL.

The paper describes what affects the drilling performance of CCL and how this knowledge can be used to design CCL with good drilling performance.

Consumers interacting with smart wearable devices is on the rise in the current health-AI market, which offers a great opportunity for companies to execute interactive marketing. However, this opportunity is mainly reliant on consumers' use of smart wearable devices. This paper aims to develop a model considering health and privacy factors to elucidate consumers' use of smart wearable devices for unleashing their full potential in interactive marketing.

The authors collected 250 samples via an online survey to validate the smart wearable devices usage model that elucidates factors that stimulate consumer usage, including privacy concerns, health consciousness and consumer innovativeness. The authors used structural equation modeling and multi-group analysis to test the hypotheses.

Privacy concerns of consumers have a negative effect on smart wearable devices usage, while health consciousness positively impacts consumers' usage of smart wearable devices. Consumer innovativeness indirectly affects smart wearable devices usage via effort expectancy. Experienced consumers are less sensitive to the performance expectancy but more affected by effort expectancy regarding smart wearable devices.

The present study contributes to the literature stream of health-AI usage by unraveling the impacts of privacy concerns and health consciousness and examining the moderating role of prior experience. The findings suggest marketers in the health-AI industry should endeavor to build transparent and sound privacy protection mechanisms and promote smart wearable devices by fostering health awareness of potential consumers.

The purpose of the research is to explore how small and medium enterprises (SMEs) in central China achieve intelligent transformation through the use of artificial intelligence (AI). Because of unequal resource allocation, constraints on the intelligent transformation of SMEs in central China are different from those in economically and technologically well-developed coastal provinces. Hence, the authors focus on SMEs in central China to identify drivers of and barriers to intelligent transformation.

The interview data were collected from 66 SMEs across 20 industries in central China. To verify the validity of the data collection method, the authors used two methods to control for retrospective bias: multi-level informants and enterprises' AI project application materials (Wei and Clegg, 2020). The final data were validated without conflicts. Next, the authors cautiously followed a two-step approach recommended by Venkatesh et al. (2010) and used NVivo 11.0 to analyze the collected text data.

SMEs in central China are enthusiastic about intelligent transformation while facing both internal and external pressures. SMEs need to pay attention to both internal (enterprise development needs, implementation cost, human resources and top management involvement) and external factors (external market pressure, convenience of AI technology and policy support) and their different impacts on intelligent transformation. However, constrained by limited resources, SMEs in central China have been forced to take a step-by-step intelligent transformation strategy based on their actual needs with the technological flexibility method in the short term.

Considering the large number of SMEs and their importance in promoting China's economic development and job creation (SME Bureau of MIIT, 2020), more research on SMEs with limited resources is needed. In the study, the authors confirmed that enterprises should handle “social responsibility” carefully because over-emphasizing it will hinder intelligent transformation. However, firms should pay attention to the role of executives in promoting intelligent transformation and make full use of policy support to access more resources.

This paper aims at defining a model to properly evaluate knowledge management (KM) value. Empirical studies have found little or no improvement in organizational performance despite large KM investments.

The KM-driven performances are rooted in knowledge resources based on the knowledge-based view. Further, the KM-driven performances are mediated by business process capabilities. Organizational learning is critically complementary to KM for being a moderator to knowledge resources. A model was proposed for defining the performance with the relationships between these issues. A survey was conducted for collecting empirical data. Partial least squares was used for path analysis.

Knowledge resources lay a foundation on the KM-driven performance through the mediator of business process capabilities. Specifically, knowledge assets and process capabilities are two different but relevant drivers in a value creation process. The findings particularly provide evidence to explain the knowledge-based view and the mediator of business process capabilities.

While an organization owns important knowledge resources in the industry, it should dedicate its effort to the improvement of business process capabilities for well-achieving final performance. The KM-driven performance should be considered for both financial and non-financial indicators in a complementary manner.

Extant theories may provide inadequate methods to evaluate KM-enabled performance. This study attempted to define an effective model for this issue. This model empirically demonstrated its capability to work on this issue.

With the development of economy, China’s OFDI constantly increase in recent year. Meanwhile, OFDI has spillover effect on economic development and technological development of home country. Thus, accurate OFDI prediction is a prerequisite for the effective development of international investment strategies. The purpose of this paper is to predict China’s OFDI accurately using a novel multivariable grey prediction model with Fourier series.

This paper applied a multivariable grey prediction model, GM(1,N), to forecast China’s OFDI. In order to improve the prediction accuracy and without changing local characteristics of grey model prediction, this paper proposed a novel grey prediction model to improve the performance of the traditional GM(1,N) model by combining with residual modification model using GM(1,1) model and Fourier series.

The coefficients indicate that the export and GDP have positive influence on China’s OFDI, and, according to the prediction result, China’s OFDI shows a growing trend in next five years.

This paper proposed an effective multivariable grey prediction model that combined the traditional GM(1,N) model with a residual modification model in order to predict China’s OFDI. Accurate forecasting of OFDI provides reference for the Chinese Government to implement international investment strategies.

The National People’s Congress (NPC) of People’s Republic of China, the highest organ of state power, is popularly seen as a rubber-stamp entity. However, it has been substantially evolving its roles to accommodate the governance discourses within China’s political system over the decades. This study aims to explore the changes of governance discourse of the NPC within China’s political system through which to offer a thorough understanding of the NPC’s evolving substantial role in current China.

This study deploys a historical approach to explore the changes of governance discourse of the NPC that has seen a growing importance in China’s political agenda, as argued by this study.

The authors find that the NPC has been substantially evolving its role within China’s political system in which the Chinese Communist Party has created different governance discourses. Besides, the NPC and its Standing Committee have asserted its authority as a substantial actor within China’s political system. The NPC is no longer functioned as a rubber-stamp institution, though it is still popularized as a rubber stamp by many scholars.

This study is a historical elaboration on the development of NPC under three governance discourses. It might be, to some extent, relatively descriptive in nature.

This study, therefore, sheds some light on a revisit on the governance discourses in current China.

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

The purpose of this paper is to provide a better understanding of the development of intelligent agriculture (IA) in China, which is an important tendency in advancing the agricultural productivity in the coming era.

Considering publications as featured evidence of an emerging phenomenon, the authors review publications of IA. Specifically, the use of term, definition and examples of IA, both English and Chinese literature, and government policies of China are all reviewed. Additionally, the authors use basic statistical and thematic analysis to help synthesizing the literature and drawing conclusions. Findings from various sources of publications supplement with each other.

IA in China has shown three main characteristics: unbalanced geographic distribution, an early stage of the trend and attention mainly focused on a limited range of technologies. Compared with the development of IA in other countries, such as Japan, India and the USA, featured with diversified properties, similarities and differences of IA development in China and in other countries are also discussed.

This general review contributes by uncovering the emergence of IA, identifying its general definition with a comprehensive set of practical examples and pointing out the present characteristics and problems of IA development in China. The general review provides a necessary summary for the policy makers and researchers to have a systematic understanding of IA and better promote its future development. At last, the paper calls for a process-based strategy with different goals at different stages, a sustainable mechanism coordinated by multiple participants, and a localized consideration for relevant policy making.

This study aims to examine the relationship between managerial ability and innovation efficiency, the mediating effect of digital transformation and the moderating effect of internal control.

This study collected A-share manufacturing listed companies in China from 2008 to 2019 and analyzed the data by means of multiple regression analysis, mediating effect test, moderating effect test and heterogeneity test. Finally, the authors conducted robustness test by remeasuring key variables and adding control variables.

The empirical results show that the higher managerial ability can improve innovation efficiency, internal control has a positive moderating effect and digital transformation plays a partial mediating effect on the relationship between managerial ability and innovation efficiency. Specially, it is found that the mediating effect of digital transformation is not significant in non-state-owned firms.

This study suggests that it is necessary to focus on the managerial ability in terms of both cultivation and supervision, to further deepen the digital transformation from the aspects of firms, government and society, especially to support the digital transformation of non-state-owned firms, and to make efforts to improve the corporate governance mechanism and internal control system, so as to better comprehensively realize the improvement of enterprise innovation efficiency.

Based on the mediating effect analysis of digital transformation and the moderating effect analysis of internal control, this study explores the role of managerial ability on innovation efficiency from a new perspective, expanding the related theoretical framework and research boundaries.