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Most employees are forced to telework due to the COVID-19 pandemic, which brings novel, disruptive, and critical challenges both in work and life. Based on event system theory and equity theory, this research explores how and when forced teleworking event strength (i.e. novelty, disruption, and criticality) affects employees’ work and life-related outcomes.

We conducted two studies to test the hypothesized moderated mediation model (Study 1: an experiment survey, N = 141; Study 2: a time-lagged survey, N = 243) with employees forced to telework from China.

The results largely support our hypotheses. Study 1 indicates that the manipulation of forced teleworking event strength (high vs low) is effective, and the main effect of forced teleworking event strength on work-family conflict is significant. Moreover, Study 2 shows that work-family conflict mediates the relationship between forced teleworking event strength (i.e. novelty, disruption, and criticality) and counterproductive work behavior (CWB). Furthermore, perceived overqualification positively moderates the relationship between work-family conflict and CWB. In detail, the relationship between work-family conflict and CWB becomes stronger when perceived overqualification is higher.

This research provides a new perspective on how forced teleworking event strength impacts CWB and advances the literature on the relevant theories.

Following the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.

Drawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.

The results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.

The conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

Ti6Al4V is a widely used metal for biomedical application due to its excellent corrosion resistance, biocompatibility and mechanical strength. However, a coupling reaction of friction and corrosion is the critical reason for the failure of implants during the long-term service in human body, shortening the life expectancy and clinical efficacy of prosthesis. Hence, this study aims to find a feasible approach to modify the service performances of Ti6Al4V.

Selective laser melting (SLM), as one of the emerging metal-based additive manufacturing (AM) technologies is capable for fabricating patient-specific personalized customization of artificial prosthesis joints, owing to its high adaptability for complex structures. This study is concerned with the tribocorrosion behavior of SLM fabricated Ti6Al4V substrate enhanced by laser rescanning and graphene oxide (GO) mixing. The tribocorrosion tests were performed on a ball-on-plate configuration under the medium of simulated body fluid (SBF). Moreover, the surface morphologies, microstructures, microhardness and contact angle tests were used to further reveal the in-situ strengthening mechanism of GO/Ti6Al4V nanocomposites.

The results suggest that the strengthening method of GO mixing and laser rescanning shows its capability to enhance the wear resistance of Ti6Al4V by improving surface morphologies and promoting the generation of hard phases. The wear volume of R-GO/Ti6Al4V is 5.1 × 10⁻² mm³, which is 25.0% lower than that of pure SLM-produced Ti6Al4V. Moreover, a wear-accelerated corrosion of the Ti6Al4V occurs in SBF medium, leading to a drop in the open circuit potential (OCP), but R-GO/Ti6Al4V has the lowest tendency to corrosion. Compared to that of pure Ti6Al4V, the microhardness and contact angle of R-GO/Ti6Al4V were increased by 32.89% and 32.60%, respectively.

Previous investigations related to SLM of Ti6Al4V have focused on improving its density, friction and mechanical performances by process optimization or mixing reinforcement phase. The authors innovatively found that the combination of laser rescanning and GO mixing can synergistically enhance the tribocorrosion properties of titanium alloy, which is a feasible way to prolong the service lives of medical implants.

How to model blind image deblurring that arises when a camera undergoes ego-motion while observing a static and close scene. In particular, this paper aims to detail how the blurry image can be restored under a sequence of the linear model of the point spread function (PSF) that are derived from the 6-degree of freedom (DOF) camera’s accurate path during the long exposure time.

There are two existing techniques, namely, an estimation of the PSF and a blind image deconvolution. Based on online and short-period inertial measurement unit (IMU) self-calibration, this motion path has discretized a sequence of the uniform speed of 3-DOF rectilinear motion, which unites with a 3-DOF rotational motion to form a discrete 6-DOF camera’s path. These PSFs are evaluated through the discrete path, then combine with a blurry image to restoration through deconvolution.

This paper describes to build a hardware attachment, which is composed of a consumer camera, an inexpensive IMU and a 3-DOF motion mechanism to the best of the knowledge, together with experimental results demonstrating its overall effectiveness.

First, the paper proposes that a high-precision 6-DOF motion platform periodically adjusts the speed of a three-axis rotational motion and a three-axis rectilinear motion in a short time to compensate the bias of the gyroscope and the accelerometer. Second, this paper establishes a model of 6-DOF motion and emphasizes on rotational motion, translational motion and scene depth motion. Third, this paper addresses a novel model of the discrete path that the motion during long exposure time is discretized at a uniform speed, then to estimate a sequence of PSFs.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.