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The purpose of this paper is to provide an effective members-adding method for truss topology optimization in plastic design.

With the help of the distribution of principal stress trajectories, obtained by finite element analysis of the design domain, ineffective zones for force transmission paths can be found, namely, areas whose nodes may have ersatz nodal displacements. Members connected by these nodes are eliminated and the reduced ground structure is used for optimization. Adding members in short to long order and limiting the number of members properly with the most strained ones added, large-scale truss problems in one load case and multiple-load cases are optimized.

Inefficient members (i.e. bars that fulfil the adding criterion but make no contribution to the optimal structure) added to the ground structure in each iterative step are reduced. Fewer members are used for optimization than before; therefore, faster solution convergence and less computation time are achieved with the optimized result unchanged.

The proposed members-adding method in the paper can alleviate the phenomenon of ersatz nodal displacements, enhance computational efficiency and save calculating resources effectively.

For any 3D model with chambers to be fabricated in powder-bed additive manufacturing processes such as SLM and SLS, powders are trapped in the chambers of the finished model. This paper aims to design a shortest network with the least number of outlets for efficiently leaking the trapped powders.

This paper proposes a nonlinear objective with linear constraints for solving the channel design problem and a particle swarm optimization algorithm to solve the nonlinear system.

Structural optimization for the channel network leads to fairly short channels in the interior of the 3D models and very few outlets on the model surface, which achieves the cleaning of the powders while causing almost the least changes to the model.

This paper reveals the NP-harness of computing the shortest channel network with the least number of outlets. The proposed approach helps the design of lightweight models using the powder-bed additive manufacturing techniques.

The purpose of this paper is to propose an agile assistant robot to be used as a mobile partner with two rotational motions and one translational motion. This robot possesses the rolling function and three operating abilities to assistant human beings in the industrial environment.

The main body of the robot is a typical 4-RSR (where R denotes a revolute joint and S denotes a spherical joint) parallel mechanism. The mechanism can reach any position on the ground by two rolling modes (the equivalent Watt linkage rolling mode and the equivalent 6R linkage rolling mode), and the robot can work as a spotlight or a worktable in operating modes at the target location. The mobility, rolling modes, operating modes and kinematics are analyzed.

Based on the results of kinematics of this assistant robot, the upper platform of the 4-RSR rolling mechanism has two rotational motions and one translational motion which can be used in the industry. The proposed concept is verified by experiments on a physical prototype.

This paper also discusses the application to industrial cases where cooperation between workers and robots is required.

The work presented in this paper is a novel exploration to apply parallel mechanisms to the field of assistant rolling robots. It is also a new attempt to use the rolling mechanism in the field of mobile operating robots for industry tasks.

In online user innovation communities (UICs), firms adopt external innovations beyond their internal resources and capabilities. However, little is known about the influences of organizational adoption or detailed adoption patterns on subsequent user innovation. This study aims to examine the influence of organizational adoption, including its level and timing, on users' subsequent innovation behavior and performance.

This research model was validated using a secondary dataset of 17,661 user–innovation pairs from an online UIC. The effect of organizational adoption on users' subsequent innovation likelihood was measured by conducting a panel logistic regression. Furthermore, the effects of organizational adoption on subsequent innovation’ quality and homogeneity and those of the adoption level and timing on subsequent innovation likelihood were tested using Heckman's two-step approach.

The authors found that organizational adoption negatively affects the likelihood of subsequent innovation and its homogeneity but positively affects its quality. Moreover, more timely and lower-level adoption can increase the likelihood of users' subsequent innovation.

This study comprehensively explores organizational adoption's effects on users' subsequent innovation behavior and performance, contributing to the literature on UICs and user innovation adoption. It also provides valuable practical implications for firms on how to optimize their adoption decisions to maintain the quantity, quality, and diversity of user innovations.

Live-streaming e-commerce is becoming a new way for many consumers to shop. During the live broadcast process, the interaction between anchors and customers plays a decisive role on consumers' purchasing decisions. This study aims to explore how two types of interaction between the anchor and the customers (i.e. task-oriented interaction and relationship-oriented interaction) affect customers' purchase decisions.

The study establishes a model based on online trust theory and multi-sensor interaction theory. To validate the model, we carried out five simulated live-streaming events and collected data through a scenario-based survey of the viewers participating in the live-streaming (N = 244). Structural equation modeling was employed to test the hypotheses.

Both task-oriented interaction and relationship-oriented interaction have a positive impact on users' purchase decisions through the mediation of virtual touch, emotional trust and cognitive trust. Sense of power has opposite moderating effects on the impacts of relationship-oriented interaction on emotional trust and cognitive trust.

This study enriches the theory of live-streaming e-commerce by demonstrating the decisive roles of two types of anchor–customer interaction, the mediation roles of virtual touch, cognitive trust, and emotional trust in customer purchase decisions, as well as the moderating effect of sense of power on customer decision-making processes. The findings provide practical insights for anchors and live-streaming platforms about how they should arrange live-streaming content to enhance consumer purchasing decisions.

A rash of security incidents in ride-sharing have made discovering the mechanisms to repair consumers' trust essential for the information technology (IT)-enabled ride-sharing platforms. The purpose of this paper is to explore how the two response strategies (i.e. security policies [SPs] and apologies) of platforms repair passengers' trust and whether the two implementation approaches of SPs (i.e. pull and push) lead to different results in repairing passengers' trust in the platforms.

A field survey based on a real scenario (n = 238) and an experiment (n = 245) were conducted to test the hypotheses empirically. Structural equation modeling and one-way analysis of variance (ANOVA) are employed in the data analyses.

This study finds that (1) both SPs and apologies aid in repairing trust; (2) repaired trust fully mediates the influence of SPs on continuance usage and partially mediates the influence of apologies on continuance usage; (3) security polices and the three dimensions of apologies play different roles in repairing trust and retaining passengers and (4) both pull-based and push-based SPs can repair the violated trust; however, the effect of the pull approach is greater than that of the push approach.

The findings provide guidelines for ride-sharing platforms in taking appropriate actions to repair users' trust after security incidents.

The findings reveal the mechanism of trust repairing in the fields of ride-sharing and extend the contents of the trust theory and pull–push theory.

This study aims to explore how and when learning from others promotes creative performance over the contributor’s tenure in the context of open innovation communities.

The authors analyze a publicly available data set that includes 25,923 innovative items developed by 2,194 contributors from an open innovation community of an online game spanning eight years. Logistic regression model is used for analyzing the data.

The results show that multicultural experiences are negatively related to contributor’s creative performance, and this negative relationship weakens as contributor’s tenure increases. While diverse skills are positively related to contributor’s creative performance, and this positive relationship strengthens as contributor’s tenure increases.

This research highlights the importance of online team collaboration in knowledge transfer through learning from others in open innovation communities. By identifying two outcomes of learning from others through online team collaboration, the authors demonstrate the double-edged role of learning from others and advance the understanding on how the effect of learning from others varies over the contributor’s tenure. These results expand the understanding of online team collaboration and provide a new perspective for research on learning from others.

Live streaming commerce has emerged as an essential strategy for vendors to effectively promote their products due to its unique content presentation and real-time interaction. This study aims to investigate the influence of viewer-streamer interaction and viewer-viewer interaction on consumer trust and the subsequent impact of trust on consumers' purchase intention within the live streaming commerce context.

A survey questionnaire was conducted to collect data, and 403 experienced live streaming users in China were recruited. Covariance-based structural equation modeling (CB-SEM) was used for data analysis.

The results indicated that viewer-streamer interaction factors (i.e., personalization and responsiveness) and viewer-viewer interaction factors (i.e., co-viewer involvement and bullet-screen mutuality) significantly influence trust in streamers and co-viewers. Additionally, drawing on trust transfer theory, trust in streamers and co-viewers positively influences trust in products, while trust in co-viewers also positively influences both trust in streamers and products. Furthermore, all three forms of trust positively impact consumers' purchase intentions.

This study enriches the extant literature by investigating interaction-based trust-building mechanisms and uncovering the transfer relationships among three trust targets (streamers, co-viewers and products). Furthermore, this study provides some practical guidelines to the streamers and practitioners for promoting consumers’ trust and purchase intention in live streaming commerce.

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.

To support the standardized evaluation of bicyclist automatic emergency braking (AEB) systems, test scenarios, test procedures and test system hardware and software tools have been investigated and developed by the Transportation Active Safety Institute (TASI) at Indiana University-Purdue University Indianapolis. This paper aims to focus on the development of test scenarios and bicyclist surrogate for evaluating vehicle–bicyclist AEB systems.

The harmonized general estimates system (GES)/FARS 2010-2011 crash data and TASI 110-car naturalistic driving data (NDD) are used to determine the crash geometries and environmental factors of crash scenarios including lighting conditions, vehicle speeds, bicyclist speeds, etc. A surrogate bicyclist including a bicycle rider and a bicycle surrogate is designed to match the visual and radar characteristics of bicyclists in the USA. A bicycle target is designed with both leg pedaling and wheel rotation to produce proper micro-Doppler features and generate realistic motion for camera-based AEB systems.

Based on the analysis of the harmonized GES/FARS crash data, five crash scenarios are recommended for performance testing of bicyclist AEB systems. Combined with TASI 110-car naturalistic driving data, the crash environmental factors including lighting conditions, obscuring objects, vehicle speed and bicyclist speed are determined. The surrogate bicyclist was designed to represent the visual and radar characteristics of the real bicyclists in the USA. The height of the bicycle rider mannequin is 173 cm, representing the weighted height of 50th percentile US male and female adults. The size and shape of the surrogate bicycle were determined as 26-inch wheel and mountain/road bicycle frame, respectively. Both leg pedaling motion and wheel rotation are suggested to produce proper micro-Doppler features and support the camera-based AEB systems.

The results have demonstrated that the developed scenarios, test procedures and bicyclist surrogate will provide effective objective methods and necessary hardware and software tools for the evaluation and validation of bicyclist AEB systems. This is crucial for the development of advanced driver assistance systems.