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The purpose of this paper is to explain progress, or the lack of it, in achieving workplace gender equality goals prescribed by affirmative action regulation by using concepts from soft regulation and organizational learning.

The research design is a longitudinal study (2002-2012) of a critical case, that of a single large organization in the male-dominated steel manufacturing, distribution and mining industries. The case focusses on the evidence about organizational learning to be found in that organization’s reports to government on its activities to promote workplace gender equality.

While other factors play a role, the apparent failure of the soft regulation to generate a significant shift in gender equality outcomes may also be attributed to ineffective organizational learning, demonstrated by the absence of systematic reflection within the organization on how to improve workplace gender equality, and the lack of firm targets and external benchmarking.

Self-reported data may be overstated or incomplete. More research is needed to confirm the nature of the specific learning processes occurring within organizations.

Absent the advent of hard sanctions in workplace gender equality regulation, the responsible government agencies may find it valuable to focus on ways to encourage target organizations to develop competence in organizational learning.

More effective gender equality regulation may change organizational policy and practice and improve work opportunities for women.

Rather than concluding that the only alternative, when soft regulation is unsuccessful, is hard regulation, this paper shifts the focus to ways that soft regulatory processes might be improved to strengthen their effect.

The purpose of this paper is to figure out the exact knowledge demand of makers. Furthermore, the paper aims to construct the knowledge space based on makers’ knowledge demand.

The first step of this research is to explore the makers’ knowledge demand by the methods of field investigation, non-interventional study and in-depth interviews. On that basis, the elements, principles, framework and mechanism of the knowledge space were discussed. The grounded theory and NVivo software were used in the data analysis.

The investigation shows that interest and competition are the main motivations for makers’ knowledge demand. The findings also indicate that the characteristics are active knowledge needs, “informal learning” preferences, divergent thinking, close community ties and interdisciplinary. According to the makers’ knowledge demand node conceptual model, the content of makers’ knowledge demand is principle knowledge, empirical knowledge and knowledge situation. Based on the above findings, the elements, principles, operation framework and inherent operation mechanism of the knowledge space construction are systemically described.

This study gives precise details about makers’ knowledge demand and the construction of makers’ knowledge space by libraries. This is the first research that comprehensively explores the knowledge demand of makers. The findings can help the library plan and implement the construction of makerspace, and also improve the service for makers.

This paper aims to verify that additive manufacturing technology could be used for the redesign and rapid manufacturing of tools and determine whether the mechanical performance of such tools can satisfy the practical operating requirements.

A special key was selected as the research object in this paper. The special key was innovatively redesigned and manufactured directly using selective laser melting (SLM). The function and critical geometries of the special key were first analysed, which was followed by discussions on the geometrical constraints in the manufacturing of typical geometrical features using SLM technology. Next, the special key was redesigned based on the SLM geometrical constraints and the functional requirements. Finally, the key was manufactured using SLM, and the mechanical performance characteristics of the key were determined.

The minimal geometrical feature was 0.2 mm when manufacturing thin walls using SLM. The reliable building angle of an overhanging surface was 40°. The top surface quality of the part could be greatly improved through laser surface re-melting. The volume of the redesigned special key based on the SLM process was only one-third to one-fourth of the original key. The mechanical properties, such as tensile strength and micro-hardness, of the samples manufactured using SLM were able to reach the practical operating requirements.

It is completely feasible to redesign and manufacture precision tools based on the innovative approach of SLM. The advantages of the redesigned tools includes the lack of design restrictions that hinder traditional manufacturing methods, material savings, ability to produce tools that cannot be easily copied and rapid production speed for a small number of tools.

As the role of AI on human teams shifts from a tool to a teammate, the implementation of AI teammates into knowledge-intensive crowdsourcing (KI-C) contest teams represents a forward-thinking and feasible solution to improve team performance. Since contest teams are characterized by virtuality, temporality, competitiveness, and skill diversity, the human-AI interaction mechanism underlying conventional teams is no longer applicable. This study empirically analyzes the effects of AI teammate attributes on human team members’ willingness to adopt AI in crowdsourcing contests.

A questionnaire-based online experiment was designed to perform behavioral data collection. We obtained 206 valid anonymized samples from 28 provinces in China. The Ordinary Least Squares (OLS) model was used to test the proposed hypotheses.

We find that the transparency and explainability of AI teammates have mediating effects on human team members’ willingness to adopt AI through trust. Due to the different tendencies exhibited by members with regard to three types of cognitive load, nonlinear U-shaped relationships are observed among explainability, cognitive load, and willingness to adopt AI.

We provide design ideas for human-AI team mechanisms in KI-C scenarios, and rationally explain how the U-shaped relationship between AI explainability and cognitive load emerges.

Laser powder bed fusion (LPBF) additive manufacturing (AM) enables the design of complex parts using materials that are otherwise difficult to fabricate. Due to the high cost of machines, the parts produced by LBPF are expensive. Both researchers and industry are therefore focused on lowering costs by improving productivity while ensuring part quality. The purpose of this study is to quantify the productivity gains from using laser beam shaping, multi-laser printing and the use of large build chambers to print larger size parts.

This paper performs an expert elicitation with 18 experts.

This paper finds that experts believe that larger parts are less likely to print successfully. Increasing the part footprint is more detrimental to print success than increasing part height. Experts also believe that beam shaping is expected to provide limited print time improvement (median 4% reduction, 90% CI: 2%–25%) while improving part quality, whereas going from one to two lasers is expected to provide a median of 25% (90% CI: 10%–45%) print time improvement but degrade part quality. Through cost analysis of a representative part, this paper shows that the uncertainty in build success rates for large parts dominates expected cost reductions from laser beam shaping or multi-laser printing.

The study has three key limitations. First, it is possible that the sample of experts who agreed to take the survey biases the results. By definition, these are individuals who are willing to share what they know. There may be other experts who have a different view of the efficacy of the technologies evaluated here, but that view might be based on proprietary knowledge, which those experts are unable to share. Second, an elicitation captures what is known at a moment in time. As technology improves and as widespread deployment results in learning, the most consequential finding − that experts believed that success rates for large builds are likely to be low − may become less valid. Third, the overarching goal of this study is to assess technologies to improve AM productivity for high performance metal parts. A single study can only partially achieve this goal. The selection of technologies is constrained by both the desire to keep the study tractable and the suitability of expert elicitation as a method. For example, expert elicitation is not appropriate to assess the efficacy of technologies where sufficient empirical data or analytical techniques exist.

The results show that AM research and policy initiatives, including standards and regulatory schemes, must support efforts to improve the repeatability and reliability of the technological innovations that are needed to deploy AM in cost-critical or high throughput applications. These results also reinforce the criticality of workforce development components of existing (and future) AM policy initiatives. The elicitation revealed a significant number of factors that must be considered and potentially managed to ensure successful builds. Notably, no experts interviewed discussed all factors. While this may be a consequence of availability bias, it suggests that inexperienced AM users and nonexpert decision-makers, including managers, who would like to adopt new AM technologies, may be unaware of the myriad mechanisms by which build failure can occur and may fail to take mitigating action. This result contradicts a common belief that complicated parts can be fabricated with little to no expertise (assuming access to a design file for the part). Workforce development programs will be essential to help AM users develop the knowledge required to successfully implement metal AM.

Several strategies, including increasing the build volume to print larger parts or more parts at a time, using multiple lasers and beam shaping are proposed to improve the productivity of AM. However, the real-world efficacy of these strategies is not known. This work pools the judgment of experts to give decision-makers some insight into the current, real-world efficacy of these approaches.