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Entrepreneurial action under uncertainty has captured the interest of scholars and practitioners alike. However, this growing body of research has yet to connect entrepreneurial action with actual actions of entrepreneurs. We combine insights from effectuation theory and the psychology of entrepreneurship to investigate drivers of entrepreneurial actions involved in starting and running ventures, particularly optimism, self-efficacy and the use of causal and effectual logics.

The study employs a unique mixed-method approach combining a survey with experience sampling data. After measuring demographics, preference for effectual or causal logics and psychological variables, the 197 US entrepreneurs sampled entrepreneurs logged their daily actions in the form of asks during 60 consecutive days.

Results suggest that self-efficacy and causal logics are key drivers of entrepreneurial actions, while optimism serves as a deterrent. Interestingly, the impact of self-efficacy on actions is moderated by the entrepreneur’s experience level, measured both in years and past asking experience.

The potential of the ask as the basic mechanism for entrepreneurial actions is explored. Based on the findings, the authors offer new and fertile insights by linking psychological traits to entrepreneurial actions, causal and effectual logics and entrepreneurial expertise development.

This study aims to present and evaluate a novel analytical model to predict the structural properties of parts fabricated by fused filament fabrication (FFF) along any non-orthogonal direction.

A new analytical model to estimate the ultimate tensile stress (UTS) and elastic modulus (E) of polylactic acid (PLA)-FFF parts fabricated in any non-orthogonal build orientation, is proposed. The new model is based on an ellipsoid, two angles that define the orientation with respect to the build axes, the infill value and the structural properties along the build axes. The proposed model is evaluated by comparing the UTS and E properties predicted by this model, with the results obtained from experimental tensile tests on PLA-FFF specimens manufactured using variable infill values and non-orthogonal build orientations.

The proposed model is able to predict with good precision the structural properties of PLA-FFF parts along any direction and infill value.

Although the study and results are limited to the UTS and E tensile properties of PLA-FFF components, the model may be extended to other materials or similar additive manufacturing processes.

The new proposed model is able to determine the structural properties of FFF components in any direction, so it can be used during the design process of FFF parts, reducing the need for experimental tests and speeding up the product development process.

Existing models to predict the structural properties of FFF components are limited to orthogonal build orientations (X, Y and Z); however, the new proposed model is able to predict the tensile properties in any direction and infill value. In addition, a new set of experimental data about the structural behaviour of PLA-FFF parts along non-orthogonal build orientations is provided, extending the existing results in the literature.

This study aims to research the time-varying mesh stiffness (TVMS) model for orthogonal face gear drives considering elastohydrodynamic lubrication (EHL) and provide a theoretical basis for understanding the dynamic characteristics of face gear drives.

Considering EHL, a novel model is proposed to calculate the TVMS of orthogonal face gears using the deformation compatibility condition. First, the tooth surface equations of orthogonal face gears are derived according to the tooth surface generation principle. Then, the oil film thickness on the tooth surface of face gears is obtained by solving the governing equations of EHL. Furthermore, the proposed model is used to calculate the TVMS of face gears along the mesh cycle and is verified. Finally, the effects of module, tooth number of shaper cutter and pressure angle on mesh stiffness are analyzed.

The results indicate that when the contact ratio is greater than 1 and less than or equal to 2, the TVMS of face gears exhibits a phenomenon of double-single tooth alternating meshing where sudden changes occur. As the module increases, the overall mesh stiffness of face gears increases, and the magnitude of the sudden change at the moment of single-double tooth alternating meshing gradually increases. As the tooth number of shaper cutter and pressure angle increase, so does the TVMS of face gears. When the effect of oil film is considered, the calculated TVMS of face gears slightly increases overall and the increase in average oil film thickness leads to a rise in the TVMS. This study provides a theoretical basis for understanding the dynamic characteristics of face gear drives.

This study’s originality and value lie in its comprehensive approach, which includes conducting analysis based on loaded tooth contact, considering the influence of elastohydrodynamic lubrication, proposing a novel analytical–finite–element model, calculating TVMS of face gears, verifying the proposed model and analyzing the effects of typical structural parameters and oil film thickness.

The investigation aims to improve Nd: YAG laser technology for precision cutting of carbon fiber reinforcing polymers (CFRPs), specifically those containing newly created resin (NDR) from the polyethylene and polyurea group, is the goal of the study. The focus is on showing how Nd: YAG lasers may be used to precisely cut CFRP with NDR materials, emphasizing how useful they are for creating intricate and long-lasting components.

The study employs a systematic approach that includes complicated factorial designs, Taguchi L27 orthogonal array trials, Gray relational analysis (GRA) and machine learning predictions. The effects of laser cutting factors on CFRP with NDR geometry are investigated experimentally, with the goal of optimizing the cutting process for greater quality and efficiency. The approach employs data-driven decision-making with GRA, which improves cut quality and manufacturing efficiency while producing high-quality CFRP composites. Integration of machine learning models into the optimization process significantly boosts the precision and cost-effectiveness of laser cutting operations for CFRP materials.

The work uses Taguchi L27 orthogonal array trials for systematically explore the effects of specified parameters on CFRP cutting. The cutting process is then optimized using GRA, which identifies influential elements and determines the ideal parameter combination. In this paper, initially machining parameters are established at level L3P3C3A2, and the optimal machining parameters are determined to be at levels L3P2C3A3 and L3P2C1A2, based on predictions and experimental results. Furthermore, the study uses machine learning prediction models to continuously update and optimize kerf parameters, resulting in high-quality cuts at a lower cost. Overall, the study presents a holistic method to optimize CFRP cutting processes employing sophisticated techniques such as GRA and machine learning, resulting in better quality and efficiency in manufacturing operations.

The novel concept is in precisely measuring the kerf width and deviation in CFRP samples of NDR using sophisticated imaging techniques like SEM, which improves analysis and precision. The newly produced resin from the polyethylene and polyurea group with carbon fiber offers a more precise and comprehensive understanding of the material's behavior under different cutting settings, which makes it novel for kerf width and kerf deviation in their studies. To optimize laser cutting settings in real time while considering laser machining conditions, the study incorporates material insights into machine learning models.

This study investigates the application of collaborative inquiry within innovation management, employing platform thinking to address challenges of generalizability and relevance. The aim is to integrate Collaborative Inquiry methods, characterized by participatory, diffuse, and reflective practices, to transform research into a tool for impactful change in organizations in the field of innovation management.

A longitudinal participatory case study approach focuses on the IDeaLs case—a research platform that collaborated with multiple companies over several years. The data gathered and analyzed comes from the research project within the research platforms over the first two editions and from the research platform management and coordination activities.

The study introduces the Collaborative Research Platform Approach (CRPA), demonstrating its effectiveness in addressing typical constraints of traditional research methodologies through a real-world application within the IDeaLs case. The findings highlight the CRPA's potential in fostering a dynamic, co-creative research environment that bridges theoretical knowledge with practical applications, thus enhancing both scholarly and organizational outcomes while pursuing a future change within the organizations.

There are two main research implications. First, it proposes platform thinking as a theoretical lens to read a multi-stakeholder phenomenon in the research domain, confirming its nature of value-creation mechanisms, using it outside the business model and strategic space. Second, it offers a methodological contribution by presenting the CRPA framework.

The CRPA framework offers organizations a structured approach to managing collaborative research projects that align with both academic rigor and practical relevance. Companies engaged in the study reported enhanced ability to implement actionable insights from research, influencing real-time decision-making processes.

By fostering collaborative engagements across multiple stakeholders, the CRPA promotes a research culture that values inclusivity and practical impact, potentially leading to broader societal benefits through improved innovation management practices.

This paper contributes to the innovation management field by proposing the CRPA, which integrates principles of Platform Thinking with Collaborative Inquiry. This novel approach is designed to improve the applicability and scope of innovation research, offering a robust framework that enhances engagement and utility across academic and business domains. It uses platforms as a theoretical lens to read a multi-stakeholder environment in the research domain.

This study investigates the sustainability models of non-profit and hybrid organizations, which aim to balance economic, social and environmental objectives. The research introduces the Sustainability Model Canvas to analyze these organizations and identify common patterns, unique characteristics and managerial insights to balance the triple bottom line.

The research utilizes the Sustainability Model Canvas to examine the sustainability models of 200 non-profit and hybrid organizations. Data were collected from secondary sources, including articles, reports and websites. The analysis was conducted using the activity system theoretical framework, which helped to identify design elements and themes within the business models of the studied organizations.

The study reveals four primary sustainability model patterns: donated income, earned income, public income and auto-generated income. An additional mixed approach pattern is identified, combining elements from the four primary patterns. The research highlights the parallels between these sustainability models and multi-sided platform business models, offering managerial suggestions for leveraging these patterns to achieve sustainability.

The study is based on secondary data, which may limit the depth of insights compared to primary data collection. At the same time, the chance to consider hybrid organization through multi-sided platform lenses provides relevant contributions to both the literature streams.

The identified sustainability model patterns and managerial suggestions can serve as blueprints for non-profit and hybrid organizations aiming to design or innovate their sustainability models. The Sustainability Model Canvas offers a practical tool for organizations to visualize and balance their triple bottom line objectives.

The research underscores the importance of integrating social and environmental considerations into business models, promoting a holistic approach to sustainability that can lead to broader social and environmental benefits.

This research contributes to the business model literature by extending the focus beyond traditional profit-oriented organizations to include non-profit and hybrid organizations. The introduction of the Sustainability Model Canvas provides a new tool for designing and analyzing sustainability-oriented business models. The study also suggests considering sustainability models as multi-sided platforms, offering new insights for both academic and practical applications.

In this chapter, the authors critically examine the application of unmeasured latent method factors (ULMFs) in human resource and organizational behavior (HROB) research, focusing on addressing common method variance (CMV). The authors explore the development and usage of ULMF to mitigate CMV and highlight key debates concerning measurement error in the HROB literature. The authors also discuss the implications of biased effect sizes and how such bias can lead HR professionals to oversell interventions. The authors provide evidence supporting the effectiveness of ULMF when a specific assumption is held: a single latent method factor contributes to the data. However, the authors dispute this assumption, noting that CMV is likely multidimensional; that is, it is complex and difficult to fix with statistical methods alone. Importantly, the authors highlight the significance of maintaining a multidimensional view of CMV, challenging the simplification of a CMV as a single source. The authors close by offering recommendations for using ULMFs in practice as well as more research into more complex forms of CMV.

The purpose of this research is to present an integrated methodological framework to aid in performance stewardship of management institutions according to their strategies based on a holistic evaluation encompassing social, economic and environmental dimensions.

A Mamdani fuzzy inference system (FIS) approach was adopted to design the quantitative models with respect to balanced scorecard (BSC) perspectives to demonstrate dynamic capability. Individual models were developed for each perspective of BSC using Mamdani FIS. Data was collected from subject matter experts in management education.

The proposed methodology is able to successfully compute the scores for each perspective. Effective placement, teaching learning process, faculty development and systematic feedback from the stakeholders were found to be the key drivers for revenue generation. The model is validated as the results were well accepted by the head of the institution after implementation.

The model resulting from this study will assist the institution to cyclically assess its performance, thus enabling continuous improvement. The strategy map provides the causality of the objectives across the four perspectives to aid the practitioners to better strategize. Also this study contributes to the literature of BSC as well to the applications of multi-criteria decision-making (MCDM) techniques.

Mamdani FIS integrated BSC model is a significant contribution to the academia of management education to quantitatively compute the performance of institutions. This quantified model reduces the ambiguity for practitioners to decide the performance levels for each metric and the priorities of metrics.

The phase-field method of interfacial damage is used to simulate the damage in composite structures containing the brittle orthotropic materials and their interface.

In the brittle fracture modeling, the strain tensor is decomposed into positive and negative parts characterizing tension and compression behaviors. By requiring an elastic energy preserving transformation involving the elastic stiffness tensor, these two strain parts must satisfy the orthogonality condition in the sense that the elastic stiffness tensor responds as a metric. However, most of the recent phase-field methods for brittle fracture do not verify this orthogonality condition. Additionally, to describe the damage in structures with anisotropic phases, recent studies have used multiple phase-field variables, with each preferential orientation represented by a phase-field variable to describe the bulk damage of component materials. This approach increases the complexity of simulation procedure. These disadvantages motivate the present study aimed at enhancing the simulation method.

The present study improves the phase-field method of interfacial damage by (1) incorporating the strain orthogonality condition into the phase-field method; (2) using only one phase-field variable instead of multiple phase-field variables to simulate damage in component orthotropic phases; and (3) investigating the interaction between interfacial damage and bulk damage as well as the effect of orientation tensor of preferential orientation in each orthotropic phase and the interfacial parameters on crack branching in composite structures.

Through several simulation examples, the present simulation method is proven to be accurate, effective, and helps the simulation process simpler than previous relevant methods.

Indoor hallways are the most common and indispensable part of people’s daily life, commercial and industrial activities. This paper aims to achieve high-precision and dense 3D reconstruction of the narrow and long indoor hallway and proposes a 3D, dense 3D reconstruction, indoor hallway, rotating LiDAR reconstruction system based on rotating LiDAR.

This paper develops an orthogonal biaxial rotating LiDAR sensing device for low texture and narrow structures in hallways, which can capture panoramic point clouds containing rich features. A discrete interval scanning method is proposed considering the characteristics of the indoor hallway environment and rotating LiDAR. Considering the error model of LiDAR, this paper proposes a confidence-based point cloud fusion method to improve reconstruction accuracy.

In two different indoor hallway environments, the 3D reconstruction system proposed in this paper can obtain high-precision and dense reconstruction models. Meanwhile, the confidence-based point cloud fusion algorithm has been proven to improve the accuracy of 3D reconstruction.

A 3D reconstruction system was designed to obtain a high-precision and dense indoor hallway environment model. A discrete interval scanning method suitable for rotating LiDAR and hallway environments was proposed. A confidence-based point cloud fusion algorithm was designed to improve the accuracy of LiDAR 3D reconstruction. The entire system showed satisfactory performance in experiments.