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This paper aims to solve the problem of the inability to apply learning methods for robot motion skills based on dynamic movement primitives (DMPs) in tasks with explicit environmental constraints, while ensuring the reliability of the robot system.

The authors propose a novel DMP that takes into account environmental constraints to enhance the generality of the robot motion skill learning method. First, based on the real-time state of the robot and environmental constraints, the task space is divided into different regions and different control strategies are used in each region. Second, to ensure the effectiveness of the generalized skills (trajectories), the control barrier function is extended to DMP to enforce constraint conditions. Finally, a skill modeling and learning algorithm flow is proposed that takes into account environmental constraints within DMPs.

By designing numerical simulation and prototype demonstration experiments to study skill learning and generalization under constrained environments. The experimental results demonstrate that the proposed method is capable of generating motion skills that satisfy environmental constraints. It ensures that robots remain in a safe position throughout the execution of generation skills, thereby avoiding any adverse impact on the surrounding environment.

This paper explores further applications of generalized motion skill learning methods on robots, enhancing the efficiency of robot operations in constrained environments, particularly in non-point-constrained environments. The improved methods are applicable to different types of robots.

This study aims to examine the effectiveness of the debt modification system (DMS) in Korea. We find that DMS does have a positive effect in increasing the credit scores and annual income of DMS users. We also find that a debt management plan (DMP) is more effective in raising credit scores than personal rehabilitation (PR). However, the credit scores of DMS users in the first half of 2019 (551.1–626.1 points) are at a very low level, making it difficult to access low-interest unsecured loans from banks. Therefore, DMS in Kores is still insufficient to support the return of debt-ridden consumers to normal financial life and provide opportunities for a fresh start.

At the end of a building’s lifecycle, there are several limitations to the decision-making process (DMP). There is a lack of data available from the building’s history, the difficulty in assessing the condition of a building and the variety of stakeholders’ needs that have to be satisfied. The purpose of this paper is to answer the question: how would end-of-life (EOL) DMP change when buildings will have been digitally built? The answer will be illustrated through a conceptual framework.

A qualitative analysis of the existing literature has been performed to identify the elements within building information modelling (BIM) and advanced digital technologies that could be of support to the DMP. The findings have been collected and summarised in a conceptual framework that has been validated and enhanced through online interviews with industry experts.

The enhanced framework has identified that BIM technology would bring the benefit of providing the initial digital data source, from which machine learning and data analytics would then extract the relevant data needed to measure accurately the criteria during the analysis of the EOL options put on the table.

The findings of this research could contribute to developing the software modules making the bridge between BIM and machine learning technologies, to implement them in the EOL DMP.

This paper seeks to investigate how seriously diversity issues are considered by municipal governments in North Carolina and to identify specific diversity management practices (DMPs) that are adopted more often by municipalities. It also aims to examine whether the adoption levels of DMPs are influenced by demographic and economic factors and the various backgrounds of city managers.

Data about adopted DMPs and city managers' backgrounds were collected by surveying all municipalities in North Carolina with populations of at least 5,000 (response rate: 50 percent). An index was constructed to determine the adoption level of DMPs, and the cities were divided into four groups, based on their index scores (i.e. DMP scores). Analysis of variance and correlations were used to test the relationship between a number of factors and the index score.

A number of DMPs were identified as being more popular among municipalities that took diversity and its related issues more seriously, and other DMPs as being less popular among cities that did not pay particular attention to diversity. It was also found that the adoption level of DMPs was significantly affected by population size, the heterogeneity of population, urbanization level, and city manager's age.

The DMPs that were found suitable for each one of the four groups of cities can be used as a guide when cities in a particular group want to adopt more DMPs to support and encourage diversity at work. According to the findings, cities need to be more proactive in managing diversity by introducing appropriate DMPs when their demographics are changing substantially.

This is one of the first studies to identify the more popular DMPs among municipalities with different attention levels toward diversity and its related issues. The study also contributes to the construction of a comprehensive diversity management model that explains how cities respond to changing demographics.

This paper aims to increase the safety of the robots’ operation by developing a novel method for real-time implementation of velocity scaling and obstacle avoidance as the two widely accepted safety increasing concepts.

A fuzzy version of dynamic movement primitive (DMP) framework is proposed as a real-time trajectory generator with imbedded velocity scaling capability. Time constant of the DMP system is determined by a fuzzy system which makes decisions based on the distance from obstacle to the robot’s workspace and its velocity projection toward the workspace. Moreover, a combination of the DMP framework with a human-like steering mechanism and a novel configuration of virtual impedances is proposed for real-time obstacle avoidance.

The results confirm the effectiveness of the proposed method in real-time implementation of the velocity scaling and obstacle avoidance concepts in different cases of single and multiple stationary obstacles as well as moving obstacles.

As the provided experiments indicate, the proposed method can effectively increase the real-time safety of the robots’ operations. This is achieved by developing a simple method with low computational loads.

This paper proposes a novel method for real-time implementation of velocity scaling and obstacle avoidance concepts. This method eliminates the need for modification of original DMP formulation. The velocity scaling concept is implemented by using a fuzzy system to adjust the DMP’s time constant. Furthermore, the novel impedance configuration makes it possible to obtain a non-oscillatory convergence to the desired path, in all degrees of freedom.

The purpose of this paper is to present a method which enables a robot to learn both motion skills and stiffness profiles from humans through kinesthetic human-robot cooperation.

Admittance control is applied to allow robot-compliant behaviors when following the reference trajectories. By extending the dynamical movement primitives (DMP) model, a new concept of DMP and stiffness primitives is introduced to encode a kinesthetic demonstration as a combination of trajectories and stiffness profiles, which are subsequently transferred to the robot. Electromyographic signals are extracted from a human’s upper limbs to obtain target stiffness profiles. By monitoring vibrations of the end-effector velocities, a stability observer is developed. The virtual damping coefficient of admittance controller is adjusted accordingly to eliminate the vibrations.

The performance of the proposed methods is evaluated experimentally. The result shows that the robot can perform tasks in a variable stiffness mode as like the human dose in the teaching phase.

DMP has been widely used as a teaching by demonstration method to represent movements of humans and robots. The proposed method extends the DMP framework to allow a robot to learn not only motion skills but also stiffness profiles. Additionally, the authors proposed a stability observer to eliminate vibrations when the robot is disturbed by environment.

Rational use of the information available to companies is one of the strategic concerns for both family businesses and non-family businesses. The aim is to make the best use of the information resulting from data analysis in order to gain strategic advantage and to be more competitive as a company in all its functional areas. Experience indicates that emotions play an important role, especially in family businesses. Many maturation models exist to analyze decision-making processes (DMPs), but none from the perspective of family business cognition. The authors start from their own “Circumplex Hierarchical Representation of Organisation Maturity Assessment” (CHROMA) model (Parra, Tort-Martorell, Ruiz-Viñals, & Alvarez-Gomez, 2017), which was created to support decision processes in family businesses. This model was proven successful in the analysis of DMPs in large family businesses. The model was simplified as CHROMA-SHADE (Parra, Tort-Martorell, Ruiz-Viñals, & Alvarez-Gomez, 2019), more adequate for the analysis of small and medium-sized family businesses. Despite being a good DMP analysis instrument, intangible aspects such as family values and emotions have not yet been included. Both entrepreneurial emotions and entrepreneurial cognition must be taken into account when analyzing the DMP of the family business. In this chapter, the authors wish to explore attributes that can be introduced into a new dimension in the CHROMA model in order to make it more specific in the analysis of DMPs of family businesses regardless of size. The resulting FAMILY-CHROMA model represents a specific maturation model to evaluate DMPs based on the use of business information of family businesses.

This study aims to examine the necessity effects of big data analytics capabilities (BDAC) on decision-making performance (DMP), particularly in the public sector.

The authors used the combined methods of partial least square structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) to test the hypothesized relationships.

The findings show that the presence of all three BDAC (infrastructure, management and personnel) is significant and necessary to achieve higher levels of DMP. Specifically, the results revealed big data management capabilities to be of higher necessity to achieve the highest possible DMP. The findings provide public-sector practitioners with insights to support the development of their BDAC.

Time-sensitive domains such as the public sector require insight and quality decision-making to create public value and achieve competitive advantage. This study examined BDAC in light of the combined methods of (PLS-SEM) and NCA to test the hypothesized relationships in the public sector context.

This study investigates the relationships between knowledge management capabilities (KMC), supply chain agility (SCA) and disruption mitigation performance (DMP) in the U.S. fashion retail industry (FRI) during turbulent times, such as a pandemic.

An online survey was used to collect 320 responses from U.S. fashion retail professionals. Structural equation modeling was used for analysis.

Among the two KMCs, knowledge infrastructure capabilities act as enabling factors for knowledge process capabilities (KPC) in U.S. fashion retail settings. The KPC were found to be positively associated with SCA, and SCA was positively associated with both pre- and post-DMP of U.S. fashion retailers.

This study adds to the literature on KMC, SCA and DMP from the FRI context and illustrates the impact of effective organizational knowledge management for supply chain (SC) disruption mitigation through agility in a volatile market.

The results inform fashion retail companies on how to transform their organizational dimensions through effective management of knowledge, i.e. digital escalation and innovation, to establish an agile and sustainable SC to mitigate future market disruptions.

The paper aims to establish the role of enterprise information systems strategies (ISS) enabled by business strategies for attaining organizational innovativeness (ORIN) mediated by performance (decision-making and business processes) under environmental turbulence.

The research framework is developed based on theoretical grounding and validated with the help of 408 responses from Brazil using SmartPLS path modeling.

The results of the research suggest that the resource orchestra of enterprise information systems strategy-enabled strategy-making can be a viable alternative to enhance innovation activities in the organizations through the mediated role of performance (decision-making and business process).

The research demonstrates the role of business function (information systems) strategy enabled overall business strategy-making for achieving innovations in the organization. Fortune organizations are exploiting the information systems strategy enabled business strategy for innovations in the organization; such as Amazon, Walmart, Costco, etc.

The proposed and validated model is a contribution to the enterprise information systems strategy theory. This model presents the role of resource orchestras in achieving innovations in organizations.