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To address the absence of Lean in transportation logistics in the digital era, this study aims to investigate the application of Lean transportation (LT) tools to reduce waste and facilitate the digital transformation of dedicated road transportation in the offshore industry.

The study adopts action research with a multimethod approach, including a scoping review, focus groups (FG) and participant observation. The research is conducted within the offshore supply chain of a major oil and gas company.

Implementing LT’s continuous improvement tools, particularly value stream mapping (VSM), reduces offshore transportation waste and provides empirical evidence about the intersection of Lean and digital technologies. Applying techniques drawn from organisational learning theory (OLT), stakeholders involved in VSM mapping and FGs engage in problem-solving and develop action plans, driving digital transformation. Waste reduction in loading and unloading stages leads to control actions, automation and process improvements, significantly reducing downtime. This results in an annual monetary gain of US$1.3m. The study also identifies waste related to human effort and underutilised digital resources.

This study contributes to theory and practice by using action research and LT techniques in a real intervention case. From the lens of OLT, it highlights the potential of LT tools for digital transformation and demonstrates the convergence of waste reduction through Lean and Industry 4.0 technologies in the offshore supply chain. Practical outputs, including a benchmarking questionnaire and a plan-do-check-act cycle, are provided for other companies in the same industry segment.

The credit of agricultural product quality and safety reflects the ability of the main actors involved in the supply chain to provide reliable agricultural products to consumers. To fundamentally solve the problem of agricultural product quality and safety, it is worth studying how to make the credit awareness and integrity self-discipline of the supply chain agriculture-related subjects strengthened and the role and value of credit supervision given full play. Starting from the application of blockchain in the agricultural product supply chain, this paper aims to investigate the main factors affecting the credit regulation of agricultural product quality.

Using the DEMATEL-ISM (decision-making trial and evaluation laboratory–interpretative structural modeling) method, we analyze the credit influencing factors of agricultural quality and safety empowered by blockchain technology, find the causal relationship between the crucial influencing factors and deeply explore the hierarchical transmission relationship between the influencing factors. Then, the path analysis in structural equation modeling is utilized to verify and measure the significance and effect value of the transmission relationship among the crucial influencing factors of credit regulation.

The results show that the quality and safety credit regulation of agricultural products is influenced by a combination of direct and deep influencing factors. Long-term stable cooperative relationship, Quality and safety credit evaluation, Supply chain risk control ability, Quality and safety testing, Constraints of the smart contract are the main influence path of blockchain embedded in agricultural product supply chain quality and safety credit supervision.

Credit supervision is an important means to improve the ability and level of social governance and standardize the market order. From the perspective of blockchain embedded in the agricultural supply chain, the regulatory body is transformed from the product body to the supply chain body. Take the credit supervision of supply chain subjects as the basis of agricultural product quality supervision. With the help of blockchain technology to improve the effectiveness of agricultural product quality and safety credit supervision, credit supervision is used to constrain and incentivize the behavior of agricultural subjects.

Insufficient time allocation for the bidding period occurs, causing drawbacks to both parties, the client and the bidder. Hence, this study aims to evaluate the time allocated for preparing a bid proposal as per the National Competitive Bidding (NCB) in the Sri Lankan context.

The study has adopted a mixed method approach and expert interviews and document review to detect, analyse and validate the issues, and solutions based on NCB along with the adequacy of the allocated bidding period used as main data collection tools. Both qualitative and quantitative data were analysed through manual content analysis and inferential analysis respectively.

Overall, 24 local issues with the existing competitive bidding process and solutions for each were identified. Among the 24 local issues, it was unanimously agreed by all interviewees that three specific issues require attention and improvement. These issues are related to the standard and incompleteness of bidding documents, inaccurate BOQ quantities measured by the consultant or the main contractor, and the excessive number of bidding document amendments by the consultant. It was revealed that a maximum of 42 calendar days (6 weeks) is sufficient for the bidding process while a minimum of 21 calendar days (3 weeks) is insufficient.

The findings of this study would be recommended that Information and Communication Technology Agency (ICTA) understand the necessity of revising the NCB reference to the time allocated for the preparation of bids. By recognising the importance of sufficient time allocation for bid preparation, this research serves as a practical guide for authorities involved in policy formulation, aiding them in implementing revisions that align with the dynamic requirements of bidding procedures.

This study aims to analyse the influence of digitalisation on the management control function of small and medium-sized enterprises (SMEs). In particular, it aims to illuminate how digitalisation influences management control elements, organisation and roles/competencies and to identify obstacles to digitalisation of management control in SMEs and measures taken to overcome them.

The study is based on guideline-supported expert interviews conducted with 14 financial managers from SMEs in Germany, Austria and Switzerland.

This study reveals the influence of digitalisation on management control elements, organisation, and roles/competencies. The automation and standardisation of management control processes result in new elements for management control, such as strategic support for management. In addition, the increased availability and transparency of data enable the use of instruments within a company that allow for quick analyses of the company's development. Digitalisation leads to the integration of management control into the corporate network and, thus, a change in the organisation of management control. It also triggers the expansion of management control competencies, especially IT competencies. A shortage of internal digitalisation resources, unclear corporate roadmaps, and a lack of managerial experience loom as central challenges for digitalising the management control function. Measures derived from the interviews can help SMEs overcome the obstacles to the digitalisation of management control.

This research is the first interview-based study of the impact of digitalisation on management control in SMEs, potential obstacles to that digitalisation, and measures to overcome those obstacles. Thus, it contributes to the emerging debate on factors that may explain why SMEs lag in terms of the digitalisation of their internal processes.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

The purpose of the article is to identify relevant criteria for decision support in the implementation of waste-to-energy (WtE)-based systems.

The methodology is a simple case study with a qualitative approach. Five experts involved in the project of a thermoelectric power plant qualitatively evaluated, on a Likert scale, a decision model with 15 indicators derived from recent studies. The research object was the first stage of a project to implement a thermoelectric plant employing municipal solid waste (MSW) in southern Brazil.

The study identified 15 criteria supporting the decision-making process regarding WtE implementation for MSW in a mid-sized city in southern Brazil. The study identified that compliance with MSW legislation, compliance with energy legislation, initial investment and public health impact are the most influential criteria. The study offered two models for decision processes: a simplified one and a complete one, with ten and fifteen indicators, respectively.

The study concerns mid-sized municipalities in southern Brazil.

Municipal public managers have now a methodology based on qualitative evaluation that admits multiple perspectives, such as technical, economic, environmental and social, to support decision-making processes on WtE technologies for MSW.

MSW management initiatives can yield jobs and revenues for vulnerable populations and provide a correct destination for MSW, mainly in developing countries.

The main originality is that now municipal public decision-makers have a structured model based on four constructs (technical, economic, environmental and social) deployed in 15 indicators to support decision-making processes involving WtE and MSW management.

The purpose of this paper is to provide an overview of what (service) experience is and examine it using three distinct perspectives: customer experience (CX), employee experience (EX) and human experience (HX).

The present conceptualization blends the marketing and organizational behavior/human resources management (OB/HRM) disciplines to clarify and reflect over the meaning of (service) experience. The marketing discipline illuminates the concept of CX, whereas the OB/HRM discipline illuminates the concept of EX. The concept of HX, which transcends CX and EX, is examined in light of its recent development in service research. For each of the three concepts, key themes are identified, and future research directions are proposed.

Because the goal that individuals seek to achieve depends on the role they are enacting, each of the three perspectives on experience (CX, EX and HX) should have a different focal point. CX requires to focus on the process of solving customer goals. EX necessitates to think in terms of organizational context and job content that support employees. Finally, the focus of HX should be on well-being via enhanced gratification, and reduced violation, of basic human needs.

This paper offers an interdisciplinary perspective on (service) experience and simultaneously addresses CX, EX and HX in order to reconcile the different perspectives on experience in service research.

Component positioning is an important part of aircraft assembly, aiming at the problem that it is difficult to accurately fall into the corresponding ball socket for the ball head connected with aircraft component. This study aims to propose a ball head adaptive positioning method based on impedance control.

First, a target impedance model for ball head positioning is constructed, and a reference positioning trajectory is generated online based on the contact force between the ball head and the ball socket. Second, the target impedance parameters were optimized based on the artificial fish swarm algorithm. Third, to improve the robustness of the impedance controller in unknown environments, a controller is designed based on model reference adaptive control (MRAC) theory and an adaptive impedance control model is built in the Simulink environment. Finally, a series of ball head positioning experiments are carried out.

During the positioning of the ball head, the contact force between the ball head and the ball socket is maintained at a low level. After the positioning, the horizontal contact force between the ball head and the socket is less than 2 N. When the position of the contact environment has the same change during ball head positioning, the contact force between the ball head and the ball socket under standard impedance control will increase to 44 N, while the contact force of the ball head and the ball socket under adaptive impedance control will only increase to 19 N.

In this paper, impedance control is used to decouple the force-position relationship of the ball head during positioning, which makes the entire process of ball head positioning complete under low stress conditions. At the same time, by constructing an adaptive impedance controller based on MRAC, the robustness of the positioning system under changes in the contact environment position is greatly improved.

This paper aims to determine the effect of a set of factors (attitude toward entrepreneurship, perceived behavioral control or entrepreneurial self-efficacy, previous experience, internal locus of control and entrepreneurial alertness) on the entrepreneurial intentions of Chilean secondary vocational school students.

An online survey was sent to all secondary-level students from four Chilean vocational schools (3,052 students). A total of 1,644 valid questionnaires were received. The data were analyzed using partial least squares structural equation modeling (PLS-SEM).

The results show that attitude toward entrepreneurship and perceived behavioral control positively and significantly impact the students’ entrepreneurial intentions, but not the previous experience.

Since this research was carried out in a specific reality, the results may lack generalizability. Also, the proposed model requires further development through the incorporation of other factors. It is then suggested to replicate this study in other countries as well as to incorporate new variables and test new relationships.

Entrepreneurship education programs at the secondary level should emphasize changing/stimulating adolescents’ attitudes toward entrepreneurship as well as improving their confidence in their entrepreneurial abilities.

This is one of the few studies that provide empirical evidence about the determinants of entrepreneurial intentions in adolescents, particularly in Latin America, and with a large sample.

El propósito de este estudio es determinar el efecto de un conjunto de factores (actitud hacia el emprendimiento, control conductual percibido o autoeficacia emprendedora, experiencia previa, locus de control interno y alerta emprendedora) sobre las intenciones emprendedoras de estudiantes secundarios técnico-profesionales chilenos.

Una encuesta online fue enviada a todos los estudiantes de nivel secundario de cuatro escuelas técnico-profesionales chilenas (3,052 estudiantes). Se recibieron un total de 1,644 cuestionarios válidos. Los datos obtenidos fueron analizados a través de una técnica de modelización de ecuaciones estructurales (PLS-SEM).

Los resultados muestran que la actitud y el control conductual percibido impactan positiva y significativamente sobre las intenciones emprendedoras de los estudiantes, no así la experiencia previa.

Dado que esta investigación se llevó a cabo en una realidad específica, se debe ser cauteloso a la hora de generalizar sus resultados. Asimismo, el modelo propuesto requiere un mayor desarrollo a través de la incorporación de otras variables. Se sugiere entonces replicar este estudio en otros países, así como incorporar nuevas variables y someter a prueba nuevas relaciones.

Los programas de educación emprendedora en el nivel secundario debiesen poner énfasis en cambiar/estimular las actitudes hacia el emprendimiento de los adolescentes, así como en mejorar su confianza en sus propias habilidades emprendedoras.

Este es uno de los pocos estudios que aporta evidencia empírica acerca de los determinantes de las intenciones de emprender en adolescentes, particularmente en América Latina y con una muestra amplia.