Search results

This study demonstrates a practical use of Actor-Network Theory (ANT), showing methodological, predictive and unforeseen issues that emerged during the data collection and analysis phases and how they were addressed during the development of this research.

Based on the research of reapplication of a “tecnologia social” (TS) of entrepreneurial education, this article explores the author’s reflections on the adoption of ANT as a theoretical-methodological approach, highlighting the practical implications of a social material theory during fieldwork.

The adoption of ANT places the researcher in front of methodological issues not always foreseen in the research design. Four moments to a practical path through the engagement of ANT agency are highlighted: the network of actors, monitoring of actors, interpretation of data collected and writing results. These moments correspond to methodological issues that the authors faced during the practical journey of the research. At each moment, the challenge aroused is discussed and the methodological choice chosen to address the issue is presented.

The engagement with ANT has enormous potential in the study of management and organizations phenomenon, but its methodological implications in practice are still challenging. The authors seek to share this investigation and engagement in ANT so that other researchers have a reference and a starting point to employ and engage in this theoretical-methodological lens. Thus, it may be possible to anticipate certain difficulties in future research designs and to glimpse at potential developments and paths that the research may lead.

Based on cognitive evaluation theory and gamification affordances, this study aims to understand how gamification affordances influence users’ intention to engage in sustainable behaviour and how new trends in Ant Forest influence its impact on green intrinsic motivation to support sustainable behaviours.

The authors developed a research model to explore the mechanisms underlying gamification affordances, psychological needs and green intrinsic motivation. Partial least squares structural equation modelling was used to assess the survey data (n = 393) and test the research model.

The results show that different gamification affordances can satisfy users’ needs for autonomy, competence and relatedness, which positively influences their green intrinsic motivation and engagement in sustainable behaviours. However, some affordances, such as competition, might negatively impact these psychological needs.

This research updates information system research on environmental sustainability and the Ant Forest context. The authors provide a new framework that links gamification affordances, psychological needs and sustainable behaviour. The study also examines changing trends in Ant Forest and their implications.

With the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.

An improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.

Experimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.

The proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.

Application of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.

Results achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.

The methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.

This paper aims to present a set of processes for obtaining the global spraying trajectory of a cold spraying robot on a complex surface.

The complex workpiece surfaces in the project are first divided by triangular meshing. Then, the geodesic curve method is applied for local path planning. Finally, the subsurface trajectory combination optimization problem is modeled as a GTSP problem and solved by the ant colony algorithm, where the evaluation scores and the uniform design method are used to determine the optimal parameter combination of the algorithm. A global optimized spraying trajectory is thus obtained.

The simulation results show that the proposed processes can achieve the shortest global spraying trajectory. Moreover, the cold spraying experiment on the IRB4600 six-joint robot verifies that the spraying trajectory obtained by the processes can ensure a uniform coating thickness.

The proposed processes address the issue of different parameter combinations, leading to different results when using the ant colony algorithm. The two methods for obtaining the optimal parameter combinations can solve this problem quickly and effectively, and guarantee that the processes obtain the optimal global spraying trajectory.

Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However, the classic approach to estimating such parameters is perceived to be imprecise. Herein, the essential features and performances of the ant colony, bee colony and elephant herd optimisation approaches are introduced to the experimental chemist and chemical engineer engaged in adsorption research for aqueous systems. Key research and development directions, believed to harness these algorithms for real-scale water treatment (which falls within the wide-ranging coverage of the Sustainable Development Goal 6 (SDG 6) ‘Clean Water and Sanitation for All’), are also proposed. The ant colony, bee colony and elephant herd optimisations have higher precision and accuracy, and are particularly efficient in finding the global optimum solution. It is hoped that the discussions can stimulate both the experimental chemist and chemical engineer to delineate the progress achieved so far and collaborate further to devise strategies for integrating these intelligent optimisations in the design and operation of real multicomponent multi-complexity adsorption systems for water purification.

The purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.

This method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.

The algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.

This paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

In this study, the authors use the actor-network theory (ANT) as a theoretical framework to better understand constructing learning as part of the networking process to produce innovations. Focussing on the antecedents of innovation within three teams in an engineering company, the authors propose a framework to enhance understanding of the innovative processes. The authors apply ANT to examine how informal learning is distributed amongst human and non-human actors.

Based on 27 interviews in a large Australian engineering company, the authors' qualitative investigation shows that innovation can have very different antecedents. The authors mobilised ANT as the authors' vantage point to explore inanimate actors and their effect on social processes or, more specifically, networks and informal learning.

The authors propose a framework to better understand innovative processes by exploring the network aspects of non-human actors and their connection to learning. More specifically, findings contribute towards a more granulated understanding of how networks, learning and non-human actors contribute towards innovations in organisations.

This study has three significant implications for managers and organisations looking to improve their innovation processes. Firstly, fostering open communication is essential for developing successful innovation processes. Secondly, a close relationship with the customer and/or the final users has often been found to positively contribute to innovation processes. Finally, intrateam motivation is also critical when it comes to creating an environment that supports innovation processes.

Surprisingly, leadership, communication and motivation did not give the best innovative outcome as the authors expected. Challenging traditional theorisations, low teamwork spirit and high individual performance orientation were some of the powerful drivers of highly innovative teams.

The purpose of this paper is to help better understand the translation process of the management control system (MCS) of privatised solid waste management (SWM) towards creating a stable network.

Drawing on the actor network theory (ANT), the case of a privatised SWM was studied. Data were collected from all entities involved in the privatisation process of SWM, which include Department A, Corporation X and the private sector concessionaire. Six documents were reviewed, 20 interviews were conducted and two observations were carried out.

The findings reveal that the control mechanism of SWM is complex, involving the interaction between human and non-human actors. Non-human actors include the key performance indicators (KPIs) and the concessionaire agreement (CA), which are the main control mechanisms towards creating a stable SWM network. Essentially, stability is achieved when the KPIs and CA can influence the activities of both intra- and inter-organisational relationships.

This paper provides a better understanding of the translation process of the MCS that adds to the stability of the network of a privatised SWM from the lens of the ANT.