Actor-network Research Articles

(Dis)entangling actor-network theory and assemblage theory in consumer and marketing scholarship: a review and future directions

ABSTRACT Studies that embrace socio-material theories, such as Actor-Network Theory (ANT) and Assemblage Theory (AT), have proliferated in consumer and marketing research. Yet, despite their popularity, little effort has been devoted to reviewing the use of ANT and AT in these fields. This article reports on a systematic theory-based review of 44 articles published in 23 journals from 2003 to 2022. Through this review, we take stock of the divergent ways in which ANT and AT are deployed separately or are combined by scholars and find that both theories most often address aspects of relationality, object agency, and becoming in marketing contexts. Inspired by discussions from other fields, we reflect on how marketing scholars navigate the theories’ similarities and (in)compatibilities and offer directions for future research.

An enterprise technical architecture for the implementation of the South African National Health Insurance

AbstractThe South African National Health Insurance (NHI) faces challenges due to a lack of an enterprise technical architecture (ETA) solution, for the implementation. This study aims to propose an ETA, to enable and support the implementation of the South African ETA. The qualitative method was employed in the data collection. Actor‐network theory (ANT) was applied as a lens to examine the data. The findings reveal several critical influencing factors, including readiness assessment, geographical locations of stakeholders; diversity of healthcare facilities; flexibility of technology solutions; and synchronization of processes and patients' data. The study highlights the need to provide governance for the technologies that influence the implementation of NHI. Based on the influencing factors, a framework was developed. The framework is intended to provide architectural governance (principles, standards, and policies), to efficiently and effectively, facilitate, support, and enable the implementation of the South African NHI.

Spatio‐temporal dynamic navigation for electric vehicle charging using deep reinforcement learning

AbstractThis paper considers the real‐time spatio‐temporal electric vehicle charging navigation problem in a dynamic environment by utilizing a shortest path‐based reinforcement learning approach. In a data sharing system including transportation network, an electric vehicle (EV) and EV charging stations (EVCSs), it is aimed to determine the most convenient EVCS and the optimal path for reducing the travel, charging and waiting costs. To estimate the waiting times at EVCSs, Gaussian process regression algorithm is integrated using a real‐time dataset comprising of state‐of‐charge and arrival‐departure times of EVs. The optimization problem is modelled as a Markov decision process with unknown transition probability to overcome the uncertainties arising from time‐varying variables. A recently proposed on‐policy actor–critic method, phasic policy gradient (PPG) which extends the proximal policy optimization algorithm with an auxiliary optimization phase to improve training by distilling features from the critic to the actor network, is used to make EVCS decisions on the network where EV travels through the optimal path from origin node to EVCS by considering dynamic traffic conditions, unit value of EV owner and time‐of‐use charging price. Three case studies are carried out for 24 nodes Sioux‐Falls benchmark network. It is shown that phasic policy gradient achieves an average of 9% better reward compared to proximal policy optimization and the total time decreases by 7–10% when EV owner cost is considered.

Autonomous Cooperative Hunting with Rule-Based and Self-Learning Control for Multiagent Systems

This paper considers the problem of autonomous cooperative hunting in an unknown dynamic environment, where a group of mobile agents collaborate to capture a moving target. Due to the decentralized decision-making nature of multi-agent systems and the presence of real-world constraints, it is a challenging task. To solve this problem, an artificial rule based hunting algorithm (AR-HA) is firstly developed based on the principles of attraction and repulsion with heading adjustment, and each agent is controlled by the designed rules. Then, to further enhance the stability of cooperative hunting, a self-learning algorithm based on Twin Delayed Deep Deterministic policy gradient (SL-TD3) is proposed. Each agent is governed by its own SL-TD3 controller and learns independently from its interaction with the environment, taking advantage of the reward function designed based on the control rules of AR-HA. Besides, in order to improve training efficiency, imitation learning is employed to initialize the actor network. Experiments on both virtual and real robots demonstrate the effectiveness of the proposed algorithms for autonomous cooperative hunting.

Reinforcement learning-adaptive fault-tolerant IGC method for a class of aircraft with non-affine characteristics and multiple uncertainties

Abstract In this paper, a brand-new adaptive fault-tolerant non-affine integrated guidance and control method based on reinforcement learning is proposed for a class of skid-to-turn (STT) missile. Firstly, considering the non-affine characteristics of the missile, a new non-affine integrated guidance and control (NAIGC) design model is constructed. For the NAIGC system, an adaptive expansion integral system is introduced to address the issue of challenging control brought on by the non-affine form of the control signal. Subsequently, the hyperbolic tangent function and adaptive boundary estimation are utilised to lessen the jitter due to disturbances in the control system and the deviation caused by actuator failures while taking into account the uncertainty in the NAIGC system. Importantly, actor-critic is introduced into the control framework, where the actor network aims to deal with the multiple uncertainties of the subsystem and generate the control input based on the critic results. Eventually, not only is the stability of the NAIGC closed-loop system demonstrated using Lyapunov theory, but also the validity and superiority of the method are verified by numerical simulations.

Interconnected influence: Unraveling purdue pharmaceutical's role in the global response to the opioid crisis

BackgroundThe global pharmaceutical industry has a long history of prioritizing profits over public health through widespread practices such as price gouging, deceptive marketing, and fraud. A prominent example of this issue is the mislabeling and mass-marketing of OxyContin by Purdue Pharmaceuticals (Purdue) that catalyzed the opioid crises in and beyond the United States. MethodsGuided by Actor-Network Theory, this case study employs Visual Network Analysis to map the actors-networks involved in responding to the harms caused by Purdue. Data was generated from peer-reviewed and grey literature published between 2007 and 2022 (n = 40) and imported into Gephi visualization software where centrality metrics were applied. ResultsA total of 39 actors and 99 relationships were visualized based on the relational thinking that actors who are heavily interconnected with others are rendered important. Centrality measures identified the socio-technical centrality of Purdue in influencing the response to the harms it caused. Purdue exerted influence through various avenues, most prominently through the creation and cooptation of pain advocacy groups, their close ties with United States elected officials, and through embedding pro-opioid messaging in international guidance documents. In doing so, Purdue was able to extend the reach and impact of their opioid promotion, while simultaneously limiting the capacity of regulatory bodies to pursue accountability and implement policies to mitigate opioid-related harms. ConclusionThis study advances understandings of the complex interplay between transnational pharmaceutical companies, global health systems, regulatory bodies, and public health. In doing so, we underscore the need for stronger regulation and increased transparency surrounding the interactions between pharma, patient groups, governments, and international organizations to better address and prevent future harms.

GWO-ANFIS-RD3PG: A reinforcement learning approach with dynamic adjustment and dual replay mechanism for building energy forecasting

Reliable prediction of building energy consumption is essential for effective and sustainable energy management. Traditional forecasting methods, however, face challenges when dealing with sudden changes in energy consumption. To address the above-mentioned issue, we introduce the GWO-ANFIS-RD3PG prediction model in this paper. This model aims to ensure global forecasting accuracy while minimizing prediction errors at sudden change points. A Grey Wolf Optimization (GWO) algorithm with the Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to predict the energy consumption type and its fluctuation magnitude for the next time step. Within the Recurrent Dual Experience Replay Buffer DDPG (RD3PG) prediction module, we employ Long Short-Term Memory (LSTM), as the actor network in the Deep Deterministic Policy Gradient (DDPG), to account for long-term dependencies in time series data. Notably, we introduce an adaptive action modification mechanism that allows the agent to optimize actions based on the output of the aforementioned ANFIS, enabling real-time adjustments during sudden change. To further enhance the model’s performance, we adopt a dual experience replay mechanism to store data samples from sudden change points, capturing insight information during these anomalies. Experimental results on two real-world datasets demonstrate that this method reduces MAE by 5.81% and 13.47%, and RMSE by 5.69% and 15.21% compared to the state-of-art models, showing the significance of the proposed method for energy efficiency improvement in real-world building operations.

Multi-attribute dynamic attenuation learning improved spiking actor network

Deep reinforcement learning (DRL) has shown promising results in solving robotic control and decision tasks, which can learn the high-dimensional state and action information well. Despite their successes, conventional neural-based DRL models are criticized for low energy efficiency, making them laborious to be widely applied in low-power electronics. With more biologically plausible plasticity principles, spiking neural networks (SNNs) are now considered an energy-efficient and robust alternative. The most existing dynamics and learning paradigms for spiking neurons with a common Leaky Integrate-and-Fire (LIF) neuron model often result in relatively low efficiency and poor robustness. To address these limitations, we propose a multi-attribute dynamic attenuation learning improved spiking actor network (MADA-SAN) for reinforcement learning to achieve effective decision-making. The resistance, membrane voltage and membrane current of spiking neurons are updated from a fixed value into dynamic attenuation. By enhancing the temporal relation dependencies in neurons, this model can learn the spatio-temporal relevance of complex continuous information well. Extensive experimental results show MADA-SAN performs better than its counterpart deep actor network on six continuous control tasks from OpenAI gym. Besides, we further validated the proposed MADA-LIF can achieve comparable performance with other state-of-the-art algorithms on MNIST and DVS-gesture recognition tasks.

Robust and energy-efficient RPL optimization algorithm with scalable deep reinforcement learning for IIoT

The increasing complexity and quantity of the Industrial Internet of Things (IIoT) pose new challenges to the traditional routing protocol for low-power and lossy networks (RPL) in terms of dynamic management, data transmission reliability, and energy efficiency optimization. This paper proposes a scalable deep reinforcement learning (DRL) algorithm with a multi-attention actor double critic model for routing optimization (MADC) to meet the requirements of IIoT for efficient and intelligent routing decisions while improving data transmission reliability and energy efficiency. Specifically, MADC employs the centralized training and decentralized execution (CTDE) learning paradigm to decouple the model’s training and inference tasks, which reduces the difficulty and computational cost of model learning and improves the training efficiency. In addition, a lightweight actor network based on multi-scale convolutional attention mechanism is designed in MADC, which can provide intelligent and real-time decision-making capabilities for resource-constrained nodes with low computational and storage complexities. Moreover, a scalable critic network utilizing multiple attention mechanisms is proposed. It is not only suitable for dynamic and changing network environments but also can more comprehensively and accurately evaluate local observation states, providing more accurate and efficient guidance for model optimization. Furthermore, MADC incorporates a double critic network architecture to mitigate potential overestimation issues during training, thereby ensuring the model’s robustness and reliability. Simulation results demonstrate that MADC outperforms existing RPL optimization algorithms in terms of energy efficiency, data transmission reliability, and adaptability.

Unveiling the missing innovation – an actor–network theory analysis of regulatory efforts towards universal design

Unveiling the missing innovation – an actor–network theory analysis of regulatory efforts towards universal design

Alternative organization without vertical hierarchies of spatial scale?

Vertical hierarchies of spatial scale—where society in general, and organizations in particular, are differentiated into levels of spatial size—have often been criticized by those encouraging alternatives to capitalist organizing. These vertical scalings are said to promote authoritarianism, a monolithic capitalist economic system, uneven concentrations of social and organizational power and wealth, and ecologically destructive organizational growth. Consequently, so the argument goes, alternative organizing must scale horizontally whereby smaller organizations autonomously associate together into loose networks or federations. The aim of this contribution is to problematize such critiques of vertical hierarchies of scaling for alternative organization. To do so, I combine Management and Organization Studies (MOS) scholarship on scaling alternative organization with two theorizations of the politics of scale in human geography—namely those derived from Neo-Marxist Political Economy and Actor-Network Theories. My argument is that all those looking to understand and encourage the scaling of alternatives to capitalist organizing should engage with and not simply reject vertical scalar hierarchies.

Under the iceberg of “one-stop”: explore multi-actor engagement in one-stop digital cultural platforms through actor-network and commons theories

PurposeThis study investigates the roles and interactions of various actors of one-stop digital culture platforms (ODCPs) by applying actor-network theory (ANT).Design/methodology/approachData were collected from focus group interviews with 19 participants and over 20 policy texts related to the digitization of cultural services in China. Utilizing ANT as the analytical framework, we employed the grounded theory to analyze the dataset.FindingsThe empirical results indicate that: (1) governments, cultural institutions, enterprises and users each play dual roles, contributing to the functionality and development of ODCPs; (2) introduces the theory of commons to reconceptualize ODCPs as commons, highlighting the need for collective management and shared responsibility in the governance of ODCPs.Originality/valueThis study identifies and elaborates on the different roles of stakeholders and addresses conceptual ambiguities within ODCP projects. By introducing the theory of commons into the governance of ODCPs, this research provides new theoretical guidance for decision-makers and practitioners involved in ODCP projects.

(Re)framing used goods: marketing strategies in second-hand retail

PurposeThis paper aims to identify marketing strategies incentivizing consumers to purchase used goods and explain how socio-material arrangements frame second-hand retail (SHR). The growing significance and professionalization of SHR underscore the pivotal role of marketing, necessitating an exploration of how second-hand stores can stimulate the consumption of used items.Design/methodology/approachEthnographically inspired fieldwork was conducted across a second-hand shopping mall with 17 stores in Sweden, utilizing actor-network theory (ANT) concepts to examine marketing activities and how these form strategies.FindingsThe findings reveal two marketing strategies: enrolling sustainability supporters and second-hand shoppers. Additionally, the findings provide examples of how SHR can be framed as environmentally friendly, socially sustainable, value for money, creatively experiential and facilitating sustainable consumer behavior.Practical implicationsUnderstanding the framing processes inherent in second-hand retailing presents opportunities to reinforce the transition to a circular economy. Second-hand retailers can leverage marketing to imbue used goods with greater significance for consumers, which necessitates marketing competencies.Originality/valueThe paper delves into activities that enhance the attractiveness of used goods, a research area that warrants increased attention.

Sporting mega-events, physical activity, and Actor Network Theory: Mapping actants of movement at the 2023 FIFA Women's World Cup

This article explores the potential of Bruno Latour's Actor Network Theory (ANT) for rethinking the relationship between sporting mega-events and physical activity (PA). Engaging with ANT as theory and methodology, we explore how PA is a result of human–nonhuman interactions occurring during and around sporting events. More specifically, based upon a collaborative ethnography of the 2023 FIFA (Fédération Internationale de Football Association) Women's World Cup (FWWC23), we focus on humans and nonhumans (football, physical spaces, and seats) as actants (intermediaries and mediators) that enabled and/or constrained PA. In so doing, we provide an alternative to PA initiatives (such as the collaboration between FIFA and the World Health Organization with the BeActive #BringtheMoves campaign at the FWWC23) that focus on human intentionality and view the body as controllable. Instead, in this article we recognize PA as a complex phenomenon dis/re/assembling as objects, humans, and actants interact in (un)expected ways. Simultaneously, we discuss both the affordances and challenges offered by ANT in sporting mega-event literature and for research and policies that link sporting events and PA.

Mapping epistemic pluralism: A network analysis of discursive practices in communities promoting refused knowledge about healthcare and wellbeing

This article presents an analysis of discourses performed in communities that share and disseminate knowledge refused by institutional science. The study focuses on an online community concerned with alkaline water, food, and lifestyle, aiming at understanding how promoters of refused knowledge in this community enrol other forms of knowledge, including science. Theoretically, this work is framed in Science & Technology Studies, and in the tradition of actor-network theory, situating itself in a recent turn that takes epistemic instability and pluralism into due consideration, thus overcoming opaque views of the opposition between science and non-science. Empirically, this fine-grained analytic purpose is addressed by a mixed-method strategy in which discursive practices are observed through a web-ethnography conducted between January 2020 and December 2021 on the relevant online spaces and then analysed qualitatively and quantitively by means of formal techniques. Relying on the tools of social network analysis, the discursive space of the community under study is formalised as a two-mode network of knowledge claims and heterogeneous actors enrolled in discourse to sustain those claims. Then, community detection is performed to map the different assemblages of claims and actors and the relevant repertoires characterising those assemblages. Finally, multiple correspondence analysis applied to two-mode networks is used to highlight the dimensions of concern and meaning expressed in the knowledge organisation of this community.

Between institutions and narratives: Understanding collective action in innovation policy processes

AbstractExplanations for collective action focus on both institutions and narratives. On the one hand, institutional approaches emphasize the role of rules that guide human behavior. On the other hand, accounting for the narratives through which policy actors make sense of their actions helps in understanding strategic behavior. However, applying institutional and narrative perspectives together is daunting, in part because there has not been a common way to integrate the two approaches. In this article, we draw from Actor‐Network Theory (ANT) to elaborate a novel analytical approach that combines ANT with the Institutional Grammar Tool (IGT) and the Narrative Policy Framework (NPF). We use IGT's and NPF's analytical categories in a processual perspective to examine how policy‐makers strategically use institutions and narratives to create and stabilize a network of actors in innovation policy processes. We illustrate our approach through an in‐depth analysis of the development of a smart city.

Hybrid learning-based visual path following for an industrial robot

Abstract This study proposes a novel hybrid learning approach for developing a visual path-following algorithm for industrial robots. The process involves three steps: data collection from a simulation environment, network training, and testing on a real robot. The actor network is trained using supervised learning for 500 epochs. A semitrained network is then obtained at the $250^{th}$ epoch. This network is further trained for another 250 epochs using reinforcement learning methods within the simulation environment. Networks trained with supervised learning (500 epochs) and the proposed hybrid learning method (250 epochs each of supervised and reinforcement learning) are compared. The hybrid learning approach achieves a significantly lower average error (30.9 mm) compared with supervised learning (39.3 mm) on real-world images. Additionally, the hybrid approach exhibits faster processing times (31.7 s) compared with supervised learning (35.0 s). The proposed method is implemented on a KUKA Agilus KR6 R900 six-axis robot, demonstrating its effectiveness. Furthermore, the hybrid approach reduces the total power consumption of the robot’s motors compared with the supervised learning method. These results suggest that the hybrid learning approach offers a more effective and efficient solution for visual path following in industrial robots compared with traditional supervised learning.

Merging Worklife Organizational Innovation and Educational Programs: Promoting Continuous Adaptations to the Global Economy

AbstractThis essay honors the accomplishments of Morten Levin both by analyzing some of the key innovative educational programs he created and by extending the elements of that analysis to two other cases, that of Aker Solutions and the Mondragon Cooperatives. We acknowledge that the relationship between higher education institutions and manufacturing-service-public sector organizations is multiplex and has undergone multiple transformations over the decades. It is also affected by the national political economy and the scope and diversity of the higher education sector itself. Creating sustained and yet dynamic relations among these actors in service of both higher education and the efficacy and innovativeness of worklife organizations is a significant challenge which all the cases we present have addressed with some success. What the efforts have in common is a democratizing action research built on an ethos and worldview of the benefits of collaboration and co-creation, and organizational capabilities built on Actor Network strategies to promote collaboration and innovation as a way of adapting to the rapidly changing world conditions effectively. We argue that there are many more such examples in existence and that a key role for action research is to document them, show how more collaborative and adaptive organizational processes are possible and show how neoliberal New Public Management is an authoritarian “wrong turn” with dire consequences. Ultimately, this essay is an argument against the “iron cage” view of bureaucracy as always coercive and stultifying and show that it is not bureaucracy but the authoritarian approaches to creating coercive bureaucracies that destroy organizational collaborations and innovation.

What Next for Actor Network Theory? Inventing around Latour on a planet in distress

This is a reflection piece on the theme ‘what will happen to actor-network theory (ANT) after Latour?’ We distinguish three scenarios but focus on the one we see as the least probable but most interesting for sociology and related disciplines. With inspiration from the anthropologist Marilyn Strathern, we think of this third scenario as a way of “inventing around” Latour. In this scenario, classical ANT tenets are prolonged and refracted through Latour's later work on modes of existence and critical zones. This mutant ANT is subject to further transformations through encounters with empirical situations and concepts from many other places. We use two brief cases for illustration. The first considers the empirical potentials and possible pitfalls of Latour's modes of existence in relation to the emergence of carbon markets as a form of Anthropocene politics. The second examines recent urban upheavals and transformations in Bangkok through the lens of urban critical zones, which make visible problems of co-existence that shade into pluriversal cosmopolitics because the city is full of beings that are both modern and amodern. These cases point the way to a scenario in which ANT is still unfinished, and far from “uncritical”. It is an “indisciplinary” scenario, where ANT with sociology and adjacent disciplines venture into complex landscapes of planetary ecological disruptions and postcolonial tensions without any false protection from certified theory and pre-defined politics.

A ‘translation’ perspective on socio-ecological displacement: insights from the 2014 Tokwe-Mukorsi flood in Zimbabwe

With socio-ecological displacement increasingly becoming an existential threat to many communities worldwide, there is a need for deepened understanding of salient socio-political dynamics that complicate displacement processes. Such understanding is crucial for reducing the risks associated with relocation as well as enhancing relocation management and building resilience among victims. This article utilises the concept of ‘translation’ in Actor-Network Theory (ANT) to examine the socio-political dynamics that characterised the 2014 Tokwe-Mukorsi forced displacement of over 3000 rural households in Southern Zimbabwe. Drawing on the narrations of victims in focus group discussions and of government officials (key-informants), as well as on relevant secondary data, the article engages the shifts in position by the dominant actor in the displacement and relocation processes, not to mention the imposition of new interpretations of reality, including the redefinition and distribution of roles, with a view to highlighting the ultimate determinants of resettlement outcomes. The paper found, in the main, that the power struggles between protagonists and the impact of those struggles on the relocation process, as well as the state’s dominance, ultimately exacerbated the victims’ distress.