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This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

In this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g. selecting input features, considering scarce resources and constrains) that cannot be solved by classical machine learning. The authors include a comparative study to build intrusion detection based on statistical machine learning and representational learning, using knowledge discovery in databases (KDD) Cup99 and Installation Support Center of Expertise (ISCX) 2012.

The methodology applies a data analytics approach, consisting of data exploration and machine learning model training and evaluation. To build a network-based intrusion detection system, the authors apply dueling double deep Q-networks architecture enabled with costly features, k-nearest neighbors (K-NN), support-vector machines (SVM) and convolution neural networks (CNN).

Machine learning-based intrusion detection are trained on historical datasets which lead to model drift and lack of generalization whereas RL is trained with data collected through interactions. RL is bound to learn from its interactions with a stochastic environment in the absence of a training dataset whereas supervised learning simply learns from collected data and require less computational resources.

All machine learning models have achieved high accuracy values and performance. One potential reason is that both datasets are simulated, and not realistic. It was not clear whether a validation was ever performed to show that data were collected from real network traffics.

The study provides guidelines to implement IDS with classical supervised learning, deep learning and RL.

The research applied the dueling double deep Q-networks architecture enabled with costly features to build network-based intrusion detection from network traffics. This research presents a comparative study of reinforcement-based instruction detection with counterparts built with statistical and representational machine learning.

This paper aims to identify ethical challenges of using artificial intelligence (AI)-based accounting systems for decision-making and discusses its findings based on Rest's four-component model of antecedents for ethical decision-making. This study derives implications for accounting and auditing scholars and practitioners.

This research is rooted in the hermeneutics tradition of interpretative accounting research, in which the reader and the texts engage in a form of dialogue. To substantiate this dialogue, the authors conduct a theoretically informed, narrative (semi-systematic) literature review spanning the years 2015–2020. This review's narrative is driven by the depicted contexts and the accounting/auditing practices found in selected articles are used as sample instead of the research or methods.

In the thematic coding of the selected papers the authors identify five major ethical challenges of AI-based decision-making in accounting: objectivity, privacy, transparency, accountability and trustworthiness. Using Rest's component model of antecedents for ethical decision-making as a stable framework for our structure, the authors critically discuss the challenges and their relevance for a future human–machine collaboration within varying agency between humans and AI.

This paper contributes to the literature on accounting as a subjectivising as well as mediating practice in a socio-material context. It does so by providing a solid base of arguments that AI alone, despite its enabling and mediating role in accounting, cannot make ethical accounting decisions because it lacks the necessary preconditions in terms of Rest's model of antecedents. What is more, as AI is bound to pre-set goals and subjected to human made conditions despite its autonomous learning and adaptive practices, it lacks true agency. As a consequence, accountability needs to be shared between humans and AI. The authors suggest that related governance as well as internal and external auditing processes need to be adapted in terms of skills and awareness to ensure an ethical AI-based decision-making.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

This paper aims to reinforce the arguments for applying the social support concept in social marketing.

This paper aims to conceptually outline the potential positive contribution of social support for social marketing practice as a tool to induce behavior change.

This paper focuses on the philosophical principle of social exchange, highlights the consumer-centered perspective of social marketing, which implies the natural evaluation of the social networks of influence and support and presents social support as a mechanism to induce long-term behavior change.

No empirical (qualitative or quantitative) investigations were used to test the application of the concept in practical interventions.

This paper provides significant insights for intervention developers that can be used to program and theoretically justify future social marketing interventions applying the social support concept.

Empirical research concluded for a positive relation between social support and human health and well-being. Thus, increasing the use of the concept in social marketing can serve to attain these social goals.

The concept of social support has gained considerable interest in the areas of behavioral medicine and health psychology. Despite such interest, it is still not clear how it can be approached in social marketing as there is a lack of conceptual literature discussing social support from a social marketing perspective, the number of social marketing interventions operationalizing the concept is limited and, till date, no research has focused in comprehensively establishing a theoretical rationale to operationalize the concept in social marketing.

The purpose of this study is to understand how neuroscientific tools are used and discussed in ongoing research on strategy in organizations.

The authors used a bibliometric study of bibliographic pairing to answer the research question. They collected data from the Web of Science and Scopus databases using the keywords “neuroscience*,” “neurostrategy*” and “neuroscientific*.”

This study presents a framework that relates fundamental aspects discussed in current research using neuroscientific tools: Neuroscience and its research tools in organizations; emotions and information processing; interdisciplinary application of neuroscientific tools; and moral and ethical influences in the leaders' decision-making process.

The inclusion of neuroscientific tools in Strategic Management research is still under development. There are criticisms and challenges related to the limitations and potential to support future research.

Despite recognizing the potential of neuroscientific tools in the mind and brain relationship, this study suggests that at this stage, because of criticisms and challenges, they should be used as support and in addition to other traditional research techniques to assess constructs and mechanisms related to strategic decisions and choices in organizations.

Neuroscientific methods in organizational studies can provide insights into individual reactions to ethical issues and raise challenging normative questions about the nature of moral responsibility, autonomy, intention and free will, offering multiple perspectives in the field of business ethics.

In addition to presenting the potential and challenges of using scientific tools in strategic management studies, this study helps create methodological paths for studies in strategic management.

Young people’s attachment to their smartphones is well-documented, with smartphones often described as prostheses. While prior studies typically assume a clear human/machine divide, this paper aims to build on posthuman perspectives, exploring intercorporeality, the blurring of human/technology boundaries, between emerging adults and their smartphones. The paper aims to discuss these issues.

Drawing on assemblage theory, this interpretive study uses smartphone diaries and friendship pair/small group discussions with 27 British emerging adults.

Participants in this study are characterized as homo prostheticus, living with and through their phones, treating them as extensions of their mind and part of their selves as they navigated between their online and offline, private and social lives. Homo prostheticus was part of a broader assemblage or amalgamation of human and non-human components. As these components interacted with each other, the assemblage could be strengthened or weakened by various technological, personal and social factors.

These qualitative findings are based on a particular sample at a particular point in time, within a particular culture. Further research could explore intercorporeality in human–smartphone relationships among other groups, in other cultures.

Although other studies have used prosthetic metaphors, this paper contributes to understanding of smartphones as a prostheses in the lives of emerging adults, highlighting intercorporeality as a key feature of homo prostheticus. It also uses assemblage theory to contextualize homo prostheticus and explores factors strengthening or weakening the broader human–smartphone assemblage.