Disentangling circular economy practices and firm's sustainability performance: A systematic literature review of past achievements and future promises.

This paper systematically and critically reviews the literature on the intersection of circular economy practices (CEPs) and sustainability performance (SP). We synthesize and analyze the extant literature to uncover the knowledge gaps, highlight the contradictory views, and provide a comprehensive overview of the field. Following a detailed database search, we selected 104 empirical studies published in peer-reviewed journals for analysis. Our review reports the publication trends, top publishing journal outlets, research methodologies, and empirical contexts. We outline the theoretical underpinnings, identify the diverse circular economy practices and the key factors that impact circular economy practices and sustainable performance. The review shows a propensity for most authors to reuse established theories or not use theory at all, revealing the need for theory development. Furthermore, our analysis revealed that R&D and innovation, digital technologies, organizational capabilities/resources, and stakeholder and institutional pressure substantially influence the CEPs - SP relationship. Through our detailed assessment of the existing literature, we identified and proposed several themes and avenues for future research.

Whole-life greenhouse gas emission reduction and removal strategies for buildings: Impacts and diffusion potentials across EU Member States.

As the European Union (EU) is aiming to realize climate neutrality by 2050, there is a need to investigate greenhouse gas (GHG) reduction and carbon dioxide removal strategies (CRRS) from a life cycle perspective. Existing literature lacks harmonization of building-related strategies considering the whole-life cycle of buildings and the interlinkages across life cycle stages. The aim and novelty of this study was to systematically identify, classify and quantify the impacts of CRRS, as well as assess their applicability in different EU Member States. We identified a total of 35 measures grouped in 11 CRRS for the whole-life cycle of buildings. We classified these measures according to various criteria, such as the avoid-shift-improve framework or the life cycle stages influenced. We then assessed the potential diffusion of these strategies in each EU Member State up to 2050 via qualitative assessment criteria. We could achieve notable short-term reductions in GHG emissions by improving use-phase energy use, selecting low-carbon materials or reducing the per capital space demand. In the medium to long term, the applicability and reduction potential of strategies such as circularity and prioritizing renovation over new construction will increase as supply chains and skills develop across the EU. Due to their different potentials and times of implementation, the entire range of strategies is needed to support building and construction transition efforts.

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking.

Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. Despite many security approaches to detect DDoS attacks, it remains an open research challenge. Therefore, this study presents a systematic literature review (SLR) to systematically investigate and critically analyze the existing DDoS attack approaches based on machine learning (ML), deep learning (DL), or hybrid approaches published between 2014 and 2022. We followed a predefined SLR protocol in two stages on eight online databases to comprehensively cover relevant studies. The two stages involve automatic and manual searching, resulting in 70 studies being identified as definitive primary studies. The trend indicates that the number of studies on SDN DDoS attacks has increased dramatically in the last few years. The analysis showed that the existing detection approaches primarily utilize ensemble, hybrid, and single ML-DL. Private synthetic datasets, followed by unrealistic datasets, are the most frequently used to evaluate those approaches. In addition, the review argues that the limited literature studies demand additional focus on resolving the remaining challenges and open issues stated in this SLR.

E-Cardiac Care: A Comprehensive Systematic Literature Review.

The Internet of Things (IoT) is a complete ecosystem encompassing various communication technologies, sensors, hardware, and software. IoT cutting-edge technologies and Artificial Intelligence (AI) have enhanced the traditional healthcare system considerably. The conventional healthcare system faces many challenges, including avoidable long wait times, high costs, a conventional method of payment, unnecessary long travel to medical centers, and mandatory periodic doctor visits. A Smart healthcare system, Internet of Things (IoT), and AI are arguably the best-suited tailor-made solutions for all the flaws related to traditional healthcare systems. The primary goal of this study is to determine the impact of IoT, AI, various communication technologies, sensor networks, and disease detection/diagnosis in Cardiac healthcare through a systematic analysis of scholarly articles. Hence, a total of 104 fundamental studies are analyzed for the research questions purposefully defined for this systematic study. The review results show that deep learning emerges as a promising technology along with the combination of IoT in the domain of E-Cardiac care with enhanced accuracy and real-time clinical monitoring. This study also pins down the key benefits and significant challenges for E-Cardiology in the domains of IoT and AI. It further identifies the gaps and future research directions related to E-Cardiology, monitoring various Cardiac parameters, and diagnosis patterns.

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things.

The IETF Routing Over Low power and Lossy network (ROLL) working group defined IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to facilitate efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Limited resources of 6LoWPAN nodes make it challenging to secure the environment, leaving it vulnerable to threats and security attacks. Machine Learning (ML) and Deep Learning (DL) approaches have shown promise as effective and efficient mechanisms for detecting anomalous behaviors in RPL-based 6LoWPAN. Therefore, this paper systematically reviews and critically analyzes the research landscape on ML, DL, and combined ML-DL approaches applied to detect attacks in RPL networks. In addition, this study examined existing datasets designed explicitly for the RPL network. This work collects relevant studies from five major databases: Google Scholar, Springer Link, Scopus, Science Direct, and IEEE Xplore® digital library. Furthermore, 15,543 studies, retrieved from January 2016 to mid-2021, were refined according to the assigned inclusion criteria and designed research questions resulting in 49 studies. Finally, a conclusive discussion highlights the issues and challenges in the existing studies and proposes several future research directions.

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review.

The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.

Recycling behaviour: Mapping knowledge domain through bibliometrics and text mining.

In recent years, the research on human behaviour in relation to waste management has increased at an exponential rate. At the same time, the expanding academic literature on this topic makes it more difficult to understand the main areas of interest, the leading institutions and authors, the possible interconnections among different disciplines, and the gaps. This paper maps knowledge domain on recycling behaviour through bibliometric analysis and text mining in order to identify current trends, research networks and hot topics. 2061 articles between 1975 and 2020 from three different databases are examined with an interdisciplinary approach. The findings reveal that 60% of papers have been published between 2015 and 2020, and this topic is of global interest. Leading countries are mainly located in Europe, North America and Commonwealth; however, China and Malaysia are also assuming a driving role. Bibliometrics and text mining provide the intellectual configuration of the knowledge on recycling behaviour; co-word analysis individuates conceptual sub-domains in food waste, determinants of recycling behaviour, waste management system, waste electrical and electronic equipment (WEEE), higher-level education, plastic bags, and local government. Overall, waste management and related human behaviour represent a universal challenge requiring a structured and interdisciplinary approach at all levels (individual, institutions, industry, academia). Lastly, this paper offers some suggestions for future research such as smart city design, sensor network system, consumer responsibilisation, the adoption of a more comprehensive view of the areas of investigation through the holistic analysis of all stakeholders.

Applications of deep learning for phishing detection: a systematic literature review.

Phishing attacks aim to steal confidential information using sophisticated methods, techniques, and tools such as phishing through content injection, social engineering, online social networks, and mobile applications. To avoid and mitigate the risks of these attacks, several phishing detection approaches were developed, among which deep learning algorithms provided promising results. However, the results and the corresponding lessons learned are fragmented over many different studies and there is a lack of a systematic overview of the use of deep learning algorithms in phishing detection. Hence, we performed a systematic literature review (SLR) to identify, assess, and synthesize the results on deep learning approaches for phishing detection as reported by the selected scientific publications. We address nine research questions and provide an overview of how deep learning algorithms have been used for phishing detection from several aspects. In total, 43 journal articles were selected from electronic databases to derive the answers for the defined research questions. Our SLR study shows that except for one study, all the provided models applied supervised deep learning algorithms. The widely used data sources were URL-related data, third party information on the website, website content-related data, and email. The most used deep learning algorithms were deep neural networks (DNN), convolutional neural networks, and recurrent neural networks/long short-term memory networks. DNN and hybrid deep learning algorithms provided the best performance among other deep learning-based algorithms. 72% of the studies did not apply any feature selection algorithm to build the prediction model. PhishTank was the most used dataset among other datasets. While Keras and Tensorflow were the most preferred deep learning frameworks, 46% of the articles did not mention any framework. This study also highlights several challenges for phishing detection to pave the way for further research.

Disinformation as an obstructionist strategy in climate change mitigation: a review of the scientific literature for a systemic understanding of the phenomenon.

Background: This study examines the scientific misinformation about climate change, in particular obstructionist strategies. The study aims to understand their impact on public perception and climate policy and emphasises the need for a systemic understanding that includes the financial, economic and political roots. Methods: A systematic literature review (SLR) was conducted using the PRISMA 2020 model. The sample consisted of 75 articles published between 2019 and 2023, sourced from Web of Science, Scopus and Google Scholar. Methodological triangulation was performed to improve the analysis. Results: The results show that technological approaches to misinformation detection, such as immunisation and fact-checking, are widely used. However, few studies look in depth at the operational structures that support systematic disinformation. Conclusions: The study emphasises the urgent need to expand and deepen research on climate disinformation and argues for more global, comparative and adequately funded studies. It emphasises the importance of addressing the systemic complexity of disinformation and integrating different theoretical and methodological approaches. This will help to develop effective measures against hidden networks of influence and mitigate their disruptive effects. The research findings are relevant for policymakers, scientists, academics, the media and the public and will help to improve strategies to combat climate misinformation and promote science-based climate action.

A systematic review of primary school teachers' wellbeing: room for a holistic approach.

Although the investigation of mental health and wellbeing in education has shown an exponential increase on an international scale, attention has primarily been paid to students, leaving the concept of teacher wellbeing comparatively overlooked. Extant literature offers numerous divergent descriptions, with some academics even avoiding an explicit definition of the term. Thus, there are limitations and inconsistencies in understanding teacher wellbeing as a unique construct. The aim of the current study was three-fold; (1) to assess the extent to which existing research reflects the multidimensional nature of the term teacher wellbeing, (2) to determine whether a holistic construct of teacher wellbeing could be justified, and (3) to evaluate the methodological quality of studies identified. A systematic review following the PRISMA statement was applied to peer-reviewed papers published between 2016 and 2021. Following the screening of 1,676 studies, this paper reports on findings drawn from a final sample of 61 articles conceptualizing teacher wellbeing. Studies were organized by their dominant discourses, namely negativity/ deficiency, positivity/ flourishing, and/or professionalism. Findings illustrate that teacher wellbeing was primarily conceptualized with a professionalism approach (with 18 of the identified studies taking solely this perspective). This is not completely consistent with the prior work that focused on stress and burnout (negativity/ deficiency approach) while exploring teachers' mental health and wellbeing. More importantly, there were only 6 studies that considered all three discourses together. This paper argues that important information is lost through neglecting alternative lenses, requiring further attention in order to address teacher wellbeing comprehensively. Such an endeavor is essential for shaping interventions and strategies aimed not only at enhancing teacher wellbeing but also at improving student outcomes and, ultimately, the overall quality of education. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021278549PROSPERO, CRD42021278549.

Exploring the potential of waste plastic-modified asphalt: a systematic review of blending ratios, mixing conditions, and rheological properties.

In the present study, a systematic literature review (SLR) is conducted to collect, compile, and summarize the findings of previous studies in a meaningful and systematic way. This review focuses on the ideal blending ratios, mixing parameters, and the physical, thermal, and rheological performance of waste plastic-modified asphalt. It highlights the most significant research results about the challenges like phase separation, low-temperature performance, and workability for waste plastic-modified asphalt and progress in this domain. The results point out that the use of chemical and physical additives can help in the reduction of phase separation. Furthermore, this paper debates the aging characteristics and it was seen that the integration of waste plastic in asphalt has shown to slow down the aging process of the binder. The review article put forward details of various field projects across the globe utilizing waste plastic. The review concludes by presenting key findings, identifying research gaps, and suggesting future directions to advance the knowledge and to fully comprehend the possible application of waste plastic-modified bitumen in sustainable road construction.

Revealing trends in academic publishing on immersive journalism through a dataset analysis of metaverse and extended technologies from 2017 to 2022.

This article presents the data obtained from a Systematic Literature Review (SLR) on the use of metaverse and extended technologies for immersive journalism [1]. Boolean operators, both in English and Spanish, were used to retrieve scientific literature using Publish or Perish 8 software on Scopus, Web of Science and Google Scholar between 2017 and 2022. After finding all the scientific literature, a methodological process was carried out using selection criteria and following the PRISMA model to obtain a total sample of 61 scientific articles. The DESLOCIS framework was used for the evaluation and quantitative and qualitative analysis of the retrieved data. The first dataset [2] contains the metadata of the retrieved publications according to the phases of the PRISMA statement. The second dataset [3] contains the characteristics of these publications according to the DESLOCIS framework. The data offer the possibility to develop new longitudinal studies and meta-analyzes in the field of immersive journalism.

Comparison of the efficacy and safety of rapid-acting insulin analogs, lispro versus aspart, in the treatment of diabetes: a systematic review of randomized controlled trials.

INTRODUCTION: We evaluated a potential move from one rapid-acting insulin analog to another, or their biosimilars, to aid better and faster decisions for diabetes management. METHODS: A systematic literature review was performed according to PRISMA reporting guidelines. The MEDLINE/EMBASE/COCHRANE databases were searched for randomized control trials (RCTs) comparing aspart/lispro in type-1 (T1D) and type-2 (T2D) diabetes. The methodological quality of the included studies was assessed using the Cochrane Collaboration's risk of bias assessment criteria. RESULTS: Of the 753 records retrieved, the six selected efficacy/safety RCTs and the additional three hand-searched pharmacokinetics/pharmacodynamics RCTs showed some heterogeneity in the presentation of the continuous variables; however, collectively, the outcomes demonstrated that lispro and aspart had comparable efficacy and safety in adult patients with T1D and T2D. Both treatments yielded a similar decrease in glycated hemoglobin (HbA1c) and had similar dosing and weight changes, with similar treatment-emergent adverse events (TEAE) and serious adverse event (SAE) reporting, similar hypoglycemic episodes in both T1D and T2D populations, and no clinically significant differences for hyperglycemia, occlusions or other infusion site/set complications. CONCLUSIONS: Aspart and lispro demonstrate comparative safety and efficacy in patients with T1D/T2D. Since both are deemed equally suitable for controlling prandial glycemic excursions and both have similar safety attributes, they may be used interchangeably in clinical practice. PROSPERO REGISTRATION NUMBER: CRD42023376793.

Methods for Indirect Treatment Comparison: Results from a Systematic Literature Review.

INTRODUCTION: Health technology assessment (HTA) agencies express a clear preference for randomized controlled trials when assessing the comparative efficacy of two or more treatments. However, an indirect treatment comparison (ITC) is often necessary where a direct comparison is unavailable or, in some cases, not possible. Numerous ITC techniques are described in the literature. A systematic literature review (SLR) was conducted to identify all the relevant literature on existing ITC techniques, provide a comprehensive description of each technique and evaluate their strengths and limitations from an HTA perspective in order to develop guidance on the most appropriate method to use in different scenarios. METHODS: Electronic database searches of Embase and PubMed, as well as grey literature searches, were conducted on 15 November 2021. Eligible articles were peer-reviewed papers that specifically described the methods used for different ITC techniques and were written in English. The review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: A total of 73 articles were included in the SLR, reporting on seven different ITC techniques. All reported techniques were forms of adjusted ITC. Network meta-analysis (NMA) was the most frequently described technique (in 79.5% of the included articles), followed by matching-adjusted indirect comparison (MAIC) (30.1%), network meta-regression (24.7%), the Bucher method (23.3%), simulated treatment comparison (STC) (21.9%), propensity score matching (4.1%) and inverse probability of treatment weighting (4.1%). The appropriate choice of ITC technique is critical and should be based on the feasibility of a connected network, the evidence of heterogeneity between and within studies, the overall number of relevant studies and the availability of individual patient-level data (IPD). MAIC and STC were found to be common techniques in the case of single-arm studies, which are increasingly being conducted in oncology and rare diseases, whilst the Bucher method and NMA provide suitable options where no IPD is available. CONCLUSION: ITCs can provide alternative evidence where direct comparative evidence may be missing. ITCs are currently considered by HTA agencies on a case-by-case basis; however, their acceptability remains low. Clearer international consensus and guidance on the methods to use for different ITC techniques is needed to improve the quality of ITCs submitted to HTA agencies. ITC techniques continue to evolve quickly, and more efficient techniques may become available in the future.

Datasets on the assessment of the scientific publication's corpora in circular economy and bioenergy approached from education and communication.

This article presents three datasets that specifically depict scientific literature published from 2009 to 2019 and that represent the overlaps between circular economy, bioenergy, education, and communication. All datasets have been obtained through an exhaustive methodological process based on a Systematic Literature Review (SLR). To collect data, we determined 12 Boolean Operators with words related to circular economy, bioenergy, communication, and education. Then, using the Publish or Perish software, 36 queries were made in the Web of Science, Scopus, and Google Scholar databases. Once the articles were retrieved, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) mode and PRISMA checklist were applied. 74 articles were then manually selected depending on their relationship with the field. Using the DESLOCIS framework, a wide evaluation of the articles was carried out focusing on the design, data collection, and analysis techniques. Thus, the first data set contains the metadata and metrics of the publications. The second data set details the analytical framework used. The third includes the analysis of the publication's corpora. Together, the data presents opportunities for longitudinal studies and meta-reviews in circular economy and bioenergy areas approached from perspectives of education and communication.

A systematic review and future research agenda on detection of polycystic ovary syndrome (PCOS) with computer-aided techniques.

Polycystic Ovary Syndrome (PCOS) is among the most prevalent endocrinological abnormalities seen in reproductive female bodies posing serious health hazards. The correctness of interpreting this condition depends heavily on the wide spectrum of associated symptoms and the doctor's expertise, making real-time clinical detection quite challenging. Thus, investigations on computer-aided PCOS detection systems have recently been explored by several researchers worldwide as a potential replacement for manual assessment. This review study's objective is to analyze the relevant research works on computer-assisted methods for automatically identifying PCOS through a systematic literature review (SLR) methodology as well as investigate the research limitations and explore potential future research scopes in this domain. 28 articles have been selected using the PRISMA approach based on a set of inclusion-exclusion criteria for conducting the review. The data synthesis of the selected articles has been conducted using six data exploration themes. As outcomes, the SLR explored the topical association between the studies; their research profiles; objectives; data size, type, and sources; methodologies applied for the detection of PCOS; and lastly the research outcomes along with their evaluation measures and performances. The study also highlights areas for future research directions examining the study gaps to enhance the current efforts for autonomous PCOS identification; such as integrating advanced techniques with the current methods; developing interactive software systems; exploring deep learning and unsupervised machine learning techniques; enhancing datasets and country context; and investigating more unknown factors behind PCOS. Thus, this SLR provides a state-of-the-art paradigm of autonomous PCOS detection which will support significantly efficient clinical assessment, diagnosis and treatment of PCOS.

Integrating the digital twin concept into the evaluation of reconfigurable manufacturing systems (RMS): literature review and research trend.

With the rapid advent of new information technologies (Big Data analytics, cyber-physical systems, such as IoT, cloud computing and artificial intelligence), digital twins are being used more and more in smart manufacturing. Despite the fact that their use in industry has attracted the attention of many practitioners and researchers, there is still a need for an integrated and comprehensive digital twin framework for reconfigurable manufacturing systems. To close this research gap, we present evidence from a systematic literature review, including 76 papers from high-quality journals. This paper presents the current research trends on evaluation and the digital twin in reconfigurable manufacturing systems, highlighting application areas and key methodologies and tools. The originality of this paper lies in its proposal of interesting avenues for future research on the integration of the digital twin in the evaluation of RMS. The benefits of digital twins are multiple such as evaluation of current and future capabilities of an RMS during its life cycle, early discovery of system performance deficiencies and production optimization. The idea is to implement a digital twin that links the virtual and physical environments. Finally, important issues and emerging trends in the literature are highlighted to encourage researchers and practitioners to develop studies in this area that are strongly related to the Industry 4.0 environment.

Impact of employee digital competence on the relationship between digital autonomy and innovative work behavior: a systematic review.

With the advent of the COVID-19 pandemic, the level of concern regarding employee digital competence has increased significantly. Several studies provide different surveys, but they cannot describe the relationship between digital autonomy and innovative work behaviour concerning the impact of employee digital competence. Hence, it is necessary to conduct a survey that provides a deeper understanding of these concerns and suggests a suitable study for other researchers. Using scientific publication databases and adhering to the PRISMA statement, this systematic literature review aims to offer a current overview of employee digital competence impact on the relationship between digital autonomy and innovative work behaviour from 2015 to 2022, covering definitions, research purposes, methodologies, outcomes, and limitations. When reviewing the selected articles, 18 articles were examined under relationship topics, and 12 articles reported on impact topics under different tasks. The main findings highlight the significance of digital competence and autonomy in promoting employee creativity, learning, and sharing knowledge. According to the review findings, employees with greater digital autonomy are more likely to engage in innovative work, leading to improved job performance and empowerment. Therefore, the development of digital autonomy prioritizes organizations by providing access to digital tools, training, and a supportive work environment. Overall, the current review indicates a strong positive correlation between digital autonomy, innovative work behaviour, and employee impact. This underscores the importance for organizations to not only participate in digital competence and skills, but also to create a culture that values autonomy, creativity, and innovation among its employees.

Lean six sigma in the healthcare sector: A systematic literature review.

Healthcare is a very important sector as our lives depend on it. During the novel corona virus pandemic, it was evident that our healthcare organizations still lack in terms of efficiency and productivity. Especially in the developing nations, the problems were much bigger. Lean Six Sigma (LSS) is a methodology which when implemented in an organization, helps to increase the process capability and the efficiency, by reducing the defects and wastes. The present study systematically reviews the research studies conducted on LSS in the healthcare sector. It was found that comparatively less studies are focused on improving the medical processes, most of the studies targeted the management processes. Moreover, lesser number of studies were being conducted for developing nations, but now it seems that the focus of research scholars has shifted towards the developing nations also. But it was observed that the studies in these nations were majorly empirical in nature, very few studies were conceptual or exploratory. There is a need for guiding healthcare professionals on creating a continuous improvement environment, which sustains the improvements achieved after LSS implementation.

Affect and Cognition in Managerial Decision Making: A Systematic Literature Review of Neuroscience Evidence.

How do affect and cognition interact in managerial decision making? Over the last decades, scholars have investigated how managers make decisions. However, what remains largely unknown is the interplay of affective states and cognition during the decision-making process. We offer a systematization of the contributions produced on the role of affect and cognition in managerial decision making by considering the recent cross-fertilization of management studies with the neuroscience domain. We implement a Systematic Literature Review of 23 selected contributions dealing with the role of affect and cognition in managerial decisions that adopted neuroscience techniques/points of view. Collected papers have been analyzed by considering the so-called reflexive (X-) and reflective (C-) systems in social cognitive neuroscience and the type of decisions investigated in the literature. Results obtained help to support an emerging "unified" mind processing theory for which the two systems of our mind are not in conflict and for which affective states have a driving role toward cognition. A research agenda for future studies is provided to scholars who are interested in advancing the investigation of affect and cognition in managerial decision making, also through neuroscience techniques - with the consideration that these works should be at the service of the behavioral strategy field.