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This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

This paper aims to investigate how congruent keywords are used in information security policies (ISPs) to pinpoint and guide clear actionable advice and suggest a metric for measuring the quality of keyword use in ISPs.

A qualitative content analysis of 15 ISPs from public agencies in Sweden was conducted with the aid of Orange Data Mining Software. The authors extracted 890 sentences from these ISPs that included one or more of the analyzed keywords. These sentences were analyzed using the new metric – keyword loss of specificity – to assess to what extent the selected keywords were used for pinpointing and guiding actionable advice. Thus, the authors classified the extracted sentences as either actionable advice or other information, depending on the type of information conveyed.

The results show a significant keyword loss of specificity in relation to pieces of actionable advice in ISPs provided by Swedish public agencies. About two-thirds of the sentences in which the analyzed keywords were used focused on information other than actionable advice. Such dual use of keywords reduces the possibility of pinpointing and communicating clear, actionable advice.

The suggested metric provides a means to assess the quality of how keywords are used in ISPs for different purposes. The results show that more research is needed on how keywords are used in ISPs.

The authors recommended that ISP designers exercise caution when using keywords in ISPs and maintain coherency in their use of keywords. ISP designers can use the suggested metrics to assess the quality of actionable advice in their ISPs.

The keyword loss of specificity metric adds to the few quantitative metrics available to assess ISP quality. To the best of the authors’ knowledge, applying this metric is a first attempt to measure the quality of actionable advice in ISPs.

This article conceptualises how the economic well-being of an entrepreneurial household affects its members' mental accounting process to establish its affordable loss for a plunge decision.

The article used research literature to analyze the resources available for entrepreneurial endeavours against a household's ability to maintain acceptable minimum material living standards, juxtaposing income and wealth against competing consumption and investment opportunities.

Mentally accounting for whether household resources can meet minimum material living standards is central to entrepreneurs' ability to raise affordable loss and decide to invest in a new venture. The article proposes that entrepreneurial households establish affordable loss by availing their money exceeding that required to maintain acceptable minimum material living standards. In low-income households, the author assumes that members are not employed and can thus avail their time (versus money) towards affordable loss.

Economic well-being introduces mental accounts of income and wealth and a hedonic reference outcome in the material living standards of households required to meet basic needs. The article introduces the tension entrepreneurial households face between using their income and wealth towards investing in a new business and maintaining their material living standards. It introduces the idea that a loss can be “affordable” according to an entrepreneurial household's ability to remain above its acceptable minimum material living standard. This view prompts scholars to consider a household unit of analysis and avoid assuming an entrepreneur makes the plunge decision in isolation.

This study aims at developing an understanding of action pathways when adverse events force micro-enterprises to change their operations.

This qualitative study draws upon empirical data collected from entrepreneurs facing the same adverse event—the COVID-19 pandemic—to build theory on different types of actions that micro-enterprises take and what leads up to these actions.

The findings suggest three types of action pathways. The first pathway is set off by losses stretched out over time and generates open-ended actions. The second pathway is set off by immediate losses and generates survival-oriented actions. The third pathway is set off by potential long-term losses and generates developmental-oriented actions.

This study offers novel insights into action pathways in response to adverse events, heterogeneity of such actions and processes that precede the choice of actions. It also expands the existing literature by showcasing actual actions instead of desired actions, which have already been extensively studied.

COVID-19 remains a public health emergency of international concern. Efforts at the global and national levels are being made to control its spread. The Nigerian Correctional Service is also proactive in the fight against the disease by organizing COVID-19 awareness training for correctional officers. We conducted a pre- and post-test assessment of COVID-19 knowledge among correctional officers in Enugu State Command to determine the impact of awareness training on their knowledge level. The study also assessed correctional officers’ attitude and preventive practices towards the disease.

The mean knowledge score was 19.34 out of 25, and the awareness training significantly improved the participants’ COVID-19 knowledge. We found a significant moderate, positive correlation between knowledge and attitude/practice, and a significantly higher knowledge level among those with higher educational qualifications.

Regular hand washing with soap and water (87.9%), wearing face masks (84.4%), and social distancing (83%) were practiced by the majority of the participants. The majority of the participants (53.2%) received COVID-19 information from multiple sources including the Nigeria Centre for Disease Control and the World Health Organization.

COVID-19 has immensely disrupted business dynamism, providing catalyst innovation opportunities and transposing society's perception of disruptive technology (DT). This research increases the understanding of the impact of the pandemic in influencing the way organizations perceive DT and whether any mitigating factors were considered when deciding to adopt new technology during the pandemic.

A qualitative approach was adopted in this research, consisting of 14 semi-structured interviews with eight senior managers and six employees, representing both the private and public sectors in New Zealand. All participants had in-depth knowledge of organizational DT adoption during the pandemic. Two separate sets of semi-structured interviews were used to enable comparison between senior managers' and employees' experiences of organizational adoption of DT post-emergence of COVID-19. Due to the nature of this research being conducted on organizational adoption of DT during the pandemic, time constraints and sample size were two of the key limitations of this research. Specifically, potential participants widely cited unavailability due to additional pressure from COVID-19. Given the limited research in this area, this study is explorative by nature and adds significant insights to the literature.

The findings suggest that COVID-19 has contributed towards an increased acceptance of, reliance on and adoption of DT across both organizational and social landscapes. The authors found that one of the reasons COVID-19 expedites the adoption of DT correlates with the notion of technology dependency, with organizations citing DT as a viable part of a business continuity plan (BCP) to counter the unpredictability of ongoing disruptive events associated with COVID-19 or any similar disruption which may be on the horizon. These findings are highly relevant as they suggest that the labor market in New Zealand is flexible so organizations and employees can adapt to DT and COVID-19.

This research adds much-needed insight into the emerging field of research that examines COVID-19's impact on the adoption of DT from both management and employee perspectives.

This study focuses on the classification of targets with varying shapes using radar cross section (RCS), which is influenced by the target’s shape. This study aims to develop a robust classification method by considering an incident angle with minor random fluctuations and using a physical optics simulation to generate data sets.

The approach involves several supervised machine learning and classification methods, including traditional algorithms and a deep neural network classifier. It uses histogram-based definitions of the RCS for feature extraction, with an emphasis on resilience against noise in the RCS data. Data enrichment techniques are incorporated, including the use of noise-impacted histogram data sets.

The classification algorithms are extensively evaluated, highlighting their efficacy in feature extraction from RCS histograms. Among the studied algorithms, the K-nearest neighbour is found to be the most accurate of the traditional methods, but it is surpassed in accuracy by a deep learning network classifier. The results demonstrate the robustness of the feature extraction from the RCS histograms, motivated by mm-wave radar applications.

This study presents a novel approach to target classification that extends beyond traditional methods by integrating deep neural networks and focusing on histogram-based methodologies. It also incorporates data enrichment techniques to enhance the analysis, providing a comprehensive perspective for target detection using RCS.

This paper aims to clarify how organizations manage their participation in networks to share and jointly create knowledge but also risk unwanted knowledge spillovers at the same time. As formal governance, trust and observation are less applicable in informal networks, the authors need to understand how members address the need to protect knowledge by informal practices. The study aims to investigate how the application of knowledge protection practices affects knowledge sharing in networks. The insights are relevant for organizational and network management to control knowledge risks but harvest the benefits of network engagement.

The authors opted for an exploratory study based on 60 semi-structured interviews with members of 10 networks. In two rounds, network managers, representatives and members of the networks were interviewed. The second round of interviews was used to validate the intermediate findings. The data were complemented by documentary analysis, including network descriptions.

Through analyzing and building on the theory of psychological contracts, two informal practices of knowledge protection were found in networks of organizations: exclude crucial topics and share on selected topics and exclude details and share a selected level of detail. The authors explored how these two practices are enacted in networks of organizations with psychological contracts.

Counter to intuition that the protection of knowledge can be strengthened only at the expense of knowledge sharing and vice versa, networks benefitted from more focused and increased knowledge sharing while reducing the risk of losing competitive knowledge by performing these knowledge protection practices.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.