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This article empirically tests the impact of risk appetite of the executive team on the re-innovation strategy after technological innovation failure using a panel regression model from the perspective of regional financial development level of enterprises.

By means of time series global principal component analysis and panel regression model method, the study validated and analyzed the impact of risk appetite of the executive team on the re-innovation strategy after enterprise technological innovation failure.

The research found that the higher the risk appetite of executive team, the more inclined the enterprise is to choose the “focusing on quantity, ignoring quality” re-innovation strategy after technological innovation failure. The better the financial development level of the region where the enterprise is located, the better it can effectively reduce the re-innovation strategy of “focusing on quantity, ignoring quality” of the enterprise due to the high risk appetite of the executive team.

The findings of this study are helpful in improving the financial development level of the region where the enterprise is located. It can help the executive team of the enterprise to more objectively choose the innovation strategy after technological innovation failure, and reduce the phenomenon that the executive team of the enterprise only pays attention to the quantity of re-innovation and underestimates the quality of re-innovation after technological innovation failure due to its high risk appetite.

This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.

To address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.

This work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.

This research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.

Many entrepreneurs often struggle with the fear of failure, which can be detrimental to both their business and personal well-being. To better understand the factors that contribute to this fear, the authors conducted research on the impact of various obstacles, such as limited financial resources, risk aversion, stress and hard work avoidance, and prior business failures. Additionally, the authors explored the effects of social capital in mitigating these obstacles and their relationship to fear of failure in entrepreneurship.

The authors conducted a survey with 440 young Iraqi entrepreneurs using non-probabilistic and purposive methods. The survey instrument included multiple measuring scales, which were provided in both English and Arabic. The authors analysed valid responses using structural equation modelling (SEM) with partial least squares (PLS).

The findings show that the fear of failure in entrepreneurship is negatively influenced by factors such as limited financial access, risk aversion, and past business failures. However, aversion to stress and hard work did not have a significant impact. The findings also show that social capital could potentially mitigate these negative factors.

The theoretical and practical implications of this study manifest in revealing the difficulties entrepreneurs encounter in developing countries like Iraq, where entrepreneurship is vital for economic growth. The study's limitations stem from its focus on one country and the use of a single survey method. Future research could use varied methods across multiple countries for a more comprehensive view.

This study sheds light on the factors that are obstacles for entrepreneurs to starting a business in emerging economies like Iraq.

This study aims to propose a facilitating methodology for the application of Fuzzy FMEA (Failure Mode and Effect Analysis), comparing the traditional approach with fuzzy variations, supported by a case application in the aeronautical sector.

Based on experts' opinions in risk analysis within the aeronautical sector, rules governing the relationship between severity, occurrence, detection and risk factor were defined. This served as input for developing a fuzzyfied FMEA tool using the Matlab Fuzzy Logic Toolbox. The tool was applied to the sealing process in a company within the aeronautical sector, using triangular and trapezoidal membership functions, and the results were compared with the traditional FMEA approach.

The results of the comparative application of traditional FMEA and fuzzyfied FMEA using triangular and trapezoidal functions have yielded valuable insights into risk analysis. The findings indicated that fuzzyfied FMEA maintained coherence with the traditional analysis in identifying higher-risk effects, aligning with the prioritization of critical failure modes. Additionally, fuzzyfied FMEA allowed for a more refined prioritization by accounting for variations in each variable through fuzzy rules, thereby improving the accuracy of risk analysis and providing a more realistic representation of potential hazards. The application of the developed fuzzyfied FMEA approach showed promise in enhancing risk assessment in the aeronautical sector by considering uncertainties and offering a more detailed and context-specific analysis compared to conventional FMEA.

This study emphasizes the potential of fuzzyfied FMEA in enhancing risk assessment by accurately identifying critical failure modes and providing a more realistic representation of potential hazards. The application case reveals that the proposed tool can be integrated with expert knowledge to improve decision-making processes and risk mitigation strategies within the aeronautical industry. Due to its straightforward approach, this facilitating methodology could also prove beneficial in other industrial sectors.

This paper presents the development and application of a facilitating methodology for implementing Fuzzy FMEA, comparing it with the traditional approach and incorporating variations using triangular and trapezoidal functions. This proposed methodology uses the Toolbox Fuzzy Logic of Matlab to create a fuzzyfied FMEA tool, enabling a more nuanced and context-specific risk analysis by considering uncertainties.

An integrated intuitionistic fuzzy (IF) modelling-based framework for examining the performance analysis of a packaging unit (PU) in three different stages has been proposed.

For the series and parallel configuration of PU, a mathematical model based on the intuitionistic fuzzy Lambda–Tau (IFLT) approach was developed in order to calculate various reliability parameters at various spreads. For determining membership and non-membership function-based reliability parameters for the top event, AND/OR gate transitions expression was employed.

For 15%–30% spread, unit’s availability for the membership function falls by 0.006442%, and it falls even more by 0.014907% with an increase in spread from 30% to 45%. In contrast, for 15%–30% spread, the availability of non-membership function-based systems reduces by 0.007491% and further diminishes. Risk analysis has presented applying an emerging approach called intuitionistic fuzzy failure mode and effect analysis (IFFMEA). For each of the stated failure causes, the output values of the intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED)-based IFFMEA have been tabulated. Failure causes like HP1, MT6, FB9, EL16, DR23, GR27, categorized under subsystems, namely hopper, motor, fluidized bed dryer, distributor, grader and bin, respectively, with corresponding IFFMEA output scores 1.0975, 1.0190, 0.8543, 1.0228, 0.9026, 1.0021, were the most critical one to contribute in the system’s failure.

The limitation of the proposed framework lies in the fact that the results obtained for both reliability and risk aspects mainly depend on the correctness of raw data provided by the experts. Also, an approximate model of PU is obtained from plant experts to carry performance analysis, and hence more attention is required in constructing the model. Under IFLT, reliability parameters of PU have been calculated at various spreads to study and analyse the failure behaviour of the unit for both membership and non-membership function in the IFS of [0.6,0.8]. For both membership- and non-membership-based results, availability of the considered system shows decreasing trend. To improve the performance of the considered system, risk assessment was carried using IFFMEA technique, ranking all the critical failure causes against IFHWED score value, on which more attention should be paid so as to avoid sudden failure of unit.

The livelihood of millions of farmers and workers depends on sugar industries. So perpetual running of these industries is very important from this viewpoint. On the basis of findings of reliability parameters, the maintenance manager could frame a correct maintenance policy for long-run availability of the sugar mills. This long-run availability will generate revenue, which, in turn, will ensure the livelihood of the farmers.

Mathematical modelling of the considered unit has been done applying basic expressions of AND/OR gate. IFTOPSIS approach has been implemented for ranking result comparison obtained under IFFMEA approach. Eventually, sensitivity analysis was also presented to demonstrate the stability of ranking of failure causes of PU.

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

This study aims to quantify and prioritize the main causes of lean wastes and to apply reduction methods by employing better waste cause identification methodologies.

We employed fuzzy techniques for order preference by similarity to the ideal solution (FTOPSIS), fuzzy analytical hierarchy process (FAHP), and failure mode effect analysis (FMEA) to determine the causes of defects. To determine the current defect cause identification procedures, time studies, checklists, and process flow charts were employed. The study focuses on the sewing department of a clothing industry in Addis Ababa, Ethiopia.

These techniques outperform conventional techniques and offer a better solution for challenging decision-making situations. Each lean waste’s FMEA criteria, such as severity, occurrence, and detectability, were examined. A pairwise comparison revealed that defect has a larger effect than other lean wastes. Defects were mostly caused by inadequate operator training. To minimize lean waste, prioritizing their causes is crucial.

The research focuses on a case company and the result could not be generalized for the whole industry.

The study used quantitative approaches to quantify and prioritize the causes of lean waste in the garment industry and provides insight for industrialists to focus on the waste causes to improve their quality performance.

The methodology of integrating FMEA with FAHP and FTOPSIS was the new contribution to have a better solution to decision variables by considering the severity, occurrence, and detectability of the causes of wastes. The data collection approach was based on experts’ focus group discussion to rate the main causes of defects which could provide optimal values of defect cause prioritization.

Organizational technologies can be classified according to the roles they play as either commodity or strategic. Commodity technologies support common operations, while strategic technologies address perceived threats to competitiveness, often identified by strategic foresight. These must go through an adoption process before playing an effective role in strategy execution. The adoption process includes known activities, ranging from sourcing (itself from in-house development to turn-key acquisition) to operational integration. This paper aims to reveal strategic technology adoption risks that arise during strategy execution.

A gradually developed causal loop diagram model, supported by general literature, introduces three general classes of technology adoption risks: mismatched requirements, supplier dependence and unmanaged life cycles.

Rather than managed, these risks are incurred or avoided depending on decisions made during the adoption process.

Despite the scarce literature coverage for the approach, examples revealing the presence of adoption risks are nevertheless available in the well-documented history of enterprise resource planning (ERP).

Although ERP is presented as a general-purpose strategic technology, the unique business features of maritime container terminals pose serious challenges to its adoption, which provides additional support to the discussion and reinforces the conclusions.

The approach to identifying risks in strategic technology adoption departs from the current risk paradigm in two significant ways. First, it emphasizes policy decision-making rather than external events. Second, it views risks as systemic rather than occurring independently.

Bank failures are critical events that have far-reaching implications for the financial system and various stakeholders. This study aims to focus on analyzing the phenomenon of small bank failures in the USA.

This study adopts the coarsened exact matching (CEM) technique to enhance the reliability of the analysis. By matching similar observed characteristics, the CEM approach helps to address potential selectivity bias and facilitates a more accurate estimation of the treatment effect. This study uses a data set covering the period from 2000 through 2019 and includes 523 failed bank observations and 43,605 nonfailed bank observations.

The results reveal several key findings. Small banks, especially those with lower yields on earning assets, those with lower charge-offs on loans and leases, those with higher core capital ratios and those with higher Fed Funds rates are found to be more susceptible to failure.

Some results align with initial predictions, whereas others present contrasting outcomes.

This study underscores the significance of understanding the factors contributing to bank failure and emphasizes the importance of studying small bank failures in particular.

This study uses the CEM method. CEM is a comprehensive approach that combines matching, sample trimming and reweighting techniques. When applying CEM, researchers carefully select a set of core variables to achieve balance between the treated and control groups. The CEM process involves discretizing each continuous variable into distinct bins or categories, a process known as “coarsening.” It then requires an exact match among these binned variables between the treated and control units, which constitutes the matching step in CEM.

This paper provides a structural model to value startup companies and determine the optimal level of research and development (R&D) spending by these companies.

This paper describes a new variant of float-the-money options, which can act as a financial instrument for financing R&D expenses for a specific time horizon or development stage, allowing the investor to share in the startup's value appreciation over that duration. Another innovation of this paper is that it develops a structural model for evaluating optimal level of R&D spending over a given time horizon. The paper deploys the Gompertz-Cox model for the R&D project outcomes, which facilitates investigation of how increased level of R&D input can enhance the company's value growth.

The author first introduces a time-varying drift term into standard Black-Scholes model to account for the varying growth rates of the startup at different stages, and the author interprets venture capital's investment in the startup as a “float-the-money” option. The author then incorporates the probabilities of startup failures at multiple stages into their financial valuation. The author gets a closed-form pricing formula for the contingent option of value appreciation. Finally, the author utilizes Cox proportional hazards model to analyze the optimal level of R&D input that maximizes the return on investment.

The integrated contingent claims model links the change in the financial valuation of startups with the incremental R&D spending. The Gompertz-Cox contingency model for R&D success rate is used to quantify the optimal level of R&D input. This model assumption may be simplistic, but nevertheless illustrative.

Once supplemented with actual transaction data, the model can serve as a reference benchmark valuation of new project deals and previously invested projects seeking exit.

The integrated structural model can potentially have much wider applications beyond valuation of startup companies. For instance, in valuing a company's risk management, the level of R&D spending in the model can be replaced by the company's budget for risk management. As another promising application, in evaluating a country's economic growth rate in the face of rising climate risks, the level of R&D spending in this paper can be replaced by a country's investment in addressing climate risks.

This paper is the first to develop an integrated valuation model for startups by combining the real-world R&D project contingencies with risk-neutral valuation of the potential payoffs.