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The objective of this work is to demonstrate the relationships between the two main processes of research and development (R&D) activities: the knowledge generation phase (KPP) and the knowledge commercialization, or transfer, phase (KCP), in a sector that is intensive in this type of activity, such as the pharmaceutical sector. In addition, within the framework of the general objective of this work, the authors propose two other objectives: (1) make advances in network efficiency measurement models, and (2) determine the factors associated with efficiency in the KPP and in the KCP in companies of the pharmaceutical sector in Spain.

A Network Data Envelopment Analysis (NDEA) model (Färe and Grosskopf, 2000) with categorical variables (Lee et al., 2020; Yeh and Chang, 2020) has been applied, and a sensitivity analysis of the obtained results has been performed through a DEA model of categorical variables, in accordance with the work of Banker and Morey (1986), to corroborate the results of the proposed model. The sample is made up of 77 companies in the pharmaceutical sector in Spain.

The results obtained point to a greater efficiency of pharmaceutical companies in the KPP, rather than in the KCP. Furthermore, the study finds that 1) alliances between companies have been the accelerating factors of efficiency in the KCP (but patents have slowed this down the most); 2) the quality of R&D and the number of R&D personnel are the factors that most affect efficiency in the KPP; and 3) the quality of R&D again, the benefits obtained and the position in the market are the factors that most affect efficiency in the KCP.

The authors have not found studies that show whether the efficiency obtained by R&D-intensive companies in the KPP phase is related to better results in terms of efficiency in the KCP phase. No papers have been found that analyse the role of alliances between R&D-intensive companies and patents, as agents that facilitate efficiency in the KCP phase, covering the gap in the research on both problems. Notwithstanding, this work opens up a research path which is related to the improvement of network efficiency models (since it includes categorical variables) and the assessment of the opinions of those who are responsible for R&D departments; it can be applied to decision-making on the aspects to improve efficiency in R&D-intensive companies.

This exploratory research aims to identify the effect of perceived stress level and self-efficacy on management quality and what practices and theories need to be enhanced to improve management quality under volatility business environments.

The study surveyed 291 working women, using the Perceived Stress Scale and the General Self-Efficacy Scale. Latent class analysis (LCA) for classifications of respondents, using categorical observed variables and MANCOVA, are applied to determine the relationship between stress and self-efficacy on the assigned classes.

The study suggests that in a highly volatile business environment, where stress is high, affecting management quality, managers as individuals fall into one of four classes that describe their techniques of coping with the stress, namely Uncommitted Experimenters, Try Anything, Intrinsically Motivated and Externally Motivated. Techniques of stress management classification are significantly related to the combined perceived stress and self-efficacy measures, with Externally Motivated respondents as the classification with a significant mean difference.

The main limitation of the study at hand refers to the sample size versus the number of potential factors of stress. This limitation highlights the need for further data gathering and research in this area, as stress is a critical factor of performance and often ignored in traditional management theories. Another limitation of this study is the lack of in-depth analysis of the use of meditation; its benefits and how to best use this practice in traditional work settings.

The outcome of the study could have significant implications for quality of management in business, private and social sectors by providing meditation as a tool for employees and stakeholders to handle stress in conflict zones.

Using stress management techniques might prove to be a low-cost tool for better quality management of human assets.

The authors study focuses on women in volatile economic turmoil, natural devastations, conflict areas and politically insecure environments. This socioeconomic segment was rarely scrutinized despite its direct effect on a large number of economies hosting a sizeable portion of the world’s population. Interesting potential results highlight the relationship between the respondents in the Intrinsically Motivated class and stress reduction for the benefit of management quality.

COVID-19 has had various effects on women’s labour supply worldwide. This study investigates how women’s labour market outcomes in the MENA region have been affected by the stringency of governments’ COVID-19 responses and school closures. We examine whether women, particularly those with children at young age, reduced their labour supply to take care of their families during the pandemic.

To investigate whether having a family results in an extra penalty to women’s labour market outcomes, we compare single women to married women and mothers. Using the ERF COVID-19 MENA Monitor Household Surveys, we analyse the key conditions underlying women’s labour market outcomes: (1) wage earnings and labour market status including remaining formally employed, informally, unpaid or self-employed, unemployed or out of the labour force and (2) becoming permanently terminated, being suspended, seeing a reduction in the hours worked or wages, or seeing a delay in one’s wage payments because of COVID-19. Ordered probit and multinomial logit are employed in the case of categorical outcomes, and linear models for wage earnings.

Women, regardless of whether they have children or not, appear to join the labour market out of necessity to help their families in the times of crisis. Child-caring women who are economically inactive are also more likely to enter the labour market. There is little difference between the negative experiences of women with children and child-free women in regard to their monthly pay reduction or delay, or contract termination, but women with children were more likely to experience reduction in hours worked throughout the pandemic.

These findings may not have causal interpretation facilitating accurate inference. This is because of potential omitted variables such as endogenous motivation of women in different circumstances, latent changes in the division of domestic work between care-giving and other household members, or selective sample attrition.

Our analysis explores the multiple channels in which the pandemic has affected the labour outcomes of MENA-region women. Our findings highlight the challenges that hamper the labour market participation of women, and suggest that public policy should strive to balance the share of unpaid care work between men and women and increase men’s involvement, through measures that support child-bearing age women’s engagement in the private sector during crises, invest in childcare services and support decent job creation for all.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices.

Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R² of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers.

By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making.

A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable.

The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Several factors influence the costs of buildings. Thus, identifying the cost significant factors can assist to improve the accuracy of project cost forecasts during the planning phase. This paper aims to identify the cost significant parameters and explore the potential for improving the accuracy of cost forecasts for buildings using machine learning techniques and large data sets.

The Australian State of Victoria Building Authority data sets, which comprise various parameters such as cost of the buildings, materials used, gross floor areas (GFA) and type of buildings, have been used. Five different machine learning regression models, such as decision tree, linear regression, random forest, gradient boosting and k-nearest neighbor were used.

The findings of the study showed that among the chosen models, linear regression provided the worst outcome (r² = 0.38) while decision tree (r² = 0.66) and gradient boosting (r² = 0.62) provided the best outcome. Among the analyzed features, the class of buildings explained about 34% of the variations, followed by GFA and walls, which both accounted for 26% of the variations.

The output of this research can provide important information regarding the factors that have major impacts on the costs of buildings in the Australian construction industry. The study revealed that the cost of buildings is highly influenced by their classes.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

The purpose of this study is to determine the nature of financial inclusion for individuals with various types of disabilities.

Data from 2015, 2017 and 2019 FDIC Survey of Household Use of Banking and Financial Services was pooled, and binary logistic regressions were used to investigate differences in barriers to financial inclusion (e.g. unbanked) between people with different types of disabilities (e.g. cognitive) and those without such disabilities.

Using five separate barrier measures, the authors found specific disability types face different barriers to financial inclusion. For example, respondents with cognitive, ambulatory or two or more disabilities were more likely to use nonbank transaction products and alternative financial services. And, those with vision or cognitive disabilities were more likely to be denied or receive reduced credit. When examining aggregate barriers to financial inclusion (total number of barriers faced) respondents with cognitive, ambulatory, hearing or two or more disabilities experienced the lowest degree of financial inclusion in the authors’ dataset.

Causal inference cannot be made due to the cross-sectional nature of the data. The data only covers the US population, and the measurement of disability type could include those with short-term impairments. Further, there may be an omitted variable bias.

Best practices to maximize financial inclusion for those with different disability types should address accessibility issues, bank staff education, financial literacy education and poverty issues. Additional government policies and oversight are also needed to protect and enhance the overall financial inclusion of people with disabilities.

To the best of the authors’ knowledge, this paper is the first to examine the relationship between various barriers to financial inclusion and aggregate barriers to financial inclusion by disability type. Specific disability types are found to face different barriers to financial inclusion.

Previous evidence has shown a generally positive relationship between continuously developed innovation, known as innovation persistence and employment growth in firms. This study investigates whether firm size moderates this relationship and how, considering persistent product and process innovation.

The authors studied the influence of firm size on the relationship between innovation persistence and employment using a 10-year panel database of firms based on national innovation surveys. The authors consider firm size as sales and measure innovation persistence through the hazard rate of innovation spells. To assess the main model, they use a system generalized method of moments (GMM) estimator.

The authors' main findings indicate that firm size negatively moderates the relationship between persistent innovation and employment growth. These results suggest that the positive effects of product and process persistent innovation on employment growth decrease as firm size increases. The authors also find evidence indicating that the moderator role of firm size is greater when firms innovate more persistently. Robustness tests with different specifications confirm the results.

The authors show that firm size negatively affects the strength of the relationship between innovation persistence and employment growth in product and process innovations. The authors also show that the moderator role of firm size is greater when firms are more persistent in generating product and process innovation. Additionally, using a panel dataset, they provide evidence from a sample of firms in a developing country where no studies on this matter have previously been conducted.