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This study examines the importance of financial literacy in explaining financial resilience in four continental European countries during the coronavirus disease 2019 (COVID-19) crisis while controlling for a wide set of additional determinants.

Variable importance may vary with the technique applied. Therefore, different classification techniques, such as logistic regression, partial proportional odds regression, and conditional random forest, have been employed. The analysis relies on the Survey of Health, Ageing and Retirement in Europe in the context of COVID-19, collecting 4,781 observations from France, Germany, Italy, and Spain.

In line with previous studies, financial resilience is found to increase with financial literacy that consistently ranks in the midfield in terms of variable importance among all explanatory variables.

The findings reveal the most important features that improve financial resilience. Financial literacy is one of the few determinants of financial resilience that can be actively shaped. To increase preparedness for future crises, a policy mix of financial education, regulation, and nudging may help increase financial literacy and, subsequently, financial resilience.

The better the financial literacy, the more protected individuals are from macroeconomic shocks. However, most previous studies do not rely on data samples that cover such crises. Moreover, most of the previous studies rely on single classification techniques, while this study applied traditional and data-mining techniques to assess feature importance.

In short-term forecasting of day-ahead electricity prices, incorporating intraday dependencies is vital for accurate predictions. However, it quickly leads to dimensionality problems, i.e. ill-defined models with too many parameters, which require an adequate remedy. This study addresses this issue.

In an application for the German/Austrian market, this study derives variable importance scores from a random forest algorithm, feeds the identified variables into a support vector machine and compares the resulting forecasting technique to other approaches (such as dynamic factor models, penalized regressions or Bayesian shrinkage) that are commonly used to resolve dimensionality problems.

This study develops full importance profiles stating which hours of which past days have the highest predictive power for specific hours in the future. Using the profile information in the forecasting setup leads to very promising results compared to the alternatives. Furthermore, the importance profiles provide a possible explanation why some forecasting methods are more accurate for certain hours of the day than others. They also help to explain why simple forecast combination schemes tend to outperform the full battery of models considered in the comprehensive comparative study.

With the information contained in the variable importance scores and the results of the extensive model comparison, this study essentially provides guidelines for variable and model selection in future electricity market research.

Whilst criticisms of current research methodology and method when assessing the performance of a building can be suggested, no practical or achievable alternatives have been found. It suggests that existing approaches fail to consider the often‐irrational perceptions of humans who inhabit the buildings. This paper proposes a change in both methodology and method when assessing building performance.

The method developed for this research project involved a four‐stage process which was designed to achieve consistency with the methodology. Fundamental to the methodological approach taken and developed in this research is the concept of beta press. The variables used for analysis must be those chosen by participants in the building and not the alpha press variables of non‐participant observers. The method chosen for collection of the participant variables was focus group meetings incorporating a modified Delphi selection exercise. A survey sought information on the occupier's personal working space.

There are a number of variables that are performing well in terms of matching occupiers' rating of importance and their satisfaction with the subject building. There are also a number of variables where perceived performance exceeds importance. Only where there is a significant difference in the distribution is there an issue to be identified.

Further research could be to develop this methodological approach to investigate the possibility of correlating the results with investment performance of buildings. This may seek to establish relationships between yields, rent levels and occupancy rates with the perception mapping of the occupants. In the short‐term research of this nature may be used by organisations to better understand the behaviour of their workforce in the work environment. It can isolate issues or clusters of issues that can be addressed and immediate results can be considered.

This research has provided a justification and rationale for an alternative methodological approach to the evaluation of building performance. Previous research in this field has been largely positivist and has not considered a behavioural dimension.

Although significant advances have been made in customer service research, a majority of this research has concentrated on defining and measuring the importance of customer service in isolation from the other components of the marketing mix. In order to achieve a competitive advantage from customer service, it is necessary to establish service levels as part of the firm′s overall marketing strategy. This monograph reviews the development of customer service; evaluates past customer service research; presents a methodology for integrating customer service and marketing strategy, and provides some suggestions for future research.

This study empirically demonstrates a contradiction between pillar 3 of Basel norms III and the designation of Systemically Important Banks (SIBs), also known as Too Big to Fail (TBTF). The objective of this study is threefold, which has been approached in a phased manner. The first is to determine the systemic importance of the banks under study; second, to examine if market discipline exists at different levels of systemic importance of banks and lastly, to examine if the strength of market discipline varies at different levels of systemic importance.

This study is based on all the public and private sector banks operating in the Indian banking sector. The Gaussian Mixture Model algorithm has been utilized to classify banks into distinct levels of systemic importance. Thereafter, market discipline has been observed by analyzing depositors' sentiments toward banks' risk (CAMEL indicators). The analysis has been performed by employing the system Generalized Method of Moments (GMM) to estimate models with different dependent variables.

The findings affirm the existence of market discipline across all levels of systemic importance. However, the strength of market discipline varies with the systemic importance of the banks, with weak market discipline being a negative externality of the SIBs designation.

By employing the Gaussian Mixture Model algorithm to develop a framework for categorizing banks on the basis of their systemic importance, this study is the first to go beyond the conventional method as outlined by the Reserve Bank of India (RBI).

The purpose of this paper is to develop a method to evaluate the quality of service delivery process designs, based on how closely they meet process requirements of key stakeholders while taking variability into account.

A Monte Carlo computer simulation of the flowchart of the service delivery process is used to capture the effects of multiple types of variabilities and parametric uncertainties on process variables of interest, and the Taguchi quality loss framework is applied to estimate overall process quality. As an example, a proposed modification to a patient‐treatment process in a hospital emergency department is evaluated.

This paper demonstrated a method that service managers can use to evaluate the quality of service delivery process designs.

This method can evaluate modifications to various aspects of a service delivery process, and can assist managers to fail‐safe the entire process. Furthermore, it characterises process quality using a range of probable values instead of a single value, such as the process mean, and thus provides a more realistic representation of quality.

This paper contributes to service engineering in two ways: by adapting the Taguchi quality loss approach, commonly used to assess manufacturing process quality, to a service delivery process; and by incorporating uncertainty, due to imprecise knowledge of process parameters, into the quality assessment.

A focus on product aspects that are most important to target consumers helps communicating product advantage to consumers, both in product design and in marketing. In previous studies, relations between the importance of functional and expressive product aspects and gender, age and social class have been indicated. However, knowledge of the relation of demographic variables to more specific product aspects is more informative for product development and marketing purposes. The purpose of this paper is to provide an insight into the relative importance of product aspects for different consumer groups as important information for product development and marketing. This study seeks to identify relations between gender, age, education and income, and the importance of aesthetic aspects, symbolic aspects, functionalities, ease of use and quality in buying a product.

Consumers (n=247) covering a range in age, gender, education and income, indicated the importance of several product aspects in the purchase decision for ten differentiated product categories. Next to relations between demographic variables and product aspect importances, the influence of social significance of the product category on these relations was assessed.

A number of significant relations between age, gender, education and income and the importance of aesthetic and symbolic aspects, functionalities, ease of use and quality in making a purchase decision have been found.

Relationships of several demographic variables with more specific product aspects – namely aesthetic, symbolic, functionalities, ease of use and quality – have been identified. Knowledge of these relations will help companies to better adjust their products and marketing efforts to fit consumer preferences.

Businesses need to make quick decisions and adjustments to fulfill the growing online demand. Previous studies examined various factors affecting the online sales performance of products such as books, electronics and movies; however, they paid limited attention toward the local brand clothing products. The current study investigates the importance of different kinds of seller-generated and consumer-generated signals such as price, discount, product ratings, review volume, review sentiment, number of questions and interaction between some of these factors for predicting the sales performance of clothing products.

The multiple linear regressions has been employed to investigate the influence of various predictor variables on sales performance. The study also examines the importance of these predictor variables by using different machine learning models, including random forest (RF), neural networks and support vector regression (SVR).

The findings of the study emphasize the importance of price and discount rates offered on the product. The quantitative characteristics of reviews, such as review volume and average rating, have been found to be more important predictors than sentiment strengths. However, the sentiment strength of reviews with higher helpfulness scores plays a significant role in predicting sales performance.

The study highlights the varying importance of seller-based and consumer-based signals in predicting sales performance. It also investigates the interaction effect of these two kinds of signals. The consumer-generated signals have been further divided into two components based on social influence theory, and the interaction effects of these components have also been examined.

Debates in the literature on entrepreneurship concentrate on whether the focus should be on psychological variables or the external environment. Despite most studies being on the former, many others argue that the external environment is more useful in understanding business start‐ups. This paper seeks to examine the relative influence of both types of variables.

Data were collected from 337 Chinese respondents in three different groups: first, people who do not want to start a business; second, people planning to set up a business; and finally, entrepreneurs who had started successful businesses. Respondents were assessed on three psychological/behavior variables (achievement striving, social networking/Guanxi, and optimism), and one external environment variable (perceived importance of a favorable business environment).

Group comparisons revealed that psychological characteristics as well as the business environment were both significant predictors. Psychological characteristics were a stronger influence for potential entrepreneurs and the business environment stronger for successful entrepreneurs.

The results help clarify debates regarding the relative importance of personal characteristics and the business environment to entrepreneurial motivation as these variables explained 54 percent of the variance for motivation. Although only one ethnic group (the Chinese) was studied, the results for the personality variables largely matched those in the Western literature, which suggests generalizability of the findings. Also, the environmental influence measure could be valuable in future research.

Both psychological characteristics and an environmental measure are evaluated to compare their influences on entrepreneurial motivation.

Customer satisfaction is perceived as being a key driver of long‐term relationships between suppliers and buyers, especially when customers are well acquainted with products and markets, and when industries are highly competitive, as is the case for Norwegian fish exporters. The purpose of this paper is to identify the most influential drivers (antecedents) of customer satisfaction.

The data source is a market survey. Items measuring customer satisfaction as well as importance and performance (satisfaction) of drivers of customer satisfaction have been included in a questionnaire answered by 128 customers from approximately 25 countries, and analysed by use of multiple regression, factor analyses and importance‐performance grids.

Managers of the exporting companies should provide quality products and focus on service quality aspects in order to attain a high level of customer satisfaction. Prices have not been identified as satisfiers; however, according to the item‐based importance‐performance grid, competitive prices are important and can perhaps be perceived as “hygienes”, focusing more on what is important for customer loyalty.

The research should be expanded to other business‐to‐business contexts (industries).

The study gives practical insight regarding drivers of customer satisfaction so that key success factors for the future can be identified.

Few studies focus both on importance and performance (explicit and implicit importance) of drivers of customer satisfaction. By allocating resources in a cost‐effective way to activities that are key drivers for customer satisfaction, managers may attain increased customer satisfaction, increased customer loyalty and increased customer profitability in the future.