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The purpose of this paper is to evaluate the functioning of the main Spanish and Portuguese containers ports to observe if they are operating below their production capabilities.

To achieve the above-mentioned objective, one possible method is to calculate the data envelopment analysis (DEA) efficiency, and the scale efficiency (SE) of targets, and in order to consider the variability across different samples, a bootstrap scheme has been applied.

The results showed that the DEA bootstrap-based approach can not only select a suitable unit which accords with a port’s actual input capabilities, but also provides a more accurate result. The bootstrapped results indicate that all ports do not need to develop future investments to expand port infrastructure.

The proposed DEA bootstrap-based approach provides useful implications in the robust measurement of port efficiency considering different samples. The study proves the usefulness of this approach as a decision-making tool in port efficiency.

This study is one of the first studies to apply bootstrap to measure port efficiency under the background of the Spain and Portugal case. In the first stage, two models of DEA have been used to obtain the pure technical, and the technical and SE, and both the input-oriented options: constant return scale and variable return scale. In the second stage, the bootstrap method has been applied in order to determine efficiency rankings of Iberian Peninsula container ports taking into consideration different samples. Confidence interval estimates of efficiency for each port are reported. This paper provides useful insights into the application of a DEA bootstrap-based approach as a modeling tool to aid decision making in measuring port efficiency.

The purpose of this article is to present an empirical analysis of complex sample data with regard to the biasing effect of non‐independence of observations on standard error parameter estimates. Using field data structured in the form of repeated measurements it is to be shown, in a two‐factor confirmatory factor analysis model, how the bias in SE can be derived when the non‐independence is ignored.

Three estimation procedures are compared: normal asymptotic theory (maximum likelihood); non‐parametric standard error estimation (naïve bootstrap); and sandwich (robust covariance matrix) estimation (pseudo‐maximum likelihood).

The study reveals that, when using either normal asymptotic theory or non‐parametric standard error estimation, the SE bias produced by the non‐independence of observations can be noteworthy.

Considering the methodological constraints in employing field data, the three analyses examined must be interpreted independently and as a result taxonomic generalisations are limited. However, the study still provides “case study” evidence suggesting the existence of the relationship between non‐independence of observations and standard error bias estimates.

Given the increasing popularity of structural equation models in the social sciences and in particular in the marketing discipline, the paper provides a theoretical and practical insight into how to treat repeated measures and clustered data in general, adding to previous methodological research. Some conclusions and suggestions for researchers who make use of partial least squares modelling are also drawn.

To solve the problem of simulation evaluation with small samples, a fresh approach of grey estimation is presented based on classical statistical theory and grey system theory. The purpose of this paper is to make full use of the difference of data distribution and avoid the marginal data being ignored.

Based upon the grey distribution characteristics of small sample data, the definition about a new concept of grey relational similarity measure comes into being. At the same time, the concept of sample weight is proposed according to the grey relational similarity measure. Based on the new definition of grey weight, the grey point estimation and grey confidence interval are studied. Then the improved Bootstrap resampling is designed by uniform distribution and randomness as an important supplement of the grey estimation. In addition, the accuracy of grey bilateral and unilateral confidence intervals is introduced by using the new grey relational similarity measure approach.

The new small sample evaluation method can realize the effective expansion and enrichment of data and avoid the excessive concentration of data. This method is an organic fusion of grey estimation and improved Bootstrap method. Several examples are used to demonstrate the feasibility and validity of the proposed methods to illustrate the credibility of some simulation data, which has no need to know the probability distribution of small samples.

This research has completed the combination of grey estimation and improved Bootstrap, which makes more reasonable use of the value of different data than the unimproved method.

The primary objective of this study was to recognize critical indicators in predicting blood glucose (BG) through data-driven methods and to compare the prediction performance of four tree-based ensemble models, i.e. bagging with tree regressors (bagging-decision tree [Bagging-DT]), AdaBoost with tree regressors (Adaboost-DT), random forest (RF) and gradient boosting decision tree (GBDT).

This study proposed a majority voting feature selection method by combining lasso regression with the Akaike information criterion (AIC) (LR-AIC), lasso regression with the Bayesian information criterion (BIC) (LR-BIC) and RF to select indicators with excellent predictive performance from initial 38 indicators in 5,642 samples. The selected features were deployed to build the tree-based ensemble models. The 10-fold cross-validation (CV) method was used to evaluate the performance of each ensemble model.

The results of feature selection indicated that age, corpuscular hemoglobin concentration (CHC), red blood cell volume distribution width (RBCVDW), red blood cell volume and leucocyte count are five most important clinical/physical indicators in BG prediction. Furthermore, this study also found that the GBDT ensemble model combined with the proposed majority voting feature selection method is better than other three models with respect to prediction performance and stability.

This study proposed a novel BG prediction framework for better predictive analytics in health care.

This study incorporated medical background and machine learning technology to reduce diabetes morbidity and formulate precise medical schemes.

The majority voting feature selection method combined with the GBDT ensemble model provides an effective decision-making tool for predicting BG and detecting diabetes risk in advance.

Under extensive pressure from normal market competition, frequent technological change and extreme exogenous shock, firms are facing severe challenge nowadays. How to withstand discontinuous crises and respond to normal risks through improving resilience (RE) is an important question worth researching. Thus, drawing on the strategic entrepreneurship theory, the purpose of this study is exploring the relationship between entrepreneurial orientation (EO) and RE, and combining digitization to discuss the role of digital business capability (DBC), digital business model innovation (DBMI) and environmental hostility (EH).

Based on survey data from 203 Chinese firms, using the methods of linear regression and bootstrap to test our hypothesis. Furthermore, fuzzy-set qualitative comparative analysis (FsQCA) is used to identify previously unknown combinations which lead to strong/weak RE in digital context.

First, EO positively influenced DBC and RE. Second, DBMI promoted RE, DBC and DBMI served as sequential mediators that linked EO and RE. Third, EH positively moderated the effects of EO on RE. Further the study revealed that different configuration of DBMI and dimensions of EO and DBC can explain RE.

The study explains mechanism of RE from perspective of digitization. The conclusion is good for further consolidating strategic entrepreneurship theory, and providing a new frame for firms to build the ability of antifragile.

The purpose of this paper is to explore the relationships between managerial control strategies, role-stress and employee adaptiveness among call centre employees.

Based on a conceptual model, a questionnaire-based survey methodology is adopted. Data were collected from call centre employees in India and the data were analysed through PLS methodology.

The study finds that outcome control and activity control increase role-stress while capability control does not have a significant impact. The interaction between outcome control and activity control also tends to impact role-stress of employees. Role-stress felt by employees has significant negative impact on employee adaptiveness.

The sampling approach was convenience based affecting the generalisability of the results.

The paper provides guidelines for utilising managerial control approaches in a service setting.

The paper looks at managerial control approaches in a service setting – a topic not quite researched before.

The purpose of this paper is to develop a more robust methodology for asset allocation for the property investment market which takes into account inherent valuation and data issues.

The methodology applied is that of a bootstrap, borrowed from Carlstein, and is applied to an investment universe consisting of UK equities, gilts and property. The bootstrap selectively re‐samples the return time series by maintaining the economic cycle. The resulting return series is then used in the standard mean‐variance optimisation (MVO) on an unconstrained basis. Finally, a “sanity” test is applied on the correlation matrix to ensure that spurious instances do not skew the results.

The bootstrapped optimisation provides a range within which the portfolio weights can be manoeuvred instead of a static point under the standard MVO. It provides a more robust methodology for asset allocation and without giving any undue significance to one year of extreme result.

The current analysis is based on unconstrained portfolio optimisation, with a very limited investment universe. Additionally, by conforming with the MVO methodology, normality of asset returns is implicitly assumed, which is clearly not the case in the data used. Future work will also focus on an all‐property portfolio.

The proposed methodology will prove to be useful for making asset allocation decisions, particularly in turbulent financial markets.

The paper focuses solely on bootstrapping with the IPD UK annual index and is particularly significant after one year of extremely poor performance of UK property. The results will be of use to fund managers and portfolio analysts.

The study aims to examine the relationship among economic policy uncertainty (EPU), geopolitical-risks (GPR), the interaction (EPGR) of EPU and GPR and the returns of gold, silver, platinum, palladium and rhodium using monthly data from January (1997) to May (2021).

The paper employs the Markov-switching and the novel Shi et al. (2020) bootstrap time-varying Granger-causality approach.

Though the Markov-switching shows variation in the responses of precious metals to EPU, GPR and EPGR across low and high states, the paper observes the safe-haven potential of the precious metals in the high regime while the hedging potency is also evident in the results. To further substantiate the safe-haven and hedging properties, the time-varying Granger-causality shows the causal effect of EPU on all the selected precious metal returns coinciding with global events. While the authors show that GPR Granger causes platinum, palladium and rhodium consistently under the rolling/recursive-evolving tests, the authors cannot find the causal effect of GPR on gold and silver returns across the algorithms. The paper also observes persistence in the causal effect of EPGR on palladium and platinum across all the algorithms, while gold and rhodium only show consistency in the responses under the rolling- and recursive-evolving algorithms given the conditions of homoscedasticity and heteroscedasticity.

The authors' results are essential to investors and policymakers since both typically leverage the hedging and safe-haven characteristics of precious metals to obviate downside risks during highly uncertain periods.

The authors' techniques allow examining the hedging and safe-haven properties of precious metals across regimes and date-stamp critical periods of causation inherent in the relationship.

This study aims to examine influence of transformational and transactional leadership styles on employees’ organisational citizenship behaviours (OCBs), as well as the mediating role of job involvement in the Ghanaian hospitality industry.

Data were gathered from 258 employees in some selected hotels and restaurants in the Greater Accra Region of Ghana through a survey and analysed using covariance-based structural equation modelling.

The results indicated that both leadership styles influenced employees’ OCBs positively. Furthermore, job involvement positively influenced OCB and mediated between transformational leadership and OCB but not between transactional leadership and employees’ OCBs.

The study reaffirms the importance of employees’ OCBs and recommends that hotels and restaurants must encourage their supervisors to exhibit more transformational leadership behaviours (motivational, inspirational and visionary behaviours), as well as a combination of transformational and transactional leadership behaviours which can influence their employees to go beyond formal requirements, and get more involved in their jobs to the benefit of the organisations.

This study reveals the extent to which internal motivations of employees, specifically their job involvement, causes their extra-role behaviours and influences the leaders–OCB relationships from a developing country perspective.

More companies embrace flexible work arrangements (FWA) as one of their employee retention strategies, yet its effectiveness is not consistent. Generally, past researchers use the social exchange theory to explain how FWA lowers turnover intention, while the rest adopts the border theory to justify why FWA can be ineffective. Here, the authors compare the competing theories for the first time to differentiate the theoretical reasoning of three forms of FWA (flex time, flex leave and homeworking). Two mediators (organisational commitment and work−family conflicts) are chosen to represent the mechanism of each theory.

The authors employ the latest wave of the International Social Survey Program (ISSP) Work Orientation Module from 2015. Based on nationally representative data from 35 nations and 17,604 participants, the authors employed simple mediation and parallel double-mediation models via bootstrapping procedures to investigate the theoretical reasoning behind each FWA.

The results indicate that organisational commitment and work−family conflicts as significant mediators in all models, supporting both theories. The authors first tested each mediator in separated models. In models concerning the social exchange theory, all FWA lead to increased organisational commitment before lowering turnover intention, implying the beneficial outcomes of FWA. However, findings also support the border theory's perspective where flex time and homeworking increase turnover intention through heightened work−family conflicts. The parallel double-mediation further suggests that all three FWA forms have their unique theoretical framework, impacting turnover intention differently.

Both the social exchange theory and border theory are well-developed theories but grounded on different theoretical reasoning. This is the first paper that compares both theoretical perspectives in the context of FWA. It offers a new perspective in explaining the inconclusive effectiveness of FWA and provides future researchers a more integrated interpretation and prediction of FWA's impact on turnover intention.