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The aim of this work is to implement bootstrapping methods into software tools, based on Java.

This paper presents a category of software e‐tools aimed at simulating laboratory works and experiments.

Both students and teaching staff use traditional statistical methods to infer the truth from sample data gathered in laboratory experiments. However, the repeated laboratory experiments mean the consumption of a great deal of substances and reactants. At the same time, there are some ethically motivated reasons to reduce the number of animals used in experimentation. Using a bootstrapping tool and computer power, the experimenter can repeat the original experiment on computer, obtaining pseudo‐data as plausible as those obtained from the original experiment.

Provides data on implementing bootstrapping methods into software e‐tools, simulating laboratory experiments in didactic and research activities.

Heavy-tailed distributions present a tough setting for inference. They are also common in industrial applications, particularly with internet transaction datasets, and machine learners often analyze such data without considering the biases and risks associated with the misuse of standard tools. This chapter outlines a procedure for inference about the mean of a (possibly conditional) heavy-tailed distribution that combines nonparametric analysis for the bulk of the support with Bayesian parametric modeling – motivated from extreme value theory – for the heavy tail. The procedure is fast and massively scalable. The work should find application in settings wherever correct inference is important and reward tails are heavy; we illustrate the framework in causal inference for A/B experiments involving hundreds of millions of users of eBay.com.

Prior research in tourism and hospitality domain calls for closer attention to model specification when using partial least square-structural equation modeling (PLS-SEM), including the choice of software and algorithm for PLS model estimation. This paper aims to illustrate the significance of choosing appropriate algorithms for testing the nature of relationships by comparing findings using two different PLS-SEM software packages.

Using a field experiment, relationships between visual servicescape aesthetics and affective responses are conceptualized based on literature in environmental psychology and marketing domains. With photographic surrogates as stimuli in two different hospitality service contexts – spa and upscale restaurant – data are collected from 350 respondents.

By comparing results of SmartPLS 3.2 and WarpPLS 5.0 software and theoretical understanding from environmental psychology literature, it is illustrated that the results and their interpretations may not be in line with theory if model specifications are not correctly implemented and are not addressed through usage of software with a relevant algorithm to test them.

The study highlights the implications for model specification issues such as type of variables and nature of relationships that tourism and hospitality researchers often face and also how use of appropriate algorithms can overcome limitations of model testing for complex models and provide empirical rigor to support theory.

本论文使用两种不同的PLS-SEM处理软件来测试理论模型。通过解析模型设定参数问题, 特别是通过结构关系本性分析, 本论文指出选择合适的软件测试模型在酒店旅游领域的PLS研究中是非常关键的。

本论文借助图像拍摄手段采用实验的采样方式, 在两个不同的酒店服务场所—按摩和高档餐厅—搜集350份数据。本论文采用Smart PLS 3.2 和Warp PLS 5.0 软件来测试PLS-SEM。 这两款软件支持线性和非线性理论关系的比较。

通过Smart PLS 3.2 和Warp PLS 5.0 软件得出的报告结果分析, 不同软件处理PLS得出的结果可能有偏差, 而且会不符合理论设定。如果模型设定参数不正确, 通过使用合适的PLS-SEM软件和相关的数据分析加以辅助, 可能会解决参数不正确的问题。

本论文的比较分析结果可能会帮助到酒店和旅游领域的研究者们, 在做对有关PLS-SEM软件选择的时候, 哪些软件可以更加合适的测试模型有着参考意义。

本论文重点指出了模型设定参数的相关问题, 比如旅游酒店领域常见的变量种类和关系属性等。本论文还研究了如何选择合适的数据分析方法来克服测试复杂模型时的限制, 并且提供实践结果来支撑理论。

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.

While crop insurance ratemaking has been studied for many decades, it is still faced with many challenges. Crop insurance premium rates (PRs) are traditionally determined only by point estimation, and this approach may lead to uncertainty because it is sensitive to the underwriter’s assumptions regarding the trend, yield distribution, and other issues such as data scarcity and credibility. Thus, the purpose of this paper is to obtain the interval estimate for the PR, which can provide additional information about the accuracy of the point estimate.

A bootstrap method based on the loss cost ratio ratemaking approach is proposed. Using Monte Carlo experiments, the performance of this method is tested against several popular methods. To measure the efficiency of the confidence interval (CI) estimators, the actual coverage probabilities and the average widths of these intervals are calculated.

The proposed method is shown to be as efficient as the non-parametric kernel method, and has the features of flexibility and robustness, and can provide insight for underwriters regarding uncertainty based on the width of the CI.

Comprehensive comparisons are conducted to show the advantage and the efficiency of the proposed method. In addition, a significant empirical example is given to show how to use the CIs to support ratemaking.

The purpose of this paper is to assess the performance of Greek fossil fuel‐fired power stations employing a data envelopment analysis (DEA) model combined with bootstrapping.

DEA is used to derive aggregate performance indicators using data on inputs and desirable and undesirable outputs for a sample of fossil fuel‐fired power stations. The statistical significance of the derived aggregate performance indicators is assessed via the bootstrapping approach.

The results suggest that the power stations in the sample are considerably more inefficient than revealed by the initial point estimates of inefficiency. Moreover, the non‐lignite‐fired stations of the sample are on an average more efficient than the lignite‐fired stations.

DEA represents a useful framework for exploring the current state to derive aggregate performance indicators of power stations, and moreover, the statistical properties of these metrics can be assessed via the bootstrapping approach.

The bootstrapping approach in DEA shows its superiority over DEA models that do not address the uncertainty surrounding point estimates. The DEA bootstrapping model used in this study to model environmental performance in the power station electricity production setting provides bias correction and confidence intervals for the point estimates and it is therefore more preferable.

The derivation of aggregate performance indicators of Greek fossil fuel‐fired power stations is an important addition to the existing literature on energy economics. The paper is also innovated in providing the statistical properties of the derived performance metrics.

The purpose of this study was to examine the extent to which organisational capabilities do impact the performance of small- and medium-sized enterprises (SMEs) in Ghana.

A cross-sectional survey design was used for the study. Data was collected from 306 SMEs from different sectors of the economy. The partial least square structural equation modelling was used to analyse the relationships between organisational capabilities and SMEs’ performance measured by their financial viability.

The findings reveal as predicted that four out of the five organisational capabilities tested were indeed important predictors of SMEs’ financial viability. Specifically, managerial capability, supply chain capability, operations capability and marketing capability were found to positively and significantly impact SMEs’ financial viability. The findings further reveal that firm size does not moderate the relationship between these capabilities and financial viability.

This study was undertaken in a developing economy with peculiar business operating conditions and, thus, may limit the generalisability of the findings.

The findings suggest that key organisational capability development is critical for enhancing the financial viability of firms, confirming four of such critical capabilities that are needed by SMEs. The findings further suggest the need for firms irrespective of size to develop organisational capabilities.

This study has empirically established that developing managerial capability, supply chain capability, operations capability and marketing capability are important success factors if SMEs, irrespective of size, intend to enhance their financial viability.

The purpose of this paper is to provide preliminary efficiency assessment of Arab microfinance institutions (MFIs) within the period 2002–2012. Microfinance is defined as the provision of financial services to poor and low-income households and their microenterprises on a sustainable basis.

The authors first present the main features of microfinance in the Middle East and North Africa (MENA) region. Second, based on a simple of 72 microfinance institutions issued from ten countries of the region, they develop a bootstrap–data envelopment analysis (bootstrap–DEA) framework to measure Arab MFIs’ efficiency. Finally, they apply parametric and non-parametric tests to compare the performance and identify factors that contribute to the efficiency of Arab Islamic microfinance institutions.

Efficiency scores of the MENA region exhibit high variability, both across time and countries. Significant difference in efficiency was found due to MFI age or regulation. Results also reveal the ability of Arab MFIs to combine social and financial performance and their solidity in time of crisis.

In this paper, the authors apply DEA–bootstrap method on a large sample of Arab MFI with special look at the peer group differences. Unlike most previous relevant studies, the paper overcomes many of the drawbacks of the DEA method by using, in addition to the DEA–bootstrap approach, a test of return to scale and a combination of three procedures to detect outliers. Furthermore, this paper analyses the efficiency of MFI in the MENA region in the light of financial crises and Arab Spring.

The purpose of the study is to measure the mediating relationship of online buying intentions between narcissism, materialism and brand experience and their effect on luxury purchase behaviour of luxury items by Phuket, Thailand customers. The study has used primary data collection techniques to gather data and used purposive sampling to analyse the data. The study's sample size is 250 respondents who prefer to buy luxury items. The research has used Smart PLS software to measure the relationship through bootstrapping and algorithms. The study has found significant positive mediation of online buying intentions between narcissism, materialism and brand experience and their effect on luxury purchase behaviour of luxury items. This study suggests the theoretical and practical implications for purchasing luxury behaviour from managerial, policymakers and researched perspectives. This study also discusses the future directions and limitations.

This study aims to comprehend the ambidexterity and organizational learning capability construct in the Indian E-commerce industry context.

The survey method was adopted for this study. A survey was circulated among the personnel working in E-commerce companies in India. The focus was on people working in managerial positions and had at least three years of experience in the same industry.

This paper investigates the link between two dimensions of ambidexterity, i.e., exploration, exploitation and learning capability in firm performance. The paper also establishes the moderating effect of the learning capability on the two dimensions of ambidexterity and firm’s performance.

Our focus was to cover most of the E-commerce companies, yet to generalize the research the analysis needs to be conducted with even more E-commerce companies. Although we took extraordinary care to gather data from multiple resources and discarded the data that was incomplete or was from lower level employees yet, we need a larger sample to establish the causal claim of our model.

We reason that learning capability of a firm impacts the two dimensions and firms should focus both on external and internal knowledge to benefit from the ambidexterity efforts.

Learning capability influences a firm’s performance and has managerial implications. The analysis’ results on the India based ecommerce companies differs from prior research done in more developed countries and other industries.

No prior research has been done from this perspective in the Indian context, and thus our work opens up new avenues for researchers to look at.Keywords Ambidexterity, Firm performance, Learning capability