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Purpose – Partial least squares (PLS) path modeling has become a pivotal empirical research method in international marketing. Owing to group comparisons' important role in research on international marketing, we provide researchers with recommendations on how to conduct multigroup analyses in PLS path modeling.

Methodology/approach – We review available multigroup analysis methods in PLS path modeling and introduce a novel confidence set approach. A characterization of each method's strengths and limitations and a comparison of their outcomes by means of an empirical example extend the existing knowledge of multigroup analysis methods. Moreover, we provide an omnibus test of group differences (OTG), which allows testing the differences across more than two groups.

Findings – The empirical comparison results suggest that Keil et al.'s (2000) parametric approach can generally be considered more liberal in terms of rendering a certain difference significant. Conversely, the novel confidence set approach and Henseler's (2007) approach are more conservative.

Originality/value of paper – This study is the first to deliver an in-depth analysis and a comparison of the available procedures with which to statistically assess differences between group-specific parameters in PLS path modeling. Moreover, we offer two important methodological extensions of existing research (i.e., the confidence set approach and OTG). This contribution is particularly valuable for international marketing researchers, as it offers recommendations regarding empirical applications and paves the way for future research studies aimed at comparing the approaches' properties on the basis of simulated data.

Bootstrapping is a practitioner-based term adopted in entrepreneurship to describe the techniques employed in micro, small and medium-sized enterprises (MSMEs) to minimise the need for external funding by securing resources at little or no cost and applying strategies to effectively use resources. Working capital management (WCM) is a term used in financial management to define a set of practices used to manage business resources, including cash management. This paper explores the overlap and divergence between these two disciplinary distinct concepts.

A dual methodology is employed. First, the usage of the two terms in prior literature is analysed and synthesised. Second, the study uses factor analysis to explore how bootstrapping practices described by owners of 167 established MSMEs relate to the components of WCM in financial management.

The factor analysis identifies two main bootstrapping practices employed by MSMEs: (1) delaying payments and owner-related bootstrapping and (2) customer-related bootstrapping. Delaying payments is an integral practice in trade payables management and customer-related bootstrapping includes practices that are integral to trade receivables management. Therefore, links between bootstrapping practices and WCM practices are firmly established.

The study is not without limitations. Based on cross-sectional evidence for established firms in Ireland only, future studies could explore cross-country longitudinal panel data to fully examine life cycle and sectoral effects, as well as other external shocks (for example, COVID-19) on bootstrapping and WCM practices. This study does not explain why some factors (for example, joint utilisation and inventory management) are present in some bootstrapping studies and not in others; further case study research might help explain this. Finally, changes in the business environment facing start-ups and established enterprise, including increased digitalisation, online trading, self-employment, remote hub working and sustainability, offer new avenues for bootstrapping research.

This is the first study to comprehensively explore the conceptual and empirical links between bootstrapping and WCM. This study will enable researchers and practitioners in these two distinct disciplines to learn from each other. Accounting researchers and practitioners can broaden their understanding of how WCM “works” in MSME settings. Similarly, entrepreneurship researchers and practitioners can deepen their understanding of how bootstrapping can be adopted by businesses to manage resources effectively.

The purpose of this paper is to investigate relationships between financial condition and the use of bootstrapping in small firms.

A total of 901 manufacturing firms were selected from a comprehensive financial database containing income statement and balance sheet records. Surveys were sent to each of these firms and 186 firms returned usable surveys. Survey responses on bootstrapping techniques were compared with financial data for these 186 firms. Both the initial financial status and the financial status of these firms two years later were analyzed.

The results indicate that highly levered, illiquid, and underperforming firms were more likely to use certain bootstrapping methods than other firms, and that the methods they used may have been detrimental to future firm performance.

The findings support a resource dependence model and raise questions as to why financially constrained firms chose to use particular bootstrapping methods. Generalizability of the study is limited due to the homogeneous sample of manufacturing firms and due to the sample and recall biases associated with surveys.

Small business owners should understand the options available for bootstrapping as well as the importance of proactive as opposed to reactive financial strategy.

This is one of the first empirical studies that investigates the relationship between financial condition and bootstrapping, and it provides insight into how and when small firms bootstrap as well as some potential implications of bootstrapping on firm outcomes.

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 current paper's goal is to examine the productivity of the closed banking sector evidenced from Ethiopia. In addition, the inclusion of intangibles on productivity was examined in the current paper.

First, the standard Malmquist Productivity Index (MPI) was employed for 13 commercial banks for both stages. Second, by excluding the state-owned commercial bank, the analysis employed a bootstrapped MPI for the robust and comprehensive conclusion. Furthermore, from 2010 to 2019, the fixed effect Ordinary Least Square (OLS) regression with balanced panel data was used.

The standard MPI in both stages shows that the productivity of Ethiopian commercial banks is declining. The technological shock was the main reason for the loss. The catch-up in both stages scored above unity, mainly due to the pure efficiency change. Besides, when combined with tangible resources, the inclusion of resource-based view (RBV) proxy variables reduces technological shock regress and ultimately improves productivity change. The bootstrapped MPI also reveals that technological shock is the primary source of the productivity decline. However, efficiency change also contributes to the productivity decline based on this estimation.

Future research could examine the more extensive productivity analysis by considering the primary sources of data collections for resource-based variables.

According to the study's results, banking regulatory authorities and bank management, including the shareholders, should continue to invest in cutting-edge technology to improve the productivity of the banking sector.

This is the first comprehensive study of productivity for Ethiopian commercial banks based on the standard MPI, bootstrapped MPI, and OLS by incorporating all resources into the analysis.

The paper's aim is to assess performance of firms in the UK retail sector.

Economic efficiencies of 41 retail companies working in the UK between 2000 and 2005 are examined in this study using three related methodologies: data envelopment analysis (DEA), Malmquist productivity index (MPI), a bootstrapped Tobit regression model. DEA is used to calculate technical and scale efficiencies of companies. Two outputs (turnover, profit before taxation) and three inputs (total assets, shareholders funds, and number of employees) are employed for the efficiency measurement. MPI is used to analyze the patterns of efficiency change over the six year period 2000‐2005. DEA efficiencies are then used to test important hypotheses on the impact of environmental variables, namely head office location, type of ownership, years of incorporation, legal form and retail characteristic, on the functioning of the UK retail sector using bootstrapped Tobit regression.

DEA analysis has shown that only ten retail companies are considered as efficient under CRS assumption, and 16 firms under VRS assumption in 2005. MPI results have indicated that about 50 percent of retail companies have registered progress in terms of MPI during 2000 and 2005. Twenty out of 41 retail companies have adopted advanced and efficient retailing technologies during this period. Three environmental variables, namely the type of ownership, legal form and retail characteristic, have been found to play significant roles influencing retail efficiency using bootstrapped Tobit regression.

Data availability has limited the level of analysis in some parts of this study, especially in the bootstrapped Tobit regression.

This study seems to be the first in applying productivity analysis using DEA for the UK retail sector.

The Indian leather industry contributes to economic growth at a significant environmental cost. Due to the rising global demand for sustainable leather products, promoting efficient input utilisation has become vital. This study measures input efficiency and its determinants for leather industry in order for it to improve its future performance.

In the first stage, bootstrap data envelopment analysis (DEA) approach is used for measuring efficiency and analysing firms' differences based on their geographical location, organisational structures, urban-rural location and sub-industrial groups. A second stage regression examines efficiency determinants using size, age, skill and capital-labour intensity as the explanatory variables.

Efficiency result shows a significant potential of minimising inputs by 47% provided the firms adopt best practices. West Bengal firms, urban located firms, individual and proprietorship owned firms and leather consumer goods firms are found to be relatively efficient to their counterparts. Size, skilled managerial staff and labour-intensive firms positively affect efficiency.

Construction of well-connected roads for accessing urban retail markets and provision of reliable electricity would improve efficiency of rural firms. Small-scale enterprises have a larger share in Indian leather industry; therefore, policy should focus on enhancing the firms' scale and investing in training facilities to skill employed labour for ensuring optimal use of inputs.

Previous studies on the leather industry have used the conventional DEA efficiency measurement approach. This study uses DEA bootstrapping model for robust efficiency estimates and provides consistent inferences about the determinants.

The main purpose of this paper is to determine the bias-corrected efficiencies and rankings of the selected hotels and restaurants (H&Rs) in India.

The data for the Indian H&R sector are collected from the Prowess database. The bootstrap data envelopment analysis (DEA) based on a constant return to scale (CRS), variable return to scale-input oriented (VRS-IP) and variable return to scale-output oriented (VRS-OP) are applied on H&Rs to obtain the bias-corrected efficiencies.

It is found that relative efficiencies using basic DEA methods of all the 45 H&Rs of India are overestimated. These efficiencies are corrected using bias correction through bootstrap DEA methods. The bounds for the efficiencies of each H&R are computed using all the adopted methods. All H&Rs are ranked using bias-corrected efficiencies, and the linear trend between ranks suggests that the H&Rs are ranked almost similarly by all the adopted methods.

To improve efficiency, Indian H&R companies must rethink their personnel needs by enhancing their workforce management capabilities. The government needs to extend more support to this sector by introducing a liberal legislation framework and supporting infrastructure policies.

There is a paucity of studies on H&Rs in India. The current study focused on measuring bias-corrected efficiencies of the selected H&Rs of India. This study is one of the few initiatives to explore bias-corrected efficiencies extensively using the bootstrap DEA method.

In order to determine the status quo of PLS path modeling in international marketing research, we conducted an exhaustive literature review. An evaluation of double-blind reviewed journals through important academic publishing databases (e.g., ABI/Inform, Elsevier ScienceDirect, Emerald Insight, Google Scholar, PsycINFO, Swetswise) revealed that more than 30 academic articles in the domain of international marketing (in a broad sense) used PLS path modeling as means of statistical analysis. We assessed what the main motivation for the use of PLS was in respect of each article. Moreover, we checked for applications of PLS in combination with one or more additional methods, and whether the main reason for conducting any additional method(s) was mentioned.

The purpose of this paper is to develop several predictive models for estimating the structural construction cost and establish range estimation for the structural construction cost using design information available in early stages of residential building projects.

Information about residential building projects is collected based on project documents from construction companies with regard to the design parameters and the actual structural construction costs at completion. Storey enclosure method (SEM) is fundamental for determining the building design parameters, forming the potential variables and developing the cost estimation models using regression analysis. Nonparametric bootstrap method is used to establish range estimation for the structural construction cost.

A model which is developed from an integration of advanced SEM, principle component analysis and regression analysis is robust in terms of predictability. In terms of range estimation, cumulative probability-based range estimates and confidence intervals are established. While cumulative probability-based range estimates provide information about the level of uncertainty included in the estimate, confidence intervals provide information about the variability of the estimate. Such information could be very crucial for management decisions in early stages of residential building projects.

This study could provide practitioners with a better understanding of the uncertainty and variability included in the cost estimate. Hence, they could make effective improvements on cost-related management approaches to enhance project cost performance.