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The purpose of this paper is to show that intentional demand forecast bias can improve warehouse capacity planning and labour efficiency. It presents an empirical methodology to detect and implement forecast bias.

A forecast model integrates historical demand information and expert forecasts to support active bias management. A non-linear relationship between labour productivity and forecast bias is employed to optimise efficiency. The business analytic methods are illustrated by a case study in a consumer electronics warehouse, supplemented by a survey among 30 warehouses.

Results indicate that warehouse management systematically over-forecasts order sizes. The case study shows that optimal bias for picking and loading is 30-70 per cent with efficiency gains of 5-10 per cent, whereas the labour-intensive packing stage does not benefit from bias. The survey results confirm productivity effects of forecast bias.

Warehouse managers can apply the methodology in their own situation if they systematically register demand forecasts, actual order sizes and labour productivity per warehouse stage. Application is illustrated for a single warehouse, and studies for alternative product categories and labour processes are of interest.

Intentional forecast bias can lead to smoother workflows in warehouses and thus result in higher labour efficiency. Required data include historical data on demand forecasts, order sizes and labour productivity. Implementation depends on labour hiring strategies and cost structures.

Operational data support evidence-based warehouse labour management. The case study validates earlier conceptual studies based on artificial data.

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.

The aims of the paper are to evaluate the relevance of environmental factors (seasonality, size and quality) on hotels’ performance and benchmarks; to measure the bias in efficiency resulting from a failure to control for these sources of heterogeneity; and to propose some managerial policies to handle for environmental heterogeneity.

The sample is constituted by 2,705 hotels operating in Emilia-Romagna (Italy). The metafrontier approach is used to identify the different production processes and measure technical efficiency scores.

Different production processes exist among accommodation firms due to environmental features; not considering heterogeneity in technological sets produces high levels of bias in the efficiency measurement, albeit the ranking of hotels tends to be fairly consistent; the star rating is the primary source of efficiency bias followed by seasonality, while size has a minor impact.

Future research could be directed to analyse the relevance of environmental heterogeneity in other areas; study the dynamics; investigate agglomeration effects; and use other methodological tools.

The analysis proposes new managerial interventions: targeted strategies to different groups; creation of networks of enterprises, clustered mainly in respect to size for highly rated enterprises and seasonality for low-rated enterprises; and incentives to annual hotels and raise in the product quality.

This paper simultaneously considers several environmental factors affecting heterogeneity in hotel production processes; investigates the effect of heterogeneity on either the efficiency scores or the ranking of hotels; and focuses on micro, low-quality or seasonal hotels.

The study attempts to examine the bias-adjusted financial and operational efficiency estimates of microfinance institutions (MFIs) operating in the Eastern Europe and Central Asia (ECA) region during the financial year 2017–2018. In addition, the study also identifies the responsible factors determining the financial and operational performances of MFIs operating in the ECA region.

The study employs two-stage bootstrap data envelopment analysis (DEA). In the first stage, the authors incorporate the bootstrap procedure in the DEA framework as suggested by Simar and Wilson (2000) to estimate the bias-corrected efficiency scores of 67 sample MFIs. In order to identify the drivers of efficiency level, the study deploys the bootstrap truncated regression model following the Simar and Wilson (2007) guidelines in the second stage of analysis.

The authors note from the empirical results that MFIs operating in the ECA region are relatively more financially efficient (0.588) than socially efficient (0.496). However, none of the MFIs were found to be operating at best-practice frontier while considering the bias-adjusted efficiency estimates. Further, the results of second stage of analysis confirm that corporate governance, that is, board size has positive and statistically significant impact on MFIs’ performances. In addition, the bad credit quality deteriorates both financial revenue and operational efficiency. Moreover, the MFIs’ size, profit status and debt-to-equity ratio were also found to be statistically significant to determine the operational and financial efficiency of MFIs in the ECA region.

The study provides the robust efficiency estimates and factors responsible to determine the financial and operational efficiency of MFIs operating in the ECA region. Further, the empirical results of the study provide the inputs and further direction to the policymakers, regulators, practitioners and managers in framing the policy and optimal operating strategies for ECA MFIs industry.

The study extends the DEA analysis by incorporating the bootstrap procedure in DEA model to estimate the bias-adjusted efficiency scores which are more reliable and robust. In addition, bootstrap truncated regression has been applied to identify the drivers of efficiency. Moreover, in the literature there is no single study which has deployed the double bootstrap DEA framework to examine the financial and operational efficiency estimates and its drivers.

The purpose of this paper is to conduct a behavioral analysis, through overconfidence, in order to understand how this cognitive bias could affect risk taking and inefficiency in Islamic and conventional banks operating in the MENA region.

To achieve the objective, the authors considered two overconfidence proxies, namely loan growth rate and net interest margin. Using the generalized method of moments method regressions for panel data, the authors found that the two overconfidence proxies have an effect on the risk exposure and consequently on the efficiency level of Islamic and conventional banks.

In general, overconfidence bias causes excessive risk taking and the degradation of the cost efficiency level. Moreover, these effects emerge with a delay of three to four years and have implications that are not too different for both types of banks.

The main motivation underlying this research study is the relatively new field of behavioral finance way in treating the topic of overconfidence. The particularity of the overconfidence bias topic is its assumption that financial decisions can be influenced by cognitive biases, ignoring the fact of a predetermined risk-return calculation.

DEA is a favored method to investigate the efficiency of institutions that provide educational services. We measure the efficiency of German universities especially from the students’ perspective. Since 1998, the Centrum für Hochschulentwicklung (CHE) evaluates German universities annually. The CHE ranking consists of three ranking groups for different indicators, but they do not create a hierarchy of the universities. Thus, a differentiation of the universities ranked in the same group is not possible. Based on the CHE data set, especially the surveys among students, we evaluate teaching performance from the students’ point of view using data envelopment analysis (DEA). DEA enables us to identify departments that – in the students’ perspective – are efficient in the sense that they provide high quality of education. As a method for performance evaluation, we apply a DEA bootstrap approach. By the use of this approach, we incorporate stochastic influences in the data and derive confidence intervals for the efficiency. Based on data generated by the bootstrap procedure, we are able to identify stochastic efficient departments. These universities serve as a benchmark to improve teaching performance.

This paper examines the technical efficiency (TE) of Indian commercial banks during 1998–2015.

This study uses mathematical programming-based data envelopment analysis (DEA) methodology to measure technical efficiency of Indian banks. Further, Simar and Wilson (2007) double bootstrap procedure is applied to examine the determinants of efficiency of the Indian banks, by examining the effects of various bank specific and other contextual variables.

The results indicate substantial upward bias in the conventional efficiency estimates of the Indian commercial banks. Needless to note, such upward bias is consistent with the theoretical postulates. The bootstrapped regression results show that increasing capital adequacy ratio is positively associated with bank efficiency. The popular belief that non-performing assets have a dampening effect on performance of banks is validated. Among others, ownership category is observed to be an important determining factor of bank efficiency. Specifically, state-owned banks (SOBs) are relatively lagging behind the foreign banks. Moreover, larger banks are observed to have a significantly higher level of efficiency, therefore, recent official policy initiatives toward consolidation of SOBs are validated.

As this study uses Simar and Wilson (2007) bootstrap approach, it enables the authors to have an estimate of the extent of bias in the traditional DEA TE scores. It also helps us drawing consistent inferences by rectifying the problem of serial correlation in the conventional second stage regression in this regard.

This study aims to explore the nexus of equality and efficiency by considering public hospitals' development dynamics, capacity and technology indicators.

Data was collected from the Ministry of Health Public Hospital Almanacs from 2014 to 2017. The Gini index (GI) is used to estimate the inequality of distribution of hospital performance indicators. A bias-corrected efficiency analysis is calculated to obtain efficiency scores of public hospitals for the year 2017. A path analysis is then constructed to better identify patterns of causation among a set of development, equality and efficiency variables.

A redefined path model highlights that development dynamics, equality and efficiency are causally related and health technology (path coefficient = 0.57; t = 19.07; p < 0.01) and health services utilization (path coefficient = 0.24; t = 8; p < 0.01) effects public hospital efficiency. The final path model fit well (X²/df = 50.99/8 = 6; RMSEA = 0.089; NFI = 0.95; CFI = 0.96; GFI = 0.98; AGFI = 0.94). Study findings indicate high inequalities in distribution of health technologies (GI > 0.85), number of surgical operations (GI > 0.70) and number of inpatients (GI > 0.60) among public hospitals for the years 2014–2017.

Study results highlight that, hospital managers should prioritize equal distribution of health technology and health services utilization indicators to better orchestrate equity-efficiency trade-off in their operations.

The purpose of this paper is to examine the operational efficiency and effects of market concentration and diversification on the efficiency of Chinese and Indian banks in the 1997-2011 period.

This study employs the two-stage bootstrap procedure of Simar and Wilson (2007) to obtain valid inferences on the efficiency scores and the efficiency determinants.

Using data set for each country separately, the authors found that the bias-corrected cost efficiency displays an upward trend in Chinese and Indian banks. This trend is consistent with profit efficiency among Chinese banks, but the trend is unclear in Indian banks. Market concentration is negatively related to cost and profit efficiencies of Chinese banks. However, market concentration is positively associated with cost efficiency, but unrelated to profit efficiency of Indian banks. In Chinese banks, diversification of revenue, earning assets and non-lending earning assets are associated with increasing profit efficiency, but their effects to cost efficiency are not clear. In Indian banks, diversification of earning assets increases profit efficiency while there are cost efficiency losses from diversification of revenue and earning assets.

Bank regulators and supervisors in China should consider establishing policies to reduce market concentration and encourage diversification of revenue, earning assets and non-lending earning assets, while increasing concentration and diversification of earning assets should be encouraged in Indian banks.

To the best of the authors’ knowledge, this is the first study employing the double bootstrap procedure proposed by Simar and Wilson (2007) which can address the problem of the two-stage data envelopment analysis or SFA estimator in the efficiency literature on Chinese and Indian banks that efficiency scores obtained in the first stage are inter-dependent, and hence violating the basic assumption in regression analysis in the second stage.

Data envelopment analysis (DEA) is based on the production possibility set that involves the process of converting resources or inputs to outputs. Accordingly, most DEA models include endogenous variables and need an additional step to find the influence of exogenous variables on the process. The purpose of this paper is to examine the relationship between the efficiency scores of DEA and the exogenous variables using truncated regression analysis with double bootstrapping along with two additional methods.

First, the authors employ DEA for benchmarking the comparative efficiency of the health care institutes. Next, the authors run and compare truncated, ordinary least square (OLS) and Tobit regression analysis using the double bootstrapping algorithm for finding the influence of exogenous variables on the efficiency of the health care institutes.

The authors confirmed the amount of bias for the Tobit and OLS regression models, which was caused by serially correlated errors. Accordingly, the authors chose results from the truncated regression model with double bootstrapping for examining the influence of exogenous or environment variables on the efficiency scores.

The study includes cross-sectional data on health care institutes in the state of Washington, USA. Collecting data in various states or regions over time is left for future studies.

In this study, three exogenous variables such as Medicaid revenues, locations of health care institutes and ownership types are significant for explaining the relationship between the efficiency scores and a group of the exogenous variables. Managers and policy makers need to pay attention to these variables along with endogenous variables for promoting the sustainability of the health care institutes.

The study demonstrates the usefulness of the truncated regression analysis with double bootstrapping for confirming the relationship between the efficiency scores of DEA and a group of exogenous variables, which is rare in the DEA literature.