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The purpose of this paper is to investigate how warehouse management, understood as a cluster of planning and control decisions and procedures, is organized and driven by task complexity (TC) and market dynamics (MD).

A multi‐variable conceptual model is developed based on the literature and tested among 215 warehouses using a survey.

The results suggest that TC and MD are the main drivers of warehouse management, measured by planning extensiveness (PE), decision rules complexity, and control sophistication. Differences between production and distribution warehouses are found with respect to the relationship between assortment changes and PE. Furthermore, TC appears to be a main driver of the specificity of the warehouse management (information) system (WMS).

This paper is based on 215 warehouses in The Netherlands and Flanders (Belgium); future research may test the model on a different sample. More research should be conducted to further validate the measures of the core dimensions of warehouse management.

Different levels of TC and MD characterize warehouses. Such a characterization is a first step in determining generic warehouse functionalities and helping managers to decide on the best software for their warehouse operations.

The paper defines the core dimensions of warehouse management, makes them measurable, tests them and assesses how these drivers impact specificity of WMS. The paper shows that PE in production warehouses is driven by different variables than in distribution centers.

Due to the importance of efficiency and responsiveness measures rather than just efficiency measures, this research recognizes both measures when considering overall performance of warehouse operations. Thus, the purpose of this paper is to prioritize overall performance measures associated with warehouse operations in manufacturing, third-party logistics service provider and retail industry supply chains.

The study uses an integrated approach that involves the Q-sort method to group measures into four categories. Fuzzy analytical hierarchy process was then used to prioritize individual performance measures within each category and integer liner programming model was used to validate prioritized categories, using the judgment of multiple decision makers across three industries.

The result shows that the financial category is a dominating performance category in managing warehouse operations across all three industries selected. Within the financial category, cost of insurance accounted for 25 percent of total weight of the category, and is considered to be a powerful measure. The financial category is verified by multiple decision makers across three industries, as the most important performance category.

As part of adopting the proposed methodology in practice, it needs to be guided by overall methodology appropriate for industry-specific contexts.

Key novel aspects of this study are to categorize warehouse operations measures and analyze their perspectives in different industries, understand dominant categories of warehouse operations measures in the contemporary supply chain and finally to explore to what extent current practices lead to achieving efficiency and responsiveness in the selected industries.

This study addresses the empirical results of the spillover effect with export as the primary economic activity that enhances local businesses' total factor productivity (TFP). A learning mechanism is expected to be generated and used as the basis for the policy implication.

This study adopted the Cobb–Douglas function and multiple estimation approaches, including the generalized method of moments, the Olley–Pakes and the Levinsohn–Petrin estimation techniques. The findings were estimated based on the panel data of a Vietnamese local businesses survey conducted by the General Statistics Office of Vietnam (GSO) from 2010 to 2019.

The results showed that the highest TFP belongs to the businesses in the Southeast region, the Mekong Delta region, the mining industry and the foreign-invested enterprises. The lowest impacted TFP are businesses in the Northwest region and agricultural, forestry and fishery sectors. In addition, the estimated results also show that the positive spillover effect on TFP is shown through forward and backward linkage. The negative spillover effect is expressed through the backward and horizontal channels.

This study offers original empirical evidence on the learning mechanisms via which exports contribute to productivity improvement in a developing Asian economy, so making a valuable contribution to the existing academic literature in this domain. The findings of this research make a valuable contribution to the advancement of understanding on the many ways via which spillover effects manifest such as horizontal, forward, backward and supplied-backward linkage.

The study's findings indicate that it is advisable for governments to give priority to the development and improvement of forward and supply chain linkages between exporters and local suppliers. This approach is recommended in order to optimize the advantages derived from export spillovers. At the organizational level, it is imperative for enterprises to strengthen their technological and managerial skills in order to efficiently incorporate knowledge spillovers that originate from overseas partners and trade counterparts.

This study sheds new evidence on the export spillover effect on productivity in emerging economies, with Vietnam as the case study. The paper contributes to the research's originality by adopting novel methodological aspects to estimate local businesses' impact on total factor productivity.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0373

The purpose of this paper is to attempt to measure the performance of the Indian banking sector in terms of efficiency and productivity levels and their determinants during the post-reform period.

The present study is a novel attempt as it has used pooled data for a duration of 15 years (i.e. 1997/1998-2010/2011) from 59 selected banks for estimating the Hicks-Moorsteen (HM) total factor productivity (TFP) index.

Poor technical efficiency has experienced with scale efficiency change exerting dominant factors; whereas relatively better productivity growth has been experienced by the banks with major contributions from technical change components. The study found relatively underestimated efficiency and productivity levels by traditional data envelopment analysis-based Malmquist index. Additionally, the study brings into account the results for external and environmental determining factors contributing to the TFP growth.

Using HMTFP indices has helped to eliminate certain drawbacks of data envelopment and provided the more elaborative decomposition of productivity growth along with their components so as to have lucid and multidimensional insights about the performance of the Indian banking industry after the initiation of financial reforms.

Recent literature recognizes the role of warehouses in enhancing the overall logistics performance. Thus, lean thinking has recently found its way in supporting warehouse and distribution centre operations. The purpose of this paper is to examine the relationships among warehouse waste reduction practices, warehouse operational performance, distribution performance and business performance.

A two-stage study was conducted. First, a Delphi technique was adopted to develop a relevant questionnaire. Second, this questionnaire was used to measure the degree of waste reduction in the different warehouse activities and to test the developed research hypotheses. The authors test the hypotheses with a sample of Middle Eastern warehouse operators.

There exists a positive relationship between warehouse waste reduction level and both warehouse operational performance and distribution performance. There was no direct relationship between warehouse waste reduction level and business performance. However, results revealed that the relationship between warehouse waste reduction level and business performance was mediated by warehouse operational performance and distribution performance.

The developed instrument provides a guide for logistics managers as to understand how to reduce waste in each warehousing activity. The results also inform logistics managers of how distribution performance can be improved through lean warehousing. The resulting performance improvements in the distribution operations will ultimately be reflected in the logistics performance of downstream retailers.

The study develops an original instrument for measuring waste reduction in warehouses, and provides insights into the evolving lean warehousing research area. This is the first scholarly work to uncover the relationships among warehouse waste reduction practices, warehouse operational performance, distribution performance and business performance.

This paper focuses on quantifying and ranking the “7-Deadly” Wastes in the warehouse environment. The purpose of this paper is to develop a prioritized road map that would guide the implementation of the “7-Deadly” Wastes and their related waste-reduction practices in a warehouse environment for improving warehouse operational performance.

A four-stage approach was used to achieve the objectives of the study. A Delphi technique was employed to develop indicators of waste-reduction practices at the warehouse activities level and categorize them according to the “7-Deadly” wastes. Whereas categories ranked based on statistical analysis, a regression analysis was used to explore the impacts of this ranking.

The results of the regression analysis showed a significant influence of the ranked “7-Deadly” wastes on warehouse operational performance. The paper develops a road map for implementing waste-reduction practices in the warehouse.

The paper proposes a road map comprising warehouse waste categories. It develops an integrated approach to quantify, rank and prioritize initiatives to adopt the categories of the “7-Deadly” Wastes that would (not) affect warehouse operational performance.

The purpose of this paper is to discover the most important productivity determinants of Yemeni microfinance institutions. In addition, this study tests the most appropriate tool to measure productivity in such unique industry.

The authors applied data envelopment analysis (DEA) with the variable return to scale after testing the technology return to scale assumption. Then, they used DEA with bootstrapping technique to overcome the borne biasness in the conventional DEA analysis. Finally, the authors presented the Hicks–Moorsteen (total factor productivity [TFP]) as the most suitable tool for the technology presented in this study.

In this paper, the authors found a prolonged deterioration in the productivity scores of microfinance institutions in Yemen. This study highlights the importance of operating in rural areas to improve micro finance institutions’ (MFIs’) productivity. In contrast, they found no significant differences in productivity, neither between microfinance banks and non-governmental organizations nor between Islamic and non-Islamic MFIs.

This study extends previous research in the area of productivity and its determinants. It also adds to the body of productivity knowledge and methodology within the context of the microfinance industry in Yemen.

The study discovered new productivity determinants and re-assessed the importance of some already known ones. These determinants have been studied for the first time in Yemen’s microfinance industry and have contributed to answer the question of what is the most suitable productivity method that should be used. This study proved that the Hicks–Moorsteen TFP and the variable return to scale assumption are the only suitable methods to study productivity in the microfinance industry.

The authors analyse a growth model to explain how economic fluctuations are primarily driven by productive capacities (i.e. capacity utilization driven by innovations and know-how) and productive inefficiencies.

This study’s methodology consists of the combination of the economic growth model, à la Solow–Swan, with a sigmoidal production function (in capital), which may explain growth, poverty traps or fluctuations depending on the relative levels of inefficiencies, productive capacities or lack of know-how.

The authors show that economies may experience economic growth, poverty traps and/or fluctuations (i.e. cycles). Economic growth is reached when an economy experiences both a low level of inefficiencies and a high level of productive capacities while an economy falls into a poverty trap when there is a high level of inefficiencies in production. Instead, the economy gets in cycles when there is a large level of the lack of know-how and low levels of productive capacity.

The authors conclude that more capital per capita (greater savings and investment) and greater productive capacity (with less lack of know-how) are the economic policy keys for an economy being on the path of sustained economic growth.