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The purpose of the paper is to develop a method to integrate the schedule-based analysis with a productivity-based analysis to prove and support the result of the damages calculation.

In this paper, a “cost and schedule impact integration” (CSI₂) model is proposed to objectively show and estimate lost productivity due to changes in construction projects.

A schedule-based analysis to include separate tracking of change order costs can be used to predict productivity due to the delay and disruption; changes in construction projects almost always result in delay and disruption. However, the schedule-based analysis needs to be integrated with a productivity-based analysis to prove and support the result of the damages calculation.

The results of this study expand upon construction practices for proving and quantifying lost productivity due to changes in construction projects.

The contribution of the paper is summarized as the introduction of a “schedule impact analysis” into a “cost impact analysis” technique to assess the damages, as well as to demonstrate the labor productivity impact due to delay and disruption in construction projects.

The conceptual problem associated with marketing productivity analysis is examined followed by an examination of currrent practice in marketing productivity in the following areas — on the product line, in advertising and promotional mix, in the salesforce, in distribution and in customer activity tracking. It provides UK companies with some guidance on how they can improve their performance measurement using marketing information systems and reorganising existing information for more effective marketing action. The research concentrates on 50 well‐known British companies in oil, chemicals, various engineering disciplines, food, pharmaceuticals, insurance, construction and chain‐store retailing. The findings are based on 28 viable responses, and a further 21 (different) responses from companies which were personally visited. Although the research techniques need to be refined they conclude that the management of resources invested in marketing activities can never be refined to the point where an incremental investment in any specific marketing application can be measured with great accuracy. Yet a great deal of measurement is possible and marketing managers can be well enough informed about the behaviour of marketing inputs so that allocation decisions in future periods will benefit.?

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

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.

Railway freight transportation in Europe is in a state of change. The system has been freed from protection from the beginning of 2007 and market forces will start to act. Demand for, and the market share of, this form of transportation has been declining for decades. Therefore, there is a need to know more about the efficiency and productivity of this sector, in order to understand the nature and magnitude of potential restructuring changes. This paper aims to address this issue.

Data envelopment analysis (DEA) is used to analyze different European countries throughout the longitudinal observation period of 1980 to 2003. Also partial productivity analysis is used to support DEA evaluation.

Based on DEA results, it is found that those countries that were showing the highest efficiency levels in the 1980s, without exception, experienced an efficiency collapse in the 1990s. These include both former Eastern Bloc and West European countries. Based on partial productivity analysis, it is proposed that productivity of locomotives and railway tracks should be the primary target of productivity improvement in these formerly highly efficient countries. The efficiency analysis shows also that currently the most efficient railway freight transportation is located in the Baltic States, namely Estonia and Latvia. If one does not include data from Estonia and Latvia in the overall analysis, in a European context the only truly improving performance indicators are partial productivities of freight wagons, and staff.

In contrast to previous efficiency studies in this sector, railway freight transportation shows high differences between European countries; it is necessary to address further research in the efficiency and productivity area in this respect to identify ongoing structural change.

Restructuring in European railways is just taking its first steps, and a combination of downsizing and greater freight volumes is needed if this mode of transportation is going to prosper in the future, and take market share back from others. Restructuring within this sector will most probably result in increased internationalization, mergers and acquisitions, and intermodal transportation solutions. These all represent new avenues for research.

This article helps to advise clear remedies for European railways and suggests interesting avenues for further research.

This paper aims to explore the level of productivity of Islamic banks specifically in selected Southeast Asian Countries from the period 2006 to 2014. Besides, this study also investigates the potential determinants of bank-specific characteristics and macroeconomic conditions that may influence the productivity of banking sector.

The present study gathers data on the 29 Islamic banks from Southeast Asian countries, namely, Brunei, Indonesia and Malaysia. The productivity level of the Islamic banks is evaluated using the data envelopment analysis-based Malmquist productivity index method. The authors then used a panel regression analysis framework based on the ordinary least square to identify potential determinants.

The domestic and foreign Islamic banks have exhibited progress in total factor productivity change solely attributed to the increase in efficiency change (EFFCH) which were mainly managerial rather than scale related. Foreign-owned banks have been slightly more productive compared to their domestic-owned bank counterparts, attributed to a higher EFFCH but insignificantly different. Furthermore, capitalisation, liquidity and world financial crisis determinants have significantly influenced productivity level of Islamic banks.

The study on the productivity of Islamic banking is still in its formative stage. To date, very limited study has been conducted to examine the productivity level in Southeast Asian, which is a strong regional hub for Islamic banking. This study intends to fill the gaps with a specific focus on the productivity level, specifically narrowing down to Southeast Asian countries in the domestic and foreign Islamic banking sector.

The purpose of this paper is to explore the effects of cross-border acquisitions on the efficiency and productivity of acquired companies in the cement industry in the context of a transitional economy.

The Data Envelopment Analysis (DEA) and Malmquist Productivity Index were used to assess the efficiency and productivity of the acquired companies over the period 2000–2018. DEA and Malmquist index are combined with bootstrapping to perform succinct statistical inferences for determining the accuracy of results. The study assesses partial efficiency and productivity of three inputs: material, capital and labour, as well as the total factor efficiency and productivity of the acquired companies in the short and long term after the acquisitions.

The research results suggest that efficiency of material, efficiency of labour and the total factor efficiency of the acquired companies are higher after the acquisitions than before, while efficiency of capital is lower. In addition, the results show that the acquisitions had a positive impact on total factor productivity of the acquired companies.

The results of this study have practical implications for managers, especially for policy-makers and industry analysts in deciding whether to encourage or discourage cross-border acquisitions in transitional economies.

The study contributes to a better understanding of the impact of cross-border acquisitions on efficiency and productivity of acquired companies in the manufacturing industry. Research in transitional economies related to subject matter is limited, and this study is the first empirical investigation of the effect of cross-border acquisitions on the efficiency and productivity in the cement industry in Serbia by applying the Data Envelopment Analysis.

Within the service sectors, Knowledge Intensive Business Services (KIBS) play an important role in local and regional economies as sources of competitive advantages and providing knowledge-intensive inputs to the business process of small and medium-sized enterprises. This study aims to analyze the changes in financial performance of KIBS industry in Italy over the period from 2012 to 2017.

This paper examines the efficiency of the KIBS firms by applying data envelopment analysis (DEA) to compute the Malmquist Productivity Index for the period under investigation. The DEA-based Malmquist productivity analysis is applied at firm level using a sample consisting 1.674 companies, representative of the Italian KIBS sector and related to three different NACE activity code (72-computing services; 73-research and development; 74 other professional business activities). The efficiency measures are then used to characterize KIBS firm financial performance through the analysis of average productivity patterns grouped by Italian geographical regions. The Malmquist productivity measures are decomposed into two components: efficiency change and technical change index. The overall analysis is coupled with a financial ratio analysis approach, selecting return on equity (ROE) and leverage ratio as descriptor to validate the results and better characterize differences in efficiency patterns among geographic-based groups of KIBS companies.

Over the period 2015-2017, the results show that the average annual growth of the overall Malmquist productivity index was positive in nine Italian regions that represent only 17 per cent of the total KIBS firms selected. On the other side, a decrease of the average performance measure is observed for the five geographic areas that contribute to 75.7 per cent of the total sample. In general, the technological change component, as a measure of innovation, strongly limits the productivity growth behavior of KIBS industry for all geographic regions. The use of selected financial ratio does not provide additional insight to the performance investigation and further in-depth studies are needed to better evaluate the correlation between average productivity results and regional business dynamics.

The study investigates the applicability of DEA-based Malmquist indices to the analysis of the productivity behavior of KIBS industry at regional level. It will be of value to provide first evidence to the policymakers to understand industry growth pattern in time frame selected and relate them to additional business factors to detect specific industry constraints.

The analysis in this paper contributes to the existing body of knowledge on industry performance measurement by applying specific analytical techniques to the productivity of Italian KIBS companies. The paper also contributes to the limited body of academic literature investigating KIBS industry at national level proposing a methodological framework that constitutes a first attempt to track average productivity behavior at regional level.

The purpose of this study is to investigate efficiency and productivity of Turkey’s both brokerage sector and intermediary institutions (IIs) that have been active in Turkish capital markets.

Data envelopment analysis (DEA) and Malmquist total factor productivity index (MPI) are used to analyze efficiency and productivity of Turkey’s both brokerage sector and 51 Turkish IIs constantly operated between the years 2008 and 2018. Paid-in capital, administrative expenses and trading volumes are used as input, while net trading commissions and net profit/loss are used as output in analysis. The calculations of this analysis are made with DEAP 2.2 program and Python.

The results reveal that during the analysis period, percentage of efficient institutions among 51 IIs was between 18% and 39% while the sector’s mean efficiency score ranged between 52% and 65%. While 2009 is the year with the highest number of efficient institutions, 2013 is observed to be the least. Finally, the results of productivity analysis indicate that all types of IIs are not fully productive during the related period. The striking finding obtained is that though there is a decrease in total productivity change, the technological change has a positive effect on their productivity change.

This study is a double-layered research paper that includes efficiency analysis by DEA in the first step and productivity analysis by using MPI in the second step.

A review is made of corporate practices in reporting performance so as to extract elements for constructing a productivity calculus for reflecting corporate performance. A communications strategy reflecting critical value‐added productivity changes is then explained. We propose multi‐directionality embedded in corporate value‐added performance changes be communicated through symbolism of “bread” or “wooden rice box”. The Internet is now streamlined as part of standard office systems. It is therefore timely to enable the e‐versions of annual reports with manipulative controls for in‐depth strategic analyses of directional changes in corporate value‐added performances.