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The purpose of this paper is to investigate the implications of supply chain strategies that manufacturing companies can use to minimise or overcome natural resource scarcity, and ultimately improve resource efficiency and achieve competitive advantage. The relationship between resource efficiency and competitive advantage is also explored.

The proposed research model draws on resource dependence theory. Data were collected from 183 logistics, purchasing, sustainability and supply chain managers from various manufacturing companies and analysed by applying the partial least squares structural equation modelling technique.

The results indicate that both buffering and bridging strategies improve resource efficiency; however, only bridging strategies seem to lead to firm’s competitive advantage in terms of ownership and accessibility to resources. The relationship between resource efficiency and competitive advantage is not supported.

Future research could confirm the robustness of these findings by using a larger sample size and taking into account other supply chain members.

This research provides guidance to managers faced with the growing risk of resource scarcity to achieve a resource efficient supply chain and an advantage over competitors.

Studies have explored the appropriate strategies for minimising dependencies caused by the scarcity of natural resources in the field of supply chain management; however, there is limited empirical work on investigating the impact of these strategies on resource efficiency and competitive advantage.

This study explores the applicability of the resource‐based view at the organizational unit level by investigating why resource utilization, as measured by efficiency, might differ within a firm. Using a downstream petroleum firm as the context for this study, the data envelopment analysis framework is applied to examine resource input congestion of its DCs (i.e. distribution centers). The study also provides a more granular analysis by decomposing distribution efficiency into managerial, scale, and programmatic efficiency, and examines the impact of corporate‐level decision making by including non‐discretionary variables. The analysis identifies opportunities to improve efficiency at the organizational unit level, using alternative views of the operational problem. The approach also provides practicing managers with an objective means to evaluate performance at the level of the organizational unit. Both the efficiency view and the managerial performance view are discussed simultaneously from a strategic view of firm resources.

Drawing on the systems theory and the natural resource-based view, the purpose of this paper is to advance an integrated resource efficiency view (IREV) and derive a composite “integrated resource efficiency index” (IRE-index) for assessing the environmental, economic, and social resource efficiencies of production economies.

Using sub-national input-output data, the IRE-index builds on the human development index (HDI) and the OECD green growth indicators by including functions for environmental resource efficiency, energy, and material productivity. The study uses multiple regressions to examine and compare the IRE-index of 40 countries, including 34 OECD nations. The study further compares the IRE-index to similar composite indicators such as the human sustainable development index (HSDI) and the ecological footprint.

The IRE-index reveals a discrepancy between social development and resource efficiency in many of the world’s wealthiest production economies. Findings also show that material productivity has been the key driver for observed improvements in IRE over time. The index is a robust macro-level methodology for assessing resource efficiency and sustainability, with implications for production operations in global supply chains.

The IREV and IRE-index both contribute towards advancing green supply chain management and sustainability, and country-level resource efficiency accounting and reporting. The IRE-index is a useful composite for capturing aggregate environmental, economic, and social resource efficiencies of production economies. The paper clearly outlines the managerial, academic, and policy implications of the IREV and resulting index.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.

The purpose of this paper is to explore how the acquisition experience – an acquiring firm’s experience of acquiring and integrating the resources of an acquired firm – affects the production resource efficiency of the acquiring firm.

The authors used data obtained from US manufacturing industries over the 1992–2014 period. The sample includes 784 acquisitions by 417 firms. The proposed hypotheses were tested through econometric analysis.

Results show that the acquisition experience has a positive association with production resource efficiency. The acquisition experience is most positively associated with acquiring firms’ production efficiency when they successfully accomplished previous performance outcomes. While the literature has recognized the relatedness of acquiring and acquired firms as a contextual moderator, the interaction of the related acquisition and the acquisition experience has no impact on efficiency benefits.

This study enhances the understanding of how prior acquisition experience can be leveraged by acquiring firms to gain efficiency benefits in the manufacturing industry.

The study investigates the effects of energy resource efficiency on the triple themes of sustainable development (economic, social and environmental dimensions). We adopt a quantitative research method, and the required macroeconomic data were extracted from World Development Indicators for a period of 30 years (1991–2020). The extracted data were analysed using correlation analysis and linear regression. Ultimately, the estimations from the three models produced mixed results. Energy resource efficiency (EFF) exerts a significant positive effect on economic sustainability (ECS), a significant negative effect on social sustainability (SOS) and a significant negative effect on environmental sustainability (EVS). However, claims on government (COG) exerted an insignificant negative effect on ECS, an insignificant negative effect on SOS and a significant positive effect on environmental sustainability (EVS). In practical terms, the findings are consistent with previous empirical studies, and they also validate X-efficiency theory (XET) and resource curse theory (RCT). The study concludes with implications, limitations and further research directions.

This paper seeks to rediscover the most suitable efficiency evaluation variables (input and output variables) for digital libraries and to employ the data envelopment analysis (DEA) model to measure the resource utilization efficiency of university libraries.

In order to analyze and evaluate university library efficiency, the paper introduces the DEA‐CCR Model and the DEA‐BCC Model. Based on these research tools, the Technical Efficiency (CCR*BCC) was determined. First, a reference group was created with a 100 percent efficiency rate, then the factors contributing to inefficient DMUs were analyzed, and the difference in the efficiency rate compared according to the different governing bodies of the libraries. Finally, the difference of efficiency according to the introduction and rejection of electronic resources was analyzed. It was possible to measure the technical efficiency, pure‐technical efficiency, and scale efficiency.

The results showed that the efficiency of university libraries varied significantly according to whether or not electronic resources were included in the evaluation. In addition, the findings confirmed decision making units (DMUs) have a 100 percent efficiency rate and a low efficiency rate as well as proposed benchmarking DMUs for inefficient DMUs and a direction for future improvements.

The paper identifies that there was a significant difference in efficiency, according to the presence of electronic resources in university libraries.

Resource scheduling is the study of how to effectively measure, evaluate, analyze, and dispatch resources in order to meet the demands of corresponding tasks. Aiming at the problem of resource scheduling in the private cloud environment, the purpose of this paper is to propose a resource scheduling approach from an efficiency priority point of view.

To measure the computational efficiencies for the resource nodes in a private cloud environment, the data envelopment analysis (DEA) approach is incorporated and a suitable DEA model is proposed. Then, based on the efficiency scores calculated by the proposed DEA model for the resource nodes, the 0-1 programming technique is introduced to build a simple resource scheduling model.

The proposed DEA model not only has the ability of ranking all the decision-making units into different positions but also can handle non-discretionary inputs and undesirable outputs when evaluating the resource nodes. Furthermore, the resource scheduling model can generate for the calculation tasks an optimal resource scheduling scheme that has the highest total computational efficiency.

The proposed method may also be used in studies of resource scheduling studies in the environments of public clouds and hybrid clouds.

The proposed approach can achieve the goal of resource scheduling in private cloud computing platforms by attaining the highest total computational efficiency, which is very significant in practice.

This paper uses an efficiency priority point of view to solve the problem of resource scheduling in private cloud environments.

As a transition economy, China is interested in allocating its limited innovation resources economically, reasonably and efficiently to produce as many outputs as possible with its limited financial and human resources. Nonetheless, what is the efficiency of the allocation of innovative resources for civil–military integration enterprises, and what factors hinder its efficiency improvement? The purpose of this paper is to explore these problems.

The improved two-stage network data envelopment analysis (DEA) method is used to measure the overall efficiency and stage efficiency of the innovation resource allocation of 58 Chinese civil–military integration listed companies from 2010 to 2016. Tobit model is used to analyze the influencing factors of resource allocation efficiency.

The results indicate that the overall efficiency and stage efficiency of innovation resource allocation fluctuate in varying degrees during the period. The optimization of overall efficiency is restricted by lower efficiency of innovation achievement transformation. Enterprise scale was found to have a significant negative impact on both overall and two-stage efficiencies. Proportion of research and development (R&D) personnel had a positive effect on the overall and two-stage efficiency. Government support had a significant positive effect on the stage of innovation resource development and overall efficiency.

Previous research studies have used either the DEA or stochastic frontier analysis method to measure the efficiency of innovation activities as a whole and ignored the stage of initial investment to final output in innovation activities. That is, the process in which initial input of R&D resources becomes innovation output, and then becomes economic benefits. Therefore, this paper studies the efficiency of innovation resource allocation of civil–military integration listed companies. The improved two-stage chain network DEA method and Tobit model were used.

The purpose of this study is twofold. First, this research estimates banks' efficiencies from the perspectives of resource utilization and investment after incorporating risk measures as an exogenous input in the investment-efficiency stage. Second, the current study examines the relationship between intellectual capital (IC) and banks' efficiencies.

First, this study uses a dynamic network data envelopment analysis approach in investigating the efficiencies of 24 Taiwanese banks in 2007–2018 from two perspectives. Second, this research utilizes various regression techniques, namely, ordinary least squares (OLS), robust least squares and truncated regression, to gauge the impact of IC on banks' efficiencies. Typically, IC is determined based on a monetary value-based measure and value-added intellectual coefficient (VAICTM).

Resource-utilization (investment) efficiencies were observed as 0.941 (0.964), thereby contributing to the mean overall efficiency of the sample banks at 0.952. However, the related efficiency changes decline over the sample period, thereby suggesting that the average banks' efficiencies hardly increase. Regression analyses show a significantly positive relationship between IC and banks' overall resource-utilization and investment efficiencies.

Overall, this study suggests that researchers should consider risks when estimating banks' efficiencies owing to their connection to banks' investment performance. From banks' dynamic two-stage efficiencies, this study demonstrated that investments in IC will bring improved future economic benefits.

Different from prior studies, this study improves banks' efficiency evaluation models by incorporating risk measures and assuming weighted periods for the 2007–2008 global financial crisis. Moreover, the use of monetary value-based measure of IC provides consistent results as the commonly-used VAICTM does.