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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.

Drawing on the concept of superior resource, capability and processes of the resource-based theory of the firm, the purpose of the current study is to analyze the influence of firms’ winner-picking strategic approach on firm performance (FP) via a direct and indirect mechanism.

Using survey data of 104 diversified manufacturing firms, the current study analyzed the conditional indirect effect of firms’ strategic approach on efficient resource allocation with the help of Statistical Analysis Software (SAS) process macros.

The study found that firms’ choices of winner-picking approach can undermine the resource allocation efficiency when not perfectly blended with firms’ access to the resource. Furthermore, the effect of winner-picking strategy (WPS) on resource allocation efficiency via firms’ competitive advantage (CA) can be greater when both strategic choice and resources are employed adequately.

Despite making a unique contribution, the present study has a few limitations requiring researchers’ attention to be tackled in the forthcoming. This includes a little amount of data, a self-reporting technique and failure to include all the possible reasons that could lead to inefficient resource allocation.

The present research has potential applications for managers of the manufacturing industry in a period of sheer uncertainty [coronavirus disease 2019 (COVID-19)]. First, the study alerts managers about the challenges of underinvestment and overinvestment while allocating resources. At the same time, this study provides an important implication for managing the importance of firms’ access to capital (AC).

The current study has made a sizeable impression in the literature on internal resource allocation and resource-based theory of the firm by recommending a model that augments the theoretical foundation of strategic management of the firms. As there are only a handful of studies on this grave issue in the context of developing economies, thus, closely considering these insights would be helping for the firms for allocating resources efficiently in the manufacturing industry.

The existing three-stage network Data Envelopment Analysis (DEA) models with shared input are self-assessment model that are prone to extreme efficiency scores in pursuit of decision-making units (DMUs) efficiency maximization. This study aims to solve the sorting failure problem of the three-stage network DEA model with shared input and applies the proposed model to evaluate innovation resource allocation efficiency of Chinese industrial enterprises.

A three-stage network cross-DEA model considering shared input is proposed by incorporating the cross-efficiency model into the three-stage network DEA model. An application of the proposed model in the innovation resource allocation of industrial enterprise is implemented in 30 provinces of China during 2015–2019.

The efficiency of DMU would be overestimated if the decision-maker preference is overlooked. Moreover, the innovation resource allocation performance of Chinese industrial enterprises had a different spatial distribution, with high in eastern and central China and low in western China. Eastern China was good at knowledge production and technology development but not good at commercial transformation. Northeast China performed well in technology development and commercial conversion but not in knowledge production. The central China did not perform well in terms of technology development.

A three-stage network DEA model with shared input is proposed for the first time, which makes up for the problem of sorting failure of the general three-stage network model.

The purpose of this paper is to study where to place industrial solid waste treatment centers among the 16 prefecture-level cities under its jurisdiction.

This paper adopts the cross-efficiency data envelopment analysis (DEA) model, with the industrial land price and average annual salary per capita as inputs, while coverage, total transportation distance, number of industrial enterprises and total amount of industrial solid waste are used as outputs.

Based on the spatial efficiency scores calculated by using the new presented models, the authors find that the most efficient construction site are Chizhou, Chuzhou, Suzhou and Bengbu. That is quite different from the results obtained by using traditional approach.

This paper evaluates the spatial efficiency by using combinations of the four locations as the decision-making units of the DEA model, which could be used as an objective way to allocate limited resource. In addition to the resource allocation of the industrial solid waste treatment center, the method in this paper can also be applied to other spatial aspects of resource allocation.

Drawing on the concept of resource-based theory of the firm; the purpose of this study is to analyze the influence of firms’ strategic approaches on the firm performance via indirect effect using a multilevel, bottom-up approach.

Using the survey method, the present study obtains data from 104 diversified manufacturing firms and analyzes the bottom-up effect of firms’ strategic approach on efficient resource allocation using Mplus.

Given the prevailing conditions, the study found that the motive of most firms is growth rather than risk mitigation or collaboration in the manufacturing sector of Pakistan. Furthermore, the study found that the bottom-level employees’ information asymmetry has a significant impact on the strategic resource allocation decision, which can lead to resource allocation inefficiency.

Despite making a unique contribution, the present study has few limitations requiring researchers’ attention to in the forthcoming. These include a low amount of data, self-reporting technique and failure to include all the possible reason that could cause resource allocation inefficiency.

The present research has potential applications for managers of the manufacturing industry. First, the study alerts managers about the challenges of resource allocation. At the same time, this study provides critical implication for managing bottom-level employees.

The current study has made a sizable impression in the literature of resource-based theory of the firm by recommending a model that augments the theoretical foundation of strategic management of the firm. So, closely considering these insights would be helping for the firms for allocating resources efficiently in the manufacturing industry.

The purpose of this paper is to investigate how customer relationship management (CRM) implementation affects internal capital allocation efficiency, the efficiency with which a firm allocates its capital across its business segments.

The authors use a statistical regression method to analyze a sample of 801 unique firms in the USA from COMPUSTAT and the Computer Intelligence database. This analysis examines the relation between CRM implementation and internal capital allocation efficiency and identifies the conditions under which firms benefit more from CRM implementation. They also use instrumental variables (IVs) to address endogenous concerns with a two-stage least squares (2SLS) model.

The authors find that CRM implementation is positively related to internal capital allocation efficiency. The results are robust to the 2SLS analysis with IVs. This positive relation is more pronounced for firms with effective internal control and for those operating in highly competitive markets.

The research implies that that CRM can have a significant cross-functional effect on corporate financing and budgeting. This also suggests that when chief marketing officers plan marketing initiatives and implement CRM, they should communicate to chief financial officers not only the direct effect but also the indirect strategic benefits of such initiatives to a firm.

The authors reveal a previously overlooked aspect of marketing accountability by suggesting marketing’s impact on internal capital markets. They also enrich the body of literature on CRM benefits by showing a cross-functional benefit from marketing to finance (or capital allocation).

Attentive to task-related information is the prerequisite for task completion. Comparing the cognition between attentive readers (AR) and inattentive readers (IAR) is of great value for improving reading services which has seldom been studied. To explore their cognitive differences, this study investigates the effectiveness, efficiency and cognitive resource allocation strategy by eye-tracking technology.

A controlled user study of two types of task, fact-finding (FF) and content understanding (CU) tasks was conducted to collect data including answer for task, fixation duration (FD), fixation count (FC), fixation duration proportion (FDP), and fixation count proportion (FCP). 24 participants were placed into AR or IAR group according to their fixation duration on paragraphs related to task.

Two types of cognitive resource allocation strategies, question-oriented (QO) and navigation-assistant (NA) were identified according to the differences in FDP and FCP. In FF task, although QO strategy was applied by the two groups, AR group was significantly more effective and efficient. In CU task, although the two groups were similar in effectiveness and efficiency, AR group promoted their strategies to NA while IAR group sticked to applying QO strategy. Furthermore, an interesting phenomenon “win by uncertainty”, which implies IAR group may get correct answer through uncertain means, such as clue, domain knowledge or guess, rather than task-related information, was observed.

This study takes a deep insight into cognition from the prospect of attentive and inattentive to task-related information. Identifying indicators about cognition helps to distinguish attentive and inattentive readers in various tasks automatically. The cognitive resource allocation strategy applied by readers sheds new light on reading skill training. A typical reading phenomenon “win by uncertainty” was found and defined. Understanding the phenomenon is of great value for satisfying reader information need and enhancing their deep learning.

Software-define vehicular networks (SDVN) assure the direct programmability for controlling the vehicles with improved accuracy and flexibility. In this research, the resource allocation strategy is focused on which the seek-and-destroy algorithm is implemented in the controller in such a way that an effective allocation of the resources is done based on the multi-objective function.

The purpose of this study is focuses on the resource allocation algorithm for the SDVN with the security analysis to analyse the effect of the attacks in the network. The genuine nodes in the network are granted access to the communication in the network, for which the factors such as trust, throughput, delay and packet delivery ratio are used and the algorithm used is Seek-and-Destroy optimization. Moreover, the optimal resource allocation is done using the same optimization in such a way that the network lifetime is extended.

The security analysis is undergoing in the research using the simulation of the attackers such as selective forwarding attacks, replay attacks, Sybil attacks and wormhole attacks that reveal that the replay attacks and the Sybil attacks are dangerous attacks and in future, there is a requirement for the security model, which ensures the protection against these attacks such that the network lifetime is extended for a prolonged communication. The achievement of the proposed method in the absence of the attacks is 84.8513% for the remaining nodal energy, 95.0535% for packet delivery ratio (PDR), 279.258 ms for transmission delay and 28.9572 kbps for throughput.

The seek-and-destroy algorithm is one of the swarm intelligence-based optimization designed based on the characteristics of the scroungers and defenders, which is completely novel in the area of optimizations. The diversification and intensification of the algorithm are perfectly balanced, leading to good convergence rates.

The purpose of this paper is to present an analysis of the role of entrepreneurship in promoting China's technological progress and efficiency improvement during the economic reform process of the past two decades. The paper contributes to our understanding of the drivers of economic growth in China during the economic reforms period through the study of technology development and efficiency of resource allocation.

The paper uses Malmquist productivity index and panel data cointegration covering 30 regions in China for the period 1992‐2007, to test the effect of entrepreneurship on Chinese technological progress and resource allocation efficiency.

The analysis shows that the rate of entrepreneurship in China improves the level of technological progress and efficiency of resource allocation significantly. The increased level of entrepreneurship in Beijing, Shanghai, Jiangsu, Zhejiang, Shandong, Guangdong and other developed areas is positively related to the increased level of technological development and resource allocation, whereas this was not identified in areas that are less developed. Furthermore, the analysis suggests that business level has promoted the level of technological progress in these areas.

The paper provides valuable insight into the role and contribution of entrepreneurship's in China's economic growth.

The purpose of this paper is to consider the following problem; if the manager of the parallel network systems wants to add new sub-decision making units (sub-DMUs) to each parallel network system, he/she wants to know how much new fuzzy inputs allocate to new sub-DMUs and how much outputs these new sub-DMUs produce such that the efficiency of each parallel network system improve or preserve.

Resource allocation and target setting is a famous topic in management science, therefore many managers attention to this field. Data envelopment analysis is one approach to apply the resource. Resource allocation and target setting is a famous topic in management science, therefore many managers attention to this field. Data envelopment analysis is one approach to apply the resource allocation and target setting. In real application the structure of many DMUs are network and the data of them are imprecise. In this work first the authors calculate the fuzzy efficiency of parallel systems by common set of weights method, after that the authors propose an approach to find how much fuzzy inputs allocate to new sub-DMUs, and how much new fuzzy outputs produce, where the efficiency of each parallel network system improve or preserve.

It is found the value of inputs and outputs of new sub-DMUs, where the efficiency of each system not worse than before.

The method can be used in many organizations such as banks, chain stores, car factory, etc.

For the first time the authors allocate new sub-DMUs to each system, and the data of paper are fuzzy numbers.