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Resource scarcity is a major difficulty facing firms that engage in new product development (NPD) projects. The purpose of this paper is to understand how resource allocation strategies affect NPD performance and which strategy is the best alternative, a research and development (R&D) process model is constructed using system dynamics.

Moreover, resource allocation strategies are categorized into two types: design‐stage‐first strategy and manufacturing‐stage‐first strategy, and several important indicators of performance evaluation are defined. Then different workload scenarios are developed to test the relationships between resource allocation strategy and various NPD performance measures.

The most important finding from simulation results is that a firm should allocate its resources into early development stage first in order to obtain superior R&D performance.

This paper has successfully constructed new system dynamics model for quantifying the performances of R&D process.

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.

Resource allocation is essential to infrastructure management. The purpose of this study is to develop a methodological framework for resource allocation that takes interdependencies among infrastructure systems into consideration to minimize the overall impact of infrastructure network disruptions due to extreme events.

Taking advantage of agent-based modeling techniques, the proposed methodology estimates the interdependent effects of a given infrastructure failure which are then used to optimize resource allocation such that the network-level resilience is maximized.

The findings of the study show that allocating resources with the proposed methodology, where optimal infrastructure reinforcement interventions are implemented, can improve the resilience of infrastructure networks with respect to both direct and interdependent risks of extreme events. These findings are also verified by the results of two case studies.

As the two case studies have shown, the proposed methodological framework can be applied to the resource allocation process in asset management practices.

The proposed methodology improves the resilience of the infrastructure network, which can alleviate the social and economic impact of extreme events on communities.

Capitalizing on the combination of agent-based modeling and simulation-based optimization techniques, this study fulfills a critical gap in infrastructure asset management by incorporating infrastructure interdependence and resilience concepts into the resource allocation process.

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 basic assumption in strategic management is that consistently high performing companies are able to adapt effectively to external shocks. While adaptation of allocation of resources and its constraints have been investigated, it is important to also consider the allocation of attention. Therefore, this study seeks to examine the differences in the patterns in the allocation of resources and attention in a comparative case study with focus on Southwest Airlines. This study illustrates that the comparison of the patterns of allocation of resource and attention is very promising for the explanation of consistent superior performance.

This paper analyzes Federal Aviation Administration and American Transport Association data in order to determine actual resource allocation. Moreover, textual analysis of annual reports serves as basis for examining the patterns of allocation of attention.

The results of this paper reveal a striking divergence of allocation of resources and attention (particularly attention to differentiation) for Southwest Airlines – the consistently high performing firm in the US airline industry.

The major limitation of the current study is the fact that it is a single industry study. It would be very interesting to replicate this study in other industries.

This study shows the importance of allocation of attention for firm performance. This is particularly relevant for resource intensive industries such as the airline industry where organizational inertia makes it hard to move resources fast. Yet, attention appears to have a great potential for firm performance and can be changed more easily.

Despite great interest in allocation of resources and attention in strategy research, authors rarely combine these two perspectives. Nadkarni and Barr present a notable exception. Yet, the latter authors focus on one specific aspect of adaptation of strategic actions, i.e. the timeliness of response. The present study takes a more comprehensive view of adaptation, e.g. the respective changes in slopes of adaptation.

The purpose of the study was to find out the determinants of strategy implementation by the international reproductive health (RH) non-governmental organizations (NGOs) in Kenya for attainment of MDG 5 by 2015. The objectives of the study were to analyse the effects of the determinants of strategy implementation for attainment of the Millennium Development Goal (MDG) 5 by international RH NGOs operating in Kenya. This study was guided by three specific objectives: to analyse the effect of policy regulations on strategy implementation by international RH NGOs in attainment of MDG 5; to determine the effect of management competencies on strategy implementation by international RH NGOs in attainment of MDG 5 and to explore the effect of resource allocation on strategy implementation by international RH NGOs in attainment of MDG 5 by 2015 in Kenya.

A cross-sectional descriptive study design was carried out from January to March 2012 in Nairobi, Kenya. The study populations comprised of programme staff and RH technical advisors for the international RH NGOs operating in Kenya and headquartered in Nairobi. The sampling design was a census method since all the ten international RH NGOs based in Nairobi were selected. Qualitative and quantitative methods of data collection were used using both closed and open-ended questionnaires. The quantitative data were analysed using SPSS version 20 for both descriptive and inferential analysis. Qualitative data were analysed descriptively. Measures of location and variability were used for analysis and hypothesis tested using Fisher ' s Exact test and regression analysis.

The research findings showed that policy regulations, management competencies and the resource allocations determine successful implementation of the strategic plans. Inadequate resource allocations have a direct bearing on the implementation of the RH programmes which invariably affects the attainment of the MDG 5 by 2015. The study did not accept the null hypotheses and therefore showed that there is relationship between policy regulations, management competencies and resource allocations and the implementation of strategic management plans by international RH NGOs operating in Kenya.

Although numerous studies acknowledge that strategies frequently fail not because of inadequate strategy formulation, but because of insufficient implementation, strategy implementation has received less research attention than strategy formulation. This paper addresses this gap.

The purpose of this paper is to present a competitive defender‐attacker risk model that assumes a dual exponential relationship between defender (C_i) and attacker (A_i) resource allocation: v_i(A_i,C_i)=e^−α_ic_i−e^{−α_iC_i−γ_iA_i}.

Network risk is defined in terms of degree sequence, g, node/link damage, d, and probability of failure, v: R=∑g_iv_id_i. The paper finds the optimal allocation of resources (A_i, C_i) that minimizes R from the defender's point of view, and maximizes R from the attacker's point of view.

The effectiveness of the optimal min‐max strategy is compared with three allocation strategies: random, non‐network, and network. It is shown that total network risk is minimized by the non‐network strategy, because this strategy considers damage values and ignores network topology in the definition of risk.

The method is illustrated by applying it to critical infrastructure – a hypothetical water‐and‐power network.

The purpose of this paper is to explore the relationship between effective strategies to improve the quality and quality management of allocated resources for the successful implementation of the strategies. For this purpose, three quality management resources (human, organizational and technological) and eight different strategies related to quality are considered.

The paper employs the fuzzy analytic network process (FANP) to prioritize and model the interactions between eight strategies, the three types of resources (human, organizational and technological) needed for effective strategy implementation and the ability to enhance quality. Then, Goal Programming (GP) is formulated by the output of the FANP to identify the extent to which each single strategy is inhibited by a lack of (or overloaded by) resources.

The first three priorities of strategies identified by the FANP include continuous management of quality system, continuous use of human knowledge and continuous approach toward target, and the order of resources is as follows: human resources, organizational resources and technological resources. The results obtained showed the largest share of human resources and its crucial role in improving the quality of the products. The contribution of organizational resources amounts to half of the contribution of human resources.

The main contribution of this paper is to employ the FANP to prioritize, whereas in prior studies in this area, priorities were conducted as definitive, and uncertainty in the opinion of experts was not considered. In this paper, the FANP–GP combined method is used.

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.

Modelling construction resources and their dynamic interactions and constraints are a challenging problem. The allocation of these resources to competing activities is usually a function required in any scheduling process. Performing such allocation under a dynamic and diverse set of constraints adds more complexity to the problem. This study seeks a structured approach for representing resources and their allocation to different activities through the use of an agent‐oriented modelling framework.

A model is developed for a real case of assembly operations of industrial construction modules. The model follows a multi‐agent resource allocation structure and is implemented within an agent‐based simulation environment. The model is used to evaluate the effects of different optimization algorithms and modelling parameters on the generation of a construction schedule. Different experiments run through the model and their results are analyzed and discussed.

The model showed sensitivity only under large and continuous workloads. Overall the structured approach followed in developing the model provided a flexible medium for experimenting with different elements of the resource allocation problem.

The work is limited to the studied case and the results cannot be generalized beyond similar cases. The modelling approach used in the study provides a platform that can facilitate future research in construction resource allocation strategies.

The presented work demonstrates a new approach for modelling construction resource allocation problems that enables structured experimentation with alternative allocation algorithms. It also presents a novel way for modelling modular industrial construction operations.