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Problem definition: We consider a two-sided market competition problem where two platforms, such as Uber and Lyft, compete on both supply and demand sides and study the impact of precommitments in a variety of practically motivated instruments on the equilibrium outcomes. Academic/practical relevance: We extend a set of classic oligopoly pricing results to account for two-sided competition under demand uncertainty. Methodology: We investigate multi-stage competition games. Results: We start with a sufficiently low demand uncertainty. First, we show that a precommitment made on the less competitive (demand or supply) side (on price or wage) has a less intense outcome than no commitment (i.e., spot-market price and wage competition). Then we show that, somewhat surprisingly, if the competition intensities of both sides are sufficiently close, the commission precommitment, where the platforms first compete in setting their commission rates and then their prices, is less profitable than no precommitment at all, and vice versa. Furthermore, we show that the capacity precommitment, in which the platforms first commit to a matching capacity and then set price and wage simultaneously subject to the precommitted capacity, leads to the most profitable outcome of all competition modes and extends the celebrated Kreps-Scheinkman equivalency to the two-sided market (without demand uncertainty). Then we extend the comparisons of various competition modes to account for a relatively high demand uncertainty. We show that the comparison between the spot-market price and wage competition and the commission precommitment stays the same as that with a sufficiently low demand uncertainty. In addition, the more flexible competition modes, such as no commitment and commission precommitment, benefit from higher demand uncertainty (with a fixed mean demand) because of their operational flexibility in response to the market changes. Further, a relatively high demand uncertainty may undermine or enhance the value of the wage precommitment, as opposed to no commitment. Finally, we also account for platforms with asymmetric parameters and matching friction and find that our main insights tend to be robust. Managerial implications: Our results caution platforms that a precommitment to the wrong instrument can be worse than no commitment at all. Moreover, the regulation of classifying gig workers as employees, despite many of its benefits to workers, may lead to a less competitive market outcome and, surprisingly, hurt gig workers by paying them lower wages. Funding: M. Hu was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757, RGPIN-2021-04295]. Y. Liu was supported by the Hong Kong Research Grants Council, Direct Allocation Grant [Project ID P0036818]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1173 .

Different forms of coworking spaces have spread worldwide that use various artifacts (e.g., office settings, decor, furniture, equipment) to strengthen autonomy, flexibility, spontaneous interactions, and intrinsic motivation. However, the various manifestations of coworking spaces limit clearly defined boundary conditions for their successful implementation and demand a profound understanding of the interactions that take place among actors and artifacts. Using a flexible pattern matching approach, our study explains that artifacts can contour and create the possibilities for actors and define the borders, e.g., when, where, and with whom to communicate and work in coworking spaces. Our findings indicate that actors and artifacts are constitutively entangled in coworking spaces and play a key role in the community development process by fostering a shared understanding and identity, mutual interdependency, and common practices.

Unmanned aerial vehicles (UAVs) have been widely applied in civilian and military applications due to their high autonomy and strong adaptability. Although UAVs can achieve effective cost reduction and flexibility enhancement in the development of large-scale systems, they result in a serious path planning and task allocation problem. Coverage path planning, which tries to seek flight paths to cover all of regions of interest, is one of the key technologies in achieving autonomous driving of UAVs and difficult to obtain optimal solutions because of its NP-Hard computational complexity. In this paper, we study the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions. First, with models of separated regions and heterogeneous UAVs, we propose an exact formulation based on mixed integer linear programming to fully search the solution space and produce optimal flight paths for autonomous UAVs. Then, inspired from density-based clustering methods, we design an original clustering-based algorithm to classify regions into clusters and obtain approximate optimal point-to-point paths for UAVs such that coverage tasks would be carried out correctly and efficiently. Experiments with randomly generated regions are conducted to demonstrate the efficiency and effectiveness of the proposed approach.

A duty of loyalty prohibits fiduciaries from appropriating business opportunities from their companies. Starting in 2000, Delaware, followed by several other states, allowed boards to waive their duty. We show that public firms covered by waiver laws invest less in R&D, produce fewer and less valuable patents, and exhibit abnormally high inventor departures. Remaining innovation activities contribute less to firm value, a fact confirmed by the market reaction when firms reveal their curtailed internal growth opportunities by announcing acquisitions. Consistent with the laws’ intent to provide contracting flexibility to emerging firms, we find evidence of positive impacts for small firms. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

In this paper, we introduce, model and solve the pickup and delivery problem with time windows, multiple stacks, and handling operations (PDPTWMS-H). In the PDPTWMS-H, a fleet of vehicles based at a depot is used to complete a set of requests which consist of transporting items from a pickup location to a delivery location. The vehicles have multiple stacks operated using last-in-first-out (LIFO) loading which requires the vehicle to be rear-loaded and items can only be unloaded if they are closest to the back door. In the PDPTWMS-H, additional handling operations, referred to as rehandling, are allowed and an additional handling time might be incurred when rehandling items (by unloading and reloading items). The problem consists of determining the number of vehicles and the vehicle routes needed to complete the set of requests at minimal cost while respecting the possible handling operations. We model the PDPTWMS-H with a set-partitioning formulation and resort to branch-price-and-cut (BPC) for its solution. To solve the pricing problem, we derive a unified labeling algorithm that is able to cope with the different rehandling possibilities. The labeling algorithm keeps track about the information of on-board items such that symmetries with respect to both stacks and item positions are reduced. Extensive tests are performed on benchmark instances to assess the performance of the proposed BPC methodology and to provide insights on the impact of the rehandling flexibility on solution quality and time.

This paper proposes an adaptive self-tuned current controller design for an LCL-filtered LC-tuned single-phase shunt hybrid active power filter (HAPF), which offers adaptability and flexibility of the control system to filter parameters changes. System modelling and online identification of parameters are presented. Then, a systematic self-tuned controller (STC) design is proposed to calculate the controller parameters and obtain the final control law adaptively. The exact HAPF parameters design is described and the overall closed-loop stability evaluation with considering digital control delay is discussed in this paper. To confirm the theoretical achievements, simulations and experimental tests on a real prototype system are performed. The results are in good accordance and demonstrate the effectiveness and superior performance of the suggested control technique. Furthermore, a comprehensive comparison between the proposed control technique and other controllers is provided to show the superiority of the proposed system from different aspects.

To reinforce competitiveness, companies must focus on competitive priorities including cost, quality, delivery, and flexibility. The competitiveness also depends on the adherence to environmental requirements and the ability of companies to survive unforeseen disruptions. Lean, agile, resilient, and green (LARG) paradigm is a hybrid supply chain paradigm, which can enhance competitiveness in today's complex markets. In this article, we propose a new conceptual decision-making model to evaluate LARG supply chain performance in medical equipment companies. In this regard, supply chain operations reference model attributes were utilized as performance evaluation criteria. Furthermore, the appropriate practices of LARG supply chain are selected through fuzzy Delphi method. Also, an extension of decision making trial and evaluation laboratory-based analytic network process method is proposed to uncover network relationships and obtain the weights for dimensions and elements. According to the results, the agile paradigm, responsiveness, and quick introduction of new products are determined as the most prioritized elements in their corresponding dimensions, which emphasize the importance of the customer satisfaction. Finally, the extended hesitant fuzzy linguistic VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) is applied to rank LARG performance of Iranian needles and syringes companies via qualitative group decision-making process. The results of this study are conformed to conventional selection results of needles and syringes suppliers conducted by Iran's Ministry of Health.

The increase of cyberattacks by using the Internet of Things devices has exposed multiple vulnerabilities not only on devices but also on IoT infrastructures. Now, small devices can affect different networks and their services. This paper presents a review of the vulnerabilities on wireless technologies that gives connectivity to IoT, and the experiences using Software Defined Radio SDR for implementing wireless attacks to IoT technologies are assessed. A systematic literature review was conducted. The types of vulnerabilities and attacks that can affect the wireless technologies that stand the IoT ecosystem and SDR platforms were compared, and recent methods to overcome these attacks were identified. On the IoT reference model, the perception layer was identified as the most vulnerable. Most attacks at this level occur due to hardware limitations, physical device exposure, and heterogeneity of technologies. Future cybersecurity systems based on SDR technologies have notable advantages due to their flexibility and adaptability to new communication technologies and their potential to develop advanced tools. However, the IoT’s cybersecurity challenges are so complex that merging SDR hardware with cognitive and intelligent techniques is highly advisable. Some of these techniques could include deep learning to adapt mitigation systems to rapid technological changes.

This paper documents two facts: (i) elasticities of substitution in production vary significantly across sectors, with manufacturing sectors being generally less flexible than service sectors, and (ii) during the Great Recession the rise in bond spreads varied systematically with these elasticities. Specifically, more flexible sectors paid lower spreads during the Great Recession. Moreover, among the less-flexible manufacturing sectors, sectors with relatively high flexibility and high debt saw their spreads rise less than average, while among the more-flexible service sectors the sectors with relatively high flexibility and high debt saw their spreads rise more. We interpret these results using a simple two-sector model with working capital constraints, and show that the model replicates these observations if manufacturing sectors face constraints on their purchases of intermediates while services face constraints on their purchases of labor/capital. The dynamics of intermediate prices and quantities support our results, as does a quantitative investigation of a 62-sector version of the US economy.

To model threshold weighted voting systems with multifailure modes, we analyze system performance in terms of system reliability by focusing on indecisive effect. The measurement of system reliability is generated by the decision rule where we apply dynamic process on two major aspects including the indecisive parameter θ and voters' errors resulting from various situations such as weights replacement and inputs discrepancy. The motivation is to make the existing model more applicable via the dynamic process. In this article, two major contributions are addressed as follows. First, the continuous distributed indecisive parameter gives an intuitive idea of how the model adapts from the decision rule, which provides the flexibility of adjustment to satisfy different applications. Second, the time delay degradation process on voters' errors illuminates the opposite system performance for two types of inputs by considering different indecisive effects, which leads to further investigation of model sensitivity of input types.

It is common that airlines encounter a disruption of a flight schedule, which is mainly caused by resource shortages. In case of a disruption, subject to scarce resources, most of airlines lose flexibility of performing aircraft recovery on the basis of business interest priorities and need to delay, swap, and cancel flights. This paper proposes an air-rail inter-modal strategy to incorporate a High-Speed Rail (HSR) transport mode into an aviation network for aircraft recovery. The air-rail inter-modal strategy focuses on occasionally operational integration of existing airside and HSR infrastructure capacities. It is different from air-rail cooperation implemented in Europe which emphasizes a long-term strategy. In addition to modelling the air-rail inter-modal strategy, an inter-modal time-band network is presented. Modelling is applied to a pure aviation network and the inter-modal network. Comparison results show that the inter-modal air-rail strategy helps to reduce the number of cancelled flights and the total disruption cost.

The rising volume of sensitive and personal data being harvested by data controllers has increased the security essentials in the cloud system. The cloud module is not used just to store the data, but also to process them on cloud premises. Security for the cloud premises is essential as the cloud has lot of outsourced, unprotected sensitive data for the public access. This has resulted repeated data violations, and thus there is a need for the advanced legal data protection constraints. Various studies were conducted to adopt the privacy preservation in the cloud, and most of the state-of-the-art techniques fail to handle the optimal privacy when dealing with sensitive data, as it requires separate data sanitization and restoration models. To overcome this challenge, this paper tempts to develop the privacy preservation model in the cloud environment using the advancements of artificial intelligent techniques. Artificial Intelligent capabilities are working in the business cloud computing environment to make organizations more efficient, strategic, and insight-driven. However, by hosting the data, cloud computing offers businesses high flexibility, agility, and cost savings. The two main phases of the proposed privacy preservation system are the data sanitization and restoration. Moreover, the proposed sanitization process depends on the optimal key generation, which is performed by the hybrid meta-heuristic algorithm. This hybrid algorithm merges two well-performed algorithms, such as Shark Smell Optimization (SSO) and Jaya Algorithm (JA), and thus termed as Jaya-based Shark Smell Optimization (J-SSO). The optimal key generation is accomplished by deriving a multi-objective function that involves the parameters, such as the degree of modification, hiding ratio, and information preservation ratio. Finally, the performance analysis has proved the efficiency of the proposed model over the state-of-the-art models in enhancing cloud security.

Currently, there is a beginning of liquefied natural gas (LNG) market transformation, which is deepening global supply chains through the growth of the number of small-scale projects. The main driver of this transformation was the creation of large-scale LNG hubs, which did not meet expectations for improving the efficiency of gas distribution systems (like in Europe). This has led to an understanding of the need of increasing flexibility and decentralization of gas supply projects, which is almost impossible to implement with traditional large and medium scale facilities. Increasing decentralization leads to a change in the role and composition of stakeholders, and, consequently, determines new algorithms and schemes for their interaction, which are poorly described in the scientific literature. This paper systematizes information about the specifics of such project implementation, defines a list of key stakeholders, their area of responsibility and area of interests. Proposed conceptual model of interaction between stakeholders has shown a significant dependence of small-scale projects on regional economic and infrastructure conditions, as well as on the availability of multiple distributed points of gas consumption.

Involving suppliers deep in the supply chain is critical for the success of environmental and social responsibility (ESR) initiatives. Administering ESR programs throughout a complex supply network, however, is challenging. In this paper, we apply a multiunit bilateral bargaining framework to coordinate ESR investments in a general supply network and analyze to what extent an ESR initiator should directly engage the higher-tier suppliers as opposed to delegating that responsibility to the first-tier suppliers. Our bargaining framework not only generalizes the conventional Shapley value approach by allowing the flexibility of modeling imbalanced power distribution among the firms but also provides an explicit way of implementing the resulting gain sharing among the firms through negotiated contract terms. We show that the eventual structure of ESR negotiation relationships can be derived by finding a shortest path tree in the supply network with the arc cost defined as the logarithm of the negotiating parties’ relative bargaining power. These developments allow us to analyze ESR implementation in generally extended supply networks. We find that the ESR initiator tends to delegate ESR negotiations to a supplier that is strong in negotiations with higher-tier suppliers. When the supply network is complex (i.e., wide and deep), directly engaging all suppliers can lead to a larger gain by the initiator than fully delegating the negotiations with higher-tier suppliers to the first-tier ones. However, as the network gets increasingly complex, the ESR initiator tends to directly engage a reduced percentage of higher-tier suppliers. We further extend our analysis to situations where the ESR relationships are sequentially formed in a decentralized manner, where the benefit of ESR depends on the collective choice of the firms’ investment levels, where multiple ESR programs are implemented in the network, and where ESR investments depend on the negotiation relationships. This paper was accepted by David Simchi-Levi, operations management.

Community detection is one of the popular research topics in the field of complex networks analysis. It aims to identify communities, represented as cohesive subgroups or clusters, where nodes in the same community link to each other more densely than others outside. Due to the interpretability, simplicity, flexibility, and generality, nonnegative matrix factorization (NMF) has become a very ideal model for community detection and lots of related methods have been presented. To facilitate research on NMF-based community detection, in this article, we make a comprehensive review on NMF-based methods for community detection, especially the state-of-the-art methods presented in high prestige journals or conferences. First, we introduce the basic principles of NMF and explain why NMF can detect communities and design a general framework of NMF-based community detection. Second, according to the applicable network types, we propose a taxonomy to divide the existing NMF-based methods for community detection into six categories, namely, topology networks, signed networks, attributed networks, multilayer networks, dynamic networks, and large-scale networks. We deeply analyze representative methods in every category. Finally, we summarize the common problems faced by all methods and potential solutions and propose four promising research directions. We believe that this survey can fully demonstrate the versatility of NMF-based community detection and serve as a useful guideline for researchers in related fields.

Diffusion models of evidence accumulation have successfully accounted for the distributions of response times and choice probabilities from many experimental tasks, but recently their assumption that evidence is accumulated at a constant rate to constant decision boundaries has been challenged. One model assumes that decision-makers seek to optimize their performance by using decision boundaries that collapse over time. Another model assumes that evidence does not accumulate and is represented by a stationary distribution that is gated by an urgency signal to make a response. We present explicit, integral-equation expressions for the first-passage time distributions of the urgency-gating and collapsing-bounds models and use them to identify conditions under which the models are equivalent. We combine these expressions with a dynamic model of stimulus encoding that allows the effects of perceptual and decisional integration to be distinguished. We compare the resulting models to the standard diffusion model with variability in drift rates on data from three experimental paradigms in which stimulus information was either constant or changed over time. The standard diffusion model was the best model for tasks with constant stimulus information; the models with time-varying urgency or decision bounds performed similarly to the standard diffusion model on tasks with changing stimulus information. We found little support for the claim that evidence does not accumulate and attribute the good performance of the time-varying models on changing-stimulus tasks to their increased flexibility and not to their ability to account for systematic experimental effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Due to the huge gap between the high dynamic range of natural scenes and the limited (low) range of consumer-grade cameras, a single-shot image can hardly record all the information of a scene. Multi-exposure image fusion (MEF) has been an effective way to solve this problem by integrating multiple shots with different exposures, which is in nature an enhancement problem. During fusion, two perceptual factors including the informativeness and the visual realism should be concerned simultaneously. To achieve the goal, this paper presents a deep perceptual enhancement network for MEF, termed as DPE-MEF. Specifically, the proposed DPE-MEF contains two modules, one of which responds to gather content details from inputs while the other takes care of color mapping/correction for final results. Both extensive experimental results and ablation studies are conducted to show the efficacy of our design, and demonstrate its superiority over other state-of-the-art alternatives both quantitatively and qualitatively. We also verify the flexibility of the proposed strategy on improving the exposure quality of single images. Moreover, our DPE-MEF can fuse 720p images in more than 60 pairs per second on an Nvidia 2080Ti GPU, making it attractive for practical use. Our code is available at https://github.com/dongdong4fei/DPE-MEF .

This paper addresses the cyber resilience issues of multi-vector energy distribution systems (MEDS) caused by false data injection FDI, considering the uncertainty from renewable resources. A novel two-stage distributionally robust optimization (DRO) is proposed to realize the day-ahead and real-time resilience improvement. The first stage determines an initial plan for day-ahead reserve preparation and the second stage makes adjustment and takes resilience-based actions after potential load redistribution (LR) attacks and renewable output deviation. The ambiguity set is based on both the Wasserstein distance and moment information. Compared to robust optimization which considers the worst case, DRO yields less-conservative solutions and thus provides more economic operation schemes. The Wasserstein-metric based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Case studies are demonstrated on two representative MEDS networked with energy hubs, i.e., a 33-bus-20-node MEDS and a 69-bus-20-node-MEDS, illustrating the effectiveness of the proposed cyber-secured model.

Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems., Accepted in IEEE TPAMI

The traditional paradigm for pharmaceutical manufacturing is focused primarily upon centralized facilities that enable mass production and distribution. While this system reliably maintains high product quality and reproducibility, its rigidity imposes limitations upon new manufacturing innovations that could improve efficiency and support supply chain resiliency. Agile manufacturing methodologies, which leverage flexibility through portability and decentralization, allow manufacturers to respond to patient needs on demand and present a potential solution to enable timely access to critical medicines. Agile approaches are particularly applicable to the production of small-batch, personalized therapies, which must be customized for each individual patient close to the point-of-care. However, despite significant progress in the advancement of agile-enabling technologies across several different industries, there are substantial global regulatory challenges that encumber the adoption of agile manufacturing techniques in the pharmaceutical industry. This review provides an overview of regulatory barriers as well as emerging opportunities to facilitate the use of agile manufacturing for the production of pharmaceutical products. Future-oriented approaches for incorporating agile methodologies within the global regulatory framework are also proposed. Collaboration between regulators and manufacturers to cohesively navigate the regulatory waters is ultimately needed to best serve patients in the rapidly-changing healthcare environment.

Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges required to be considered. Mobile crowd sensing (MCS) embeds three beneficial characteristics: 1) cost-effectiveness; 2) scalability; and 3) mobility and robustness. With the Internet of Things becoming a reality, smartphones are widely becoming available even in remote areas. Hence, both the MCS characteristics and the plug-and-play widely available infrastructure provide huge opportunities for MCS-enabled smart agriculture, opening up several new opportunities at the application level. In this article, we extensively evaluate agriculture mobile crowd sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we offer a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low-cost requirements. However, we note that AMCSs may still possess limitations regarding data integrity and quality to be considered a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns MCS-enabled agriculture by putting forward seven potential applications of AMCS-enabled agriculture. Finally, we propose general research based on agricultural characteristics and discuss a special case based on the solar insecticidal lamp maintenance problem.

Automotive cyber-physical systems need to be rigorously checked and tested under various physical conditions. Automakers aim to improve development efficiency of the automotive cyber-physical systems in the fierce market competition. However, the actual development process suffers from the challenges of long development cycle and poor scalability. To tackle these challenges, this article develops a digital twinning based adaptive development environment for automotive cyber-physical systems, which addresses two critical problems: each physical entity (i.e., electronic control unit, component, test source, etc.) needs to clone a corresponding digital twin; digital twins and the physical entities need to interact closely. The first problem is addressed through proposing an integrated digital twinning clone flow. The second problem is addressed through developing a smart digital twinning board. Our case study with the automotive body control system demonstrates that the adaptive development environment achieves a high adaptability with short development cycle, low complexity, low cost, high scalability, and high flexibility, which meet various automotive cyber-physical design requirements during the development process.

I review and integrate a wide range of literature that has examined how geographic mobility of high-skilled workers creates value for organizations and individuals. Drawing on this interdisciplinary literature, I document that geographic mobility creates value by facilitating the transfer and recombination of knowledge, transfer of social capital, organizational norms, and financial capital, as well as by creating opportunities for individuals to develop skills, seek resources, and experience wage increases. I also review the literature around geographic immobility and synthesize this body of research under a framework of geographic mobility frictions that constrain and add costs to geographic mobility. I enumerate four key types of frictions: regulatory frictions, occupational/organizational frictions, personal frictions, and economic/environmental frictions, which act as impediments to geographic mobility. I then propose a research agenda around studying whether and how provisioning geographic flexibility through ‘work-from-anywhere’ (WFA) policies might help individuals and firms capture value from geographic mobility and mitigate adverse effects of geographic mobility frictions. I also outline future research questions related to how adoption of geographic flexibility might alter future patterns of geographic mobility, and the future geography of work.

Alfalfa is a valuable and widely adapted forage crop, and its nutritive value directly affects animal performance and ultimately affects the profitability of livestock production. Traditional nutritive value measurement method is labor-intensive and time-consuming and thus hinders the determination of alfalfa nutritive values over large fields. The adoption of unmanned aerial vehicles (UAVs) facilitates the generation of images with high spatial and temporal resolutions for field-level agricultural research. Additionally, compared with other imaging modalities, hyperspectral data usually consist of hundreds of narrow spectral bands and allow the accurate detection, identification, and quantification of crop quality. Although various machine-learning methods have been developed for alfalfa quality prediction, they were all single-task models that learned independently for each quality trait and failed to utilize the underlying relatedness between each task. Inspired by the idea of multitask learning (MTL), this study aims to develop an approach that simultaneously predicts multiple quality traits. The algorithm first extracts shared information through a long short-term memory (LSTM)-based common hidden layer. To enhance the model flexibility, it is then divided into multiple branches, each containing the same or different number of task-specific fully connected hidden layers. Through comparison with multiple mainstream single-task machine-learning models, the effectiveness of the model is illustrated based on the measured alfalfa quality data and multitemporal UAV-based hyperspectral imagery.

The design of production plants in the form of a Cyber Physical Production System (CPPS) promises rapid adaptability to changing market requirements, high flexibility during production, robust behavior in the event of failure and offers the possibility of integrating customers into the production process. In contradiction to these advantages there are both a low penetration and a still low Technology Readiness Level (TRL) of operational CPPS. This article therefore presents a production system for customized beverage bottling, consisting of industrial resources from well-known machine builders and designed as a CPPS through the use of a Multi-Agent System (MAS). The premise that all products are to be manufactured in a saleable form should clarify the claim of the CPPS to be classified as an industrial plant. Its functionality is tested through an evaluation based on two use cases with different requirements. This conceptual proof should enable the future, practice-oriented investigation of urgent questions from research and industry. As a further result, a complete test method for the development and commissioning of an CPPS, starting with the design phase and ending with a valid, reliable production system, is shown.

We study a fiscal policy model in which the government is present‐biased towards public spending. Society chooses a fiscal rule to trade off the benefit of committing the government to not overspend against the benefit of granting it flexibility to react to privately observed shocks to the value of spending. Unlike prior work, we examine rules under limited enforcement: the government has full policy discretion and can only be incentivized to comply with a rule via the use of penalties which are joint and bounded. We show that optimal incentives must be bang‐bang. Moreover, under a distributional condition, the optimal rule is a maximally enforced deficit limit, triggering the maximum feasible penalty whenever violated. Violation optimally occurs under high enough shocks if and only if available penalties are weak and such shocks are relatively unlikely. We derive comparative statics showing how rules should be calibrated to features of the environment.

New product development to enhance companies’ competitiveness and reputation is one of the leading activities in manufacturing. At present, achieving successful product design has become more difficult, even for companies with extensive capabilities in the market, because of disorganisation in the fuzzy front end (FFE) of the innovation process. Tremendous amounts of information, such as data on customers, manufacturing capability, and market trend, are considered in the FFE phase to avoid common flaws in product design. Because of the high degree of uncertainties in the FFE, multidimensional and high-volume data are added from time to time at the beginning of the formal product development process. To address the above concerns, deploying big data analytics to establish industrial intelligence is an active but still under-researched area. In this paper, an intelligent product design framework is proposed to incorporate fuzzy association rule mining (FARM) and a genetic algorithm (GA) into a recursive association-rule-based fuzzy inference system to bridge the gap between customer attributes and design parameters. Considering the current incidence of epidemics, such as the COVID-19 pandemic, communication of information in the FFE stage may be hindered. Through this study, a recursive learning scheme is established, therefore, to strengthen market performance, design performance, and sustainability on product design. It is found that the industrial big data analytics in the FFE process achieve greater flexibility and self-improvement mechanism on the evolution of product design.

SUMMARYWhen faced with predatorial threats, escape towards shelter is an adaptive action that offers long-term protection against the attacker. From crustaceans to mammals, animals rely on knowledge of safe locations in the environment to instinctively execute rapid shelter-directed escape actions 1,2. While previous work has identified neural mechanisms of escape initiation3–5, it is not known how the escape circuit incorporates spatial information to execute rapid flights along the most efficient route to shelter. Here we show that mouse retrosplenial cortex (RSP) and superior colliculus (SC) form a circuit that encodes shelter direction vector and is specifically required for accurately orienting to shelter during escape. Shelter direction is encoded in RSP and SC neurons in egocentric coordinates and SC shelter-direction tuning depends on RSP activity. Inactivation of the RSP-SC pathway disrupts orientation to shelter and causes escapes away from the optimal shelter-directed route, but does not lead to generic deficits in orientation or spatial navigation. We find that the RSP and SC are monosynaptically connected and form a feedforward lateral inhibition microcircuit that strongly drives the inhibitory collicular network due to higher RSP input convergence and synaptic integration efficiency in inhibitory SC neurons. This results in broad shelter direction tuning in inhibitory SC neurons and sharply tuned excitatory SC neurons. These findings are recapitulated by a biologically-constrained spiking network model where RSP input to the local SC recurrent ring architecture generates a circular shelter-direction map. We propose that this RSP-SC circuit might be specialized for generating collicular representations of memorized spatial goals that are readily accessible to the motor system during escape, or more broadly, during navigation when the goal must be reached as fast as possible.

This paper proposes several data-based models for solar fields (SF) of concentrating solar power (CSP) plants. The proposed models estimate SF thermal output from solar resource data and aim to replace first-principles-based SF models typical in CSP generation schedulers. The advantages of the first models as opposed to the second ones are the ease in their development and their adaptability to changes in the plant, facilitating the forecast of SF thermal output. This information is very useful in order to take advantage of the operational flexibility of CSP plants, which is the main attribute that differentiates them from other renewable sources. The applicability of these models is studied in the optimal generation self-scheduling problem of a CSP plant participating in a day-ahead energy market. A simulation-based economic study has been performed using realistic data, where several scheduling strategies are evaluated in a 50 MW parabolic-trough plant with thermal storage. The scheduling strategies differ only in the model used for the SF. The study shows that the data-based models provide results very similar to those obtained with a first-principles-based SF model. Therefore, they can be used as a viable alternative, while significantly simplifying the design and development of the generation scheduler.

In this letter we consider the coordination of connected and autonomous vehicles (CAVs) with intelligent traffic control signalization to improve the performance of traffic at intersections. We propose a coordination method based on a gradient-based multi-agent system. Specifically, we consider both the traffic light and the approaching vehicles as smart connected agents and we define a suitable edge tension function for each connection between agents. The agents then run a decentralized gradient descent control policy that drives them towards a desirable sequence of intersection crossings. In the literature, the problem of coordinating CAVs at intersections has been widely explored, with different algorithms being proposed. However, the problem of considering both smart traffic lights and smart cars simultaneously is relatively unexplored, especially from a multi-agent system, gradient-based, perspective. The advantages of the edge tension approach lie in its simplicity in terms of computation and in its ability to scale to larger networks. Furthermore, the control framework is applicable under different connected vehicle penetration rates. This flexibility is a desirable advantage given the gradual nature of the implementation of autonomy in real traffic networks. The proposed strategy is evaluated in simulation and significant fuel savings and delay improvements are shown in comparison with fixed-time traffic light control.

Smart sensors, self-configuration, operational flexibility, and automatic learning, among others, are technological attributes from industry 4.0 appliable to the essential oil extraction by the steam distillation process. These operations are recognized by their simplicity. Nevertheless, lack of automatic controls, process monitoring, and self-adjustment lead to uncontrolled extraction, poor yields, low quality of products. It occurs because of overexposure to high temperatures and overspending resources like energy and water. As far as capacity utilization is concerned, the optimized process is key to planning and managing the production activities.

This study focused on spatial analysis to identify the changes in adaptability over the last five decades. The features influencing adaptability were selected from the reference study. An appropriate method was used to analyse these features through spatial analysis. Six distinctive typologies of rural houses were selected from six regions. Unlike the traditional houses, the contemporary houses in the same area reflected a different character. Urban houses built since the early and mid-20th century were compared with contemporary houses. After analysing the openness, generality, flexibility, depth, typicality, construction technique, involvement of end-users, and the feedback from the inhabitants, the study identified a significant decrease in contemporary houses' adaptability. Spatial analysis was used to quantify the different features and compare between traditional and contemporary houses. Though the adaptability had been reduced over time, the latest houses started to achieve better flexibility in some features due to government policy and implementation of statutory building regulations. Further recommendations were provided to enhance the residential architecture's adaptability in future. The study samples were selected from different regions of Bangladesh. Still, the result and policy recommendations can be helpful for other countries, especially with high population density and a developing economy.

El objetivo es describir el software disponible en la web 4.0 para la creación de recursos didácticos en las instituciones de educación superior en Venezuela durante la pandemia del Covid-19. La investigación se tipificó descriptiva apoyada en el método cualitativo. Desde la perspectiva de las fuentes el diseño es documental. Como instrumento se aplicó una matriz de análisis de contenido en una muestra teórica (50) de artículos. Los resultados indican que en la actualidad existe una diversidad de software, para que los docentes puedan elaborar materiales didácticos innovadores en el contexto universitario, la flexibilidad de estos materiales, se extiende a su uso por parte de los estudiantes, ya que pueden reconfigurarlos, modificarlos y compartirlos. Como conclusión se tiene que el docente, debe innovar constantemente haciendo uso de las múltiples herramientas disponibles en la web 4.0, para promover aprendizajes significativos en los estudiantes en pregrado.

Motivation RNA 3D motifs are recurrent substructures, modeled as networks of base pair interactions, which are crucial for understanding structure–function relationships. The task of automatically identifying such motifs is computationally hard, and remains a key challenge in the field of RNA structural biology and network analysis. State-of-the-art methods solve special cases of the motif problem by constraining the structural variability in occurrences of a motif, and narrowing the substructure search space. Results Here, we relax these constraints by posing the motif finding problem as a graph representation learning and clustering task. This framing takes advantage of the continuous nature of graph representations to model the flexibility and variability of RNA motifs in an efficient manner. We propose a set of node similarity functions, clustering methods and motif construction algorithms to recover flexible RNA motifs. Our tool, Vernal can be easily customized by users to desired levels of motif flexibility, abundance and size. We show that Vernal is able to retrieve and expand known classes of motifs, as well as to propose novel motifs. Availability and implementation The source code, data and a webserver are available at vernal.cs.mcgill.ca. We also provide a flexible interface and a user-friendly webserver to browse and download our results. Supplementary information Supplementary data are available at Bioinformatics online.

The research phase preluding a design capstone project is challenging for both students and instructors. In a situation where students are free to work on a topic of their choosing, a preset, generic research design is not suitable. Imposing a rigid framework would endanger the possibility of students truly examining and capturing the complexity of their project problematic. This would also be considered a counterpoint to the learning goal of teaching students how to engage in problematization, a skill highly valued in contemporary design practice. This reality raises the following question: how are instructors supposed to monitor and support students in this process? To address this problem, a teaching assignment called the Newspaper was designed. The Newspaper is an open and flexible framework, based on a digital platform, through which students can organize their research findings and reflections, while ensuring enough flexibility for students to deploy appropriate and relevant research methods. This assignment gives students freedom in terms of their research design, but also has certain constraints to ensure the intelligibility of the research process. This paper presents the Newspaper assignment in detail and discusses its impacts after it is used by three cohorts of industrial design students.

Purpose: The article aims to identify links between attributes of organizational structure and knowledge management processes in high technology companies. Approach/Methodology/Design: The research was conducted in 100 Polish high-tech companies. Findings: The results show that high-tech enterprises carry out different number of sub-process of knowledge management in different combinations. Surveyed companies confirm that different structural features are needed depending on the phase/sub-process of knowledge management. It is recommended to redesign the organizational structure in such a way that it corresponds to the key knowledge management processes as much as possible. Flexibility was found to have the most supportive effect on five of the seven knowledge management sub-processes studied. Practical Implications: The results can serve as a guide for managers in improving the organizational structure for knowledge management needs. Originality/Value: The attributes of organizational structure supportive and inhibiting knowledge management processes for high-tech companies were identified., peer-reviewed

Robotic systems are becoming increasingly present in every aspect of our daily private as well as working environments. The more these systems enter into our spheres of action, the more they are intended to support humans in dangerous, dull and dirty tasks, and the more flexibility is expected from them. In order to prepare robotic systems for such duties, they need to be able to perceive their environment. In robotic systems, a perception of the environment can either be achieved through internal sensors, i.e., joint angle and torque sensors, or through external sensors, e.g., vision or tactile sensors. While the use of such internal sensors may seem advantageous because no additional system has to be added, the information that can be obtained from them is rather undifferentiated when we want to gather information about the environment. Thus, this information is in most cases augmented by external sensors. Considering exteroception, the most often used technology to analyze the environment is vision. However, due to general deficiencies of the system such as their dependence on lighting conditions and the necessary line of sight, the information accessible through vision alone [1] is not sufficient to accomplish differentiated manipulation tasks [2].

Due to high penetration of Distributed Renewable Energy Resources and their inflexible dispatch nature, modern and future electrical grids are facing technical challenges. New participants can offer the flexibility required by the grid through investments in energy storage and demand response capabilities. This article provides a comprehensive review on the concept of Energy Communities, their technical and economic motivations, available resources in the literature, trading schemes, price negotiation algorithms, and benefits for the grid. Furthermore, this review describes the policy framework in the European Union to make Energy Communities a reality. Moreover, a brief survey of related projects over the world is presented, along with a discussion around benefits, obstacles, and future research opportunities in this area.

The deployment of 5G urban networks is often described as a disruptive phenomenon since it enables new emerging Internet of Things (IoT) applications such as connected vehicles. Such applications demand new spectrum regulations to decrease network investment requirements by incentivizing operator cooperation. However, currently, no clear consensus exists on the appropriate regulatory regime for such an urban deployment. This work explores two main alternative regulatory scenarios for a connected vehicle use case. Both alternatives lower implementation costs while promoting competition. The first alternative is to maintain the current scheme of spectrum assignment while facilitating additional flexibility for infrastructure sharing (ex-post competition). The second alternative is to define local areas for monopoly 5G provisioning and define the conditions for competition ex-ante. Through agent-based simulations, this work shows that a local licensing of spectrum scenario may achieve better performance than alternative scenarios with traditional spectrum assignment. Additional sensitivity checks also help detail the practical trade-offs.

Single image super-resolution (SISR) is a classic ill-posed problem in computer vision. In recent years, deep-learning-based (DL-based) models have achieved promising results with the SISR problem. However, most existing methods suffer from an intrinsic trade-off between distortion and perceptual quality. To satisfy the requirements in different real-world situations, the balance of distortion and visual quality for image super-resolution is a critical issue. In DL-based models, the uses of hybrid loss (i.e., the combination of the distortion loss and the perceptual loss) and network interpolation are two common approaches to balancing the distortion and perceptual quality of super-resolved images. However, these two kinds of methods lack flexibility and hold strict constraints on network architectures. In this paper, we propose an image-fusion interpolation method for image super-resolution, which can balance the distortion and visual quality of super-resolved images, based on the optimal transport theory in the wavelet domain. The advantage of our proposed method is that it can be applied to any pretrained DL-based model, without any requirement from the network architecture and parameters. In addition, our proposed method is parameter-free and can run fast without using a GPU. Compared with existing state-of-the-art SISR methods, experiment results show that our proposed method can achieve a better balance between the distortion and visual quality in super-resolved images.

This article discusses the plausibility of historical analogies and general theoretical schemes in the study on contemporary international processes. It explains how the complex character of contemporary international relations modifies the previous role of theoretical generalizations and lessons from the past. The uncertainty of contemporary international processes, including the waves of global economic, social and political turbulences, makes the general theoretical schemes more tentative. It reduces the role played by historical analogies which become more risky and potentially inconclusive. They change their initial explanatory functions. The novelty and past experience intertwine in the analysis of contemporary international phenomena, yet the elements of uncertainty and novelty prevail. Theories respond to these challenges with flexibility and pragmatism, including some “middle ground” positions. Their ideas are valuable, but do not change the article’s conclusion that general theoretical schemes and historical analogies offer a limited practical advice in the study on contemporary international relations.

Existing studies on the information technology (IT) usage of firms are constrained within the perspective of “IT tool” or “IT investment.” Few studies have analyzed the IT usage of firms from a new perspective. This study aims to fill this gap by examining the impact of IT usage on the innovation performance of small- and medium-sized enterprises (SMEs) from the perspective of the product value chain (PVC). A research model integrating IT usage width, IT usage frequency, manufacturing flexibility, human capital quality, and firm innovation performance was proposed for this analysis. Furthermore, a quantitative analysis was performed on a sample of more than 1300 Chinese manufacturing firms using the newest data provided by the World Bank. Results validate that IT usage width and IT usage frequency have a significantly positive impact on the innovation performance of SMEs. Manufacturing flexibility plays a partially mediating role between IT usage frequency and the innovation performance of SMEs. Moreover, human capital quality has a significantly positive moderating effect on IT usage width and the innovation performance of SMEs. IT usage width and IT usage frequency appear to be important predictors of the innovation performance of SMEs, whereas there are substantial differences in the influencing mechanism of IT usage width and IT usage frequency on the innovation performance of SMEs.

Additive manufacturing (AM) is a layer-by-layer material deposition process that allows for more manufacturing flexibility and design complexity than traditional manufacturing processes. However, the print quality in metal AM is hard to be predicted and controlled due to its high process variability. Numerous process parameters are correlated/intertwined and have a direct/indirect relationship with the final build quality. However, these interconnections among the process parameters and print qualities are not yet fully understood and identified. To achieve a functional part without additional post-processing to improve the surface finish/mechanical properties, the comprehensive process model, which includes the semantic and qualitative connections among the process inputs-signatures-qualities, is urgently needed. Currently, surrogate models (regression models/ physical-based models) are widely used for predicting part qualities, specifically thermal distortion. These surrogate models can provide a simulated result based on the user-defined parameter settings. However, the reverse determination of the highly correlated parameters that can manage the print qualities in the desired standard cannot be achieved through these surrogate models. Understanding this interconnectivity is the top priority in building process models that will help to make the process more predictable and controllable. An ontology-based process map that captures the complicated metal AM processes, including all input parameters, physical, thermal model, and resultant build qualities (microstructure and mechanical properties), is provided in this manuscript. The connections among these factors are used to diagnose the relevant process variables regarding the specific build qualities, which can further combine with experimental data to contribute to a “printable” zone. The ontology-based process map links variable selection to quality requirements, creating a printable zone. A printable zone indicates the allowable process parameter selections, contributing to the desired part quality. Ultimately, the selected variable process data from the ontological process map will scope the parametrical analysis. The implementations of the ontology-based process map and printable zone are presented to demonstrate this proposed method's effectiveness by demonstrating that the proposed printable zone's validity and desired build quality can be achieved.

Laporan keuangan berisi informasi yang berguna bagi pemangku kepentingan mengenai kondisi keuangan perusahaan dan digunakan sebagai dasar pengambilan keputusan. Oleh karena itu, laporan keuangan harus disusun dengan tepat, berkualitas, dan mencerminkan kondisi perusahaan yang sebenarnya sehingga dapat diandalkan bagi para pengguna laporan keuangan. Financial restatement disajikan dalam basis akrual sehingga dapat meningkatkan fleksibilitas manajer dalam mencatat peristiwa ekonomi. Fleksibilitas ini dapat menyebabkan salah saji informasi dalam laporan keuangan yang disebabkan oleh perubahan akuntansi yang dipersyaratkan oleh standar akuntansi dan indikasi adanya kecurangan manajemen untuk bertindak secara oportunistik sehingga menyebabkan perusahaan melakukan financial restatement. Oleh karena itu, tujuan dari penelitian ini adalah untuk menguji dan menganalisis pengaruh struktur kepemilikan, kinerja keuangan, dan ukuran perusahaan terhadap financial restatement. Objek penelitian adalah perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia (BEI) dengan periode penelitian 2014-2018. Hasil penelitian menunjukkan bahwa struktur kepemilikan berupa kepemilikan manajerial dan kepemilikan institusional tidak berpengaruh terhadap financial restatement, sedangkan kepemilikan asing berpengaruh negatif terhadap financial restatement. Kinerja keuangan dalam bentuk profitabilitas, leverage, dan aktivitas tidak berpengaruh terhadap financial restatement. Ukuran perusahaan berpengaruh positif terhadap financial restatement.

PurposeInvestigation of housing adaptability is related to understanding factors that influence project transformation. Factors may be associated with the design and project stage as also alternative uses to existing buildings. To structure methodology for criteria analysis of adaptability, the research aims to verify the importance level of factors related to adaptability and flexibility context in architecture.Design/methodology/approachEmploying a multicriteria analysis and a diagnosis performed with architecture professionals, criteria importance identified in a bibliographic survey is contacted.FindingsWith harmonious results, key elements highlighted in the diagnosis are discussed. Adaptability and flexibility are mainly related to balance in the design of spaces and their connections, so the user can have control administrating the organization of their habitat.Originality/valueAs a complex subject, involving legal, social and cultural issues, the multicriteria method supports the scientific analysis of data. The results can be used in a practical case study as assessment of housing adaptability and flexibility, as a useful tool to decision support in architectural project.

The question of balancing the competing uses of prior and late developers has long been the core issue in international water law (IWL). Yet, how IWL evolves to assure the flexibility and continuing adaptability of its norms in the context of changing circumstances of water use regimes, has not been the subject of comprehensive academic research. Based on an empirical analysis of 459 international water treaties, this research reveals that equal protection of prior use and later use is becoming an emerging trend for international treaty practices, while obligations of “due diligence” emerge to redefine the “no-harm” principle; the growing concern for environmental protection challenges the basic understanding of the concepts of “harm” and “reasonableness”. To strengthen the trend for equal protection and to ensure greater flexibility and adaptability of IWL, this paper focuses on the promotion of institutional water cooperation, the forging of common understanding over the importance of transboundary environmental protection, and the development of more up-to-date global legal frameworks.

This study addresses the problem of selecting a supplier bound by sustainability and resilience criteria, focusing on the steel industry as a case in point. A new framework has been presented for the evaluation of suppliers in this study where thirty-two factors are first recognized based on previous publications and fourteen appropriate factors among them are selected based on expert opinions. Then, Interpretive Structural Modelling is used to study the reciprocal effects of factors and MICMAC analysis is performed to select the critical factors among the fourteen factors. Afterward, the Best–Worst Method is performed for weighting the critical factors. Eventually, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied for ranking the suppliers. The results show that the criteria with higher driving powers and lower dependence have higher weights in the Best–Worst Method. Gilan steel company in Iran is examined as a case study in the steel industry to verify the efficiency of the proposed framework. A comparative analysis is performed showing that taking resilience criteria into account alters the supplier ranking results, making these criteria important from a managerial point of view. It is also suggested to managers to consider all four dimensions of these criteria when evaluating suppliers. It is observed that the reputation and position of the supplier in the industry, availability of a support supplier, strategic stock, sensitivity to the political situation, pollution control, social responsibility, and flexibility in service are the main factors to consider.

Purpose The study aims to identify the impact of monetary policy tools on the performance of banks in India, and this could be an excellent suggestion to the regulators in framing the favourable interest rates which would meet the macroeconomic objectives of the Indian economy. Design/methodology/approach The design adopted in this study is descriptive and analytical research. Correlation and regression analysis is used to determine the relationship between bank rate (BR) and the performance of public sector banks in India. The sample chosen for this study is the public sector banks actively performing in India. Findings The performance is measured by taking three factors, and they are deposits, loans and advances (L&A) and total asset value of the banks. All three factors have shown an impact of BR on them during the five years. L&A affected the least amongst the three factors, but the other two were significantly impacted by the change in BR by the Reserve Bank of India. So, there should be a favourable fluctuation in the BR which will bring flexibility in the banking system, and they can perform well in the economy and the central bank also can concentrate on the macro-economic situation in the country. Originality/value This paper helps in giving suggestions to the Central bank, researchers, financial institutions to look into the financial performance and monetary policy rates and the central bank also can concentrate on the macro-economic situation in the country.

PurposeIntegration of city systems is needed to provide flexibility, agility and access to real-time information for the creation and delivery of efficient services in a smart and sustainable city. Consequently, City Process Modelling (CPMo) becomes an essential element of connecting various city sectors. However, to date, there has been limited research on the requirements of an ideal CPMo approach and the usefulness of available Business Process Modelling (BPMo) approaches. This research develops a framework for CPMo to guide smart city developers when modelling city processes.Design/methodology/approachData from literature analysis was gathered to derive capabilities of existing BPMo techniques. Then, semi-structured interviews were conducted to thematically and qualitatively explore the requirements, challenges and success factors of CPMo.FindingsThe interview findings offered 17 requirements to be addressed by a CPMo approach, along with several challenges and success factors to be considered when implementing CPMo approaches. Then, the paper presents the results of mapping these requirements against 12 existing BPMo capabilities, identified from the literature, concluding that a significant number of requirements (which are mainly related to inputs and visualisation) have been left unfulfilled by existing BPMo approaches. Hence, developing an innovative CPMo approach is necessary to address the components of unfulfilled requirements.Originality/valueThe innovative framework presented in this paper justifies the CPMo requirements, which are unexplored in existing SCD frameworks. Moreover, it will act as a guide for smart city developers, to model cross-sectoral city processes, helping them progress their SCD road map and make their cities smart.

The emergence of logistics and supply chain management as a fully mature business discipline may depend on the development of foundational supply chain management perspectives embracing a focus on responsiveness. Hundreds of papers in our field conceptualize responsiveness and related concepts in disconnected ways ignoring this potentially valuable foundation for investigating supply chain strategic and logistical adjustments. Although these extant studies highlight many important issues related to responsiveness, their conceptualizations and nomological networks vary considerably. This diffuse focus seriously hinders efforts to create an overarching theoretical perspective in a dynamic field without one. The result is a masking of promising research directions that could help define the discipline. Drawing from the organizational economics, logistics, and supply chain management literatures, we begin the argument that responsiveness—realized through logistics and supply chain management—has strong potential as our defined foundational perspective. All roads to superior performance depend upon supply chain responsiveness to the environment, supply chain members, stakeholders, and the consumer. Our proposed Responsiveness View of supply chain management supports the exploration of how supply chains compete successfully amidst disruption and change, helping to define a young, theoretically distinct, research domain.