Investigating the impact of networking capability on firm innovation performance:using the resource-action-performance framework

The author's final peer reviewed version can be found by following the URI link. The Publisher's final version can be found by following the DOI link.Purpose The experience of successful firms has proven that one of the most important ways to promote co-learning and create successful networked innovations is the proper application of inter-organizational knowledge mechanisms. This study aims to use a resource-action-performance framework to open the black box on the relationship between networking capability and innovation performance. The research population embraces companies in the Iranian automotive industry. Design/methodology/approach Due to the latent nature of the variables studied, the required data are collected through a web-based cross-sectional survey. First, the content validity of the measurement tool is evaluated by experts. Then, a pre-test is conducted to assess the reliability of the measurement tool. All data are gathered by the Iranian Vehicle Manufacturers Association (IVMA) and Iranian Auto Parts Manufacturers Association (IAPMA) samples. The power analysis method and G*Power software are used to determine the sample size. Moreover, SmartPLS 3 and IBM SPSS 25 software are used for data analysis of the conceptual model and relating hypotheses. Findings The results of this study indicated that the relationships between networking capability, inter-organizational knowledge mechanisms and inter-organizational learning result in a self-reinforcing loop, with a marked impact on firm innovation performance. Originality/value Since there is little understanding of the interdependencies of networking capability, inter-organizational knowledge mechanisms, co-learning and their effect on firm innovation performance, most previous research studies have focused on only one or two of the above-mentioned variables. Thus, their cumulative effect has not examined yet. Looking at inter-organizational relationships from a network perspective and knowledge-based view (KBV), and to consider the simultaneous effect of knowledge mechanisms and learning as intermediary actions alongside, to consider the performance effect of the capability-building process, are the main advantages of this research

Investigation of Visual Management Cases in Construction by an Analytical Framework from Manufacturing

Along with the progress of globalization, speed and efficiency have become more critical for any industry than ever before. In this sense, the concept and methods of lean management, promoting these performances, have been deployed from manufacturing, its origin industry, to other industries. This paper deals with this management style in the construction industry, called lean construction. In particular, visual management (VM) as one effective tool in this scheme is focused on. A number of VM cases, 306 in total, was collected from both construction and manufacturing sites and investigated by the so-called 5W1H analytical framework developed in the manufacturing industry. Obtained results suggest that the VM cases in construction have common attributes such as purpose and location, target to attain, users’ attributes, timing to use and elemental technologies for case development. A comparison analysis of the VM cases from construction and those from manufacturing was also carried out, for a mutual transfer of this technology between these industries

Why do patients having coronary artery bypass grafts have different costs or length of stay? : An analysis across ten European countries

We analyse variations in costs or lengths of stay (LoS) for 66,587 patients from ten European countries receiving a coronary artery bypass graft (CABG) procedure. In five of these countries, variations in cost are analysed using log-linear models. In the other five countries, negative binomial regression models are used to explore variations in LoS. We compare how well each country’s Diagnosis Related Group (DRG) system and a set of patient-level characteristics explain these variations. The most important explanatory factors are the total number of diagnoses and procedures, although no clear effects are evident for our CABG-specific diagnostic and procedural variables. Wound infections significantly increase length of stay and costs in all countries. There is no evidence that countries using larger numbers of DRGs to group CABG patients were better at explaining variations in cost or LoS. However, refinements to the construction of DRGs to group CABG patients might recognise first and subsequent CABGs or other specific surgical procedures, such as multiple valve repair

Assessment of participatory management of irrigation schemes in Sri Lanka: Partial reforms, partial benefits

Privatization / Policy / Performance evaluation / Indicators / Operating costs / Irrigation management / Economic aspects / Returns / Participatory management / Farmer participation / Government managed irrigation systems / Small scale systems / Large-scale systems / Regression analysis / Models

How well do DRGs for appendectomy explain variations in resource use? : An analysis of patient-level data from 10 European countries

Appendectomy is a common and relatively simple procedure to remove an inflamed appendix, but the rate of appendectomy varies widely across Europe. This paper investigates factors that explain differences in resource use for appendectomy. We analysed 106,929 appendectomy patients treated in 939 hospitals in ten European countries. In stage one, we tested the performance of three models in explaining variation in the (log of) cost of the inpatient stay (seven countries) or length-of-stay (three countries). The first model used only the Diagnosis Related Groups (DRGs) to which patients were coded; the second used a core set of general patient-level and appendectomy-specific variables; and the third model combined both sets of variables. In stage two, we investigated hospital-level variation. In classifying appendectomy patients, most DRG systems take account of complex diagnoses and comorbidities, but use different numbers of DRGs (range: 2 to 8). The capacity of DRGs and patient-level variables to explain patient-level cost variation ranges from 34% in Spain to over 60% in England and France. All DRG systems can make better use of administrative data such as the patient’s age, diagnoses and procedures, and all countries have outlying hospitals that could improve their management of resources for appendectomy

Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems. However, these deep models are perceived as "black box" methods considering the lack of understanding of their internal functioning. There has been a significant recent interest in developing explainable deep learning models, and this paper is an effort in this direction. Building on a recently proposed method called Grad-CAM, we propose a generalized method called Grad-CAM++ that can provide better visual explanations of CNN model predictions, in terms of better object localization as well as explaining occurrences of multiple object instances in a single image, when compared to state-of-the-art. We provide a mathematical derivation for the proposed method, which uses a weighted combination of the positive partial derivatives of the last convolutional layer feature maps with respect to a specific class score as weights to generate a visual explanation for the corresponding class label. Our extensive experiments and evaluations, both subjective and objective, on standard datasets showed that Grad-CAM++ provides promising human-interpretable visual explanations for a given CNN architecture across multiple tasks including classification, image caption generation and 3D action recognition; as well as in new settings such as knowledge distillation.Comment: 17 Pages, 15 Figures, 11 Tables. Accepted in the proceedings of IEEE Winter Conf. on Applications of Computer Vision (WACV2018). Extended version is under review at IEEE Transactions on Pattern Analysis and Machine Intelligenc