The MNC as a Knowledge Structure The Roles of Knowledge Sources and Organizational Instruments in MNC Knowledge Management

Recent research on the differentiated MNC has concerned knowledge flows between MNC units. While linking up with this literature, we extend in two directions. First, we argue that conceptualizing the MNC as a knowledge structure furthers the understanding of intra-MNC knowledge flows. Thus, we see MNC knowledge elements as being structured along such dimensions as their type and degree of complementarity to other knowledge elements, and their sources, for example, whether they are mainly developed from external or internal knowledge sources. These dimensions matter in terms of knowledge flows, because they influence the costs and benefits of knowledge transfer and, hence, the actual level of knowledge transferred. Second, based on this conceptualization, we argue that MNC management can influence the development, characteristics and transfer of knowledge through choices regarding organizational instruments (control, motivation and context). We test six hypotheses derived from these arguments against a unique dataset on subsidiary knowledge development. The dataset includes information on organizational instruments, sources of subsidiary knowledge, and the extent of knowledge transfer to other MNC units. It covers more than 2,000 subsidiaries located in seven different European countries.Knowledge structure, complementarity, knowledge transfer, the MNC

A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks

Transfer Learning (TL) plays a crucial role when a given dataset has insufficient labeled examples to train an accurate model. In such scenarios, the knowledge accumulated within a model pre-trained on a source dataset can be transferred to a target dataset, resulting in the improvement of the target model. Though TL is found to be successful in the realm of image-based applications, its impact and practical use in Natural Language Processing (NLP) applications is still a subject of research. Due to their hierarchical architecture, Deep Neural Networks (DNN) provide flexibility and customization in adjusting their parameters and depth of layers, thereby forming an apt area for exploiting the use of TL. In this paper, we report the results and conclusions obtained from extensive empirical experiments using a Convolutional Neural Network (CNN) and try to uncover thumb rules to ensure a successful positive transfer. In addition, we also highlight the flawed means that could lead to a negative transfer. We explore the transferability of various layers and describe the effect of varying hyper-parameters on the transfer performance. Also, we present a comparison of accuracy value and model size against state-of-the-art methods. Finally, we derive inferences from the empirical results and provide best practices to achieve a successful positive transfer.Comment: 9 pages, 2 figures, accepted in SDM 201

Building brands through experiential events: when entertainment meets education

Experiential marketing is increasingly getting companies’ attention as a strategy to interact with consumers and engage them to better convey their brand image and positioning. However, its effects are still unclear both at the aggregate and at the individual levels. This paper addresses this topic and presents a field experiment investigating the effects of experiential marketing on brand image in retailing. Two similar consumer electronics stores with different strategies – traditional vs. experiential – constitutes the setting in which a field experiment has been run. Two similar samples of consumers took part in our study by visiting one of these two stores, and answering a questionnaire before and after the visit with the primary goal to investigate the brand image and its changes due to the shopping visit. Brand image was measured as the overall brand attitude – via four items – and five specific desired brand claims that the company wanted to convey to consumers. Findings show that engaged consumers through the multisensory and interactive event arranged in the experiential store register higher levels of both brand attitude and all brand claims than those visiting the traditional store, and that the increase in both the dependent variables after the visit of the experiential store is higher than the increase in the traditional store. Thus, experiential stores are not only able to entertain consumers, but they are also able to educate them, by conveying them a set of brand claims more effectively than the traditional stor

On systematic approaches for interpreted information transfer of inspection data from bridge models to structural analysis

In conjunction with the improved methods of monitoring damage and degradation processes, the interest in reliability assessment of reinforced concrete bridges is increasing in recent years. Automated imagebased inspections of the structural surface provide valuable data to extract quantitative information about deteriorations, such as crack patterns. However, the knowledge gain results from processing this information in a structural context, i.e. relating the damage artifacts to building components. This way, transformation to structural analysis is enabled. This approach sets two further requirements: availability of structural bridge information and a standardized storage for interoperability with subsequent analysis tools. Since the involved large datasets are only efficiently processed in an automated manner, the implementation of the complete workflow from damage and building data to structural analysis is targeted in this work. First, domain concepts are derived from the back-end tasks: structural analysis, damage modeling, and life-cycle assessment. The common interoperability format, the Industry Foundation Class (IFC), and processes in these domains are further assessed. The need for usercontrolled interpretation steps is identified and the developed prototype thus allows interaction at subsequent model stages. The latter has the advantage that interpretation steps can be individually separated into either a structural analysis or a damage information model or a combination of both. This approach to damage information processing from the perspective of structural analysis is then validated in different case studies

A space-time neural network

Introduced here is a novel technique which adds the dimension of time to the well known back propagation neural network algorithm. Cited here are several reasons why the inclusion of automated spatial and temporal associations are crucial to effective systems modeling. An overview of other works which also model spatiotemporal dynamics is furnished. A detailed description is given of the processes necessary to implement the space-time network algorithm. Several demonstrations that illustrate the capabilities and performance of this new architecture are given

Motivation Factors On Knowledge Sharing Among Public Sector Organizations In Malaysia

Tujuan utama kajian dijalankan adalah bagi mengenalpasti faktor-faktor yang mempengaruhi perkongsian pengetahuan di kalangan organisasi sektor kerajaan di Malaysia. A study has been conducted to explore the motivation factors on knowledge sharing among public sector organizations in Malaysia

What Are the Useful Past Inter-Organizational Relationships (IORs) for Forming Complex IORs?

Purpose: The purpose is to explore the relationship between prior and later inter-organizational relationships (IORs) by studying whether past experience can be leveraged on when forming new, more complex, IORs. Methodology: Participation in prior IORs is characterized in terms of both resource- transferring and resource-pooling IORs in home-country networks, while complex IORs are considered those with foreign partners. An empirical test on 366 Italian firms is performed using OLS with robust standard errors. Findings: Both resource-transferring and resource-pooling IORs have non-convergent effects. The former has controversial effects on the base of the position a firm occupies, that in turn affects the structure of interests between the partners. The latter has different effects in line with the information complexity of the objective of the relationship. Research Implications: Results provide support to the idea that structure of interests and information complexity represent \u201cdiscriminating characteristics\u201d that identify salient structural alternatives in the analysis of inter-firm organization. Practical Implications: The paper advances that firms can partially leverage on the exploitation of prior experience in settings that are explorative in nature, by carefully selecting within past experiences. Originality: A distinction between coordination \u201cgiving\u201d and coordination \u201ctaking\u201d IORs is proposed to discern among different types of inter-firm coordination forms

Conditional Random Fields as Recurrent Neural Networks

Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixel-level labelling tasks. One central issue in this methodology is the limited capacity of deep learning techniques to delineate visual objects. To solve this problem, we introduce a new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling. To this end, we formulate mean-field approximate inference for the Conditional Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks. This network, called CRF-RNN, is then plugged in as a part of a CNN to obtain a deep network that has desirable properties of both CNNs and CRFs. Importantly, our system fully integrates CRF modelling with CNNs, making it possible to train the whole deep network end-to-end with the usual back-propagation algorithm, avoiding offline post-processing methods for object delineation. We apply the proposed method to the problem of semantic image segmentation, obtaining top results on the challenging Pascal VOC 2012 segmentation benchmark.Comment: This paper is published in IEEE ICCV 201