Factors affecting regional SMEs progression to Digital Business Ecosystems

SMEs are widely recognized as an important driving force of economic growth, yet, their uptake of ICT is still very low. Tosupport SMEs ICT adoption and to foster regional development, in 2000, the Lisbon Strategy on the Information Society andKnowledge-based economy created a vision for 2010 towards the creation of the European Digital Business Ecosystems(DBE). This paper is positioned within that context and reports upon a project involving 6000 SMEs whose aim was tosupport ICT adoption and to encourage SME networks through the creation of a Regional Business Portal. The papere xplores factors affecting the regional SMEs participating in the DBE. An in-depth longitudinal case study approach was adopted and multiple sources of evidence were used. Many factors affecting SMEs progression to DBE were identified:including people and organization, environmental, diffusion networks, technological, regional and time factors

Social networks: evolving graphs with memory dependent edges

The plethora, and mass take up, of digital communication tech- nologies has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the ex- istence or otherwise of certain infinite products and series involving age dependent model parameters. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

An overview of the use of neural networks for data mining tasks

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.

Closed-loop feedback computation model of dynamical reputation based on the local trust evaluation in business-to-consumer e-commerce

Trust and reputation are important factors that influence the success of both traditional transactions in physical social networks and modern e-commerce in virtual Internet environments. It is difficult to define the concept of trust and quantify it because trust has both subjective and objective characteristics at the same time. A well-reported issue with reputation management system in business-to-consumer (BtoC) e-commerce is the “all good reputation” problem. In order to deal with the confusion, a new computational model of reputation is proposed in this paper. The ratings of each customer are set as basic trust score events. In addition, the time series of massive ratings are aggregated to formulate the sellers’ local temporal trust scores by Beta distribution. A logical model of trust and reputation is established based on the analysis of the dynamical relationship between trust and reputation. As for single goods with repeat transactions, an iterative mathematical model of trust and reputation is established with a closed-loop feedback mechanism. Numerical experiments on repeated transactions recorded over a period of 24 months are performed. The experimental results show that the proposed method plays guiding roles for both theoretical research into trust and reputation and the practical design of reputation systems in BtoC e-commerce.

Creating Prestige and Global Networks through Conference Rituals

An international conference is a secular ritual which serves to create, recreate and shape global-wide translocal cultural sharings. Social anthropological theories and methods are used to show that, besides being an information flow junction, the international conference is a network crossroad and a way of socialising new members into aninternational research community. It is also capable of creating prestige and honour for the individual researcher,for the arranging research team, university and city. Rituals do not merely reflect the social relations or cosmology of a society, but are events that in themselves do important things through ritual forms and symbolic statements.

The market strategies alternatives of an e-business start-up in Brazil

The e-business market is one of the fastest growing markets in Brazil, with e-business sales accounting for BRL 14.8 billion in 2010 and a growth of 40% per year (+1000% over the past 7 years). Sales-event clubs and collective bargaining websites are one of the most dynamic segments of the e-business market: the number of new players is increasing rapidly, with over 1200 collective bargaining websites currently operating in Brazil. In that context, growth and differentiation seem to be two key success factors for Coquelux. According to webshopper (23rd Edition, e-bit), growth can be achieved by targeting middle and low-income consumers from class C, who represent 50% of the total e-commerce sales. But Coquelux, which is specialized in desire and luxury brands, has built its reputation and competitive advantage through its “exclusivity”, by targeting wealthier consumers from classes A and B who are attracted by its fashionable and high-end positioning. The evolution (growth?) of this market and the development of its competition naturally raise a strategic question for Coquelux’s managers: can Coquelux grow and still maintain its competitive advantage? Should it grow by expanding its consumer base to class C? If so, how? Consumers from classes A, B or C must be targeted through the same online communication channels. Recent studies from the ABEP/ABIPEME emphasized the importance of social networks as a tool for converting new clients and gaining their loyalty, regardless of their social class. However, high-income and low-income e-consumers do not have the same consumption habits, do not respond to the same type of marketing strategies, and most importantly, do not share the same values. Thus, it seems difficult to expand Coquelux’s consumer base to class C without changing its marketing strategies and altering its image Three options were identified for Coquelux: reinforcing its leadership on the luxury segment and focusing on a small niche market (1), which would threaten its survival in the long run; completely changing its strategy and competing for a mass market through commercial brands (2), which requires major financial investments that managers don’t have access to; or finding an intermediary solution (3). This thesis’ recommendation for the third option consists in focusing on premium brands (rather than luxury) in order to increase sales volume (Coquelux’s most profitable sales happened with local desire brands) with products that appeal to class B but also attract the emerging class C which is looking for brand recognition. It could thus implement a slow entry strategy towards the mass market without damaging its main competitive advantage.

Windows of opportunities and knowledge networks: implications for the catch-up in developing countries

This document represents a doctoral thesis held under the Brazilian School of Public and Business Administration of Getulio Vargas Foundation (EBAPE/FGV), developed through the elaboration of three articles. The research that resulted in the articles is within the scope of the project entitled “Windows of opportunities and knowledge networks: implications for catch-up in developing countries”, funded by Support Programme for Research and Academic Production of Faculty (ProPesquisa) of Brazilian School of Public and Business Administration (EBAPE) of Getulio Vargas Foundation.

Markets-as-networks theory: a review

This paper exposes the development of markets-as-networks theory from formal inception in the mid-1970s until 2010 state-of-the-art, en route presenting its historical roots. This largely European-based theory challenges the conventional, dichotomous view of the business world as including firms and markets, arguing for the existence of relational governance structures (the so-called ‘‘interfirm cooperation’’) in addition to hierarchical and transactional ones.

Using remote lab networks to provide support to public secondary school education level

The advantages of networking are widely known in many areas (from business to personal ones). One particular area where networks have also proved their benefits is education. Taking the secondary school education level into account, some successful cases can be found in literature. In this paper we describe a particular remote lab network supporting physical experiments accessible to students of institutions geographically separated. The network architecture and application examples of using some of the available remote experiments are illustrated in detail. ©2008 IEEE.

Typology of companies networks for competitive assessment: an application in higher education institutions

Taking into account the changes in the market scenario by virtue of globalization, Institutes of Higher Education (IES) as well as other organizations seek their competitive stability. For that reason, it is up to organizations to adopt innovative models of management for their operations aimed at improving results. Company networks consist of a model that is perfect for uniting efforts through cooperation among partners in a given business, which can involve ties of different natures. This paper shows the development and the application of an auxiliary technique to analyze the intensity, nature and importance of internal and external relations in the formation of results for a company network. For such, a multiple case study was conducted at two IES in the State of São Paulo and their networks of partners and employees in order to observe their specificities and organizational strategies. The study demonstrated the existence of specific performance criteria (pillars) for each IES and its network, resulting from its competitive reality. It reveals evidence that the education pillar is strengthened in both cases, and the research pillar is growing, although it is the weakest. The outreach pillar is the most robust in the public IES and the financial sustainability pillar is relevant for the private IES, and it was only detected in this IES.

Knowledge Flows and Networks: the Interplay between Local and Global Linkages within Bangalore IT Cluster

This doctoral work gains deeper insight into the dynamics of knowledge flows within and across clusters, unfolding their features, directions and strategic implications. Alliances, networks and personnel mobility are acknowledged as the three main channels of inter-firm knowledge flows, thus offering three heterogeneous measures to analyze the phenomenon. The interplay between the three channels and the richness of available research methods, has allowed for the elaboration of three different papers and perspectives. The common empirical setting is the IT cluster in Bangalore, for its distinguished features as a high-tech cluster and for its steady yearly two-digit growth around the service-based business model. The first paper deploys both a firm-level and a tie-level analysis, exploring the cases of 4 domestic companies and of 2 MNCs active the cluster, according to a cluster-based perspective. The distinction between business-domain knowledge and technical knowledge emerges from the qualitative evidence, further confirmed by quantitative analyses at tie-level. At firm-level, the specialization degree seems to be influencing the kind of knowledge shared, while at tie-level both the frequency of interaction and the governance mode prove to determine differences in the distribution of knowledge flows. The second paper zooms out and considers the inter-firm networks; particularly focusing on the role of cluster boundary, internal and external networks are analyzed, in their size, long-term orientation and exploration degree. The research method is purely qualitative and allows for the observation of the evolving strategic role of internal network: from exploitation-based to exploration-based. Moreover, a causal pattern is emphasized, linking the evolution and features of the external network to the evolution and features of internal network. The final paper addresses the softer and more micro-level side of knowledge flows: personnel mobility. A social capital perspective is here developed, which considers both employees’ acquisition and employees’ loss as building inter-firm ties, thus enhancing company’s overall social capital. Negative binomial regression analyses at dyad-level test the significant impact of cluster affiliation (cluster firms vs non-cluster firms), industry affiliation (IT firms vs non-IT fims) and foreign affiliation (MNCs vs domestic firms) in shaping the uneven distribution of personnel mobility, and thus of knowledge flows, among companies.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.