The role of network competence and international business competence in multinational corporations: Subsidiary perspective

On-going process of globalization makes companies all over the world to go beyond the national markets and internationalize. Organizational form of multinational corporation (MNC) has capabilities for establishing the affiliate companies in several countries. Thus, the relocation of resources occurs and particularly, the cross-border transfer of knowledge which possesses the competitive advantage. However, differences in countries` business environments and cultures may constrain this capability. The research aim of this thesis is to investigate the role of subsidiary’s network competence (ability to build and manage the relationships with other local business units) and international business competence in relation to the benefits that MNC receives from a subsidiary. Additionally, subsidiary’s business adaptation, partnerships and knowledge transfer mechanism with parent company and external partners are investigated. This research, conducted in the Finnish-Russian context, consists of theoretical and empirical parts. The qualitative approach in the form of multiple case studies is employed. The empirical data incorporated primary and secondary data in the form of interviews collected in 2013 and 2015 years. Interviews were collected from four Finnish case companies in Saint-Petersburg and Kaluga region and five Russian partner companies. Results are drawn from two cases from Saint-Petersburg. The abductive research approach for the results analysis is adopted. The results indicate that both competencies lead to the subsidiary’s local embeddedness in the form of mutual business activities with local business partners and product adaptation for the local market needs. In addition to the monetary benefits in form of payments or turnover share, local embeddedness brings the knowledge of the local environment which is utilized by an MNC in the long-term planning. Another found tacit benefit is the access to the national market. This is strategically useful benefit not only for parent MNC but also for the subsidiary’s partners, i.e. international suppliers.

Dynamic capabilities as enablers of value co-creation between an MBA school and its students

This study presents an understanding of how a U.S. based, international MBA school has been able to achieve competitive advantage within a relatively short period of time. A framework is built to comprehend how the dynamic capability and value co-creation theories are connected and to understand how the dynamic capabilities have enabled value co-creation to happen between the school and its students, leading to such competitive advantage for the school. The data collection method followed a qualitative single-case study with a process perspective. Seven semi-structured interviews were made in September and October of 2015; one current employee of the MBA school was interviewed, with the other six being graduates and/or former employees of the MBA school. In addition, the researcher has worked as a recruiter at the MBA school, enabling to build bridges and a coherent whole of the empirical findings. Data analysis was conducted by first identifying themes from interviews, after which a narrative was written and a causal network model was built. Thus, a combination of thematic analysis, narrative and grounded theory were used as data analysis methods. This study finds that value co-creation is enabled by the dynamic capabilities of the MBA school; also capabilities would not be dynamic if value co-creation did not take place. Thus, this study presents that even though the two theories represent different level analyses, they are intertwined and together they can help to explain competitive advantage. The MBA case school’s dynamic capabilities are identified to be the sales & marketing capabilities and international market creation capabilities, thus the study finds that the MBA school does not only co-create value with existing students (customers) in the school setting, but instead, most of the value co-creation happens between the school and the student cohorts (network) already in the recruiting phase. Therefore, as a theoretical implication, the network should be considered as part of the context. The main value created seem to lie in the MBA case school’s international setting & networks. MBA schools around the world can learn from this study; schools should try to find their own niche and specialize, based on their own values and capabilities. With a differentiating focus and a unique and practical content, the schools can and should be well-marketed and proactively sold in order to receive more student applications and enhance competitive advantage. Even though an MBA school can effectively be treated as a business, as the study shows, the main emphasis should still be on providing quality education. Good content with efficient marketing can be the winning combination for an MBA school.

Adoption of Technology-Enabled Banking Self-Services: Antecedents and Consequences

School of Management Studies, Cochin University of Science and Technology

AN ARTIFICIAL NEURAL NETWORK FOR SONAR TARGET DETECTION

Neural Network has emerged as the topic of the day. The spectrum of its application is as wide as from ECG noise filtering to seismic data analysis and from elementary particle detection to electronic music composition. The focal point of the proposed work is an application of a massively parallel connectionist model network for detection of a sonar target. This task is segmented into: (i) generation of training patterns from sea noise that contains radiated noise of a target, for teaching the network;(ii) selection of suitable network topology and learning algorithm and (iii) training of the network and its subsequent testing where the network detects, in unknown patterns applied to it, the presence of the features it has already learned in. A three-layer perceptron using backpropagation learning is initially subjected to a recursive training with example patterns (derived from sea ambient noise with and without the radiated noise of a target). On every presentation, the error in the output of the network is propagated back and the weights and the bias associated with each neuron in the network are modified in proportion to this error measure. During this iterative process, the network converges and extracts the target features which get encoded into its generalized weights and biases.In every unknown pattern that the converged network subsequently confronts with, it searches for the features already learned and outputs an indication for their presence or absence. This capability for target detection is exhibited by the response of the network to various test patterns presented to it.Three network topologies are tried with two variants of backpropagation learning and a grading of the performance of each combination is subsequently made.

Development and Deployment of Wireless Sensor Network in Paddy Fields of Kuttanad

The evolution of wireless sensor network technology has enabled us to develop advanced systems for real time monitoring. In the present scenario wireless sensor networks are increasingly being used for precision agriculture. The advantages of using wireless sensor networks in agriculture are distributed data collection and monitoring, monitor and control of climate, irrigation and nutrient supply. Hence decreasing the cost of production and increasing the efficiency of production. This paper describes the development and deployment of wireless sensor network for crop monitoring in the paddy fields of Kuttanad, a region of Kerala, the southern state of India.

Estado situacional de los modelos basados en agentes y su impacto en la investigación organizacional

En un mundo hiperconectado, dinámico y cargado de incertidumbre como el actual, los métodos y modelos analíticos convencionales están mostrando sus limitaciones. Las organizaciones requieren, por tanto, herramientas útiles que empleen tecnología de información y modelos de simulación computacional como mecanismos para la toma de decisiones y la resolución de problemas. Una de las más recientes, potentes y prometedoras es el modelamiento y la simulación basados en agentes (MSBA). Muchas organizaciones, incluidas empresas consultoras, emplean esta técnica para comprender fenómenos, hacer evaluación de estrategias y resolver problemas de diversa índole. Pese a ello, no existe (hasta donde conocemos) un estado situacional acerca del MSBA y su aplicación a la investigación organizacional. Cabe anotar, además, que por su novedad no es un tema suficientemente difundido y trabajado en Latinoamérica. En consecuencia, este proyecto pretende elaborar un estado situacional sobre el MSBA y su impacto sobre la investigación organizacional.

Las TIC en la administración de la cadena de suministro y su incidencia en el desempeño operacional

Las tecnologías de la información han empezado a ser un factor importante a tener en cuenta en cada uno de los procesos que se llevan a cabo en la cadena de suministro. Su implementación y correcto uso otorgan a las empresas ventajas que favorecen el desempeño operacional a lo largo de la cadena. El desarrollo y aplicación de software han contribuido a la integración de los diferentes miembros de la cadena, de tal forma que desde los proveedores hasta el cliente final, perciben beneficios en las variables de desempeño operacional y nivel de satisfacción respectivamente. Por otra parte es importante considerar que su implementación no siempre presenta resultados positivos, por el contrario dicho proceso de implementación puede verse afectado seriamente por barreras que impiden maximizar los beneficios que otorgan las TIC.

GDASH: a grid-enabled program for structure solution from powder diffraction data

The simulated annealing approach to structure solution from powder diffraction data, as implemented in the DASH program, is easily amenable to parallelization at the individual run level. Very large scale increases in speed of execution can therefore be achieved by distributing individual DASH runs over a network of computers. The GDASH program achieves this by packaging DASH in a form that enables it to run under the Univa UD Grid MP system, which harnesses networks of existing computing resources to perform calculations.

Innovation dynamics and capability in open collaborative cyber communities: implications for cybersecurity

Cybersecurity is a complex challenge that has emerged alongside the evolving global socio-technical environment of social networks that feature connectivity across time and space in ways unimaginable even a decade ago. This paper reports on the preliminary findings of a NATO funded project that investigates the nature of innovation in open collaborative communities and its implications for cyber security. In this paper, the authors describe the framing of relevant issues, the articulation of the research questions, and the derivation of a conceptual framework based on open collaborative innovation that has emerged from preliminary field research in Russia and the UK.

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Improving CS-4 user data rate in GPRS enabled devices by using a BLER co-processor

The General Packet Radio Service (GPRS) was developed to allow packet data to be transported efficiently over an existing circuit switched radio network. The main applications for GPRS are in transporting IP datagram’s from the user’s mobile Internet browser to and from the Internet, or in telemetry equipment. A simple Error Detection and Correction (EDC) scheme to improve the GPRS Block Error Rate (BLER) performance is presented, particularly for coding scheme 4 (CS-4), however gains in other coding schemes are seen. For every GPRS radio block that is corrected by the EDC scheme, the block does not need to be retransmitted releasing bandwidth in the channel, improving throughput and the user’s application data rate. As GPRS requires intensive processing in the baseband, a viable hardware solution for a GPRS BLER co-processor is discussed that has been currently implemented in a Field Programmable Gate Array (FPGA) and presented in this paper.

Cloud optical depth retrievals from the Aerosol Robotic Network (AERONET) cloud mode observations

Cloud optical depth is one of the most poorly observed climate variables. The new “cloud mode” capability in the Aerosol Robotic Network (AERONET) will inexpensively yet dramatically increase cloud optical depth observations in both number and accuracy. Cloud mode optical depth retrievals from AERONET were evaluated at the Atmospheric Radiation Measurement program’s Oklahoma site in sky conditions ranging from broken clouds to overcast. For overcast cases, the 1.5 min average AERONET cloud mode optical depths agreed to within 15% of those from a standard ground‐based flux method. For broken cloud cases, AERONET retrievals also captured rapid variations detected by the microwave radiometer. For 3 year climatology derived from all nonprecipitating clouds, AERONET monthly mean cloud optical depths are generally larger than cloud radar retrievals because of the current cloud mode observation strategy that is biased toward measurements of optically thick clouds. This study has demonstrated a new way to enhance the existing AERONET infrastructure to observe cloud optical properties on a global scale.

Normality mining: privacy implications of behavioral profiles drawn from GPS enabled mobile phones

There is growing interest in the ways in which the location of a person can be utilized by new applications and services. Recent advances in mobile technologies have meant that the technical capability to record and transmit location data for processing is appearing in off-the-shelf handsets. This opens possibilities to profile people based on the places they visit, people they associate with, or other aspects of their complex routines determined through persistent tracking. It is possible that services offering customized information based on the results of such behavioral profiling could become commonplace. However, it may not be immediately apparent to the user that a wealth of information about them, potentially unrelated to the service, can be revealed. Further issues occur if the user agreed, while subscribing to the service, for data to be passed to third parties where it may be used to their detriment. Here, we report in detail on a short case study tracking four people, in three European member states, persistently for six weeks using mobile handsets. The GPS locations of these people have been mined to reveal places of interest and to create simple profiles. The information drawn from the profiling activity ranges from intuitive through special cases to insightful. In this paper, these results and further extensions to the technology are considered in light of European legislation to assess the privacy implications of this emerging technology.

A new South American network to study the atmospheric electric field and its variations related to geophysical phenomena

In this paper we present the capability of a new network of field mill sensors to monitor the atmospheric electric field at various locations in South America; we also show some early results. The main objective of the new network is to obtain the characteristic Universal Time diurnal curve of the atmospheric electric field in fair weather, known as the Carnegie curve. The Carnegie curve is closely related to the current sources flowing in the Global Atmospheric Electric Circuit so that another goal is the study of this relationship on various time scales (transient/monthly/seasonal/annual). Also, by operating this new network, we may also study departures of the Carnegie curve from its long term average value related to various solar, geophysical and atmospheric phenomena such as the solar cycle, solar flares and energetic charged particles, galactic cosmic rays, seismic activity and specific meteorological events. We then expect to have a better understanding of the influence of these phenomena on the Global Atmospheric Electric Circuit and its time-varying behavior.

A robust two-dimensional hydrogen-bonded network for the predictable assembly of ternary co-crystals of furosemide

Consideration of the geometrical features of the functional groups present in furosemide has enabled synthesis of a series of ternary co-crystals with predictable structural features, containing a robust asymmetric two-dimensional network.