World globalisation drives companies to undertake international expansion with the aim of retaining or growing their businesses. When companies globalize, managers encounter new challenges in making international marketing strategy (IMS) decisions, which are influenced by perceived cultural and business distance between their home- and foreign country. Telkom Indonesia International (Telin) was formed by Telkom Indonesia (i.e. the state-owned company in the telecommunication industry in Indonesia) to engage in international business within a global market. The central question in this study is to what extent do managers’ perceived cultural and business distance between home- and foreign country influence their IMS decisions? A mixed research strategy will be employed by applying qualitative and quantitative methods concurrently. The data collection will involve interviews with CEOs and managers, alongside a web survey to 55 managers of Telkom's. Results suggest important consequences for IMS decisions and emphasizes the need for dialogue on perceptions of cultural and business characteristics of countries.
DOCUMENT
Digitalization is the core component of future development in the 4.0 industrial era. It represents a powerful mechanism for enhancing the sustainable competitiveness of economies worldwide. Diverse triggering effects shape future digitalization trends. Thus, the main research goal in this study is to use sustainable competitiveness pillars (such as social, economic, environmental and energy) to evaluate international digitalization development. The proposed empirical model generates comprehensive knowledge of the sustainable competitiveness-digitalization nexus. For that purpose, a nonlinear regression has been applied on gathered annual data that consist of 33 European countries, ranging from 2010 to 2016. The dataset has been deployed using Bernoulli’s binominal distribution to derive training and testing samples and the entire analysis has been adjusted in that context. The empirical findings of artificial neural networks (ANN) suggest strong effects of the economic and energy use indicators on the digitalization progress. Nonlinear regression and ANN model summary report valuable results with a high degree of coefficient of determination (R2>0.9 for all models). Research findings state that the digitalization process is multidimensional and cannot be evaluated as an isolated phenomenon without incorporating other relevant factors that emerge in the environment. Indicators report the consumption of electrical energy in industry and households and GDP per capita to achieve the strongest effect.
MULTIFILE
Rapid creation of new services for telecommunications systems is hindered by the feature interaction problem. This is an important issue for development of IN services, not only because of interactions\namong IN services themselves but because of interactions of IN services with switch-based services and potential interactions with services not yet developed. Furthermore, the problem is fundamental to services creation; it is not restricted to IN services. Any platform for telecommunication services requires a method for dealing with the feature interaction problem. A number of approaches for managing feature interactions have been proposed. However, lack of structured ways to categorize feature interactions makes it difficult to determine if a particular approach has addressed some, if not all, classes of interactions. We describe and analyze a number of feature interactions by using two independent classification schemes. This paper is a step to achieving the goal of a coherent industry-wide collection of\nillustrative features and their interactions. The collection will helpconvey the scope of the feature interaction problem. It will also serve as a benchmark for determining the coverage of various approaches, and as a guideline for identifying potential interactions in software architectures and platforms.
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In the past decades, we have faced an increase in the digitization, digitalization, and digital transformation of our work and daily life. Breakthroughs of digital technologies in fields such as artificial intelligence, telecommunications, and data science bring solutions for large societal questions but also pose a new challenge: how to equip our (future)workforce with the necessary digital skills, knowledge, and mindset to respond to and drive digital transformation?Developing and supporting our human capital is paramount and failure to do so may leave us behind on individual (digital divide), organizational (economic disadvantages), and societal level (failure in addressing grand societal challenges). Digital transformation necessitates continuous learning approaches and scaffolding of interdisciplinary collaboration and innovation practices that match complex real-world problems. Research and industry have advocated for setting up learning communities as a space in which (future) professionals of different backgrounds can work, learn, and innovate together. However, insights into how and under which circumstances learning communities contribute to accelerated learning and innovation for digital transformation are lacking. In this project, we will study 13 existing and developing learning communities that work on challenges related to digital transformation to understand their working mechanisms. We will develop a wide variety of methods and tools to support learning communities and integrate these in a Learning Communities Incubator. These insights, methods and tools will result in more effective learning communities that will eventually (a) increase the potential of human capital to innovate and (b) accelerate the innovation for digital transformation
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