Research Question: This paper explores the potential for technology entrepreneurship development at the country level through the creation of a new composite index. Motivation: Motivation for this paper arises from the fact there is a lack of the composite indices used exclusively for technology management as identified by Jovanovic et al. (2017). Technology indices are mostly used as important components of other composite indices used for tracking a country performance from the perspective of other global phenomena (e.g. competitiveness and innovativeness). The novelty of this paper reflects in the proposed Technology Entrepreneurship Development Potential (TED-pot) index which has multiple significances. It could serve as a help for policy makers in creating national policies; other companies and countries looking for the adequate environment to invest in technology entrepreneurship projects; academics who benefit from a new country-level view on technology entrepreneurship, especially ICT entrepreneurship. Idea: The idea of the paper was to create the TED-pot index to enable the cross-countries comparisons and examine whether the potential of Serbia lies in its entrepreneurial ICT sector. Data: Four indicators included in the created index are measured by the World Bank. The index is applied on six ex-Yugoslav countries and the EU for the period 2009-2014. All the data is collected from the World Bank database. Tools: The final index value is obtained by using the simple weighted function with equal weights. The overall TED-pot has been built upon the equal weighting of the two created pillars: ICT potential (ICT-pot) and Entrepreneurial potential (E-pot). The values for each pillar are calculated by the same procedure, through the simple mean of certain indicators. Findings: According to the calculated TED-pot values, Serbia stands out as a country with the greatest potential for technology entrepreneurship development in the region. Analysing individual pillars, ICT-pot indicates Serbia has very strong ICT sector, far ahead of other countries in the region, while the E-pot values show there is a space for administration to ease and speed up the process of starting new businesses in Serbia. This is a pilot research and the first presentation of the created index, which calls for further investigation. Contribution: This paper expands exiting research related to the country-level measurement in the field of technology management and entrepreneurship, especially focusing on ICT entrepreneurship development.

The authors discuss why the current conceptual base of project management research and practice continues to attract criticism since it does not adequately address the complexity that leads to software-project failure. To do so, the study explores systems thinking and artificial neural networks to shed light on complexity in software-project behavior using nonlinear functional relationships between critical success factors and project success to utilize their connectedness as an approach in order to create projectoutcome prediction models. The artificial neural networks were used to create two project-outcome prediction models: one for a binary classification task to discriminate failed from successful projects using a multi-input-single-output configuration and one for a multi-task binary classification to discriminate success from failure in multiple project-success dimensions using a multi-input multi-output configuration. The results yielded high-performance values for a binary classification task, performed to predict overall project success, and slightly lower performance values for the multi-task binary classification, which was also performed to predict success in project-success dimensions. It was found that the nonlinear behavior of critical success factors may be used to create prediction models, by embedding equifinality and connectedness constructs that prove to be useful to understand projects as complex, multi-loop, and nonlinear systems. Further research is needed to investigate the causality between critical success factors in order to explore the possible propagation of critical success factors within a project system network and its implications on project success. INDEX TERMS Artificial neural networks, critical success factors, project success, prediction models, systems thinking.

Digitization, digitalization and digital transformation represent one of the primary incentives of today's development. To successfully implement these changes, countries need to create smart digital policies which are evidence-and databased. The study presented in this paper uses the logical clustering approach for grouping countries according to five dimensions of the Digital Economy and Society Index (DESI). Logical clustering employs Interpolative Boolean Algebra (IBA) as a consistent fuzzy approach, which means that all Boolean axioms are fulfilled. To measure proximity among countries, logical clustering uses IBAbased exclusive disjunction and logical aggregation. The general aim is to provide help in identifying directions for defining smart digital policies for achieving digital competitiveness of nations, based on the analysis of similarities among countries.The results indicate that logical clustering enables more comprehensive differentiation between clusters than the original composite index methodology does, and determination of the primary areas of action in clusters, among similar countries. Some interesting cases where logical clustering results differ from the original methodology are discussed.

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