The concept of a smart city is becoming the leading paradigm worldwide. Consequently, a creative mix of emerging technologies and open innovation is gradually becoming the defining element of smart city evolution, changing the ways in which city administrators are organizing their services and development globally. Thus, the smart city concept is becoming extremely relevant on the agendas of policy-makers as a development strategy for enhancing the quality of life of the citizen and improving the sustainability goals of their cities. Despite of the relevance of the topic, still few studies investigate how open innovation shapes the way cities become smarter or focus on the experiences of professionals to understand the concept of a smart city and its implementation. This paper fills this gap and analyzes the processes for building effective smart cities by integrating the different perspectives of smart innovations and using the core components of smart cities according to a conceptual framework developed in previous research. In so doing, it provides useful insights for smart city stakeholders in adopting social and technological innovation to improve the global competitiveness of their cities. The empirical dataset allows examining how “smart cities” are being implemented in Manchester (UK), and in Boston, Massachusetts, and San Diego City (United States of America (USA)), including archival data and in-depth interviews with core smart city stakeholders who are involved in smart city projects and programs across the cases. Results from empirical data suggest that the conceptualization of smart cities across the cases is similar with a strong emphasis on social and technological innovation aimed at addressing municipal challenges in the core sub-systems of the cities, which include mobility, environmental sustainability, entrepreneurial development, quality of life, and social cohesion. The results also reveal benefits and challenges relating to smart innovation ecosystems across the cases and the future directions of their diffusion.

The Smart City concept has attracted wide-spread interest from both industry and academia with a growing number of experimental use-cases emerging from developed countries around the world. However, despite the importance of Smart City innovation in dealing with emerging urban challenges, i.e. environmental sustainability, social cohesion, efficiency, improved quality of life, and cost effectiveness, the concept is yet to receive the attention needed among cities in developing countries, especially in Africa. Although there are ongoing efforts to conceptualise Smart Cities through associated framework models that assess their impacts, these mainly focus on key performance indicators (KPIs) and use-cases from cities in developed countries. This paper proposes a conceptual framework model to assess the impacts of Smart City innovation resulting from the introduction of new technologies to cities. To achieve this goal, existing Smart City framework models were systematically reviewed and analysed. This was supported by focus-group interviews and a survey, which were conducted in the city of Abuja and discovered how stakeholders envisioned their Smart City initiatives. This also determined the priority indicators to assess the impacts of Smartness on cities and citizens. The analysis shows that Smart City stakeholders in Abuja emphasised three components of Smart Cities, namely: Smart Infrastructure, Smart Institution, and Smart People. By analysing the empirical data from Abuja Smart City stakeholders and evidence from the literature, a conceptual framework model is proposed for Smart City innovation in Abuja and by extension, other cities with similar histories and experiences in Nigeria.

In developing countries, the importance of digital transformation in the public sector as the engine room of every Government cannot be over-emphasized. Information and Communication Technology (ICT), if its integration into development reforms is gotten right, will play a revolutionary role for developing countries to accelerate their advancements while enabling possibilities of competing with top nations for social and economic growth. However, achieving digital transformation objectives is no bed of roses. A holistic approach with a strong foundation involving a whole lot of activities that must be put in place and executed. Most of the digital transformation projects had resulted in failures without achieving initial anticipated value. Therefore, Governments must rethink their approaches and also ensure the digital transformation is holistic and end-to-end. One of the holistic approaches to digital transformation is to continuously assess and measure the performance of public institutions (PIs) to ensure benefits are realized as they spend huge amounts of public resources in digital projects. However, there is an inadequacy of a tailored and comprehensive performance assessment framework in the form of a toolkit that can be used by IT/ICT regulators and the PIs themselves to assess post-digital transformation projects implementation of developing countries. A holistic assessment toolkit that provides regulators and PIs means of assessing digital transformation maturity will help to improve benefits realization for IT investments and ultimately enhance the contribution of digital transformation projects to social-economic development of developing countries. This paper presents a holistic framework for digital transformation performance assessment by describing four digital transformation stages each with its defining characteristics, nine key assessment parameters, alignment of each parameter with the four stages to aid a better understanding of what should be achieved in each stage as public institutions move from one maturity stage to the next. It finally provides an assessment questionnaire template to assist public institutions and certified digital transformation maturity assessors in carrying out digital government transformation performance assessments.

Smart City is becoming increasingly popular all over the word, gaining the status of paradigm for steering urban and regional strategies oriented to improve the citizens' quality of life through IT-led solutions. Despite of its international relevance, still consensus on its most significant components and their reciprocal influence does not exist. Building on a wide scholarship on Key Performance Indicators (KPI) for Smart Cities, this paper suggests that System Dynamics could provide an appropriate conceptual model for interpreting the dynamic interconnections among the core components of a Smart City -i.e., Smart Infrastructures, Smart Institutions and Smart People-. At this goal, this paper offers a novel application of System Dynamic, which is used to analyze a robust set of new data, collected in three different cities. The model allows modelling and interpreting the role played by each component in implementing the Smart City paradigm and offers a new perspective on how to diversify the concept of Smart City according to site-specific conditions. Findings show that whilst a Smart City in a mature stage is highly influenced by the people and the infrastructure, for a Smart City in a developing country it is mainly the smart infrastructure component that shall be emphasized.

A fundamental task in natural language processing (NLP) is part of speech (PoS) tagging. PoS tagging is crucial to many NLP applications, including question answering, machine translation, syntactic parsing, speech recognition, and semantic parsing. PoS tagging is a task for labeling sequences in which a tagger/system tags each word with its appropriate part of speech label. In NLP, PoS tagging is often considered as a language-specific task. Similarly, Pashto is a language that has not been explored regarding PoS tagging. Therefore, this research focuses on the PoS tagging considering the Pashto language and provides a baseline accuracy. The research has twofold benefits. First, it introduces a Pashto tag set that contains 2,81,205 words of the Pashto language. All these words are tagged with 17 unique PoS tags. Second, it proposes a deep learning-based model by examining classic Recursive Neural Networks (RNN) and Bidirectional Long Short Term Memory Networks (BLSTM). The results show promising performances when used with the word embedding technique. The proposed approach achieved 98.82% accuracy as a baseline on the test dataset by using the BLSTM model along with word embedding.