This interdisciplinary article presents a concept of the 21st century and phenomena that are products of the 4th industrial revolution – big data and Artificial Intelligence technologies – as well as the opportunities of their application in public governance and social policy. This paper examines the advantages and disadvantages of big data, problems of data collection, its reliability and use. Big data can be used for the analysis and modeling of phenomena relevant to public governance and social policy. Big data consist of three main types: a) historical data, b) present data with little delay, c) prognostic data for future forecasting. The following categories of big data can be defined as: a) data from social networks, b) traditional data from business systems, c) machine-generated data, such as water extraction, pollution, satellite information. The article analyzes the advantages and disadvantages of big data. There are big data challenges such as data security, lack of cooperation in civil service and social work, in rare situations – data fragmentation, incompleteness and erroneous issues, as well as ethical issues regarding the analysis of data and its use in social policy and social administration. Big data, covered by Artificial Intelligence, can be used in public governance and social policy by identifying “the hot spots” of various phenomena, by prognosing the meanings of variables in the future on the basis of past time rows, and by calculating the optimal motion of actions in the situations where there are possible various alternatives. The technologies of Artificial Intelligence are used more profoundly in many spheres of public policy, and in the governance of COVID-19 pandemics too. The substantial advantages of the provided big data and Artificial Intelligence are a holistic improvement of public services, possibilities of personalization, the enhancement of citizen satisfaction, the diminishing of the costs of processing expenditure, the targeting of adopted and implemented decisions, more active involvement of citizens, the feedback of the preferences of policy formation and implementation, the observation of social phenomenas in real time, and possibilities for more detailed prognosing. Challenges to security of data, necessary resources and competences, the lack of cooperation in public service, especially rare instances of data fragmentation, roughness, falseness, and ethical questions regarding data analysis and application can be evaluated as the most significant problems of using big data and Artificial Intelligence technologies. Big data and their analytics conducted using Artificial Intelligence technologies can contribute to the adequacy and objectivity of decisions in public governance and social policy, effectively curbing corruption and nepotism by raising the authority and confidence of public sector organizations in governance, which is so lacking in the modern world.
The article deals with theoretical and practical interactions between public administration and business management. This paper analyses normative models of public administration (New Public Management and New Governance) and business management (Corporate Social Responsibility), and raises claims pertaining to these models. Based on this analysis, an integrated model of interaction between public and private sectors consisting of five dimensions is suggested.
The article provides the theoretical analysis of co-production phenomenon. The interests in co-production and related concepts are examined applying the methods of Google Trends statistical analysis and information visualization. The activity of business, government, society and research sectors during the last five years are compared, the trends of interest change and the balance of inter-sectoral interest in co-production is assessed. The relative evaluation of interest in co-production indicates that the situation in different sectors is not the same – the asymmetry in interest in co-production prevails. The article concludes that cooperation between public sector organizations with private sector organizations and society in providing public service as well as cooperation between scientific research, methods and technologies is developed at a different rate thus hindering breakthrough on a larger scale.
We examine the relevance of suggestive findings and assumptions about immigrant philanthropy to a diaspora from a high-income country of origin, whose members are generally highly educated and professionally employed: the Lithuanian diaspora. We investigate whether this immigrant group’s voluntary sector participation, despite these differences, may evolve similar to that of other immigrant groups studied. The study casts doubt on the generalizability of the current claims from the literature—which is largely derived from cases of immigrants migrating from the South to the North—to immigrant and diaspora groups who are highly skilled and originating from relatively more industrialized countries (North–North migration). The findings highlight the need to examine the voluntary sector participation of a greater variety of diasporas and to recognize that not all groups will behave similarly. Some of the differentiated behavior may stem from demographic characteristics specific to North–North migration.
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