REGIONAL INNOVATION SYSTEM FAILURES AND HIGHLIGHTS

The systemic analysis of innovation conceives complex analytical frameworks, with intense socio-technological aspects of knowledge generation and encompasses a detailed analysis of system failures. These frameworks are not suitable for benchmarking a wide range of regions, due to low availability of such elaborate data sources. On the other hand, metric regional innovation micro data offer the opportunity for large-scale cross-regional benchmarking exercise illustrating mainly the market failures of the innovation systems although this type of analysis does not provide any detailed systemic envisioning. Is the combination of these two analytical approaches possible? This study presents the Interaction Intension Indicator (3I) analytical framework, analysing system failures and highlights of various regional innovation deployment patterns along with the analysis of the Romanian innovation system.Regional Economic Activity: Growth, Development, and Changes, Regional innovation policies, regional innovation metrics, regional innovation systems, innovation policy assessment

A Proposed Framework of Regional Innovation System: The Case of the Kharkiv Region in Eastern Ukraine.

Regional Innovation System (RIS) model of economic growth, seeks to promote increased interaction across the government, business and academia. The importance of RIS stems from the increasing interaction of regional actors on the outcome of the innovation process. This paper proposes a framework of regional innovation system for the Kharkiv region in the Eastern Ukraine. A thorough theoretical analysis was conducted to apply the most appropriate scientific approach to the study. Qualitative research approach was applied to cover the purpose of the study and answer the research questions raised. Interviews and documentation review were carried out using research questions based on previous studies. It is concluded that the main components of the regional innovation system in the investigated region are knowledge application and exploitation subsystem, knowledge generation and diffusion subsystem. The major stakeholders of regional innovation system (academic universities; research institutes; public organizations; regional state administration; non-governmental agencies and private firms) and specific component of regional innovation system (knowledge support and promotion subsystem) are identified in the Kharkiv region. The specific paper contributes to the knowledge in region by providing a proposed framework for the Kharkiv region

Service quality perceptions in primary health care centres in Greece

Context The paper refers to the increased competition between health care providers and the need for patient-centred services in Greece. Using service quality methodology, this paper investigates service quality perceptions of patients in Greek public primary health centres. Objective To test the internal consistency and applicability of SERVQUAL in primary health care centres in Greece. Strategy SERVQUAL was used to examine whether patients have different expectations from health care providers and whether different groups of patients may consider some dimensions of care more important than others. Results The analysis showed that there were gaps in all dimensions measured by SERVQUAL. The largest gap was detected in empathy. Further analysis showed that there were also differences depending on gender, age and education levels. A separate analysis of expectations and perceptions revealed that this gap was because of differences in patients’ perceptions rather than expectations. Discussion and conclusions This paper raises a number of issues that concern the applicability of SERVQUAL in health care services and could enhance current discussions about SERVQUAL improvement. Quality of health care needs to be redefined by encompassing multiple dimensions. Beyond a simple expectations-perceptions gap, people may hold different understandings of health care that, in turn, influence their perception of the quality of services

Mobile Computing, IoT and Big Data for Urban Informatics: Challenges and Opportunities

Over the past few decades, the population in the urban areas has been increasing in a dramatic manner. Currently, about 80% of the U.S. population and about 50% of the world’s population live in urban areas and the population growth rate for urban areas is estimated to be over one million people per week. By 2050, it has been predicted that 64% of people in the developing nations and 85% of people in the developed world would be living in urban areas [1, 2]. Such a dramatic population growth in urban areas has been placing demands on urban infrastructure like never before