Innovation modes and productivity in the UK

This paper is motivated by the aim to develop appropriate indicators capturing modes of innovation by UK enterprises, examine how such innovation practices vary across regions and industries and explore the extent to which they have an impact on productivity. There is an emphasis on identifying and examining the relevance of non-technological innovation that builds on and extends previous research in this important area. Traditionally, measures of innovation have rested on single indicators such as patenting or R&D, supplemented, by product and process and process innovation outputs. More recently innovations in management, organisational and marketing areas are being brought into the picture and the relevant information collected by innovation surveys. Among indicators of innovation the distinctions between technological and non-technological innovations has often been loosely translated into either activities in manufacturing versus services, or into product and process innovations versus organisational and marketing innovations. While these simplifications of technological and non-technological innovation can be a practically useful, since data is readily available, they do not fully recognize that mixed modes of innovations are adopted by today’s firms; firms whose environments are characterised by increased competition, internationalisation and shorter product life cycles

Origin and emergence of entrepreneurship as a research field

This paper seeks to map out the emergence and evolution of entrepreneurship as an independent field in the social science literature from the early 1990s to 2009. Our analysis indicates that entrepreneurship has grown steadily during the 1990s but has truly emerged as a legitimate academic discipline in the latter part of the 2000s. The field has been dominated by researchers from Anglo-Saxon countries over the past 20 years, with particularly strong representations from the US, UK, and Canada. The results from our structural analysis, which is based on a core document approach, point to five large knowledge clusters and further 16 sub-clusters. We characterize the clusters from their cognitive structure and assess the strength of the relationships between these clusters. In addition, a list of most cited articles is presented and discussed

Towards Guidelines for Preventing Critical Requirements Engineering Problems

Context] Problems in Requirements Engineering (RE) can lead to serious consequences during the software development lifecycle. [Goal] The goal of this paper is to propose empirically-based guidelines that can be used by different types of organisations according to their size (small, medium or large) and process model (agile or plan-driven) to help them in preventing such problems. [Method] We analysed data from a survey on RE problems answered by 228 organisations in 10 different countries. [Results] We identified the most critical RE problems, their causes and mitigation actions, organizing this information by clusters of size and process model. Finally, we analysed the causes and mitigation actions of the critical problems of each cluster to get further insights into how to prevent them. [Conclusions] Based on our results, we suggest preliminary guidelines for preventing critical RE problems in response to context characteristics of the companies.Comment: Proceedings of the 42th Euromicro Conference on Software Engineering and Advanced Applications, 201

Smart-logistics for people management of innovative small and medium enterprises` development: Agile methodology

Introduction. Innovative entrepreneurship is one of the applied spheres, in which the implementation of core competencies: knowledge and skills from other areas is complicated nowadays due to challenges and rapid changes. Management systems for small and medium-sized enterprises are not so classical now, but horizontal and, what most importantly – flexible, agile is. Thus, it is proposed to turn to modern models of people management to increase the effectiveness of companies and institutions for future economy development. Aim and tasks. The article appeals to management innovation and the use of the “mobility gap” as one of the most discussed economic problems that are necessary for the urban areas and require an appropriate solution development. The proposal is to use smart logistics for an effective people management system for innovative small and medium enterprises to zoom the weight of the cluster unit. The assertion that the increasing impact of horizontal management in outsourcing services is the way for further development and modernization of tax policy for the local economy. Results. In the nearest future, the character of the local economy will change as communities develops cities, improving the quality of life, ensuring safety and neighbourhood policy and stimulating creative development. And it will create opportunities for cutting-edge initiatives, entrepreneurship and local municipalities, as well as developing relationships and competencies based on innovation and creative interaction between stakeholders in the new digital reality, while remaining people-centred. It is proposed to introduce management innovations into the management system according to the developed model according to the agile method of modernization: “to do” – list; "in progress" and motivation; “to verify” – monitoring; “done” – analysis; and business scaling. And to customize services, it is necessary the high quality achieving, speed that meets the needs of the market, and the quick creating of unique innovations. Conclusions. This methodology has been tested by the project – Erasmus 2020, despite the “lockdown” conditions, by the open data project “City Code” with the aim of creating conditions for sustainable urban development by combining: talents, technologies, tolerance, and should be used to people management of small and medium-sized innovative enterprise

Knowledge Discovery in the SCADA Databases Used for the Municipal Power Supply System

This scientific paper delves into the problems related to the develop-ment of intellectual data analysis system that could support decision making to manage municipal power supply services. The management problems of mu-nicipal power supply system have been specified taking into consideration modern tendencies shown by new technologies that allow for an increase in the energy efficiency. The analysis findings of the system problems related to the integrated computer-aided control of the power supply for the city have been given. The consideration was given to the hierarchy-level management decom-position model. The objective task targeted at an increase in the energy effi-ciency to minimize expenditures and energy losses during the generation and transportation of energy carriers to the Consumer, the optimization of power consumption at the prescribed level of the reliability of pipelines and networks and the satisfaction of Consumers has been defined. To optimize the support of the decision making a new approach to the monitoring of engineering systems and technological processes related to the energy consumption and transporta-tion using the technologies of geospatial analysis and Knowledge Discovery in databases (KDD) has been proposed. The data acquisition for analytical prob-lems is realized in the wireless heterogeneous medium, which includes soft-touch VPN segments of ZigBee technology realizing the 6LoWPAN standard over the IEEE 802.15.4 standard and also the segments of the networks of cellu-lar communications. JBoss Application Server is used as a server-based plat-form for the operation of the tools used for the retrieval of data collected from sensor nodes, PLC and energy consumption record devices. The KDD tools are developed using Java Enterprise Edition platform and Spring and ORM Hiber-nate technologies

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Campus information systems for undergraduate students in Spain: a country-wide cluster classification

The availability of broad characterizations of campus information systems for students can be useful for the strategic information management of higher education centres. First we describe and contextualize a characterization model for on-line campus information systems for undergraduate students. This serves as a framework of conceptual reference for empirical work on Spanish universities. Information was gathered by means of structured visits to institutional websites and structured interviews with undergraduate students. Analyzing the information collected enables us to classify Spanish higher education institutions into three clusters with homogeneous characteristics, in the sense of their information system and organizational aspects. Finally, we discuss future research on this subject