Mapping information economy businesses with big data: findings from the UK

Governments around the world want to develop their ICT and digital industries. Policymakers thus need a clear sense of the size and characteristics of digital businesses, but this is hard to do with conventional datasets and industry codes. This paper uses innovative ‘big data’ resources to perform an alternative analysis at company level, focusing on ICT-producing firms in the UK (which the UK government refers to as the ‘information economy’). Exploiting a combination of public, observed and modelled variables, we develop a novel ‘sectorproduct’ approach and use text mining to provide further detail on the activities of key sector-product cells. On our preferred estimates, we find that counts of information economy firms are 42% larger than SIC-based estimates, with at least 70,000 more companies. We also find ICT employment shares over double the conventional estimates, although this result is more speculative. Our findings are robust to various scope, selection and sample construction challenges. We use our experiences to reflect on the broader pros and cons of frontier data use

Web-ased innovation indicators - which firm website characteristics relate to firm-level innovation activity?

Web-based innovation indicators may provide new insights into firm-level innovation activities. However, little is known yet about the accuracy and relevance of web-based information. In this study, we use 4,485 German firms from the Mannheim Innovation Panel (MIP) 2019 to analyze which website characteristics are related to innovation activities at the firm level. Website characteristics are measured by several text mining methods and are used as features in different Random Forest classification models that are compared against each other. Our results show that the most relevant website characteristics are the website’s language, the number of subpages, and the total text length. Moreover, our website characteristics show a better performance for the prediction of product innovations and innovation expenditures than for the prediction of process innovations

An online stock brokerage platform for African markets

Applied project submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, May 2020Stock trading is a vibrant industry around the globe. The advent of the internet on the other hand has resulted in the birth of online payment systems and electronic commerce, thus enabling a lot of businesses to carry out transactions online. However, a lot of stockbrokers in African countries are yet to take advantage of these technological developments to enable their clients to buy and sell shares online. This project therefore seeks to facilitate this transition by developing an online brokerage platform that can be easily replicated to meet the needs of online investors in African stock markets and uses the Ghana Stock Exchange as an example in the development process. This paper explains in detail the development process for the online brokerage platform intended to facilitate online buying and selling of stocks. Key advantages of such a platform include allowing investors to buy and sell shares at any time, and it is time and cost effective as it eliminates the need to go through a stockbroker in order to buy or sell shares. The platform also provides educational material to give investors better insight in their trading activities. The end product of this project is a web application where users can easily create trading accounts and start the process of buying and selling shares.Ashesi Universit

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

Industrial Clusters in England

Research trialling a big data approach to identifying industry clusters, with case studies from each of the sectors studied

Designing social media analytics tools to support non-market institutions: Four case studies using Twitter data

This research investigates the design of social media tools for non-market institutions, such as local government or community groups. At the core of this practice-based research is a software tool called LocalNets. LocalNets was developed to collect, analyse and visualise data from Twitter, thereby revealing information about community structure and community assets. It is anticipated that this information could help non- market institutions and the communities with which they work. Twitter users send messages to one another using the ‘@mention’ function. This activity is made visible publicly and has the potential to indicate a Twitter user’s participation in a ‘community structure’; that is, it can reveal an interpersonal network of social connections. Twitter activity also provides data about community assets (such as parks, shops and cinemas) when tweets mention these assets’ names. The context for this research is the Creative Exchange Hub (CX), one of four Knowledge Exchange Hubs for the Creative Economy funded by the UK Arts and Humanities Research Council (AHRC). Under the theme of ‘Digital Public Space’, the CX Hub facilitated creative research collaborations between PhD researchers, academics and non-academic institutions. Building on the CX model, this PhD research forged partnerships between local councils, non-public sector institutions that work with communities, software developers and academics with relevant subject expertise. Development of the LocalNets tool was undertaken as an integral part of the research. As the software was developed, it was deployed in relevant contexts through partnerships with a range of non-market institutions, predominantly located in the UK, to explore its use in those contexts. Four projects are presented as design case studies: 1) a prototyping phase, 2) a project with the Royal Society of Arts in the London Borough of Hounslow, 3) a multi-partner project in Peterborough, and 4) a project with Newspeak House, a technology and politics co-working space located in London. The case studies were undertaken using an Action Design Research method, as articulated by Sein et al. Findings from these case studies are grouped into two categories. The first are ‘Implementation findings’ which relate specifically to the use of data from Twitter. Second there are six ‘situated design principles’ which were developed across the case studies, and which are proposed as having potential application beyond Twitter data. The ‘Implementation findings’ include that Twitter can be effective for locating participants for focus groups on community topics, and that the opinions expressed directly in tweets are rarely sufficient for the local government of community groups to respond to. These findings could benefit designers working with Twitter data. The six situated design principles were developed through the case studies: two apply Burt’s brokerage social capital theory, describing how network structure relates to social capital; two apply Donath’s signalling theory – which suggests how social media behaviours can indicate perceptions of community assets; and two situated design principles apply Borgatti and Halgin’s network flow model – a theory which draws together brokerage social capital and signalling theory. The principles are applicable to social media analytics tools and are relevant to the goals of non-market institutions. They are situated in the context of the case studies; however, they are potentially applicable to social media platforms other than Twitter. Linders identifies a paucity of research into social media tools for non-market institutions. The findings of this research, developed by deploying and testing the LocalNets social media analytics tool with non-market institutions, aim to address that research gap and to inform practitioner designers working in this area

Developing unbiased artificial intelligence in recruitment and selection : a processual framework : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Albany, Auckland, New Zealand

For several generations, scientists have attempted to build enhanced intelligence into computer systems. Recently, progress in developing and implementing Artificial Intelligence (AI) has quickened. AI is now attracting the attention of business and government leaders as a potential way to optimise decisions and performance across all management levels from operational to strategic. One of the business areas where AI is being used widely is the Recruitment and Selection (R&S) process. However, in spite of this tremendous growth in interest in AI, there is a serious lack of understanding of the potential impact of AI on human life, society and culture. One of the most significant issues is the danger of biases being built into the gathering and analysis of data and subsequent decision-making. Cognitive biases occur in algorithmic models by reflecting the implicit values of the humans involved in defining, coding, collecting, selecting or using data to train the algorithm. The biases can then be self-reinforcing using machine learning, causing AI to engage in ‘biased’ decisions. In order to use AI systems to guide managers in making effective decisions, unbiased AI is required. This study adopted an exploratory and qualitative research design to explore potential biases in the R&S process and how cognitive biases can be mitigated in the development of AI-Recruitment Systems (AIRS). The classic grounded theory was used to guide the study design, data gathering and analysis. Thirty-nine HR managers and AI developers globally were interviewed. The findings empirically represent the development process of AIRS, as well as technical and non-technical techniques in each stage of the process to mitigate cognitive biases. The study contributes to the theory of information system design by explaining the phase of retraining that correlates with continuous mutability in developing AI. AI is developed through retraining the machine learning models as part of the development process, which shows the mutability of the system. The learning process over many training cycles improves the algorithms’ accuracy. This study also extends the knowledge sharing concepts by highlighting the importance of HR managers’ and AI developers’ cross-functional knowledge sharing to mitigate cognitive biases in developing AIRS. Knowledge sharing in developing AIRS can occur in understanding the essential criteria for each job position, preparing datasets for training ML models, testing ML models, and giving feedback, retraining, and improving ML models. Finally, this study contributes to our understanding of the concept of AI transparency by identifying two known cognitive biases similar-to-me bias and stereotype bias in the R&S process that assist in assessing the ML model outcome. In addition, the AIRS process model provides a good understanding of data collection, data preparation and training and retraining the ML model and indicates the role of HR managers and AI developers to mitigate biases and their accountability for AIRS decisions. The development process of unbiased AIRS offers significant implications for the human resource field as well as other fields/industries where AI is used today, such as the education system and insurance services, to mitigate cognitive biases in the development process of AI. In addition, this study provides information about the limitations of AI systems and educates human decision makers (i.e. HR managers) to avoid building biases into their systems in the first place