Mapping the trickle out benefits of social and environmental enterprises: a pilot study of an East African eco business

• Objectives The objective of this paper is to propose a framework for mapping the sustainable development and poverty alleviation impacts of social and environmental enterprises in Africa. This framework is then piloted with reference to an East African Ecobusiness. • Prior Work This paper is based on data collected as part of a wider research project examining social and environmental enterprises across the 19 countries of Southern and Eastern Africa. In total, the sustainable development and poverty alleviation impacts of 20 in-depth case studies in 4 countries are being examined. • Approach Data was collected using in-depth interviews with multiple stakeholders associated with the case study business. Secondary materials were also analysed and a quantitative survey of customers undertaken. • Results In addition to their impacts on the environment, African eco businesses can also have substantial social, economic and wider poverty alleviation impacts. This paper maps the impacts of a case study East African ecobusiness, as part of developing a social and environmental enterprise impact framework for Africa and the wider developing world. In our case study, positive and negative impacts are identified, while questions are raised in relation to tradeoffs between social and environmental objectives and temporal dimensions of impact. The usefulness of existing frameworks for understanding the social, environmental and development impacts of these kinds of organisations are also considered. • Implications This paper outlines the necessity of building an African-centric impact map to capture the multi-level poverty alleviation and sustainable development impacts of social and environmental enterprise activity in developing world environments. The framework proposed also offers guidance to businesses operating in Africa about the factors that might be considered as part of their wider social and environmental responsibilities. • Value Assessing the impact of social and environmental enterprises, especially as a route to development within low income countries, is receiving increasing attention in academia and beyond. This paper presents a useful contribution to the scarce literature on social and environmental enterprises in Africa.

Evaluating the interdependencies between managing business change, IT sourcing and IT governance

Organisations need the right business and IT capabilities in order to achieve future business success. It follows that the sourcing of these capabilities is an important decision. Yet, there is a lack of consensus on the approach to decid-ing where and how to source the core operational capabilities. Furthermore, de-veloping its dynamic capability enables an organisation to effectively manage change its operational capabilities. Recent research has proposed that analysing business capabilities is a key pre-requisite to defining its Information Technology (IT) solutions. This research builds on these findings by considering the interde-pendencies between the dynamic business change capability and the sourcing of IT capabilities. Further it examines the decision-making oversight of these areas as implemented through IT governance. There is a good understanding of the direct impact of IT sourcing decision on operational capabilities However, there is a lack of research on the indirect impact to the capability of managing business change. Through a review of prior research and initial pilot field research, a capability framework and three main propositions are proposed, each examining a two-way interdependency. This paper describes the development of the integrated capa-bility framework and the rationale for the propositions. These respectively cover managing business change, IT sourcing and IT governance. Firstly, the sourcing of IT affects both the operational capabilities and the capability to manage business change. Similarly a business change may result in new or revised operational ca-pabilities, which can influence the IT sourcing decision resulting in a two-way rela-tionship. Secondly, this IT sourcing is directed under IT governance, which pro-vides a decision-making framework for the organisation. At the same time, the IT sourcing can have an impact on the IT governance capability, for example by out-sourcing key capabilities; hence this is potentially again a two-way relationship. Finally, there is a postulated two-way relationship between IT governance and managing business change in that IT governance provides an oversight of manag-ing business change through portfolio management while IT governance is a key element of the business change capability. Given the nature and novelty of this framework, a philosophical paradigm of constructivism is preferred. To illustrate and explore the theoretical perspectives provided, this paper reports on the find-ings of a case study incorporating eight high-level interviews with senior execu-tives in a German bank with 2300 employees. The collected data also include or-ganisational charts, annual reports, project and activity portfolio and benchmark reports for the IT budget. Recommendations are made for practitioners. An understanding of the interdependencies can support professionals in improving business success through effectively managing business change. Additionally, they can be assisted to evaluate the impact of IT sourcing decisions on the organisa-tion’s operational and dynamic capabilities, using an appropriate IT governance framework.

Are informed citizens more trusting? Transparency of performance data and trust towards a British police force

In Britain, substantial cuts in police budgets alongside controversial handling of incidents such as politically sensitive enquiries, public disorder and relations with the media have recently triggered much debate about public knowledge and trust in the police. To date, however, little academic research has investigated how knowledge of police performance impacts citizens’ trust. We address this long-standing lacuna by exploring citizens’ trust before and after exposure to real performance data in the context of a British police force. The results reveal that being informed of performance data affects citizens’ trust significantly. Furthermore, direction and degree of change in trust are related to variations across the different elements of the reported performance criteria. Interestingly, the volatility of citizens’ trust is related to initial performance perceptions (such that citizens with low initial perceptions of police performance react more significantly to evidence of both good and bad performance than citizens with high initial perceptions), and citizens’ intentions to support the police do not always correlate with their cognitive and affective trust towards the police. In discussing our findings, we explore the implications of how being transparent with performance data can both hinder and be helpful in developing citizens’ trust towards a public organisation such as the police. From our study, we pose a number of ethical challenges that practitioners face when deciding what data to highlight, to whom, and for what purpose.

Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States

Many macroeconomic series, such as U.S. real output growth, are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS specification used in the comparison uses a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to significant improvement in forecasting current and next quarter output growth, and that MIDAS is an effective way to exploit monthly data compared with alternative methods.

Pocket data mining - big data on small devices

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

'Mental mobility’ in the digital age: entrepreneurs and the online home-based business

Home-based online business ventures are an increasingly pervasive yet under-researched phenomenon. The experiences and mindset of entrepreneurs setting up and running such enterprises require better understanding. Using data from a qualitative study of 23 online home-based business entrepreneurs, we propose the augmented concept of ‘mental mobility’ to encapsulate how they approach their business activities. Drawing on Howard P. Becker's early theorising of mobility, together with Victor Turner's later notion of liminality, we conceptualise mental mobility as the process through which individuals navigate the liminal spaces between the physical and digital spheres of work and the overlapping home/workplace, enabling them to manipulate and partially reconcile the spatial, temporal and emotional tensions that are present in such work environments. Our research also holds important applications for alternative employment contexts and broader social orderings because of the increasingly pervasive and disruptive influence of technology on experiences of remunerated work.

Computationally efficient rule-based classification for continuous streaming data

Advances in hardware and software technologies allow to capture streaming data. The area of Data Stream Mining (DSM) is concerned with the analysis of these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (concept drift) in the stream in real-time in order to reflect the most recent concept in the data as accurately as possible. A recent addition to the data stream classifier toolbox is eRules which induces and updates a set of expressive rules that can easily be interpreted by humans. However, like most rule-based data stream classifiers, eRules exhibits a poor computational performance when confronted with continuous attributes. In this work, we propose an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up the real-time rule induction process while maintaining a similar level of accuracy compared with the original eRules classifier. We termed this new version of eRules with our approach G-eRules.

Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.

An investigation of the role of business analysts in IS development

Business analysis has developed since the early 1990s as an IS discipline that is concerned with understanding business problems, defining requirements and evaluating relevant solutions. However, this discipline has had limited recognition within the academic community and little research has been conducted into the practices and standards employed by business analysts. This paper reports on a study into business analysis that considered the activities conducted and the outcomes experienced on IS projects. Senior business analysts were interviewed in order to gain insights into the business analyst role and the techniques and approaches applied when conducting this work. The Context, Content, Process, Outcomes framework was adopted as a basis for developing the interview questions. The data collected was analysed using the template analysis technique and the template was based upon this framework. Additional themes concerning aspects of business analysis that may contribute to IS success emerged during data analysis. These included the key business analysis activities and the skills business analysts require to perform these activities. The organisational attitude was also identified as a key factor in enabling the use and contribution of business analysis.

Assessing macro uncertainty in real-time when data are subject to revision

Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box-Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically-estimated model of data revisions for US output growth is used to investigate small-sample properties.

Follow the leaders? An analysis of convergence and innovation of faculty recruiting practices in US business schools

The debate associated with the qualifications of business school faculty has raged since the 1959 release of the Gordon–Howell and Pierson reports, which encouraged business schools in the USA to enhance their legitimacy by increasing their faculties’ doctoral qualifications and scholarly rigor. Today, the legitimacy of specific faculty qualifications remains one of the most discussed topics in management education, attracting the interest of administrators, faculty, and accreditation agencies. Based on new institutional theory and the institutional logics perspective, this paper examines convergence and innovation in business schools through an analysis of faculty hiring criteria. The qualifications examined are academic degree, scholarly publications, teaching experience, and professional experience. Three groups of schools are examined based on type of university, position within a media ranking system, and accreditation by the Association to Advance Collegiate Schools of Business. Data are gathered using a content analysis of 441 faculty postings from business schools based in the USA over two time periods. Contrary to claims of global convergence, we find most qualifications still vary by group, even in the mature US market. Moreover, innovative hiring is more likely to be found in non-elite schools.

Predicting early data revisions to US GDP and the effects of releases on equity markets

The effects of data uncertainty on real-time decision-making can be reduced by predicting early revisions to US GDP growth. We show that survey forecasts efficiently anticipate the first-revised estimate of GDP, but that forecasting models incorporating monthly economic indicators and daily equity returns provide superior forecasts of the second-revised estimate. We consider the implications of these findings for analyses of the impact of surprises in GDP revision announcements on equity markets, and for analyses of the impact of anticipated future revisions on announcement-day returns.

The determinants of inward foreign direct investment in business services across European regions

The determinants of inward foreign direct investment in business services across European regions, Regional Studies. The role of forward linkages with manufacturing sectors and other service sectors as attractors of business services foreign direct investment (FDI) is studied at the regional level. Using data on 146 NUTS-2 regions, it is found that regions specialized in those (manufacturing) sectors that are high potential users of business services attract more FDI in the business services than other regions. Results are robust to the inclusion of the traditional determinants of foreign investments at the regional level as well as to controls for spatial dependence. The results suggest that regional policies aimed at attracting foreign investors in the business service industry might prove ineffective in the absence of a pre-existing local intermediate demand

Big data: a normal accident waiting to happen?

Widespread commercial use of the internet has significantly increased the volume and scope of data being collected by organisations. ‘Big data’ has emerged as a term to encapsulate both the technical and commercial aspects of this growing data collection activity. To date, much of the discussion of big data has centred upon its transformational potential for innovation and efficiency, yet there has been less reflection on its wider implications beyond commercial value creation. This paper builds upon normal accident theory (NAT) to analyse the broader ethical implications of big data. It argues that the strategies behind big data require organisational systems that leave them vulnerable to normal accidents, that is to say some form of accident or disaster that is both unanticipated and inevitable. Whilst NAT has previously focused on the consequences of physical accidents, this paper suggests a new form of system accident that we label data accidents. These have distinct, less tangible and more complex characteristics and raise significant questions over the role of individual privacy in a ‘data society’. The paper concludes by considering the ways in which the risks of such data accidents might be managed or mitigated.