The location of executive suites and business centers in the United States: an exploratory analysis

Increasingly, corporate occupiers seek more flexible ways of meeting their accommodation needs. One consequence of this process has been the growth of the executive suite, serviced office or business centre market. This paper, the final report of a research project funded by the Real Estate Research Institute, focuses upon the geographical distribution of business centers offering executive suites within the US. After a brief review of the development of the market, the paper examines the availability of data, provides basic descriptive statistics of the distribution of executive suites by state and by metropolitan statistical area and then attempts to model the distribution using demographic and socio-economic data at MSA level. The distribution reflects employment in key growth sectors and the position of the MSA in the urban hierarchy. An appendix presents a preliminary view of the global distribution of suites.

The cost of choice: how corporate real estate managers evaluate business space options

Firms are faced with a wider set of choices when they identify a need for new office space. They can build or purchase accommodation, lease space for long or short periods with or without the inclusion of services, or they can use “instant office” solutions provided by serviced office operators. But how do they evaluate these alternatives and are they able to make rational choices? The research found that the shortening of business horizons lead to the desire for more office space on short-term contracts often with the inclusion of at least some facilities management and business support services. The need for greater flexibility, particularly in financial terms, was highlighted as an important criteria when selecting new office accommodation. The current office portfolios held were perceived not to meet these requirements. However, there was often a lack of good quality data available within occupiers which could be used to help them analyse the range of choices in the market. Additionally, there were other organisational constraints to making decisions about inclusive real estate products. These included fragmentation of decisions-making, internal politics and the lack of assessment of business risk alongside real estate risk. Overall therefore, corporate occupiers themselves act as an interial force to the development of new and innovative real estate products.

Data interpretation in social housing management

The use of business management techniques in the public sector is not a new topic. However the increased use of the phrase "housing business management" as against that of "housing administration" reflects a change in the underlying philosophy of service delivery. The paper examines how data collection and use can be related to the operational requirements of the social landlords and highlights the problems of systems dynamics generating functionally obsolete data.

Pocket data mining: towards collaborative data mining in mobile computing environments

Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. This paper proposes a new architecture that we have prototyped for realizing the significant applications in this area. We have proposed using mobile software agents in this application for several reasons. Most importantly the autonomic intelligent behaviour of the agent technology has been the driving force for using it in this application. Other efficiency reasons are discussed in details in this paper. Experimental results showing the feasibility of the proposed architecture are presented and discussed.

An overview of the use of neural networks for data mining tasks

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.

Endogenous business cycles with frictional labour markets

We present a neoclassical model of capital accumulation with frictional labour markets. Under standard parameter values the equilibrium of the model is indeterminate and consequently displays expectations-driven business cycles – so-called endogenous business cycles. We study the properties of such cycles, and find that the model predicts the high autocorrelation in output growth and the hump-shaped impulse response of output found in US data – important features that existing endogenous real business cycle models fail to explain. The indeterminacy of the equilibrium stems from job search externalities and does not rely on increasing returns to scale as in most models.

The world at one's fingertips: interactive interpretation of environmental data

This chapter introduces the latest practices and technologies in the interactive interpretation of environmental data. With environmental data becoming ever larger, more diverse and more complex, there is a need for a new generation of tools that provides new capabilities over and above those of the standard workhorses of science. These new tools aid the scientist in discovering interesting new features (and also problems) in large datasets by allowing the data to be explored interactively using simple, intuitive graphical tools. In this way, new discoveries are made that are commonly missed by automated batch data processing. This chapter discusses the characteristics of environmental science data, common current practice in data analysis and the supporting tools and infrastructure. New approaches are introduced and illustrated from the points of view of both the end user and the underlying technology. We conclude by speculating as to future developments in the field and what must be achieved to fulfil this vision.

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.