Enabling innovation in information technology outsourcing : an empirical study

Information technology outsourcing has become a pervasive and important phenomenon in business organizations and there is substantial evidence about its benefits and pitfalls. Initially, firms used outsourcing as a way to lower costs, gain access to expertise and focus on core activities. Recently, there is a shift in focus and more firms are outsourcing to attain innovative products and services. However, current research is still unclear about how innovation can be achieved through outsourcing. Drawing predominantly from the dynamic capability theory, the objective of this paper is to explore how absorptive capacity unfolds as a process within and between firms when client firms outsource their information technology services with expectations of innovation generation. In this paper, we propose a research model that links absorptive capacity to innovation generation. We draw on three case studies to focus on how absorptive capacity, as a process, impacts innovation generation. Results show that assimilation and transformation stages are critical in generating radical innovation while acquisition and exploitation play a key role in incremental innovation. The implications of these findings for both researchers and practitioners are discussed.

The Nicholas building : a user's manual

Details of the design, construction and the 1926 opening of the Nicholas Building, located on the corner of Flinders Lane and Swanston Street, Melbourne, are presented. Information about the varied types of business houses functioning in the large premises over the years is provided, highlighting the building's lasting appeal for traders and the public.

Reliability and validity of a new Building Environmental Quality Questionnaire

This study aimed to develop and then test the reliability and validity of a new self-report questionnaire method called the building environmental quality questionnaire (BEQQ) designed to assess the perceived environmental quality in residential apartments in Hong Kong. A total of 108 (46 men and 62 women) Chinese-speaking residents, between 16 and 81 years of age, took part and completed the questionnaire study. The subjects were recruited from 12 different buildings of three distinct quality ratings (low, medium and high) assigned by the building assessment tool called the building health and hygiene index (BHHI). The study was evaluated to determine reliability and this was assessed involving 20 of the participants (18% of the total sample size). The BEQQ was found to have good test-retest reliability, with intra-class correlation coefficient (ICC) values typically around 0.70. The validity testing, also using ICCs, generated moderate to high values for all BEQQ sub-categories (the mean value was around 0.80), indicating a good consistency among residents living within the same building. Finally, the summary BEQQ scores were significantly correlated (—0.68) with the BHHI ratings as the criterion standard. It is concluded that this eight-dimension instrument would provide a short and efficient questionnaire method to obtain self-reported information to determine the perceived residential building quality. The method was shown to yield adequate reliability and has been validated for use in empirical research.

Interval-based and fuzzy set-based approaches to modeling of fuzzy inference systems with the local monotonicity property

Even though the importance of the local monotonicity property for function approximation problems is well established, there are relative few investigations addressing issues related to the fulfillment of the local monotonicity property in Fuzzy Inference System (FIS) modeling. We have previously conducted a preliminary study on the local monotonicity property of FIS models, with the assumption that the extrema point(s) (i.e., the maximum and/or minimum point(s)) is either known precisely or totally unknown. However, in some practical situations, the extrema point(s) can be known imprecisely (as an interval or a fuzzy set). In this paper, the imprecise information is exploited to construct an FIS model that fulfills the local monotonicity property. A procedure to estimate the extrema point(s) of a function is devised. Applicability of the findings to a datadriven modeling problem is further demonstrated.

Analysing building energy use using sub metering and external weather data

The Intergovernmental Panel on Climate Change and the McKinsey Greenhouse Gas abatement studies have highlighted reduction of building energy consumption as a primary cost-effective element in the abatement of Global Warming. Nevertheless, the energy investigation in most of our existing building stock remains at a novice level at best. Building sub-metering, by which we mean any secondary, hourly, metering (after the main) of various circuits, provides substantial information on when and where energy is used in specific buildings. Furthermore, combining this information with external weather data provides information beyond basic metering results. This paper discusses three case studies and explains how sub-metering, augmented by external solar and temperature data, benefits energy management and identified problems. It explains how different methods of analysing energy usage allowed: justifiable sizing of a solar photovoltaic system, with a calculated Cooling Degree Unit, identified the absence of savings from a proprietary chiller controller, and the energy variation due to user schedules and external conditions indicated anomalies in energy use. The advantages of wireless access are noted. Extracting information in graphical formats suggests better strategies to understand and control energy use.

Modeling malware propagation in smartphone social networks

Smartphones have become an integral part of our everyday lives, such as online information accessing, SMS/MMS, social networking, online banking, and other applications. The pervasive usage of smartphones also results them in enticing targets of hackers and malware writers. This is a desperate threat to legitimate users and poses considerable challenges to network security community. In this paper, we model smartphone malware propagation through combining mathematical epidemics and social relationship graph of smartphones. Moreover, we design a strategy to simulate the dynamic of SMS/MMS-based worm propagation process from one node to an entire network. The strategy integrates infection factor that evaluates the propagation degree of infected nodes, and resistance factor that offers resistance evaluation towards susceptible nodes. Extensive simulations have demonstrated that the proposed malware propagation model is effective and efficient.

Structural equation modeling for evaluating the user perceptions of E-learning effectiveness in higher education

A crucial prerequisite for sustainable e-learning is the understanding of learners’ preferences for various pedagogical strategies, technologies, and the management of learning resources. This paper presents an empirical study aiming to empirically test the theoretical (pedagogies, technologies and management) (PTM) model on the preference of learners and on the perceived impact of the effectiveness of e-learning. This study uses structural equation modelling (SEM) to identify the critical dimensions in the PTM model for augmenting the effectiveness of e-learning. This leads to the development of a PTM model with the path coefficients showing weak to strong relationships ranging from 0.15 to 0.42 with acceptable significance levels. The results support the hypothesis that management, technology, resources and metadata ontology dimensions affect the effectiveness of elearning both directly and indirectly through enhancing the management effectiveness of learning resources. However, the result does not support positive influence of pedagogical strategy per se on e-learning effectiveness. The implications of this study indicate the criticality of effective management of learning resources to enhance e-learning effectiveness.

Disputes resolution : can Bim help overcome barriers?

Many researchers consider disputes as part of the project lifecycle. Although preventative actions exist, it is not utterly possible to avoid them. Once the disputes arise, an appropriate resolution technique should be adopted. Common perception is referring to a resolution method either internally or via a third party; which may also be binding by law. The resolution process requires attention to the disputed claims. Hence, deep investigation of the claims and choosing the appropriate method is crucial for the successful project delivery and reputation of the industry.

Preparation of disputed claims and resolution process also faces many debates. Conducting To effective dispute resolution requires attention to proper preparation and presentation of the incurred events. All the required information should be acquired to estimate and present the claim, for a smooth settlement. As an integrated digital model of the project, BIM, stores all the information of the projects in detail. Retrieval of the required information for the disputed issues can easily be obtained from the model. It is also possible to embed the construction schedule, change orders and variations, specifications and financial data such as cash flow along with the multidisciplinary drawings. As this model stores all the information at every particular time and phase, disputes can be concluded quick and accurate.

In this research, using a case study and literature review, disputes and resolution processes are deeply studied. A BIM model is created to investigate benefits on overcoming the challenges; during claiming, and resolution of the disputes. It is seen that the claims are prepared faster and more accurate in a visualized environment provided by BIM. Furthermore, substantiating and presenting the disputes for the resolution purpose was incomparable to the traditional methods. The conclusions recommend that; even the project did not adopt a BIM model earlier; it can be created for a smooth process, during claiming and resolution of disputes.

A comprehensive framework for assessing the life-cycle energy of building construction assemblies

Building environmental design typically focuses on improvements to operational efficiencies such as building thermal performance and system efficiency. Often the impacts occurring across the other stages of a building's life are not considered or are seen as insignificant in comparison. However, previous research shows that embodied impacts can be just as important. There is limited consistent and comprehensive information available for building designers to make informed decisions in this area. Often the information that is available is from disparate sources, which makes comparison of alternative solutions unreliable. It is also important to ensure that strategies to reduce environmental impacts from one life cycle stage do not come at the expense of an increase in overall life-cycle impacts. A consistent and comprehensive framework for assessing and specifying building assemblies for enhanced environmental outcomes does not currently exist. This article presents the initial findings of a project that aims to establish a database of life cycle energy requirements for a broad range of construction assemblies, based on a comprehensive assessment framework. Life cycle energy requirements have been calculated for eight residential construction assemblies integrating an innovative embodied energy assessment technique with thermal performance modelling and ranked according to their performance. © #2010 Earthscan ISSN: 0003-8628.

Effect of ECG-derived respiration (EDR) on modeling ventricular repolarization dynamics in different physiological and psychological conditions

Ventricular repolarization dynamics is an important predictor of the outcome in cardiovascular diseases. Mathematical modeling of the heart rate variability (RR interval variability) and ventricular repolarization variability (QT interval variability) is one of the popular methods to understand the dynamics of ventricular repolarization. Although ECG derived respiration (EDR) was previously suggested as a surrogate of respiration, but the effect of respiratory movement on ventricular repolarization dynamics was not studied. In this study, the importance of considering the effect of respiration and the validity of using EDR as a surrogate of respiration for linear parametric modeling of ventricular repolarization variability is studied in two cases with different physiological and psychological conditions. In the first case study, we used 20 young and 20 old healthy subjects’ ECG and respiration data from Fantasia database at Physionet to analyze a bivariate QT–RR and a trivariate QT–RR–RESP or QT–RR–EDR model structure to study the aging effect on cardiac repolarization variability. In the second study, we used 16 healthy subjects’ data from drivedb (stress detection for automobile drivers) database at Physionet to do the same analysis for different psychological condition (i.e., in stressed and no stress condition). The results of our study showed that model having respiratory information (QT–RR–RESP and QT–RR–EDR) gave significantly better fit value (p < 0.05) than that of found from the QT–RR model. EDR showed statistically similar (p > 0.05) performance as that of respiration as an exogenous model input in describing repolarization variability irrespective of age and different mental conditions. Another finding of our study is that both respiration and EDR-based models can significantly (p < 0.05) differentiate the ventricular repolarization dynamics between healthy subjects of different age groups and with different psychological conditions, whereas models without respiration or EDR cannot distinguish between the groups. These results established the importance of using respiration and the validity of using EDR as a surrogate of respiration in the absence of respiration signal recording in linear parametric modeling of ventricular repolarization variability in healthy subjects.

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

Consensus guidelines for implementation of quality processes to prevent invasive fungal disease and enhanced surveillance measures during hospital building works, 2014

Healthcare-associated fungal outbreaks impose a substantial economic burden on the health system and typically result in high patient morbidity and mortality, particularly in the immunocompromised host. As the population at risk of invasive fungal infection continues to grow due to the increased burden of cancer and related factors, the need for hospitals to employ preventative measures has become increasingly important. These guidelines outline the standard quality processes hospitals need to accommodate into everyday practice and at times of healthcare-associated outbreak, including the role of antifungal stewardship programmes and best practice environmental sampling. Specific recommendations are also provided to help guide the planning and implementation of quality processes and enhanced surveillance before, during and after high-risk activities, such as hospital building works. Areas in which information is still lacking and further research is required are also highlighted.

Modeling the propagation of worms in networks: a survey

There are the two common means for propagating worms: scanning vulnerable computers in the network and spreading through topological neighbors. Modeling the propagation of worms can help us understand how worms spread and devise effective defense strategies. However, most previous researches either focus on their proposed work or pay attention to exploring detection and defense system. Few of them gives a comprehensive analysis in modeling the propagation of worms which is helpful for developing defense mechanism against worms' spreading. This paper presents a survey and comparison of worms' propagation models according to two different spreading methods of worms. We first identify worms characteristics through their spreading behavior, and then classify various target discover techniques employed by them. Furthermore, we investigate different topologies for modeling the spreading of worms, analyze various worms' propagation models and emphasize the performance of each model. Based on the analysis of worms' spreading and the existing research, an open filed and future direction with modeling the propagation of worms is provided. © 2014 IEEE.

Intervention-driven predictive framework for modeling healthcare data

Assessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks. © 2014 Springer International Publishing.

Modeling and analysis on the propagation dynamics of modern email malware

Due to the critical security threats imposed by email-based malware in recent years, modeling the propagation dynamics of email malware becomes a fundamental technique for predicting its potential damages and developing effective countermeasures. Compared to earlier versions of email malware, modern email malware exhibits two new features, reinfection and self-start. Reinfection refers to the malware behavior that modern email malware sends out malware copies whenever any healthy or infected recipients open the malicious attachment. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. In the literature, several models are proposed for email malware propagation, but they did not take into account the above two features and cannot accurately model the propagation dynamics of modern email malware. To address this problem, we derive a novel difference equation based analytical model by introducing a new concept of virtual infected user. The proposed model can precisely present the repetitious spreading process caused by reinfection and self-start and effectively overcome the associated computational challenges. We perform comprehensive empirical and theoretical study to validate the proposed analytical model. The results show our model greatly outperforms previous models in terms of estimation accuracy. © 2013 IEEE.