An Approach to Security for Unstructured Big Data

Security of Big Data is a huge concern. In a broad sense, Big Data contains two types of data: structured and unstructured. Providing security to unstructured data is more difficult than providing security to structured data. In this paper, we have developed an approach to provide adequate security to unstructured data by considering types of data and their sensitivity levels. We have reviewed the different analytics methods of Big Data to build nodes of different types of data. Each type of data has been classified to provide adequate security and enhance the overhead of the security system. To provide security to a data node, and a security suite has been designed by incorporating different security algorithms. Those security algorithms collectively form a security suite which has been interfaced with the data node. Information on data sensitivity has been collected through a survey. We have shown through several experiments on multiple computer systems with varied configurations that data classification with respect to sensitivity levels enhances the performance of the system. The experimental results show how and in what amount the designed security suite reduces overhead and increases security simultaneously.     

The contextual relevance effect on financial advertising

The contextual relevance effect is an important factor for the effectiveness of financial services advertising that corresponds to the message effects such as ad recall (R_AD), perceived contextual relevance (PCR), message involvement (MI) and attitude towards the ad (A_AD), created during the process. Thus, this study examines whether the contextual relevance effect increases R_AD, PCR, MI and A_AD. The results reveal that the contextual relevance effect increases R_AD, PCR, MI and A_AD. Moreover, MI mediates PCR on A_AD. Implications based on the findings demonstrate the importance of contextual relevance as a metric for financial services advertising effectiveness.     

Late for a very important date: financial reporting and audit implications of late 10-K filings

Delays in financial reports often reflect issues related to period-end accounting and audit processes. We investigate the impact of filing delays in connection with auditor characteristics on the quality of financial statements in a sample of firms that filed Form 10-K after the statutory due date. We find that late filing firms are associated with lower financial reporting quality compared to timely filing firms matched by propensity scores, where financial reporting quality is measured by the absolute value of performance-matched discretionary accruals and the probability of a late filing being restated in subsequent periods. Furthermore, we demonstrate that the adverse consequences of late filings can be largely mitigated by having a Big 4 auditor. The differential audit quality stems primarily from Big 4 auditors in large offices and is more pronounced when an auditor may need to draw on additional resources in a limited period.     

Contextual Innovation Management Using a Stage‐Gate Platform: The Case of Philips Shaving and Beauty

To improve its innovation process, Philips Shaving and Beauty (S&B) designed a blueprint for its innovation process. Although it has proved to be quite effective, it has experienced a lack of efficiency, in terms of frequent cost and time overruns, in the fuzzy front end of this process. We suggest a contextual innovation management approach to set up a stage‐gate‐based innovation process platform and thus improve the efficiency in the fuzzy front end, which means that, for different contexts, stage‐gate process variants will be designed from which unnecessary activities are removed and important activities are emphasized. The design is based on the identification of relevant contextual factors to develop variations of the common innovation process within Philips S&B. We distinguished different variants of the innovation processes within Philips S&B that can increase the efficiency in the fuzzy front end. Based on interviews within and outside Philips S&B, we identified problems and potential solutions with regard to efficiency in eight recently finished innovation processes. The results indicate that the most important contextual factors are the distinction between incremental and radical innovations, and between market and technology‐based innovations. We used these factors to design three variants on the basic platform of the stage‐gate process.     

A two-stage approach to defining an affected community based on the directly affected population and the sense of community

Studies have demonstrated the inadequacy of relying on existing administrative boundaries or simple proximity to define an affected community. The proposal and siting of hazardous facilities can have a range of impacts upon people across wide areas, with some more affected than others as a result of living with the physical impacts of construction or the fear associated with perceived risk. We term those most affected the directly affected population and propose a two-stage model for identifying an affected community which places those most affected at the centre of the definition. The second stage is to identify the relationships those most affected have with the wider elements of the sense of community to discover the existing community or communities which are affected. Illustrated by the siting of a low-level radioactive waste disposal facility at Dounrey in the north of Scotland, we show that elements of the lived community experience may have very different shapes, extents and conflicting interests which pose challenges for their incorporation into a siting process. The two-stage model presented in this paper, by placing those most directly affected at the centre and working from there out into the existing communities, identifies issues early in any siting process to improve their incorporation and amelioration.     

Testing Alternative Regression Frameworks for Predictive Modeling of Health Care Costs

Predictive models of health care costs have become mainstream in much health care actuarial work. The Affordable Care Act requires the use of predictive modeling-based risk-adjuster models to transfer revenue between different health exchange participants. Although the predictive accuracy of these models has been investigated in a number of studies, the accuracy and use of models for applications other than risk adjustment have not been the subject of much investigation. We investigate predictive modeling of future health care costs using several statistical techniques. Our analysis was performed based on a dataset of 30,000 insureds containing claims information from two contiguous years. The dataset contains more than 100 covariates for each insured, including detailed breakdown of past costs and causes encoded via coexisting condition flags. We discuss statistical models for the relationship between next-year costs and medical and cost information to predict the mean and quantiles of future cost, ranking risks and identifying most predictive covariates. A comparison of multiple models is presented, including (in addition to the traditional linear regression model underlying risk adjusters) Lasso GLM, multivariate adaptive regression splines, random forests, decision trees, and boosted trees. A detailed performance analysis shows that the traditional regression approach does not perform well and that more accurate models are possible.     

Investigating Adoption of Free Beta Applications in a Platform‐Based Business Ecosystem

Planning new product development (NPD) activities is becoming increasingly difficult, as contemporary businesses compete at the level of business ecosystems in addition to the firm‐level product‐market competition. These business ecosystems are built around platforms interlinking suppliers, complementors, distributors, developers, etc. together. The competitiveness of these ecosystems relies on members utilizing the shared platform for their own performance improvement, especially in terms of developing new valuable offerings for end users. Therefore, managing the development of the platform‐based applications and gaining timely end‐user input for NPD are of vital importance both to the ecosystem as a whole and to the developers. Subsequently, to succeed in NPD planning developers utilizing beta testing need a thorough understanding of the adoption dynamics of beta products. Developers need to plan for example resource allocation; development costs; and timing of commercial, end‐product launches. Therefore, the anticipation of the adoption dynamics of beta products emerges as an important antecedent in planning NPD activities when beta testing is used for gaining end‐user input to the NPD process. Consequently, we investigate how free beta software products that are built upon software platforms diffuse among their end users in a cocreation community. We specifically study whether the adoption of these beta products follows Bass or Gompertz model dynamics used in the previous literature when modeling the adoption of stand‐alone products. Further, we also investigate the forecasting abilities of these two models. Our results show that the adoption dynamics of free beta products in a cocreation community follow Gompertz's model rather than the Bass model. Additionally, we find that the Gompertz model performs better than the Bass model in forecasting both short and long out‐of‐sample time periods. We further discuss the managerial and research implications of our study.