What Data Is Necessary To Data Mine For Knowledge?

The data that most organizations possess is considered a valuable asset. To really benefit from it, organizations must be able to analyze these data efficiently and effectively. This activity is nowadays referred to as business intelligence and one class of algorithms used within this activity are called data mining methods. There are some famous success stories about knowledge discovered via data mining but there is also a lot of disappointment so far. The paper argues that one of the reasons for some failures to produce better results with data mining is the reliance on transactional and master data only. It points to other types of data that can enrich transactional data and help in this way to produce more interesting and more rewarding data patterns

INTEGRATION OF INTERNET USERS IN INNOVATION PROCESSES

Development of new products or services was originally under exclusive control of product or service suppliers. With maturation of consumers, first in the industrial sector but today also in the consumer goods sector, the situation has changed. Many new ideas for product improvement or even completely new products come from their users who possess extensive product knowledge or expertise in a specific area. Companies are increasingly trying to actively tap this potential in a process called open innovation. Companies’ efforts to integrate (potential) customers into the product development can best be observed on the internet. On a number of websites special tools are implemented which capture users input related to early or later phases of product development. In other cases, companies initiate or support virtual communities organized around a theme that relates to their offerings. For these reasons, an online survey of internet users with respect to their experiences and attitudes towards co-creation was conducted. The results show that there is a big, still untapped potential for involvement of users in co-creation

The Timing Noise of PSR 0823+26, PSR 1706-16, PSR 1749-28, PSR 2021+51 and The Anomalous Braking Indices

We have investigated the stability of the pulse frequency second derivatives ($\ddot \nu$) of PSR 0823+26, PSR 1706-16, PSR 1749-28, PSR 2021+51 which show significant quadratic trends in their pulse frequency histories in order to determine whether the observed second derivatives are secular or they arise as part of noise processes. We have used TOA data extending to more than three decades which are the longest time spans ever taken into account in pulse timing analyses. We investigated the stability of pulse frequency second derivative in the framework of low resolution noise power spectra (Deeter 1984) estimated from the residuals of pulse frequency and TOA data. We have found that the $\ddot \nu$ terms of these sources arise from the red torque noise in the fluctuations of pulse frequency derivatives which may originate from the external torques from the magnetosphere of pulsar

CAN MOBILE APPS DEFEND PRINT MEDIA?

The business model of traditional media has come under attack since the diffusion of the Internet. One of the latest disruption waves are mobile apps. In this paper, we analyze the effect a mobile app has on the lifetimes and lifetime values of customers of print media. For this purpose, we use subscription data and develop a model based on survival analysis that captures the interdependence between two content delivery channels, in our context the offline and the mobile channel. We apply our model to a large dataset received from a publisher who offers a newspaper in a print version and a version for a mobile app. The results suggest that there exists a complementary interdependence between these media, as having a subscription to one of them decreases the hazard to cancel a parallel subscription to the other one. Given this complementarity, we find that the mobile app increases the lifetimes and lifetime values of print customers and vice versa. We also analyze the attribution of these effects

When Does Brand Bidding Pay Off (Even) If Website Competition is Low?

Many companies place advertisements on search engine result pages, a practice referred to as search engine advertising (SEA). If their website also appears among the organic results, it is questionable whether SEA makes sense: Free clicks may be substituted by costly clicks on the advertisement (paid result). We propose a model that determines when paid results complement organic results and when they cannibalize them. The model explains both interaction effects by the characteristics of the triggering keyword and the specificity of the advertisement. We evaluate the model in a field experiment in the context of a quasi-monopolistic company and approx. nine million search queries that contain its brand names (brand bidding). This helps to reduce possible biases from competition and user heterogeneity. Preliminary results suggest that superior net effects can be achieved by placing ads for search queries that indicate a navigational or transactional search intention compared to informational searches

Organizational culture as a primary driver for utilizing big data analytics in organizations

Master's thesis Information Systems IS501 - University of Agder 2019Context:During this last decade we have witnessed a wave of digital disruption, where big data has had a central part. This has gotten many organizations to pay attention and investing in analytic tools for big data. Big data analytics can provide organizations with more knowledge from more data sources that can have a big impact on how organizations act. Many of the organizations that have purchased big data analytics have failed to derive benefits from it and this is demonstratedin the literature. Organizational culture is mentioned as being an important part of achieving success when adopting big data.Purpose: The purpose of this thesis is to investigate the effect that organizational culture has on big data adoption, more specifically the organizations big data analytic capabilities. To measure this, we looked at the organization’s performance.Methods: We decided to use a quantitative approach to answer the research question. In order to define the different constructs of this research, we conducted a systematic literature review where we based the study on.We conducted a survey that we distributed to organizations within Europe that were using big data. The items of the survey were carefully developed by looking at previousmeasurements of these constructs and evaluating them with our supervisor, Ilias Pappas. We managed to get 104 respondents where they were all using big data in their work. We then developed a model with three different hypotheses and analysed the responseby using partial least square path modelling (PLS-SEM). This was done by using the tool, SmartPLS.Results: Our analysis validated our three hypotheses. The first one that focused on the positive effect organizational culture have on big data analytic. Second, organizational cultures positive effect on big data analytic capabilities. Final, hypothesis showed that organizational culture had a positive effect on intangible resources.Conclusion: We can conclude the research by confirming that organizationalculture has a huge effect on big data analytic capabilities and organizations need to look at organizational factors as well as the technical when they are investing in big data solutions. Keywords: Big data, big data analytics, big data analytic capabilities, organizational culture, firm performanc