user does not know something, where there is an anomaly in the user’s state of knowledge in relation to the problem faced. A summary of the literature ( Khazer & Ganaie, 2014 ) in recent years indicates that research into information-seeking behavior mainly concentrates on two aspects of online searches: job-related information and daily information ( Figure 1 ). And health information-seeking behavior has become an increasingly important issue in daily information-seeking behavior studies ( Ajuwon & Popoola, 2015 ; Kim, 2015 ). When it comes to the process of
-40-10 model itself has rarely been debated although substantial amounts of money have been reallocated through this mechanism over the years ( Aagaard, 2011 ).
2 The process leading to the adoption of the Norwegian model
The political perception that the existing Danish core funding system was functioning inappropriately became even more outspoken after the turn of the century. It was particularly highlighted as problematic that the distribution of core funding between the universities was based on a historically conditional distribution key—regardless of whether the
purpose, identified need or sometimes understanding on how big data could even be used. Almost by an act of magic, data-driven statistics and data mining are expected to resolve industry problems by themselves.
In order to mitigate the effects of this “Industrial Big Data Hubris” it is necessary to clearly define the concept of big data in terms of its business value and the information that contributes to this value. This is of fundamental importance since there is a clear difference between data and information. Data comprises facts and figures which have been
are perceived and evaluated by customers. According to Burgoon et al. (2000), structural characteristics are represented in certain communication tools and approaches, and the experience characteristics produce the perceptionof interaction process, during which the structural characteristics play a role. The structural features of interactive tools that can support interaction are the material basis for interaction. With such a foundation, users can use the medium (i.e. interactive tools) to interact and get experience and perceptionof B2C enterprises. If
Dangzhi Zhao, Alicia Cappello and Lucinda Johnston
Citation analysis is used in research evaluation exercises around the globe, directly affecting the work and lives of millions of researchers and the expenditure of billions of dollars. It is therefore crucial to address the problems and limitations that plague it. Central amongst critiques of the current practices of citation analysis has long been that it treats all citations equally, regardless of whether they are crucial to the citing paper or perfunctory. This problem is especially troublesome when tracing or assessing research impact
. (2011) examined the factors that lead to acceptance of cloud computing platforms in colleges, and proposed an adoption model that adds personal innovativeness and teacher support to TAM3, and divided adoption behavior into practice use, future use intention, and future use ( Behrend et al., 2011 ). Their findings demonstrated that background characteristics influenced perceived usefulness, while ease of use perceptions were largely determined by first-hand experiences with the platform and instructor support. Park and Ryoo (2013) used a longitudinal design to get
the national level in Norway, while there are concerns regarding how the model is used in local contexts. Still, the effects of performance-based research funding system (PRFS) are still largely an unresolved issue, and one of the main problems is to disentangle effects of PRFS from surrounding factors (for an in-depth discussion see special issue of Journal of Informetrics , Waltman, 2017 ). In this paper, however, I will focus on a different but related question: How and why has the Norwegian model become some popular? The present paper thus attempts to explain
information.” The unifying factor across various definitions is the “science” that comprises defining appropriate questions, selecting and obtaining suitable data, and applying the correct, at times often innovative, modeling, and statistical methods.
The “science” of data science indicates a methodological and systematic approach to leveraging data as part of studying a problem or a phenomenon. Data science endeavors rely not only on data, but accurate description of the data—hence metadata. Given the reliance on metadata, one would anticipate appropriate support for
with better interactivity can help make the virtual visiting experiences more engaging and richer, through facilitating multi-dimension perceptions, observations, and manipulations (if possible) ( Tait et al., 2016 ).
As users need to perform relevant tasks in the usability tests, we designed nine information seeking tasks in four categories of different cognitive complexity levels ( Spool, 1999 ), with considerations of the contexts of Mogao Caves as well as panorama representations of cultural heritage, including colors, shapes, structures, and themes ( Shiaw
for ranking universities. Various reports in the popular press seem to be based on this incorrect perception. Also, the fact that the order by setting on the list view page was changed in only 17.4% of all sessions (see Figure 4 ) shows that many visitors of the list view page stick to the default criterion for ordering universities, probably either because they consider this to be the criterion that is recommended by CWTS or because they are not even aware that it is possible to select an alternative criterion. Hence, even though CWTS does not want to recommend a