The two major topics of this dissertation are desirability and information scent. The parallel between information retrieval and human memory is that the goal of an information retrieval system is to find the set of documents most relevant to the query whereas the goal for the human memory system is to access the relevance of items stored in memory given a memory probe (Steyvers & Griffiths, 2010). This dissertation explores the implications of computational cognitive modeling for information retrieval. Since the development of digital information technologies (DITs) has made the B2B buying process more buyer driven, our neural content modeling approach can be used to create B2B analytics that re-empower the sellers. With these experiments, we illustrate that browsing data can be combined with marketing content data to evaluate and improve the content-marketing efforts of B2B seller firms. In the first experiment, we study the content in the sellers' own channels, and in the second experiment we study paid channels. In total, the data comprises 180 million browsing sessions tracked via 11.44 million cookies from 34,170 buyer companies. The model was tested with two experiments using a dataset that combines cookie-based browsing data from 74 B2B seller companies over a period of fourteen months. In this study, we develop a neural content model to match the content that B2B sellers are providing with the type of content that buyers are seeking. #RSSOWL SAVED SEARCHES FEATURES HOW TO#The challenge is that sellers have data but do not know how to utilize it. Further, this study methodology can be adapted by researchers to understand other aspects of programming such as implementing, reusing, and maintaining code.īusiness-to-business (B2B) sellers need to enhance content marketing and analytics in an online environment. Our results give us a better understanding of the programming behavior of web-active end-users and can inform researchers and professionals how to create better support for the debugging process. We also identified the strategies used by the participants when finding and fixing bugs. Clear cues helped participants to find and fix bugs with ease while fuzzy and elusive cues led to useless foraging. On analyzing the data, we identified three types of cues: clear, fuzzy, and elusive. The programmers completed two debugging tasks using the Yahoo! Pipes web mashup environment. Through the lens of information foraging theory, we analyzed the data from a controlled lab study of eight web-active end-user programmers. Information foraging theory helps understand how users forage for information and has been successfully used to understand and model user behavior when foraging through documents, the web, user interfaces, and programming environments. To understand the foraging behavior of end-user programmers when debugging, we used information forging theory. The debugging on these platforms is challenging as end user programmers need to forage within the mashup environment to find bugs and on the web to forage for the solution to those bugs. Web-active end-user programmers spend substantial time and cognitive effort seeking information while debugging web mashups, which are platforms for creating web applications by combining data and functionality from two or more different sources.
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