Several recent studies show that previous experience can influence observers’ search strategy in a way that improves search performance. The purpose of the present study is to investigate how the experience of difficult color discriminability affects search strategies. Two participant groups either experienced difficult color discriminability in a half of the trials (i.e., hard-discrimination group) or experienced easy search in all trials (i.e., easy-discrimination group) in a dual-target search task. Participants were required to respond to the presence of a target (colored T) among distractors (colored pseudo-L). Eye movements were recorded to understand which feature information is used to guide attention, and behavioral performance was measured to compare search efficiency between the two groups. The hard-discrimination group fixated more distractors with target-dissimilar colors than the easy-discrimination group, suggesting the hard-discrimination group used shape information to guide search more than the easy-discrimination group. However, error rates and response times were not significantly different between groups. The results demonstrate that the experience of difficult color discriminability discourages observers from guiding attention by color, and encourages them to use shape information.

This dissertation is composed of three essays using labor search models to explore the role of referral-networks in the labor market. The first, "<b>The Stabilizing Effect of Referral-Networks on the Labor Market</b>," examines how the use of informal connections (i.e. referral-networks) affects the severity and duration of recessions. To do so, I develop a search-and-matching model in which there are two hiring methods, formal channels and informal channels, and workers endogenously adjust their network of informal contacts in response to shocks and government policy. I show referral-networks have a stabilizing effect on the labor market, reducing the severity of adverse economic shocks and accelerating post-recession recovery. Counterfactuals demonstrate the government must exercise caution when enacting policies intended to expedite economic recovery. Policies that generically improve worker-firm matching prolong recovery by 8 months, as they facilitate relatively more matches between workers and low-productivity firms during recessions. In contrast, policies aimed at reducing the costs of network-formation or increasing referral-network prevalence facilitate more matches between workers and high-productivity firms, expediting recovery by 3-6 months.<br> <br>The second chapter, "<b>The Impact of Referral-Networks on Sectoral Reallocation</b>," investigates a new explanation for the long-run decline in sectoral switching--the increased prevalence of referral-networks. Using data from the Current Population Survey (CPS), I first document empirically significant increase in the use of referral-networks in the job-search process by the unemployed. Moreover, this increase is concurrent with the decline in sectoral switching. The CPS is then used to estimate the effect of using referral-networks on the likelihood of an individual switching sectors at a various levels of industry classifications. For all aggregations, using referral-networks significantly reduces the probability a worker switches sectors. After controlling for demographics, these estimates imply an increase in the prevalence of referral-network use could explain as much as 5% to 40% of the decline in sectoral switching. <br><br>To better illustrate the policy implications of this finding, a discrete time sectoral-switching model is constructed using a search and matching framework with labor market referrals. The estimated model estimates a referral-switching elasticity of about -.12, which is within the empirically estimated range of -.05 to -.22 for the 2-sector industry aggregation, demonstrating that the increased of the prevalence of referrals overtime can explain about 20% of the decline in US sectoral switching. Welfare results indicate that referrals are a "benign'' cause of the decline, i.e. welfare declines upon effectively banning the use of referral-networks. These results have important implications for policymakers. They suggest that the cause of the decline in sectoral switching (and more generally job-changing) is the result of improved matching efficiency over time rather than market inefficiency. <br><br>The third chapter, "<b>Does Job-Finding Using Informal Connections Reduce Mismatch?</b>," presents evidence that nonpecuniary benefits of a job, such as hours, commute time, and work environment, are a salient factor in a worker's decision to either accept or reject the offer. Using data form the Survey of Consumer Expectations (SCE), I document three empirical facts on the use of referral-networks and mismatch. First, not all referrals reduce perceived mismatch as reported by workers. For high-skill workers, referrals from former coworkers tend to reduce perceived nonpecuniary-mismatch. For low-skill workers, referrals from friends and family tend to increase perceived non-pecuniary mismatch. <br><br>Given these empirical facts, I construct a search-and-matching model of the labor market similar to Buhrmann [2018a] where workers and firms are given types on a unit interval and suffer increasingly greater productivity losses depending on distance between the firm's type and the worker's type. I augment this baseline model with mismatch along two dimensions -- skill and nonpecuniary preferences-- and calibrate it to the US economy. Results show nonpecuniary preferences can generate more dispersion in skill-mismatch for very low-skill workers and very high-skill workers. Moreover, while referral-networks generally improve aggregate mismatch, they have a heterogeneous affect on nonpecuniary mismatch by type. For low-skill (high-skill) workers, referral-networks increase (decrease) nonpecuniary mismatch.<br><br>Overall, the results from this dissertation serve as a guide for policymakers. While government intervention may be deemed necessary in recessions, it is vital to understand the role specific matching channels serve in the economy in order for a policy to achieve the desired result. Understanding that referrals generate more high-productivity matches suggests policymakers should investigate policies aimed at improving network formation and functionality. Similarly, distinguishing between formal and informal methods of job finding are key to understanding recent labor market phenomenon. The second chapter shows informal channels have become more ubiquitous in order to facilitate matching. While this change creates patterns in the data that seem concerning, a closer investigation reveals this seems to be a result of the market simply adapting to be more efficient. Finally, understanding why people use formal and informal channels is vital to understanding worker-firm mismatch on a micro-level. While high-skill workers use informal channels to find better matches, low-skill seem to use them to find any match faster. In essence, the findings of this dissertation emphasize the need for policymakers to understand the nuanced behavior of job seekers and the differing goals of various job-finding methods. One cannot simply treat all job-finding as the same, especially if a particular method is widely used and leads to significantly different outcomes, and expect to implement efficient policy. Thus, it is important to understand how certain job-finding methods differ on a micro level and apply these finding to macro policy. <br>

Users of current web search engines are often presented with a large number of returns after submitting a search term and choosing from the list might lead to them suffering from the effect of “choice overload”, as reported in earlier work. However, these search results are typically presented in an ordered list so as to simplify the search process, which may influence search behaviour and moderate the effect of number of choices. In this thesis, the effects of the number of search returns and their ordering on user behaviour and satisfaction are explored. A mixed methods approach combining multiple data collection and analysis techniques is employed in order to investigate these effects in terms of three specific issues, namely, choice overload in search engine use, variety seeking behaviour in a situation where multiple aspects of search results are required, and the chance of encountering serendipity. The participants were given search tasks and asked to choose from the sets of returns under experimental conditions. The results from the first three experiments revealed that large numbers of search results returned from a search engine tended to be associated with more satisfaction with the selected options when the decision was made without a time limit. In addition, when time was more strongly constrained the choices from a small number of returns led to relatively higher satisfaction than for a large number. Moreover, users’ behaviour was strongly influenced by the ordering of options in that they often looked and selected options presented near the top of the result lists when they perceived the ranking was reliable. The next experiment further investigated the ranking reliance behaviour when potentially useful search results were presented in supplementary lists. The findings showed that when users required a variety of options, they relied less on the ordering and tended to adapt their search strategies to seek variety by browsing more returns through the list, selecting options located further down, and/or choosing the supplementary web pages provided. Finally, with the aim of illustrating how chance encountering can be supported, a model of an automated synonym-enhanced search was developed and employed in a real-world literature search. The results showed that the synonym search was occasionally useful for providing a variety of search results, which in turn increased users’ opportunity to come across serendipitous experiences.

Thesis (MPhil)--Stellenbosch University, 2004. / ENGLISH ABSTRACT: A growing body of studies is developing approaches to evaluate human interaction with Web search engines. Measuring the information retrieval effectiveness of World Wide Web search engines is costly because of the human relevance judgements involved. However, both for business enterprises and people it is important to know the most effective Web search engine, since such search engines help their users find a higher number of relevant Web pages with less effort. Furthermore, this information can be used for several practical purposes. This study does not attempt to describe all the currently available search engines, but provides a comparison of some, which are deemed to be among the most useful. It concentrates on search engines and their characteristics only. The goal is to help a new user get the most useful "hits" when using the various tools. / AFRIKAANSE OPSOMMING: Al hoe meer studies word gedoen om benaderings te ontwikkel vir die evaluasie van menslike interaksie met Web-soekenjins. Om te meet hoe effektief 'n soekenjin inligting op die Wêreldwye Web kan opspoor, is duur vanweë die mens se relevansiebeoordeling wat daarby betrokke is. Dit is egter belangrik dat die bestuurders van sake-ondememings en ander mense sal weet watter die mees doeltreffende soekenjins is, aangesien sulke soekenjins hulle gebruikers help om 'n hoër aantal relevante Webblaaie met minder inspanning te vind. Hierdie inligting kan ook gebruik word om 'n paar praktiese doelwitte te verwesenlik. Daar word nie gepoog om al die soekenjins wat tans beskikbaar is, te beskryf nie, maar sommige van die soekenjins wat as die nuttigste beskou word, word vergelyk. Daar word alleenlik op soekenjins en hulle kenmerke gekonsentreer. Die doel is om die nuwe gebruiker te help om die nuttigste inligting te verkry deur gebruik te maak van verskeie hulpmiddels.

Search engines have become integral to the way in which we use the Web of today. Not only are they an important real time source of links to relevant information, but they also serve as a starting point to the Web. A veritable treasure trove of the latest news, satellite images, directions from anywhere to anywhere, local traffic updates and global trends ranging from the spread of influenza to which celebrity happens to be the most popular at a particular time. The more popular search engines are collecting incredible amounts of information. In addition to indexing significant portions of the Web they record what hundreds of millions of users around the world are searching for. As more people use a particular search engine, it has the potential to record more information on what is deemed relevant (and in doing so provide better relevance in the future, thereby attracting more users). Unfortunately, the relevance derived from this cycle between the search user and the search engine comes at a cost: privacy. In this work, we take an in depth look at what privacy means within the context of search. We discuss why it is that the search engine must be considered a threat to search privacy. We then investigate potential solutions and eventually propose our own in a bid to enhance search privacy. / Thesis (PhD)--University of Pretoria, 2011. / Computer Science / unrestricted

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