Online IR system evaluation: online databases versus Web search engines

Xie, Hong (Iris)

doi:10.1108/14684520410543652

Cited by 22 publications

(9 citation statements)

References 11 publications

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“…A brief literature survey was done to develop a basic understanding of the problem, and to design appropriate questionnaires. Various aspects taken up by the previous researchers [1][2][3][4][5][6][7][8][9][10][11][12] helped us to approach the problem in a holistic manner and become clear in objectives of the present study.…”

Section: Methodsmentioning

confidence: 99%

Problems in Searching Online Databases: A Case Study of Select Central University Libraries in India

Roy¹,

Kumar²,

Satija³

2012

DJLIT

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The purpose of this paper is to investigate and determine the different challenges faced by the searchers of online database in selected central university libraries in India. Eight central universities in India were chosen on the basis of the bandwidth utilisation (higher to lowest in each group). A total of 302 library users filled in the questionnaires (out of 320 approached) in the survey all eight central university libraries across India. It finds that easy and understandable content pages are the most desirable by the users; site feasibility is directly proportional to users interaction; and the retrieval techniques vary from subject to subject. This paper affirms that the databases should be selected on the basis of their retrieval aspects and the online features. The publisher of the online databases can not overlook the retrieval features in the databases. Easy and simple retrieval features can fetch more subscribers for the vendors and publishers. This is a comprehensive study that provides statistical data on searching/retrieval problem of online databases in different central universities in India.

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Section: Methodsmentioning

confidence: 99%

Problems in Searching Online Databases: A Case Study of Select Central University Libraries in India

Roy¹,

Kumar²,

Satija³

2012

DJLIT

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“…Customers may still prefer direct channels for seeking information about the insurance products. Xie (2004) posits that customers evaluate information according to the interface devices, performance of the website and information about the service.…”

Section: Literature Reviewmentioning

confidence: 99%

Antecedents to Indian customers attitude towards online insurance services

Khare¹,

Singh

2010

IJBCG

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Proliferation of internet and its usage in services has endowed customers with multiple channels of service delivery. Online insurance refers to the selling of insurance products via the internet and is fast gaining momentum across the world as an alternative delivery channel. In India, however, with little IT penetration and awareness, a huge percentage of the population remains skeptical about online insurance. To facilitate quicker and easier transactions, insurance companies have jumped into the bandwagon of modifying their service delivery channels. The research focuses on deciphering the attitude of Indian customers towards online insurance services being adopted by Indian insurance companies.

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“…How to provide the information to online users properly is an important issue for company web sites. Xie (2004) claimed that system users evaluate information according to three factors: interface design, system performance and collection coverage of information. However, Bond (2004) found that even experienced web users may find it difficult to search for information if they lack search strategies and are unfamiliar with the information content.…”

Section: Introductionmentioning

confidence: 99%

Locating online loan applicants for an insurance company

Yang¹,

Wang²

2008

Online Information Review

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Purpose -This study aims to investigate insurance policy loan applicant characteristics. Additionally, it reveals the behaviour patterns of heavy users who have applied for at least two loans. A policy loan prediction model is established which is designed to increase loan application rates. Design/methodology/approach -The proposed model is implemented using data-mining techniques and comprises two mechanisms: a business rule generator and a recommendation mechanism. Two analytical approaches, the C.5 and Apriori algorithm, are employed to analyse the profile and browsing log DBs of insured individuals. The prediction model is verified by actual data from a Taiwanese insurance company. Findings -The data-mining results reveal that five attributes are ultimately used to establish the prediction model, namely: gender, marketing channel, insurance type, area of policy owner, and assumed interest rate. Additionally, the analytical results also indicate that insured individuals apply for loans as a result of arbitrage inducement. The accuracy of loan applicant prediction can exceed 70 per cent. Finally, some interesting patterns emerge for heavy users, such as the finding that loan applicants are used to applying for loans continuously (loan application repetition is on average two to three times). Research limitations/implications -Some policy owners who are unfamiliar with the web interface prefer to contact insurance personnel directly to discuss their insurance needs, and thus no browsing records are available for such users. In such cases only the profile could be collected and analysed. Practical implications -The proposed model enables insurance firms to locate potential loan applicants according to the data-mining results. As in the illustration scenario in the paper, insurance personnel can contact these potential loan applicants before they submit loan applications to the bank. Additionally, loan-related information is provided for online insurance users based on their browsing logs. The loan application rate is thus expected to increase, along with interest revenue. Originality/value -As long as the policy proceeds, the interest income from the policy loan seems to be a good option for extending insurance company operational earnings. Understanding the characteristics of loan applicants will provide helpful information. Besides, the proposed mechanism will be more appropriate to online users, who are unwilling to deal with unwanted information.

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Online IR system evaluation: online databases versus Web search engines

Cited by 22 publications

References 11 publications

Problems in Searching Online Databases: A Case Study of Select Central University Libraries in India

Problems in Searching Online Databases: A Case Study of Select Central University Libraries in India

Antecedents to Indian customers attitude towards online insurance services

Locating online loan applicants for an insurance company

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