J.K. Bhargava, R.K. Sricastava and S.S. Murthy
A library automation software package (SANJAY) has been developed in the CDS/ISIS V2.3 environment extensively using the Pascal interface to meet the requirements of a model…
Abstract
A library automation software package (SANJAY) has been developed in the CDS/ISIS V2.3 environment extensively using the Pascal interface to meet the requirements of a model library. Using SANJAY a user can get instant access to information, responses to queries and reports from multiple databases. It is an interactive, menu driven, and user‐friendly package which carries out routine functions of a library. The software is capable of inter‐relating two or more databases for a single application like acquisition or circulation. This paper identifies some of the problems in the CDS/ISIS V2.3 package and presents the features of the SANJAY package that overcome these problems and discusses its implementation in a government library.
G.G. Chowdhury and Sudatta Chowdhury
Automated text retrieval and library management systems have not yet taken a desired shape in Indian libraries, though efforts are being made in this direction. A number of…
Abstract
Automated text retrieval and library management systems have not yet taken a desired shape in Indian libraries, though efforts are being made in this direction. A number of software packages for this purpose have come out recently through government and private agencies. Published sources assessing this technology are yet to appear, therefore choosing the right software is difficult. This paper aims to high‐light the present Indian scenario by presenting a brief overview of 10 selected indigenous packages, namely CATMAN, CDS/ISIS, LIBRARIAN, LibSys, MAITRAYEE, MECSYS, NIRMALS, SANJAY, TULIPS, and WILISYS. The underlying framework and text retrieval and library management facilities in these packages are briefly discussed. Considering the cost aspect, it is concluded that CDS/ISIS, along with SANJAY with some further modifications, might prove to be the most suitable package for most Indian libraries.
Mamta Kayest and Sanjay Kumar Jain
Document retrieval has become a hot research topic over the past few years, and has been paid more attention in browsing and synthesizing information from different documents. The…
Abstract
Purpose
Document retrieval has become a hot research topic over the past few years, and has been paid more attention in browsing and synthesizing information from different documents. The purpose of this paper is to develop an effective document retrieval method, which focuses on reducing the time needed for the navigator to evoke the whole document based on contents, themes and concepts of documents.
Design/methodology/approach
This paper introduces an incremental learning approach for text categorization using Monarch Butterfly optimization–FireFly optimization based Neural Network (MB–FF based NN). Initially, the feature extraction is carried out on the pre-processed data using Term Frequency–Inverse Document Frequency (TF–IDF) and holoentropy to find the keywords of the document. Then, cluster-based indexing is performed using MB–FF algorithm, and finally, by matching process with the modified Bhattacharya distance measure, the document retrieval is done. In MB–FF based NN, the weights in the NN are chosen using MB–FF algorithm.
Findings
The effectiveness of the proposed MB–FF based NN is proven with an improved precision value of 0.8769, recall value of 0.7957, F-measure of 0.8143 and accuracy of 0.7815, respectively.
Originality/value
The experimental results show that the proposed MB–FF based NN is useful to companies, which have a large workforce across the country.
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V. Srilakshmi, K. Anuradha and C. Shoba Bindu
This paper aims to model a technique that categorizes the texts from huge documents. The progression in internet technologies has raised the count of document accessibility, and…
Abstract
Purpose
This paper aims to model a technique that categorizes the texts from huge documents. The progression in internet technologies has raised the count of document accessibility, and thus the documents available online become countless. The text documents comprise of research article, journal papers, newspaper, technical reports and blogs. These large documents are useful and valuable for processing real-time applications. Also, these massive documents are used in several retrieval methods. Text classification plays a vital role in information retrieval technologies and is considered as an active field for processing massive applications. The aim of text classification is to categorize the large-sized documents into different categories on the basis of its contents. There exist numerous methods for performing text-related tasks such as profiling users, sentiment analysis and identification of spams, which is considered as a supervised learning issue and is addressed with text classifier.
Design/methodology/approach
At first, the input documents are pre-processed using the stop word removal and stemming technique such that the input is made effective and capable for feature extraction. In the feature extraction process, the features are extracted using the vector space model (VSM) and then, the feature selection is done for selecting the highly relevant features to perform text categorization. Once the features are selected, the text categorization is progressed using the deep belief network (DBN). The training of the DBN is performed using the proposed grasshopper crow optimization algorithm (GCOA) that is the integration of the grasshopper optimization algorithm (GOA) and Crow search algorithm (CSA). Moreover, the hybrid weight bounding model is devised using the proposed GCOA and range degree. Thus, the proposed GCOA + DBN is used for classifying the text documents.
Findings
The performance of the proposed technique is evaluated using accuracy, precision and recall is compared with existing techniques such as naive bayes, k-nearest neighbors, support vector machine and deep convolutional neural network (DCNN) and Stochastic Gradient-CAViaR + DCNN. Here, the proposed GCOA + DBN has improved performance with the values of 0.959, 0.959 and 0.96 for precision, recall and accuracy, respectively.
Originality/value
This paper proposes a technique that categorizes the texts from massive sized documents. From the findings, it can be shown that the proposed GCOA-based DBN effectively classifies the text documents.
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Karen L. Xie and Young Jin Lee
When shopping for hotels online, consumers usually follow a sequential process of search, click-through and book. How to maximize consumer conversion on the path to purchase and…
Abstract
Purpose
When shopping for hotels online, consumers usually follow a sequential process of search, click-through and book. How to maximize consumer conversion on the path to purchase and prevent potential customers from giving up the online search remains an important topic to hotel marketers and online travel agents (OTAs). The purpose of this study is to understand how informational cues displayed in an online hotel search process, including quality indicators, brand affiliation, incentives (discounted price and promotion) and position in the search results, influence consumer conversion from one stage to another.
Design/methodology/approach
The authors collected clickstream data of hotel search from Expedia. The data include information on individual consumers’ click-through and booking, as well as events leading up to the conversions (or failure to convert) from search, click-through to book. It contains 940,164 hotels searched and displayed in 39,574 online search queries made by users in a regional US market between November 1, 2012 and June 20, 2013. The modeling strategy comprised the Heckman model and random effects model, which integrated sequential consumer behavior in different problem-solving stages while accounting for heterogeneity across different hotels online.
Findings
The authors find that consumers rely on informational cues displayed online to make decisions about hotel booking. Specifically, consumers tend to click through hotels with higher consumer-generated ratings and industry-endorsed ratings. However, they tend to rely on consumer-generated ratings rather than industry-endorsed ratings when committing to a booking. Moreover, consumers are strongly responsive to incentives (discounted price and promotion) when clicking-through and booking a hotel. Finally, the likelihood of consumer conversions from search to click-through and booking is higher for hotels with brand affiliation and higher positions in the search results.
Originality/value
This research provides critical managerial implications of online search for hotel marketers and OTAs. The results inform hotel marketers and OTAs on how consumers respond to informational cues displayed in their search process and how these informational cues influence consumer conversion from one stage to another. The sequential problem-solving process of search, click-through and booking disclosed in this study also helps hotel marketers to identify customer conversion opportunities using effective informational cues.
研究目的
当在线酒店预定时, 消费者往往遵循一系列流程, 搜索, 点击查询, 到最后预定。对于酒店营销商和线上旅游社(OTAs)来说, 如何最大化提高消费转化, 使得消费者不会半途中断, 最后预定酒店, 是一个重要话题。本论文的研究目的就是理解酒店在线搜索过程中, 信息线索如何影响每个阶段的消费转化, 其中涉及的因素有:信息质量、品牌、激励(折扣和促销)、以及搜索结果排名等。
研究设计/方法/途径
研究样本数据采集于Expedia酒店搜索点击流。其中包括个人消费者点击和预定信息、以及由搜索、点击查询到预定过程中的消费转化(或者中途转化失败)的各种事件。样本容量包括940,164家酒店, 其涉及到由美国局部市场消费者在2012年11月1日到2013年6月20日之间做出的39,574条搜索结果。 我们采用Heckman模型和随机效应模型来整合不同线性时间上的消费者行为, 同时考虑不同酒店的多样性。
研究结果
研究发现消费者使用在线信息线索来做酒店预订决策。具体来说, 消费者倾向于对于消费者评价高和行业认证高的酒店进行点击查询。然而, 相比行业认证, 消费者更倾向于借鉴消费者评价, 来做出最后预定决策。此外, 在点击查询和预定时, 消费者对于激励(折扣和促销)反应强烈。最后, 品牌和搜索排名靠前的酒店往往获得从搜索、点击查询到最后预定中更高的消费转化率。
研究原创性/价值
本论文对酒店营销商和OTAs有重要的在线搜索启示。研究结果向酒店营销商和OTAs证明消费者在搜索过程中对信息线索如何反应, 以及这些信息线索如何影响每个阶段之间的消费转化。本论文展示的从搜索、点击查询、到预定的线性决策过程对于酒店营销商们有着重大帮助, 帮助其使用信息线索找出各种消费转化机遇。
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Loan T. Le, Luan Duc Tran and Trieu Ngoc Phung
The study investigates determinants of willingness to pay (WTP) for laser land leveling (LLL) technology, its demand heterogeneity across individual farmers and plot…
Abstract
Purpose
The study investigates determinants of willingness to pay (WTP) for laser land leveling (LLL) technology, its demand heterogeneity across individual farmers and plot characteristics and the technology's empirical impact on paddy productivity.
Design/methodology/approach
The study applies the Becker-DeGroote, Marschak style to elicit the WTP for LLL technology and the Cragg model to examine the determinants of the WTP to capture both the demand decision and affordability. The randomized controlled trials (RCT) are incorporated with a production function model to analyze the technology effects on paddy productivity.
Findings
The Cragg model finds that the key demographic and behavioral traits such as age, extension services and risk acceptance significantly influence the adoption decision; however, the plot area, bank and financial capacity become predominant factors in the adoption affordability. The LLL treatment effect results in a statistically significant increase in paddy yield of 6.48%, equivalent to 492,138 kg ha-1.
Research limitations/implications
The analysis underscores the factor complexity, illustrating that the LLL-promoting interventions need to address both the adoption barriers and the enablers for greater affordability. A composite of climate-smart agricultural programs should be employed to facilitate the LLL adoption. The empirical evidence highlights the positive effect on agricultural productivity, potentially offering a significant boost to output and farmer income.
Originality/value
The study contributes to existing literature by analyzing the heterogeneous demand for LLL technology with two distinguishable features of the paddy mono-cropping system and land fragmentation and by incorporating the RCTs alongside a production function for the effects on paddy productivity.
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Taking instances from extant findings from the literature, the study aims to examine the community perception toward renewable energy (RE) off-grid (mini-grid/microgrid…
Abstract
Purpose
Taking instances from extant findings from the literature, the study aims to examine the community perception toward renewable energy (RE) off-grid (mini-grid/microgrid) intervention, the underlying rationales for engagement of communities in RE off-grid projects, the different alternatives/models to engage communities in various phases of RE off-grid project deployment.
Design/methodology/approach
The study has followed the structured literature review to explore the identified research question of the study.
Findings
Based on findings from the review, the framework for effective community engagement in RE mini-grid projects is suggested. Furthermore, the study also draws suggestions and implications for future research and practice.
Practical implications
Based on such understanding the present study offers the framework which suggests the steps for the engagement of the communities in the off-grid projects. The key steps are managing the perception of the community (including generation of awareness among the community), planning for the benefits of the community, linkage the sustainable development goals (SDG), planning for the inclusion of the community and measuring performance (in the line of social and economic criteria and SDG).
Originality/value
This study finds the gap in the literature on the nexus of community, off-grid energy projects and SDG. Following the findings from the scholars in this field, a few gaps in the policy and practice have been highlighted which could be useful for practitioners and policymakers in this area.
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Rainer Olbrich and Carsten D. Schultz
The study contributes to our understanding of search engine advertising in two main ways. Firstly, we analyze the comparative effectiveness of its campaign parameters. Secondly…
Abstract
Purpose
The study contributes to our understanding of search engine advertising in two main ways. Firstly, we analyze the comparative effectiveness of its campaign parameters. Secondly, we examine the effect of print advertising on search engine advertising
Design/methodology/approach
Based on advertising data for a three-year period, we test the hypotheses by means of a path model with the aid of partial least squares.
Findings
The advertising budget and the degree of keyword matching yield the greatest effect on the number of signed contracts. The click-through rate and the bid amount contribute, to a lesser extent, to explaining this financial target variance. The number of keywords had no significant effect. The study did not yield significant evidence of print advertising, directly affecting the number of search engine advertisement impressions, but showed an indirect effect of print advertising on the number of conversions, induced directly by search engine advertising.
Research limitations/implications
The multichannel relationship of print and search engine advertising, including its campaign parameters, provides a starting point for future research to provide a coherent methodology for capturing the necessary data, processing the underlying information and evaluating the advertising effects.
Practical implications
The multichannel effect needs to be quantified and taken into account when evaluating print advertising and search advertising campaigns and the future advertising mix is planned.
Originality/value
The study extends the field of search engine advertising in the direction of multichannel effects. In comparison to previous research, empirical evidence on the multichannel usage of print advertising and search engine advertising, related to an overall economic target, is provided.
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Surabhi Nishad, Sapna Jain and Rama Bhargava
This paper aims to study the flow and heat transfer inside a wavy enclosure filled with Cu-water nanofluid under magnetic field effect by parallel implemented meshfree approach.
Abstract
Purpose
This paper aims to study the flow and heat transfer inside a wavy enclosure filled with Cu-water nanofluid under magnetic field effect by parallel implemented meshfree approach.
Design/methodology/approach
The simulation has been carried out for a two-dimensional model with steady, laminar and incompressible flow of the nanofluid filled inside wavy enclosure in which one of the walls is sinusoidal such that the amplitude (A = 0.15) and number of undulations (n = 2) are fixed. A uniform magnetic field B0 has been applied at an inclination angle γ. The governing equations for the transport phenomena have been solved numerically by implementing element-free Galerkin method (EFGM) with the sequential as well as parallel approach. The effect of various parameters, namely, nanoparticle volume fraction (φ), Rayleigh number (Ra), Hartmann number (Ha) and magnetic field inclination angle (γ) has been studied on the natural convection flow of nanofluid.
Findings
The results are obtained in terms of average Nusselt number calculated at the cold wavy wall, streamlines and isotherms. It has been observed that the increasing value of Rayleigh number results in increased heat transfer rate while the Hartmann number retards the fluid motion. On the other hand, the magnetic field inclination angle gives rise to the heat transfer rate up to its critical value. Above this value, the heat transfer rate starts to decrease.
Originality/value
The implementation of the magnetic field and its inclination has provided very interesting results on heat and fluid flow which can be used in the drug delivery where nanofluids are used in many physiological problems. Another important novelty of the paper is that meshfree method (EFGM) has been used here because the domain is irregular. The results have been found to be very satisfactory. In addition, parallelization of the scheme (which has not been implemented earlier in such problems) improves the computational efficiency.
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Ryan Greenaway-McGrevy, Chirok Han and Donggyu Sul
This paper is concerned with estimation and inference for difference-in-difference regressions with errors that exhibit high serial dependence, including near unit roots, unit…
Abstract
This paper is concerned with estimation and inference for difference-in-difference regressions with errors that exhibit high serial dependence, including near unit roots, unit roots, and linear trends. We propose a couple of solutions based on a parametric formulation of the error covariance. First stage estimates of autoregressive structures are obtained by using the Han, Phillips, and Sul (2011, 2013) X-differencing transformation. The X-differencing method is simple to implement and is unbiased in large N settings. Compared to similar parametric methods, the approach is computationally simple and requires fewer restrictions on the permissible parameter space of the error process. Simulations suggest that our methods perform well in the finite sample across a wide range of panel dimensions and dependence structures.