Lessons from multi-agency information management projects: Case of the Online Business Licensing Service (OBLS) Project,Singapore

Multi-agency information management projects are becoming increasingly common. Yet, there is relatively limited research in this area, particularly in the context of electronic government (e-government). This paper fills this gap in the literature by examining a successful information management project called the Online Business Licensing Service (OBLS) that aims to streamline the various licenses (managed by different agencies) required to operate a business in Singapore. The findings suggest that multi-agency projects are inherently more complex than single agency projects. A set of lessons on managing such projects is presented and these lessons should prove useful to researchers and practitioners as multi-agency information management projects become increasingly more widespread.     

网络(联机)环境中情报检索语言的几个问题

网络环境下,情报检索系统的变革对情报检索语言提出许多新课题,本文就情报检索语言的电子化和联机显示,网络环境中情报检索语言的兼容,规范语言与自然语言结合应用以及超文本信息系统与情报检索语盲保证的问题进行了探讨。     

美国联机目录中检索点的多余性和唯一性研究概述——许红和兰开斯特的合作研究

联机目录可大大提高主题检索能力 ,人们早已达成共识。F· W·兰开斯特等人于 1991年提出了篇名与分类号更多的是与著作固有的主题发生重合 ,而不是提供更深层次的检索点。据此 ,许红和兰开斯特从 OCLC的联机目录中进行提样研究 ,认为联机目录在检索方面比在提供每本书详尽的主题表达式方面具有更高的区别能力。表 5。图 1。参考文献 6。     

ScienceDirect Online数据库使用统计报告的获取及利用

介绍了ScienceDirect Online数据库使用统计报告的获取及构成,并对其使用情况进行分析。     

论网络出版与电子政务建设的互动

网络出版与电子政务之间不仅有着紧密的联系或相似的特征,而且在多个方面有着相互推动的作用:首先,网络出版技术的发展促进了电子政务技术的成熟,有利于优化与重组电子政务应用场景,有利于传统政务向电子政务迈进,其专业性和法治性有利于推进电子政务建设;其次,电子政务有利于监管网络出版,电子政务的G2B模式直接推动网络出版发展,电子治理将规范网络出版并使其健康发展.     

基于.net的在线编辑系统的实现

运用Web Service技术,采用浏览器、服务器、数据库三层结构设计一个在线编辑系统方案.在.net平台上给出了该方案的具体实现,使用SOAP扩展对包含在SOAP头中的应用程序身份验证信息进行了加密,提高了系统的安全性.     

Better together: Effects of four self-efficacy-building strategies on online statistical learning

The goal of this study is to test the individual and combined effects of supplementing an online statistics lesson with four motivational strategies corresponding to Bandura’s (1997) four sources of self-efficacy (anxiety coping, modeling, mental practice, and effort feedback) on cognitive, motivational, and affective outcomes. Internet participants (N = 279) completed an online statistics module in one of six conditions with one or all four self-efficacy-building strategies (5 treatment conditions) or none of these strategies (control condition). The results indicated that the four strategies worked effectively in combination, significantly improving transfer test scores (d = 0.608), increasing self-efficacy ratings (d = 0.696), and reducing task anxiety ratings (d = −0.534), as compared with the control condition. By contrast, no motivational strategy alone was effective. The results suggest the importance of taking advantage of the power of all four sources of self-efficacy information in combination when designing motivational interventions for online mathematical lessons.     

Securing online accounts and assets: An examination of personal investments and protection motivation

This paper examines how personal investment influence the use of multi-factor authentication (MFA) for securing online accounts. We draw from the Protection Motivation Theory (PMT) that accounts for intrapersonal factors that increase one’s understanding of their threat and coping mechanisms and, subsequently, their motivation to protect such investments. We integrate size of investment – the amount of time, effort, and other personal resources that an individual has put into their online account. Based on PMT’s framework, we develop a research model that examines the influence of investment size as an information source that initiates and builds threat and coping appraisals. The model is tested with a survey of 263 responses, and the result shows that investment size influences threat and coping appraisals, which in turn increases MFA protection motivation and use. These results highlight the importance of eliciting individuals’ personal investments in order to improve their protective security behaviors.     

Does imagination compensate for the need for touch in 360-virtual shopping?

The inability to touch products is a fundamental shortcoming in online shopping because humans typically use the sense of touch to evaluate the utilitarian product functionality and to obtain hedonic sensory enjoyment, which the instrumental and autotelic need for touch capture. This study of 900 consumers looks at the interplay between need for touch and imagination to study how imagination compensates for the lack of touch when consumers shop in a 360-virtual store. The study finds that while telepresence of a 360-virtual store improves consumer attitudes toward virtual shopping, the need for hedonic sensory enjoyment – autotelic need for touch – significantly reduces this effect. Further, imagination can compensate for the need for touch; yet this finding holds only for the instrumental need for touch, not for the autotelic need for touch. Consequently, we conclude that imagination can compensate for the utilitarian need to touch products in a 360-virtual store.     

Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews

For new product development, previous segmentation methods based on demographic, psychographic, and purchase behavior information cannot identify a group of customers with unsatisfied needs. Moreover, segmentation is limited to sales promotions in marketing. Although needs-based segmentation considering customer sentiments on product features can be conducted to develop a new product concept, it cannot identify commonalities among customers owing to their diverse preferences. Therefore, this paper proposes an interpretable machine learning-based approach for customer segmentation for new product development based on the importance of product features from online product reviews. The technical challenges of determining the importance of product features in each review are identifying and interpreting the nonlinear relations between satisfaction with product features and overall customer satisfaction. In this study, interpretable machine learning is used to identify these nonlinear relations with high performance and transparency. A case study on a wearable device is conducted to validate the proposed approach. Customer segmentation using the proposed approach based on importance is compared with that employing a previous approach based on sentiments. The results show that the proposed approach presents a higher clustering performance than the previous approach and offers opportunities to identify new product concepts.