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The purpose of this paper is to examine whether corporate social media information helps improve analysts’ forecast accuracy.

This study uses hand-collected information on S&P 500 firms’ official Facebook pages and uses posts and reactions to such posts to measure corporate Facebook information. Multivariate regression models are estimated to test the relationship between analysts’ forecast accuracy and corporate Facebook information.

The results indicate that analysts forecast accuracy is unresponsive to posts. However, analyst forecast errors are decreasing in reactions to posts. These findings are robust to the inclusion of control variables, firm and time fixed effects, and alternative specifications of forecast errors and different pre-forecast time windows.

This study has some limitations. It focuses only on the S&P 500 firms, which are large and generally provide better information to the market. The sample period coincides with the early period of the corporate Facebook culture. However, more recent data sets are likely to provide stronger results.

The findings of this study provide support for “information generation” role of social media and show that reactions to corporate Facebook posts are the new and unique information generated from corporate social media activities, which help information intermediaries in improving their forecasting accuracy.

This study makes an important contribution to the literature by separating the information dissemination role of social media from information generation role and establishes the first evidence on how corporate social media information affects forecast accuracy of financial analysts.

We highlight two mechanisms of limited attention for expert information intermediaries, i.e., analysts, and the effects of such limited attention on the market price discovery process. We approach analysts' limited attention from the perspective of day-to-day arrival of information and processing of tasks. We examine the attention-limiting role of competing tasks (number of earnings announcements and forecasts for portfolio firms) and distracting events (number of earnings announcements for non-portfolio firms) in analysts' forecast accuracy and the effects of such, on the subsequent price discovery process. Our results show that competing tasks worsen analysts' forecast accuracy, and competing task induced limited attention delays the market price adjustment process. On the other hand, distracting events can improve analysts' forecast accuracy and accelerate market price adjustments when such events relate to analysts' portfolio firms through industry memberships.

This article examines important issues of knowledge management within academic libraries, and how reference librarians can become more effective as information intermediaries. Exploiting knowledge can be a major competitive advantage for libraries, which can translate into better service to users. This article focuses on librarians and the information they use in carrying out the tasks for reference librarianship, and in organizing a multi‐campus library to effectively meet the needs of a large and diverse group of faculty, students, and staff. Specifically, a new tool for managing and using informal knowledge will be described, along with the team‐based approach that was used to develop and introduce the tool into the New Brunswick Libraries at Rutgers University.

This article identifies the changes that libraries and information centres (LICs) need to undergo for networking, and lists the existing communication networks (INDONET, ERNET, NICNET, GPSS, RABMN, INET) and libraries and information networks (INFLIBNET, DELNET, BTIS, SIRNET, TIFACLINE, CALIBNET, MALIBNET, BONET, MYLIBNET, PUNENET, etc.) in India. It mentions the paradigm changes that LICs undergo, challenges to their networking, and also highlights the role of IT in transforming traditional LICs into a digital mode. It discusses the various Governmental policies that led to the development of national information infrastructure, the inadequacies of the infrastructure, the status of IT application in Government, and the challenges in converting library contents into computer readable form. It concludes that, although India has drawn up ambitious plans, it has not seen the benefits that could come from employing these facilities to improve the socio‐economic conditions of its citizens or help it emerge as an economic superpower.

This review reports on the current state and the potential of tools and systems designed to aid online searching, referred to here as online searching aids. Intermediary mechanisms are examined in terms of the two stage model, i.e. end‐user, intermediary, ‘raw database’, and different forms of user — system interaction are discussed. The evolution of the terminology of online searching aids is presented with special emphasis on the expert/non‐expert division. Terms defined include gateways, front‐end systems, intermediary systems and post‐processing. The alternative configurations that such systems can have and the approaches to the design of the user interface are discussed. The review then analyses the functions of online searching aids, i.e. logon procedures, access to hosts, help features, search formulation, query reformulation, database selection, uploading, downloading and post‐processing. Costs are then briefly examined. The review concludes by looking at future trends following recent developments in computer science and elsewhere. Distributed expert based information systems (debis), the standard generalised mark‐up language (SGML), the client‐server model, object‐orientation and parallel processing are expected to influence, if they have not done so already, the design and implementation of future online searching aids.

This paper examines the relevance of the expert systems approach to information retrieval. First, the purpose and nature of expert systems are outlined, and it is argued that only in domains with relatively limited and clear‐cut rule‐sets will such systems be viable in the near future. The expert systems approach is then related to the provision of interfaces for OPACs and IR systems. Intermediary systems, designed to clarify through dialogue the terms of a client's information need, are described, and the prospects for such systems are discussed. It is argued that relatively straightforward ride‐sets should suffice, and that useful systems may therefore be available fairly soon. Since they could facilitate access to general as well as to specialised information, the potential demand for such systems would seem to be enormous.

This paper is an exploratory study of one approach to incorporating situational information into information retrieval systems, drawing on principles and methods of discourse linguistics. A tenet of discourse linguistics is that texts of a specific type possess a structure above the syntactic level, which follows conventions known to the people using such texts to communicate. In some cases, such as literature describing work done, the structure is closely related to situations, and may therefore be a useful representational vehicle for the present purpose. Abstracts of empirical research papers exhibit a well‐defined discourse‐level structure, which is revealed by lexical clues. Two methods of detecting the structure automatically are presented: (i) a Bayesian probabilistic analysis; and (ii) a neural network model. Both methods show promise in preliminary implementations. A study of users' oral problem statements indicates that they are not amenable to the same kind of processing. However, from in‐depth interviews with users and search intermediaries, the following conclusions are drawn: (i) the notion of a generic research script is meaningful to both users and intermediaries as a high‐level description of situation; (ii) a researcher's position in the script is a predictor of the relevance of documents; and (iii) currently, intermediaries can make very little use of situational information. The implications of these findings for system design are discussed, and a system structure presented to serve as a framework for future experimental work on the factors identified in this paper. The design calls for a dialogue with the user on his or her position in a research script and incorporates features permitting discourse‐level components of abstracts to be specified in search strategies.

Selected current and recent work in the area of cognitive modelling is reviewed. Particular attention is paid to user models (that is, the model held by a system of a user). The relevance of this work to information retrieval is assessed and some attempts to include user models in IR systems are discussed. Implications are drawn for future work in IR.

This paper reviews the history of separate online system interfaces, leading to efforts to develop expert systems for searching databases, particularly for end users, and introduces the research in such expert systems. Appended is a bibliography of sources on interfaces and expert systems for online retrieval.