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This paper aims to identify and articulate critical research issues in the emerging area of real‐time knowledge management (RT‐KM) in enterprises, in order to stimulate other researchers to further pursue them. The paper creates a framework around which it identifies and examines research issues and challenges that become salient and critical when knowledge sharing and creation need to happen in near real‐time. The framework is based on two drivers: increasing the requirements to plan for quickening the action‐learning loop in the enterprise, and increasing requirements in planning for emergence. The action‐learning loop is further articulated through the “observe, orient, decide, and act” (OODA) framework that is suited to sense‐and‐respond environments. Through the framework six sets of critical research challenges are identified around RT‐KM in enterprises: challenges around managing the quality of information in RT‐KM, challenges around improving the selective and intensive aspects of managerial attention in RT‐KM, challenges around making core business processes better suited to RT‐KM, challenges around integrating multiple distributed perspectives unpredictably in RT‐KM, challenges around developing heuristics in a way that allow real‐time emergence, and challenges around effectively capturing actions and learning for later reuse in RT‐KM. The issues and challenges identified are suggestive rather than exhaustive. Based on observations from real field case studies in industry, and driven by an industry need for better RT‐KM. This paper brings together the concepts of vigilant information systems, OODA loops, and emergence and applies them through a framework to identify research issues and challenges in this new emerging area of RT‐KM.

The purpose of this paper is to study how the online temporary crowd shares knowledge in a way that fosters the integration of their diverse knowledge. Having the crowd integrate its knowledge to offer solution-ideas to ill-structured problems posed by organizations is one of the desired outcomes of crowd-based open innovation because, by integrating others’ knowledge, the ideas are more likely to consider the many divergent issues related to solving the ill-structured problem. Unfortunately, the diversity of knowledge content offered by heterogeneous specialists in the online temporary crowd makes integration difficult, and the lean social context of the crowd makes extensive dialogue to resolve integration issues impractical. The authors address this issue by exploring theoretically how the manner in which interaction is organically conducted during open innovation challenges enables the generation of integrative ideas. The authors hypothesize that, as online crowds organically share knowledge based upon successful pro-socially motivated interaction, they become more productive in generating integrative ideas.

Using a multilevel mixed-effects model, this paper analyzed 2,244 posts embedded in 747 threads with 214 integrative ideas taken from 10 open innovation challenges.

Integrative ideas were more likely to occur after pro-socially motivated interactions.

Ideas that integrate knowledge about the variety of issues that relate to solving an ill-structured problem are desired outcomes of crowd-based open innovation challenges. Given that members of the crowd in open innovation challenges rarely engage in dialogue, a new theory is needed to explain why integrative ideas emerge at all. The authors’ adaptation of pro-social motivation interaction theory helps to provide such a theoretical explanation. Practitioners of crowd-based open innovation should endeavor to implement systems that encourage the crowd members to maintain a high level of activeness in pro-socially motivated interaction to ensure that their knowledge is integrated as solutions are generated.

The present study extends the crowd-based open innovation literature by identifying new forms of social interaction that foster more integrated ideas from the crowd, suggesting the mitigating role of pro-socially motivated interaction in the negative relationship between knowledge diversity and knowledge integration. This study fills in the research gap in knowledge management research describing a need for conceptual frameworks explaining how to manage the increasing complexity of knowledge in the context of crowd-based collaboration for innovation.

This study aims to investigate the effects of project-level external knowledge search breadth and search depth on the innovation performance of open innovation (OI) projects in large firms; it further considers these effects mediated by two forms of control mechanisms (process and outcome control) when the level of project complexity and the two stages of a project – early (problem definition) and late (solution development) – are taken into account.

Based on a survey of 187 managers responsible for an OI project, the authors use theory on behavioral-based control mechanisms to explore whether the effect of external knowledge search breadth and depth on OI performance is contingent on having the right levels of control mechanisms in place.

The results showed that the control mechanism mediates the relationship between external knowledge search breadth and depth and OI project performance. Furthermore, project complexity is an important moderator of these effects, especially for outcome control.

A better OI project’s performance is not achieved by external knowledge search breadth and depth alone, but by building process and outcome control mechanism on it to balance knowledge sharing and protecting tension. Furthermore, Outcome control is only helpful with less complex OI projects.

The purpose of this paper is to quantify recent changes in students' use of laptops in one academic library and to consider the potential role of new high‐tech library spaces on laptop use.

Instead of relying on standard library exit and entrance counts, this study was based on brief, structured observations of library spaces, including counts of laptop use. By conducting such observations over regular intervals in both new and pre‐existing library spaces, the authors were able to observe changes in laptop use across both time and type of library space.

The growth in laptop use dramatically exceeded expectations: 28 percent of students used laptops in existing spaces in 2005, while 62 percent of students used laptops in the same spaces in 2008. While a new, adjoining high‐tech space also had high laptop use, the opening of this new space coincided with growth in both laptop use and overall student presence in the pre‐existing library spaces. In addition, the paper posits a variety of potential causes for these changes, including available technology and hardware, university‐wide policy, and student behavior.

The changing use of laptops in libraries is important for administrative decision making at both the library and institutional level, including decisions about facilities, technology infrastructure and support, and security.

On December 17, 2003 the Securities and Exchange Commission (SEC) approved an overhaul of the New York Stock Exchange’s (NYSE’s) system of corporate governance. After questions arose concerning the NYSE’s ability to discharge its self‐regulatory functions following the resignation of former Chairman and CEO Richard Grasso, Interim Chairman John Reed proposed new governance architecture including a newly independent Board of Directors and a separate Board of Executives designed to represent the NYSE’s various constituencies. The new architecture reflects an effort to strike a balance between an independent board of directors and the desire for input from the industry, i.e., self‐regulation. This new structure should not be seen as the SEC’s determination of the future of self‐regulation, but simply as the most recent step in refining and improving the self‐regulatory process at the NYSE and other marketplaces as well.

The process by which technological innovations developed in one institution are discovered, acquired, and adapted for use by another institution.

Under certain conditions, a mass of people can be smarter than the best expert – even if the expert is part of the group. In this paper we show how leaders can improve decision making by tapping into the collective intelligence of their organization.

Based on James Surowiecki’s four conditions of collective intelligence (cognitive diversity, independence, utilization of decentralized knowledge, and effective aggregation of dispersed knowledge), we discuss how leaders can tap into the wisdom of the crowd of their organizations.

We show how leaders can increase cognitive diversity in decision making, access decentralized knowledge in their organizations, encourage individuals to contribute their knowledge without interference from peer pressure, conformity or influence from superiors, and how knowledge can effectively be aggregated to make wiser decisions.

While various tools exist to reap the collective intelligence of a group, we argue that leaders also must change their attitudes and leadership styles. Using evidence from various studies and several examples we show what leaders can do to make smarter decisions.

Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable, only conditionally backtestable and less robust. In this chapter, we compare an innovative artificial neural network (ANN) model with a time series model in the context of forecasting VaR and ES of the univariate time series of four asset classes: US large capitalization equity index, European large cap equity index, US bond index, and US dollar versus euro exchange rate price index for the period of January 4, 1999, to December 31, 2018. In general, the ANN model has more favorable backtesting results as compared to the autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) time series model. In terms of forecasting accuracy, the ANN model has much fewer in-sample and out-of-sample exceptions than those of the ARMA-GARCH model.

Environmental engineering is primarily concerned with the application of technology to the urgent tasks of cleaning up our environment. Its practitioners generally attempt to cope with the problems of streams and waterways polluted by sewage and industrial waste, oceans damaged by oil spills and sewage sludge dumpings, air polluted with noxious fumes and land abused by solid waste disposal. But that is not all that they do. The recent energy crisis has sharply brought into focus the need for alternate energy strategies, including energy extraction from solid waste. Under current estimates, the United States will produce approximately 340 million tons of solid waste by 1980. This is equivalent to one ton of solid waste per person per year. The most widely used methods of waste disposal right now are dumping, incineration and sanitary landfill. They are expensive and they cause pollution. Instead, solid waste can be burned to produce steam which can be used for heating or to generate electricity. It can also be converted to pyrolysis gas or oil, which can be stored or transported. It is from this standpoint that environmental engineering assumes considerable importance. A report which presents an overview of the state of the art in this area is the Resource Recovery from Municipal Solid Waste. Other pertinent guides include Energy from Solid Waste, Conversion of Refuse to Energy, Recycling and Reclaiming of Municipal Solid Wastes, Resource Recovery and Recycling Handbook of Industrial Wastes, and Wasteheat Management Guidebook. No project of this nature can be undertaken without government assistance. A description of the activities of the Federal Solid Waste Management Program is available from EPA's Solid Waste Recycling Projects: A National Directory.