Computer-Aided Deliberation: Model Management and Group Decision Support

Nunamaker, Jay F.; Applegate, Lynda M.; Konsynski, Benn R.

doi:10.1287/opre.36.6.826

Cited by 217 publications

(84 citation statements)

References 24 publications

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“…A small screen may increase ranking effects because the narrow view can cause information chunking and users to lose a global perspective of the task, incurring cognitive load (Nunamaker et al 1987(Nunamaker et al , 1988. Numerous studies have documented that the small screens of mobile phones create a serious obstacle to users' navigation activities and perceptions (Chae and Kim 2004); hence, they reduce the effectiveness of the learning experience using mobile devices (Maniar et al 2008), of mobile marketing activities (Shankar et al 2010), of visualization design for mobile devices (Luca 2006), etc.…”

Section: Why Do Ranking Effects Matter?mentioning

confidence: 99%

How Is the Mobile Internet Different? Search Costs and Local Activities

Ghose

Goldfarb

Han

2013

Information Systems Research

419

238

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W e explore how Internet browsing behavior varies between mobile phones and personal computers. Smaller screen sizes on mobile phones increase the cost to the user of browsing for information. In addition, a wider range of offline locations for mobile Internet usage suggests that local activities are particularly important. Using data on user behavior at a (Twitter-like) microblogging service, we exploit exogenous variation in the ranking mechanism of posts to identify the ranking effects. We show that (1) ranking effects are higher on mobile phones suggesting higher search costs: links that appear at the top of the screen are especially likely to be clicked on mobile phones and (2) the benefit of browsing for geographically close matches is higher on mobile phones: stores located in close proximity to a user's home are much more likely to be clicked on mobile phones. Thus, the mobile Internet is somewhat less "Internet-like": search costs are higher and distance matters more. We speculate on how these changes may affect the future direction of Internet commerce.

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Section: Why Do Ranking Effects Matter?mentioning

confidence: 99%

How Is the Mobile Internet Different? Search Costs and Local Activities

Ghose

Goldfarb

Han

2013

Information Systems Research

419

238

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“…Early trials, however, revealed that people at the time struggled to imagine even the possibility of distributed teamwork. Researchers therefore developed new prototypes based on personal computers connected by a local area network (a radical new technology at the time), and built a first generation of electronic meeting rooms where people could see one another as they worked together and engaged one another through the technology (Figure 4) [51]. Researchers added additional tools to improve the ability of groups to generate, organize, and evaluate ideas [49].…”

Section: Last Research Mile For Group Support Systemsmentioning

confidence: 99%

The Last Research Mile: Achieving Both Rigor and Relevance in Information Systems Research

Nunamaker

Briggs

Derrick

et al. 2015

Journal of Management Information Systems

122

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From our desk chairs it may be tempting to work up an idea, build a quick prototype, test it in a lab, and say, "Our work here is done; the rest is merely details." More scholarly knowledge awaits discovery, however, by researchers who shepherd an information systems (IS) solution through the last research mile, that is, through successful transition to the workplace. Going the last research mile means using scientific knowledge and methods to address important unsolved classes of problems for real people with real stakes in the outcomes. The last research mile proceeds in three stages: proof-of-concept research to demonstrate the functional feasibility of a solution; proof-of-value research to investigate whether a solution can create value across a variety of conditions; and proof-of-use research to address complex issues of operational feasibility. The last research mile ends only when practitioners routinely use a solution in the field. We argue that going the last research mile negates the assumption that one must trade off rigor and relevance, showing it to be it a false dilemma. Systems researchers who take their solutions through the last research mile may ultimately have the greatest impact on science and society. We demonstrate the last research mile with cases from our own work and the work of others spanning more than forty years. As a professor of information systems at San Diego State University, Robert O. Briggs researches the cognitive foundations of collaboration and uses his findings to design new collaborative work practices and technologies. He is cofounder of the collaboration engineering field and co-inventor of the ThinkLets design pattern language for collaborative work processes. He has made exploratory, theoretical, experimental, and technological contributions to the areas of team productivity, technology-supported learning, ideation, creativity, consensus, satisfaction, willingness to change, and technology transition. He has designed collaboration systems and collaborative workspaces for industry, academia, government, and the military. He co-chairs the Collaboration Systems and Technology track for the Hawaii International Conference on System Sciences. He lectures worldwide on collaboration theory and practice, and on the logic of scientific inquiry. He has published more than two hundred scholarly works collaboration systems and technology, addressing issues team productivity, technology-supported learning, creativity, satisfaction, and technology transition. He earned his doctorate Management Information Systems at the University of Arizona in 1994. Air Force officer. He has extensive experience working with the Department of Defense. As a contractor and academic, he has been awarded contracts and grants totaling $39.85 Million over the last 12 years (principle investigator awards total $16.89 Million). Doug has published 41 peer-reviewed journal articles and conference proceedings. Douglas C Derrick Gerhard SchwabeGerhard Schwabe is a full professor of at the Universit...

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“…Anonymity influences various types of nonnormative behaviors including arguing (Lee, 2007), lying (Rockman & Northcraft, 2008), risky decisionmaking (Kiesler & Sproull, 1992;Pissarra & Jesuino, 2005), and lowered inhibition in computer-mediated communication (Sosik, Kahai, & Avolio, 1999). Anonymity makes individuals feel less compelled to conform to social norms (Kraemer & King, 1988;Jarvernpaa et al, 1988;Nunamaker, Applegate, & Konsynski, 1988;Connolly, Jessup, & Valacich, 1990;Carte & Chidambaram, 2004). …”

Section: H2bmentioning

confidence: 99%

The Effects of Media Capabilities on the Rationalization of Online Consumer Fraud

Harrison¹

2018

JAIS

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This research develops and tests a model of online consumer fraud to determine how the capabilities of communication technologies affect the rationalization of fraudulent behaviors. The model is based on research about the rationalization of fraud, media capabilities, and computer-mediated deception. This investigation empirically tests this model by analyzing 459 Facebook advertisements and 1,896 surveys completed by university students. The findings indicate that the capabilities provided by communication technologies affect the extent to which media mask cues of deceit and dehumanize others. As a result, some media capabilities increase one's willingness to engage in fraudulent behaviors while other capabilities deter those actions. Media capabilities that mask cues of deceit and reduce social presence increase the inclination of individuals to rationalize fraudulent activities, while media capabilities that expose cues of deceit and increase social presence deter individuals from rationalizing acts of fraud. Media offering greater capabilities for reprocessability and transmission velocity decrease the inclination to rationalize fraud, whereas greater capabilities for anonymity, rehearsability, and parallelism increase the inclination to rationalize fraud. In contrast, symbol set variety does not appear to significantly affect the inclination to rationalize fraud.

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Computer-Aided Deliberation: Model Management and Group Decision Support

Cited by 217 publications

References 24 publications

How Is the Mobile Internet Different? Search Costs and Local Activities

How Is the Mobile Internet Different? Search Costs and Local Activities

The Last Research Mile: Achieving Both Rigor and Relevance in Information Systems Research

The Effects of Media Capabilities on the Rationalization of Online Consumer Fraud

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