Designing technology for managing the information exchange of decision making teams

Silver, Steven D.

doi:10.1016/j.dss.2014.02.005

Cited by 13 publications

(9 citation statements)

References 49 publications

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“…A recent numerical analysis showed that regulative effects in distributed GDSS settings suppressed the bias of group members' information exchange (Silver, 2014).…”

Section: Deception In Gdss Settingsmentioning

confidence: 99%

“…One such feature is the presence of information asymmetries during the aggregation of information (Barkhi et al, 1998). The other is the decision rule complexity that characterizes this aggregation (Silver, 2014). …”

Section: Deception In Gdss Settingsmentioning

confidence: 99%

“…In addition, regulative effects may suppress biased information exchange (Silver, 2014). Therefore, under conditions of information asymmetry, the complexity of a rule that determines how imputations are aggregated into a group outcome may differentially impact the exchange of inauthentic information.…”

mentioning

confidence: 99%

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Manipulative Imputation in Distributed Decision Support Settings: The Implications of Information Asymmetry and Aggregation Complexity

Malekovic¹,

Sutanto

Goutas

2016

Decision Support Systems

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According to earlier research, distributed decision support structures are susceptible to deception. We complement the existing works by analyzing group members' attempts to manipulate group decisions supported by distributed communications. Experimentally, we manipulated two systemic features of a distributed support structure: the members' information asymmetry and decision rule complexity. Both of these features refer to structural properties of aggregated information exchange. We confirmed several hypothesized effects: An increase in the information asymmetry in the aggregation of information increases the incidence of the members' manipulative tendency. It also increases the effectiveness of the members' manipulative imputations. However, the complexity of a decision rule negatively moderates both of these effects. We point out the theoretical relevance and managerial implications of our findings. We conclude that managing team members' information asymmetry and complexity of issues under their practical consideration may result in valuable disclosures.

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“…A recent numerical analysis showed that regulative effects in distributed GDSS settings suppressed the bias of group members' information exchange (Silver, 2014).…”

Section: Deception In Gdss Settingsmentioning

confidence: 99%

Section: Deception In Gdss Settingsmentioning

confidence: 99%

See 1 more Smart Citation

Manipulative Imputation in Distributed Decision Support Settings: The Implications of Information Asymmetry and Aggregation Complexity

Malekovic¹,

Sutanto

Goutas

2016

Decision Support Systems

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“…The problem is then extended to represent social structure in teams as a regulator of the amount and type of information that members exchange [2]. The team dynamics in a collaborative environment is considered a major contributing factor to the decision making process and therefore the enjoyment of the game [27].…”

Section: Team Matchingmentioning

confidence: 99%

“…The ever increasing popularity of collaborative games, especially MMOGs and social networking games, has seen a rise in the deployment of collaborative gaming in areas such as learning, decision making and health [1][2][3] where gaming is traditionally regarded as a distraction. There are an 1 Corresponding Author estimated 400 million (and rising) MMOG players across the globe [4], while collaborative social games such as Zynga's FarmVille suite of games have gained immense popularity, commanding the engagement of 60 million monthly users on average, an estimated 1% of the world population, and more than 20% of Facebook users [5,6].…”

Section: Introductionmentioning

confidence: 99%

Lu-Lu: A framework for collaborative decision making games

Daylamani-Zad

Angelides

Agius

2016

Decision Support Systems

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This paper proposes Lu-Lu as an add-on architecture to open MMOGs and social network games, which has been developed to utilise a key set of ingredients that underline collaborative decision making games as reported within the research literature: personalisation, team matching, non-optimal decision making, leading, decisiveness index, scoring, levelling, and multiple stages. The implementation of Lu-Lu is demonstrated as an add on to the classic supply chain beer game, including customisation of Lu-Lu to facilitate information exchange through the Facebook games platform, e.g. Graph API and Scores API. Performance assessment of Lu-Lu using Behaviour Driven Development suggests a successful integration of all key ingredients within Lu-Lu's architecture, yielding autonomous behaviour that improves both player enjoyment and decision making.

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