The strategic use of expert systems for risk management in the insurance industry

Meyer, Marc H.; DeTore, Arthur; Siegel, Stephen F.; Curley, Kathleen Foley

doi:10.1145/97709.97748

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…First, social network literature on the strength of weak ties suggests that looser organizational coupling is more likely to connect partners with nonredundant, and useful information (Granovetter, ). In contrast, relationships characterized by tighter organizational coupling tend to bond cliques of mutually connected companies together (Burt, ; Krackhardt, ), which reduces the flow of nonredundant information and other resources into the network (Burt, ; Meyer, Detore, Siegel, and Curley, ). Second tighter organizational coupling can result in overembeddedness (Hagedoorn and Frankort, ).…”

Section: Theory Research Constructs and Hypothesismentioning

confidence: 99%

“…When companies are tied together by elaborate institutional, economic, and cultural relationships, technological change can be more difficult than in innovation networks characterized by looser organizational coupling (Glasmeier, ; Greenwood and Hinings, ; Uzzi, ). Such binding and solidarity among innovation network partners creates network inertia that hinders the formation of new relationships that would be potentially better equipped for performing the task (Adler and Kwon, ; Hansen, ; Kim et al, ; Meyer et al, ). In this way, tighter organizational coupling reduces the flexibility to adapt and to select partners with novel knowledge (Weick, ), which may result in lower innovation commercial performance.…”

Section: Theory Research Constructs and Hypothesismentioning

confidence: 99%

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When to Use Loose or Tight Alliance Networks for Innovation? Empirical Evidence

Halman¹,

Song²

2016

J of Product Innov Manag

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This study examines the impact of different degrees of organizational coupling among the members of innovation alliance project networks on the commercial performance of collaborative innovations. Specifically, we study how type of innovation (modular vs. architectural innovation) moderates this relationship. Using data from 664 product innovation networks from five different industries in the United States, we find that the degree of organizational coupling among innovation network members significantly affects the commercial performance of collaborative innovations and that the type of innovation has a significant moderating effect. More specifically, the impact on commercial innovation performance of organizational coupling is positive for modular innovations and negative for architectural innovations.

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Section: Theory Research Constructs and Hypothesismentioning

confidence: 99%

Section: Theory Research Constructs and Hypothesismentioning

confidence: 99%

When to Use Loose or Tight Alliance Networks for Innovation? Empirical Evidence

Halman¹,

Song²

2016

J of Product Innov Manag

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“…Mizoguchi and Motoda (1995) say that, in the top five Japanese companies, there are from 20 to 30 operating KBSs. Many insurance companies also use such systems (Meyer, Detore, Siegel, & Curley, 1992;Rowe & Wright, 1993).…”

Section: Introduction the Missing Dimensions To Analyze Systemsmentioning

confidence: 99%

User Acceptance of Interactive Systems: Lessons From Knowledge-Based and Decision Support Systems

Brézillon¹,

Pomerol²

1997

fail less learn it manage

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A realistic evaluation of the number of expert systems or knowledge-based systems (KBSs) that really are operational within companies or administrations is difficult. It seems that a large number of such systems have never been used in operations, and a rich literature stresses this point. We think that most of the references in the literature do not address some important dimensions. In this article, we discuss problems encountered in the literature according to three neglected dimensions: (1) the differences between automatic KBSs and interactive KBSs; (2) the types of data acquisition that are required; and (3) the nature and the role of future and preferences in the involved decisions. This permits us to propose an interpretation of the acceptance or the rejection of KBSs and their use along these three dimensions. Most of the previous failures and difficulties have already been encountered in several domains in which interactivity plays a crucial role. These domains are generally regrouped under the term of decision support systems (DSSs). Comparing KBS and DSS approaches permits us to propose some recommendations for an emerging approach, namely those of intelligent assistant systems that cover KBSs and DSSs.

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Expert Systems

O’Leary¹

2008

Wiley Encyclopedia of Computer Science and Engineering

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The purpose of this chapter is to review key concepts in expert systems, across the life cycle of expert system development. As a result, we will analyze the choice of the application area for system development, gathering knowledge through so‐called knowledge acquisition, choosing a knowledge representation, building in explanation and verifying and validating the system. In addition, we analyze a number of different applications of expert systems across a broad base of application areas, including medicine, geology and business. Further, we investigate some of the extensions to and emerging areas associated with expert systems.

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The strategic use of expert systems for risk management in the insurance industry

Cited by 4 publications

References 8 publications

When to Use Loose or Tight Alliance Networks for Innovation? Empirical Evidence

When to Use Loose or Tight Alliance Networks for Innovation? Empirical Evidence

User Acceptance of Interactive Systems: Lessons From Knowledge-Based and Decision Support Systems

Expert Systems

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