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Abstract

The resource-based view suggests that organizations achieve and maintain competitive advantage through effective deployment of firm-specific resources and capabilities. Because of volatile market conditions, researchers now focus on the development of dynamic capabilities that allow firms to react and create change in these dynamic environments. Despite the growing acceptance of the dynamic capabilities perspective in information systems research, the process of how organizations develop capabilities to influence the overall process of strategy formation and implementation in a dynamic and volatile environment (e.g., the information communication technology industry) is still underexplored. To address the knowledge gap, this article draws on an in-depth case study of the capability development experience of a call center in strategic transformation from an in-house customer service department to an outsourced customer service provider. We use Montealegre's (2002) process model of capability development as our analytical framework and extend it beyond the organizational perspective to include a project-level (business unit) perspective. By adopting a dual-level analysis, researchers and practitioners may obtain a more detailed and complete view of an organization's capability development, hence allaying criticism of the resource-based view as a vague and tautological concept.

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.13, S.1814-1829

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Abstract

Logical rules are essential for uncovering the logical connections between relations, which could improve the reasoning performance and provide interpretable results on knowledge graphs (KGs). Although there have been many efforts to mine meaningful logical rules over KGs, existing methods suffer from the computationally intensive searches over the rule space and a lack of scalability for large-scale KGs. Besides, they often ignore the semantics of relations which is crucial for uncovering logical connections. Recently, large language models (LLMs) have shown impressive performance in the field of natural language processing and various applications, owing to their emergent ability and generalizability. In this paper, we propose a novel framework, ChatRule, unleashing the power of large language models for mining logical rules over knowledge graphs. Specifically, the framework is initiated with an LLM-based rule generator, leveraging both the semantic and structural information of KGs to prompt LLMs to generate logical rules. To refine the generated rules, a rule ranking module estimates the rule quality by incorporating facts from existing KGs. Last, a rule validator harnesses the reasoning ability of LLMs to validate the logical correctness of ranked rules through chain-of-thought reasoning. ChatRule is evaluated on four large-scale KGs, w.r.t. different rule quality metrics and downstream tasks, showing the effectiveness and scalability of our method.