Collective intelligence applied to legal e-discovery: A ten-year case study of Australia franchise and trademark litigation

doi:10.1016/j.aei.2015.04.006

Advanced Engineering Informatics

Volume 29, Issue 4, October 2015, Pages 787-798

https://doi.org/10.1016/j.aei.2015.04.006 Get rights and content

Highlights

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E-discovery to define legal evolution using Collective Litigation Intelligence.
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A scientific approach to improve legal practice litigation research.
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Derives market entry strategies for global franchise brand expansion.

Abstract

The purpose of this research is to develop a formal knowledge e-discovery methodology, using advanced information technology and decision support analysis, to define legal case evolution based on Collective Litigation Intelligence (CLI). In this research, a decade of Australia’s retail franchise and trademark litigation cases are used as the corpus to analyze and synthesize the evolution of modern retail franchise law in Australia. The formal processes used in the legal e-discovery research include a LexisNexis search strategy to collect legal documents, text mining to find key concepts and their representing key phrases in the documents, clustering algorithms to associate the legal cases into groups, and concept lattice analysis to trace the evolutionary trends of the main groups. The case analysis discovers the fundamental issues for retail modernization, advantages and disadvantages of retail franchising systems, and the potential litigation hazards to be avoided in the Australian market. Given the growing number of legal documents in global court systems, this research provides a systematic and generalized CLI methodology to improve the efficiency and efficacy of research across international legal systems. In the context of the case study, the results demonstrate the critical importance of quickly processing and interpreting existing legal knowledge using the CLI approach. For example, a brand management company, which purchases a successful franchise in one market is under limited time constraints to evaluate the legal environment across global markets of interest. The proposed CLI methodology can be applied to derive market entry strategies to secure growth and brand expansion of a global franchise.

Introduction

This research focuses on developing a methodology for legal e-discovery and, most importantly, litigation evolution analysis using collective intelligence of precedential documents, court cases, and data existing in legal databases. The specific techniques of text mining, data mining, cluster analysis, and formal concept analysis are modified and applied as the Collective Litigation Intelligence (CLI) methodology for legal e-discovery. The case study of the Australia retail franchise and trademark litigation is used to demonstrate the effectiveness of the generalizable CLI processes. The last decade of legal cases (2004–2013), related to retail franchises and trademarks, are searched as the fact-base to derive collective litigation intelligence to project the litigation trends and evaluate hazards underlying the retail legal evolution. These franchise and trademark litigation cases and selected documents are used to demonstrate the significant findings of the CLI approach and the validity of the methodology. The paper strategically advises franchise trademark holders of market strategies for sustainable market development that avoids past mistakes and identifies new opportunities. The research results enables managers and legal advisors to answer critical questions such as “Has legislation stabilized the franchise environment?” and “What are the trademark, legal, and franchise hazards that restrict market development?” The research clusters the last decade of 35 precedent setting franchise and trademark cases into four homogenous groups. Briefly speaking, the clustering analysis yields four groups of cases with distinctive characteristics and features. The CLI’s final step is to derive the time-varied evolution trends underlying the development of the franchise and trademark legal landscape. Applying the developed CLI processes, Clusters 1, 2, and 3 are considered bellwether case clusters whereas Cluster 4 supports legislative stability and a market place of opportunity rather than a legal hazard. Franchisers and brand managers interested in the Australian market should focus on the case clusters to avoid franchise and trademark litigation and to develop opportunities that create sustainable market plans. The methodology and the case research can be rapidly repeated in other legal domains for their litigation e-discovery.

This paper is organized using the following sections. In the literature review section, the key concepts and related research of ontology, knowledge discovery, data and text mining, and formal concept analysis are reviewed, cited, and described. We refer to these papers for the readers’ further reference and study when conducting following up research for advanced theory development and applications. The “collective litigation intelligence (CLI) methodology” section describes the CLI methodology framework and logic for each detailed step. The CLI procedure is best demonstrated and described using an applied legal case study. Thus, the step-wise sub-sections including the document search strategy, the case text mining, the CLI clustering and context interpretations, and legal concept evolution lattice are presented using the Australian franchise example. Finally, the conclusion section discusses the research results, contributions, and suggested future work for a generalizable CLI methodology applicable for legal e-discovery beyond the franchise and trademark case domain.

Section snippets

Literature review

This research focuses on using knowledge discovery algorithms and methods to effectively extract litigation trends and insights from a huge collection of text documents. The key knowledge e-discovery techniques specifically applicable for CLI methodology development include the capturing of key entities in the domain ontology, knowledge discovery from text documents (KDT) and from databases (KDD), document clustering, and formal concept lattice analysis. From these critical components of

Collective litigation intelligence (CLI) methodology

This research develops the formal methodology for the discovery of collective litigation intelligence. The main processes to the CLI approach are illustrated in Fig. 3, which include the litigation case search strategy and execution, the legal document text mining, the case clustering, and the CLI concept evolution lattice analysis. As shown in Fig. 4, the CLI processes are implemented for the Australia retail franchise and trademark related litigation activities and trend analysis. The

Discussion and conclusions

This research develops a generalizable Collective Litigation Intelligence (CLI) e-discovery methodology and demonstrates the approach in practice using the case study of Australia’s franchise and trademark litigation cases. The results of CLI e-discovery for the case study show that Australia has a well regulated retail marketplace with solid legislation to support franchises and protect trademarks. There are three important points derived from this research to be considered when entering the

Acknowledgement

This research was partially supported by research grants (NSC 102-2410-H-009-053-MY3, NSC 103-2218-E-007-007) funded by the Ministry of Science and Technology. The authors appreciate the discussions with Professor Kamal Puri, the Faculty of Law at Queensland University of Technology, which helped the legal foundation of this research.

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View full text

Collective intelligence applied to legal e-discovery: A ten-year case study of Australia franchise and trademark litigation

Highlights

Abstract

Introduction

Section snippets

Literature review

Collective litigation intelligence (CLI) methodology

Discussion and conclusions

Acknowledgement

Technol. Forecast. Soc. Change

Inform. Process. Manage.

Inform. Process. Manage.

Lattice Theory

Data Mining Techniques: for Marketing, Sales, and Customer Support

Exploiting franchisee vulnerability

Franchise Law Colloquium

Knowledge discovery and data mining in databases

Handbook Software Eng. Knowledge Eng.

From data mining to knowledge discovery in databases

AI Magazine

Knowledge discovery in textual databases (KDT)

Knowledge discovery in databases: an overview

AI Magazine

Ontologies and knowledge bases, towards a terminological clarification

Technology and knowledge document cluster analysis for enterprise R&D strategic planning

Int. J. Technol. Manage.