Modeling ERP Academic Deployment via Adaptive Structuration Theory

Modeling ERP Academic Deployment via Adaptive Structuration Theory

Harold W. Webb (The University of Tampa, USA) and Cynthia LeRouge (Saint Louis University, USA)
DOI: 10.4018/978-1-60566-026-4.ch421
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Abstract

Academic/industry collaboration can change learning processes and improve outcomes by integrating resources and creating opportunities not otherwise attainable (Wohlin & Regnell, 1999). However, each institution’s culture and organizational objectives influence the collaborative relationships developed as advanced information technologies (e.g., computer aided software engineering tools, enterprise resource planning [ERP] systems, and database tools) are adopted. The challenge is to facilitate mutual understanding and acknowledge distinctions in addressing each organization’s goals. The aim of these relationships is the appropriation of ERPs in a manner that enriches educational experiences, while providing industry benefit. There are many quandaries associated with this phenomenon. How does the deployment of ERPs facilitate educational processes? To what degree should these resources be utilized? What tools and methods should be used? What is the role of the ERP vendor? Can academic independence be maintained? Without a framework to identify relevant variables, it is daunting to begin to assess the impact of varying degrees of adoption, identify effective processes of deployment, and move toward assessing costs and benefits. Though some frameworks address academic/industry collaboration (Mead et al., 1999), few have considered the implications of ERPs on the evolution of inter-institutional collaborative relationships. This exposition augments a framework for understanding the forces at work when integrating ERPs into educational settings (LeRouge & Webb, 2002, 2005). We begin our discussion by reviewing adaptive structuration theory (DeSanctis & Poole, 1994) as the foundation for the academic/industry ERP collaboration framework (LeRouge & Webb, 2002). We discuss academic/industry collaboration constructs and their relationships within the context of ERP systems and then integrate examples, findings, and issues from recent research.
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Using Ast To Model Erp Deployment In The Academy

Adaptive structuration theory (AST), an extension of structuration theory (Giddens, 1982), has been used as a framework to study organizational change processes during advanced information technology adoption (Poole & DeSanctis, 1992). Adaptive structuration takes a socio-technical perspective. Human actors and organizational context are introduced within this perspective as moderators of technology impact. The adoption of an advanced technology, therefore, is a process of organizational change resulting from the mutual influence of the technology and social processes.

The premise at hand is that in academic settings, human actors and organizational context collectively moderate the processes by which ERPs are appropriated. Such dynamic processes affect not only institutional and industry outcomes resulting from the appropriation, but also the evolution of the relationship between industry and academia. The number of academic institutions adopting ERPs is increasing (Rosemann & Maurizio, 2005). However, use is not a perfect proxy for effectiveness, as ERPs serve some institutions better than others (Antonucci, Corbitt, Stewart, & Harris, 2004).

ERP system adoption within the context of colleges of business is of interest and has considerable impact for a number of reasons: market demand, level of commitment required, interdisciplinary functionality, and level of system sophistication. To provide insight, we reintroduce our AST-based model for organizing constructs and relationships for this phenomenon (see Figure 1). We augment this model and understanding by providing recent research examples, findings, and issues related to construct attributes (provided in Tables 1 through 9).

Figure 1.

Adaptive Structuration Theory Applied to Industry/Academic Collaborations adapted from DeSanctis and Poole (1994) (LeRouge & Webb, 2002)

Key Terms in this Chapter

Technology Infrastructure: Required supporting activities and facilities including network, hardware, software, development, and maintenance.

Structure of Academic/Industry Collaboration: Representation of social practices among the stakeholders affecting academic/industry collaboration that result in the establishment of rules of practice and the provision of resources.

Joint Outcomes: The direct output and by-products of the education process including student learning, employable work force, market exposure, and contributions to research.

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