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In the early 1990s, business process reengineering (BPR) came blazing onto the business stage as a savior of under performing organizations. Early advocates of BPR (Davenport, 1993; Hammer & Champy, 1993; Harrington, 1991) touted it as the next revolution in obtaining breakthrough performance via process improvement and process change. However, BPR has failed to live up to expectations in many organizations (Bergey, Smith, Tiley, Weiderman, & Woods, 1999; Davenport, 1993; Hammer & Champy, 1993; Kotter, 1995). Some of the reasons include adoption of a flawed BPR strategy, inappropriate use of consultants, a workforce tied to old technologies, failure to invest in training, a legacy system out of control, IT architecture misaligned with BPR objectives, an inflexible management team, and a lack of long-term commitment (Bergey et al., 1999). As one can see from this list, it seems obvious that many organizations failed to realize the scope and resource requirements of BPR.

Patience is another important aspect of BPR success. BPR initiatives can lose momentum as managers face limited resources, slow pay off, diminished employee enthusiasm, and increased resistance to change (Harkness, Kettinger, & Segars, 1996). When short-term BPR results are not obtained, management tends to lose interest and top management is less willing to allocate new resources to the project (Paper, 1998a). One solution to this problem is targeting a BPR initiative that is "manageable" and that will garner quick results (Paper, 1998b). Another solution is for top management to be actively involved in the effort (Kettinger, Teng, & Guha, 1997).

Assuming that the organization understands the scope of BPR and is patient, the project still may not succeed without careful consideration of the type of process initiative. Paper (1998a) argues that the BPR initiative should be driven by a focus on the customer, strategic business issues or senior management directives. Failure to do so greatly reduces the chances for success.

IT has been touted as one of the key enablers of BPR (Davenport, 1993). However, IT can be one of the biggest obstacles if not properly aligned with business objectives (Broadbent, Weill, & St. Claire, 1999). The heritage of a legacy system can contribute greatly to BPR failure (Bergey et al., 1999). Many legacy systems are not under control because they lack proper documentation, historical measurements, and change control processes (Bergey et al., 1999; Paper, 1998b). Due to the scope and complexities inherent to a typical legacy system infrastructure, it should be treated with the same priority as the cultural and organizational structures when undergoing process change (Broadbent et al., 1999; Clark, Cavanaugh, Brown, & Sambamurthy, 1997; Cross, Earl, & Sampler, 1997).

Although the proliferation of research articles has been abundant, research findings have provided limited explanatory power concerning the underlying reasons behind BPR failure. To address this problem, several recent in-depth case studies have appeared in the IS literature to add explanatory power to this issue (Broadbent et al., 1999; Clark et al., 1997; Cooper, 2000; Cross et al., 1997; Harkness et al., 1996; Paper, 1999). However, much more work of this type needs to be undertaken. Hence, the authors embarked on an in-depth empirical study to gain insights into the IT-enabled BPR phenomenon. Our research adopted a phenomenological approach to help us gain an emic perspective, that is, a perspective of how the world really operates.

We begin by classifying BPR literature into five categories or components. The classification tool is an extension of a five-component theoretical model adapted from the literature. We use it to support inductively generated themes or categories that emerge from the research (Patton, 1990). We then introduce the case study within the context of the research methodology. The case site was chosen because of its involvement in enterprise-wide BPR facilitated and driven by data-centric enterprise technology. We conclude by articulating theory that emerged from the analysis.

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Managing Data Mining Technologies in Organizations(c) Techniques and Applications
Managing Data Mining Technologies in Organizations: Techniques and Applications
ISBN: 1591400570
EAN: 2147483647
Year: 2003
Pages: 174 © 2008-2017.
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