Predicting Implementation Failure in Organization Change

ABSTRACT Failure to successfully implement change initiatives in organizations has long been recognized as widespread, commonplace, and costly. Though estimates of implementation failure rates vary, recent studies find that failure rates may be as high as 93%. Despite the best efforts of researchers in recent decades, high failure rates continue to persist. Neither advances in organizational measures, such as culture or alignment, nor attempts to enhance decision making, nor the addition of change management techniques have led to a dramatic reduction in implementation failure or the attainment of the expected value associated with change initiatives. Our principal aim in this paper is to suggest an alternative approach that may increase the likelihood of successful implementation of organizational change initiatives and projects. We pursue this aim by developing taxonomy of implementation failure factors and by proposing a methodology for predicting these failure factors. INTRODUCTION For over a half century, researchers have studied the dynamics of organizational change in an attempt to help organizations more successfully implement it. However, failure in implementing change initiatives persists in organizations and is still recognized as widespread, commonplace, and costly (Wolf, 2006). Recent review articles have estimated that failure rates for organizational change implementation range somewhere between 28% to as high as 93% (Candido & Santos, 2008; Bridgeforth, 2000). In his review, Bridgeforth suggests that high reported failure rates have remained essentially unchanged since Greiner's (1967) meta-analysis of change studies reported an average failure rate of 73%. The higher estimates (upwards to 93%) tend to be derived from consulting company reports and executive opinions, although studies of return on investment (ROI) have estimated failure rates of between 60% and 90% (Wong, Chau, Scarbrough & Davison, 2005). Projects are abandoned about 30% of the time; perceived degree of financial objectives not attained is estimated at approximately 33%; joint ventures are said to fail 61% of the time; failed implementation of advanced manufacturing technology approaches, 86% of the time; reduction of business unit costs and improvement of earnings fail to be realized between 55 % and 70% of the time; and perceived total quality management implementations fail at the rate of 41% to 93 % (Candido & Santos, 2008). Failure rates in the Project Management and Information Technology literature are similar, with rates reported to range between 50% and 90% (Stephens, 2008; Wong, Chau, Scarbrough & Davison, 2005). Elevated failure rates are also prevalent in the reengineering literature (Grover, Jeong & Teng, 1998). Much is known about (a) how to improve strategic decision making, (b) how to successfully to choose a project with a high expected economic return, (c) how to operationalize strategies and projects, and (d) how to apply change management processes to maintain change momentum. Why then do implementation failures persist in such great numbers? Although there are numerous studies that have examined implementation success and failure, they are inconsistent (a) with respect to their definitions of what constitutes failure, (b) in the criteria they use to judge it, (c) in the industry studied,(d) in the quality of methodology used, and (e) in the unit of analysis (business process, project, strategy, etc). (See Hutzschenreuter & Kleindienst, 2006; Candido & Santos, 2008.) Candido and Santos (2008) acknowledge that true failure rates might be slightly lower than reported because of the high number of perception and estimation investigations; however, they still conclude that failure rates are very high. No one really knows what the true rate of failure is in implementation of projects and strategies and there is no clear model of how to avoid failure (Lally, 2004; Stanislao & Stanislao, 1983; Hrebiniak, 2006; Hutzschenreuter & Kleindienst, 2006). …