Higher Learning Commission

Documenting Perceptions of Organizational Change Through Reengineering at a Midwestern Comprehensive University

Chris Crawford and Kenneth L. Rigler Jr.

Process reengineering is an organizational change process that began to be promoted in the 1990s by organizational leaders such as Hammer and Champy (1993) as a way to improve organizational performance (Burke, Graham and Smith, 2005; Clemmer 1992). As a touchstone of the reengineering process, leaders typically implement a major organizational transformation and therefore are subject to high levels of resistance and failure rates (Hammer and Champy 1993; Burke, Graham and Smith 2005). Though process reengineering is often documented within business literature, the change model can be implemented in a variety of types of organizations, including institutions of higher learning. However, the organizational structure of traditional postsecondary educational institutions and their high levels of inertia (Weick 1976) create unique challenges for large-scale process reengineering initiatives. The purpose of this paper is to examine the university staff perceptions of a reengineering proposal using the Organizational Change Recipients’ Belief Scale (OCRBS) at a midwestern comprehensive university during the early stages of a process reengineering initiative.  

Background

The leadership of the university determined that the following objectives would guide the reengineering process: (1) consider new ways of doing the work of the university, (2) expand investments in talent and technology, (3) leverage the combined size and resources, (4) restructure work patterns and habits, (5) break down barriers to change and improvement, (6) achieve efficiency in operations and processes, (7) provide excellence in our service to students, and (8) ensure that departments are best positioned and properly named to create and nurture synergistic collaboration among faculty. To initiate the process of reengineering, two task forces were created: one task force concentrated efforts on the divisions of student affairs, administration and finance, and information technology and the second task force was charged with reengineering the division of academic affairs. Jointly, the task forces were charged with studying critical operational and structural issues and identifying, developing, and recommending the combination of units, realignment of reporting structures, and other functional or operational efficiencies deemed appropriate. Throughout the reengineering proposal process, feedback was solicited from the campus community through surveys, town hall meetings, and open forums. The process reengineering was finalized in the spring semester and was fully implemented by the end of the following fall semester after approval by the board of regents.

Review of Literature

Process reengineering was originally envisioned as a practical alternative to earlier total quality management (TQM) and quality improvement approaches, which often proved to be too technical to be effective for non-manufacturing enterprises and long-term in focus (Burke, Graham and Smith 2005). Reengineering is best thought of as a continuous quality improvement strategy aligned to large business processes where substantive radical strategic change is the goal rather than incremental improvement of simple work process. Often this large-scale radical rethinking of business processes employs information technology as an enabler of new business processes to achieve strategic organizational outcomes (Marjanovic 2000). The aim of reengineering is substantial improvement in organizational performance by rethinking core business processes (Attaran and Wood 1999); however, the reengineering literature generally shows limited success, with 50 percent to 70 percent of organizations failing to achieve their desired objectives (Attaran and Wood 1999). Some research indicates that reengineering efforts simultaneously improve aspects of work satisfaction while reducing other aspects of work satisfaction (Burke, Graham and Smith 2005). Gore (1999) and Marjanovic (2000) highlight the lack of focus on the human aspect when considering reengineering business process and the ultimate effect on the success when human factors are not the central aspect of reengineering efforts.

Armenakis, Harris and Feild (2001) theorize that readiness for change comprises five dimensions (i.e., discrepancy, self-efficacy, personal valence, principal support, and appropriateness) and incorporated the five dimensions into a measurable instrument called the OCRBS. In the scale, discrepancy refers to the change recipient’s belief that a need for change exists typically resulting from a clear difference between the current and desired performance, appropriateness is described as the match between the organizational context and the intended corrective actions that will thus eliminate the discrepancy, efficacy is characterized as the perceived capability to implement the change initiative, principal support refers to the support from top management and organizational change agents, and valence refers to the “attractiveness (from the change recipient’s perspective) associated with the perceived outcome of the change” (Armenakis et al. 2007, 488), which can be differentiated between both extrinsic rewards and intrinsic benefits. The OCRBS examines perceptions and progress of organizational change efforts and comprises 24 Likert-type survey items that constitute the five dimensions.

Methods

In this study, a quantitative survey method was used to examine the university staff perceptions of a reengineering proposal using the OCRBS. The study addressed the following research questions:

  1. What were the university staff perceptions toward the reengineering proposal?
  2. What were the differences in the university staff perceptions toward the reengineering proposal based on age, years of experience, division, and employee classification?

The OCRBS was integrated into an online survey along with general demographic questions relating to the participants’ role and experience at the university. An email invitation with a link to the online survey was sent to all faculty, staff, and administrators (N = 1,313) with a request to participate in the anonymous study.  

Results

Demographics.

Respondents (n = 257, 20% response) consenting to complete the survey were characterized according to the following demographic variables: age, division/college, years of experience at institution, employee classification, faculty classification, and faculty rank. Table 1 documents employee classification. Classifications represent the nature of the contract held with the employee. 

Table 1. Employee and Faculty Classification and Rank

  Professional Unclassified Faculty University Support Staff Other Missing
Number 78 96 62 12 15
Percent 29.7% 36.5% 23.6% 4.6% 5.7%
  Part-time Adjunct Temporary Tenure Track Tenured
Number 3 6 14 20 51
Percent 3.2% 6.4% 14.9% 21.3% 54.3%
  Instructor Assistant Professor Associate Professor Professor
Number 19 27 31 19
Percent 19.8% 28.1% 32.3% 19.8%

Data Analysis.

To more fully understand the impact of reengineering on the organization, a variety of ANOVAs were calculated by looking at subscale mean differences among colleges/units, years of experience, employee classification, faculty classification, and faculty rank. Relative to differences among colleges/units, Table 2 details statistical differences by subscale.

Table 2. ANOVA Results of Organizational Change Recipients’ Belief Subscales by College/Unit

  Mean df F Sig
Discrepancy Subscale 12.26 8, 236 3.435 p = .001
Appropriateness Subscale 12.98 8, 236 2.893 p = .004
Efficacy Subscale 14.79 8, 236 2.963 p = .004
Principal Support Subscale 15.46 8, 236 2.580 p = .010
Valence Subscale 8.89 8, 236 3.195 p = .002

Relative to differences among years of experience, Table 3 details statistical differences by subscale.

Table 3. ANOVA Results of Organizational Change Recipients’ Belief Subscales by Years of Experience

  Mean df F Sig
Discrepancy Subscale 12.26 4, 242 2.564 p = .039
Appropriateness Subscale 12.98 4, 242 2.718 p = .030
Efficacy Subscale 14.79 4, 242 2.203 NS
Principal Support Subscale 15.46 4, 242 1.140 NS
Valence Subscale 8.89 4, 242 3.010 p = .019

Relative to differences among employee classification, Table 4 details statistical differences by subscale.

Table 4. ANOVA Results of Organizational Change Recipients’ Belief Subscales by Employee Classification

  Mean df F Sig
Discrepancy Subscale 12.26 3, 244 3.849 p = .010
Appropriateness Subscale 12.98 3, 244 2.756 p = .043
Efficacy Subscale 14.79 3, 244 5.488 p = .001
Principal Support Subscale 15.46 3, 244 3.100 p = .027
Valence Subscale 8.89 3, 244 2.642 p = .050

Discussion

The first significant finding relates to differences among college/unit of employment. The differences across all subscales suggested that participants from some colleges/units perceived the change in a completely different manner. Notably, the Administration and Finance unit had the lowest mean scores (6.44; 7.56; 8.13; 8.94; 4.94) across all subscales, and the Information Technology unit had the highest mean scores (16.00; 16.75; 18.06; 20.31; 11.81), followed by the Academic Affairs non-teaching professionals (15.18; 15.29; 18.47; 18.53; 11.53) and the College of Business and Entrepreneurship (14.83; 16.25; 16.92; 16.08; 8.95). It is difficult to draw inferences from these differences other than to provide context. At the time of this survey, the Information Technology division had just been separated from the Administration and Finance division, as part of reengineering. In addition, the College of Business and Entrepreneurship was preparing for the split of a large department, again due to the reengineering event.  

The second interesting finding considers differences of perception related to years of employment. Although only three of the subscales proved significant, the interesting aspect focused on the mean differences in employment time. Employees with up to four years of experience (n = 70) had the highest perception mean scores (14.64; 15.70; 17.16; 17.39; 10.71), and employees with five to nine years of employment (n = 48) had the lowest subscales mean scores (11.29; 11.90; 13.13; 14.40; 7.79). Given the substantial sampling size of each increment, the finding seems to warrant further research to determine the effect.

The final interesting finding relates to employee classification. In this analysis, there were significant differences among all five subscales. The university support staff generally had the lowest perception related to the subscales (10.42; 11.50; 12.21; 13.37; 7.79) than other classifications. Faculty (13.35; 13.82; 16.88; 17.03; 9.65) and professional unclassified employees (13.69; 14.68; 15.92; 16.87; 9.83) generally had the highest perception mean scores. Understanding these perceptions is perhaps simplified by again providing context. The results of the reengineering effort were generally focused on the academic areas, which would be more populated with faculty and professional unclassified staff members. Other support units, which were more populated by university support staff, were not directly impacted by the reengineering efforts.

Conclusion

Bruckman (2008) and Yilmaz and Kilicoglu (2013) provided ample basis for the assumption that different employee classifications might impact the perception of the need for reengineering. This study found significant differences between employment units, years of employment, and employee classification. Specifically, those units with larger groups of university support staff generally rated the reengineering more poorly than units with larger numbers of faculty and professional staff members. In addition, those employees with the fewest years of experience rated reengineering less favorably. These findings support the conclusions of Bruckman (2008) and Yilmaz and Kilicoglu (2013), which suggest that fear and threats to the status quo impacted respondents’ favorability toward reengineering. Further research is needed to fully examine the longer-term impact of the process reengineering initiative at the university.

References

Armenakis, A. A., J. B. Bernerth, J. P. Pitts, and H. J. Walker. 2007. Organizational Change Recipients’ Beliefs Scale: Development of an assessment instrument. Journal of Applied Behavioral Science 43 (4): 481–505.

Armenakis, A. A., S. G. Harris, and H. S. Feild. 2001. Paradigms in organizational change: Change agent and change target perspectives. In Handbook of organizational behavior, 2nd ed., edited by R. T. Golembiewski, 631–658. New York: Marcel Dekker.

Attaran, M., and G. G. Wood. 1999. How to succeed at reengineering. Management Decision 37 (10): 752–757. doi:10.1108/00251749910302845.

Bruckman, J. C. 2008. Overcoming resistance to change: Causal factors, interventions, and critical values. Psychologist-Manager Journal 11 (2): 211–219. doi:10.1080/10887150802371708.

Burke, R. J., J. Graham, and F. Smith. 2005. Effects of reengineering on the employee satisfaction-customer satisfaction relationship. TQM Magazine 17 (4): 358–363. doi:10.1108/09544780510603198.

Clemmer, J. 1992. Firing on all cylinders: The service/quality system for high-powered corporate performance. Toronto: Macmillan.

Gore, E. W. Jr. 1999. Organizational culture, TQM, and business process reengineering: An empirical comparison. Team Performance Management: An International Journal 5 (5): 164–170.

Hammer, M., and J. Champy. 1993. Reengineering the corporation. New York: Harper.

Marjanovic, O. 2000. Supporting the “soft” side of business process reengineering. Business Process Management Journal 6 (1), 43–55. doi:10.1108/14637150010313339.

Weick, K. E. 1976. Educational organizations as loosely coupled systems. In Organization & governance in higher education, edited by M. C. Brown, 36–49. Needham Heights, MA: Pearson Custom Publishing.

Yilmaz, D., and G. Kilicoglu. 2013. Resistance to change and ways of reducing resistance in educational organizations. European Journal of Research on Education 1 (1): 14–21. http://iassr2.org/rs/010103.pdf.

 

About the Authors

Chris Crawford is Associate Provost for Institutional Effectiveness and Quality Improvement and Kenneth L. Rigler Jr. is Assistant Professor at Fort Hays State University in Hays, Kansas.

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