Antecedents to Job Success in Business Process Management: A Comparison of Two Models

Antecedents to Job Success in Business Process Management: A Comparison of Two Models

Richard J. Goeke (Widener University, USA) and Yvonne L. Antonucci (Widener University, USA)
Copyright: © 2011 |Pages: 20
DOI: 10.4018/irmj.2011010104
OnDemand PDF Download:
List Price: $37.50
10% Discount:-$3.75


Business Process Management (BPM) maximizes firm performance by transforming isolated functional activities into streamlined, cross-functional processes. Being a relatively new discipline, disagreement exists regarding the position structure and qualifications required for success. However, certain individual differences have been associated with job performance, both in general and for specific occupations. Because BPM initiatives can be expensive and risky, understanding these individual differences may help practitioners improve their chances for BPM job success. Two models of job performance have dominated organizational research; one argues that personality traits are the chief determinants, while the other argues that intelligence and experience are most important. This paper uses logistic regression to examine the efficacy of each model in predicting job performance among BPM professionals. Results indicate that intelligence, conscientiousness, and openness to experience are the most important differentiators of BPM job performance.
Article Preview


Over the past several years, interest in Business Process Management (BPM) has risen, as nearly 80% of mid- and large-size companies have implemented some type of BPM effort (Towers & Schurter, 2005). Organizations have long recognized the importance of managing and optimizing corporate components such as people, technology, strategy, structure, and tasks. However, prior efforts to optimize business processes focused on functional process automation, using information technology as the catalyst (Davenport & Short, 1990; Hammer, 1990). Such efforts often had little effect on the firm’s competitive position, because the end-to-end, cross-functional linkages important to customer satisfaction and retention remained incomplete (Hung, 2006). The focus on BPM, as the key to the coordination, optimization, and predictability of these components, is quickly evolving as the premier 21st century management practice (Hung, 2006).

Since BPM has evolved into a management discipline that treats business processes as assets to be designed, utilized, and valued, BPM initiatives often become complex, expensive, and time-consuming endeavors (Hammer, 2007). A BPM initiative is defined as any effort to improve organizational performance that takes a holistic, cross-functional view of processes, is guided by strategic imperative, and is enabled by information technology (Hung, 2006). For example, one BPM initiative at Wyeth Pharmaceuticals (now Pfizer) sought to improve the process by which products were delivered to customers. Backed by executive management and sponsored by business units, the initiative involved defining, mapping, and standardizing all processes affecting product delivery. Business managers, operations managers, and technologists across multiple departments integrated their enterprise systems with the revised, standardized business processes, which were aligned with business strategies. The initiative spanned several years, and resulted in substantially more effective and efficient product delivery (Antonucci, Boykin, & Donahue, 2009).

BPM initiatives typically require a variety of knowledge sets, including scientific management, Value Chain, Six Sigma and Lean, along with extensive experience and expertise with the firm’s own processes, capabilities, and limitations (Paim, Caulliraux, & Cardoso, 2008). Not surprisingly, organizations have expressed difficulty finding individuals qualified to lead and implement their BPM initiatives (Hill, Sinur, Flint, & Melenovsky, 2006). Therefore, staffing BPM positions with the appropriate leaders and professionals is all the more critical.

Industrial and applied psychologists have long studied the individual differences that correlate with job success. In particular, two schools of thought have emerged. The first model concerns the individual’s personality, as certain personality traits are thought to lead to or be counter-productive toward job performance (Barrick & Mount, 1991). The second model argues that an individual’s intelligence (or GMA – general mental aptitude) and experience are the prime determinants of job performance (Schmidt & Hunter, 1998; Schmidt, Hunter, Outerbridge, & Goff, 1988). However, measuring either model’s constructs in field settings has historically been problematic.

From the respondent’s perspective, taking personality and intelligence tests can be time consuming. For example, personality tests can exceed 180 items (Cattell, Cattell, & Cattell, 1993), and intelligence tests are nearly as long (Wechsler, 1997). To the extent that survey length has an inverse relationship with response rate and sample size (Sivo, Saunders, Quing, & Jiang, 2006), a survey with over 300 items would be problematic in field settings. Moreover, the mere collection of personality and intelligence data raises privacy and security issues. For example, development of the model describing the effect of intelligence on job performance relied on military datasets, because comparable data from civilian settings was not available (Schmidt, Hunter, & Outerbridge, 1986).

Complete Article List

Search this Journal:
Volume 36: 1 Issue (2023): Forthcoming, Available for Pre-Order
Volume 35: 4 Issues (2022): 3 Released, 1 Forthcoming
Volume 34: 4 Issues (2021)
Volume 33: 4 Issues (2020)
Volume 32: 4 Issues (2019)
Volume 31: 4 Issues (2018)
Volume 30: 4 Issues (2017)
Volume 29: 4 Issues (2016)
Volume 28: 4 Issues (2015)
Volume 27: 4 Issues (2014)
Volume 26: 4 Issues (2013)
Volume 25: 4 Issues (2012)
Volume 24: 4 Issues (2011)
Volume 23: 4 Issues (2010)
Volume 22: 4 Issues (2009)
Volume 21: 4 Issues (2008)
Volume 20: 4 Issues (2007)
Volume 19: 4 Issues (2006)
Volume 18: 4 Issues (2005)
Volume 17: 4 Issues (2004)
Volume 16: 4 Issues (2003)
Volume 15: 4 Issues (2002)
Volume 14: 4 Issues (2001)
Volume 13: 4 Issues (2000)
Volume 12: 4 Issues (1999)
Volume 11: 4 Issues (1998)
Volume 10: 4 Issues (1997)
Volume 9: 4 Issues (1996)
Volume 8: 4 Issues (1995)
Volume 7: 4 Issues (1994)
Volume 6: 4 Issues (1993)
Volume 5: 4 Issues (1992)
Volume 4: 4 Issues (1991)
Volume 3: 4 Issues (1990)
Volume 2: 4 Issues (1989)
Volume 1: 1 Issue (1988)
View Complete Journal Contents Listing