An Empirical Investigation of the Viable System Model In Three Human Service Agencies

Background and Purpose: Employee engagement is viewed as a critical component of organizational performance (Cartwright & Holmes, 2006; Saks, 2006) and encompasses employees' perceptions of the effectiveness of their organization (Denison, 1995), their satisfaction with their employer's support for professional growth and development (Goodman, 2001; Quinn, 1988), and internal and external issues, such as funding, organizational culture, communication and managerial styles, trust, respect and leadership (Kimberly, 2008; Packard, 2010; Schalock & Bonham, 2003; Zammuto, 2000). This multidimensional way of conceptualizing organizational performance naturally lends itself to systems approaches to understanding this behavior, specifically, Stafford Beer's Viable System Model. In this model, Beer posits that a viable system is any system organized in such a way as to meet the demands of surviving in a changing environment. In this model, an organization is divided into five functions: System 1 direct service staff, System 2 policies and procedures, System 3 resources, and System 4-5 the executive leadership (Beer, 1984, 1985). This framework has had limited treatment in the human services (Fitch, 2006), but has been used extensively in operational research (Espejo & Gill, 1997; Leonard, 2000; Schwaninger, 2006; Vidgen, 1998; Yolles, 2004). This study sought to empirically validate whether or not the viable system model can be used to understand organizational functioning.

Methodology: Using data from the Survey of Employee Engagement (SEE), 1809 employees from a child welfare agency, 2928 from a public welfare agency, and 1018 from a youth services agency completed an online survey. The results were analyzed using structural equation modeling (SEM). The SEE encompasses 71 items across 14 constructs which were mapped onto Beer's viable system model. The response rate ranged from 67% to 83% across the agencies. The SEM was comprised of items representing Systems 1-5 as constructs with the addition of two constructs: one focused on the informational needs of the workers (labeled Variety Amplification), and the second construct functioned as the dependent variable and represented overall organizational performance. Missing data ranged from 0-5% and was addressed by AMOS' full information maximum likelihood method.

Results: The final model had fit indices over .90. The independent variables predicted 83% of the variance in the dependent variable for the child welfare agency, 81% for the public welfare agency, and 89% for the youth services agency. Among the constructs, Variety Amplification explained the most variance for two agencies while System 2 policies and procedures did so for the other, which has implications for worker decision making. In sum, the Viable System Model demonstrated a remarkable ability to explain organizational functioning in three different agencies.

Conclusion/Implications: The benefit of SEM over the more traditional reporting out of employee engagement scale scores is that it provides a simultaneous analysis of the variables against each other as they predict organizational performance. The findings perfectly align with Beer's hypothesized viable system model and lends further credence to its importance in understanding organizational performance. It lets public welfare administrators know where to focus their efforts in increasing organizational performance within existing resource constraints.