An expectancy‐equity model of productive system performance

Abstract

Although numerous behavioral variables (e.g., personality traits, incentives, nature of supervision) have documented relationships with quantity and quality of output, i.e., performance, in production systems, existing production and operations management models are dominated by technological variables. The article addresses three questions: 1. Which technological and behavioral variables influence productive system performance? 2. How do behavioral and technological variables interact? 3. Which variables are most important under different circumstances in determining performance?An expectancy‐equity model, patterned on research by Lawler [22], presents proposed relationships among variables in production systems, which are classified along a continuum from high to low process predictability. Predominant determinants of performance in various types of systems (e.g., continuous process, job shop, R&D project) are considered in light of the model. Implications for designers and analysts of productive systems and for future research are discussed.

Incorporating behavioral theory in OM empirical models

“…In the 1980 inaugural issue of the Journal of Operations Management, Powell and Johnson (1980) stressed the need to introduce behavioral factors into research models of operational processes and performance. More recently, Hopp's (2004) article on the 50th anniversary of Management Science has issued a similar call, emphasizing that understanding the nature of a firm's operations “does not just require a theory of human motivation and a theory of material flow; it also requires a means for describing the interaction between the two.” In recent years, there has been a growing interest in the introduction of behavioral theory as a key element of empirical models of operational dynamics and performance.…”

The relationship between team autonomy and new product development performance under different levels of technological turbulence⋆

“…In this study, we build on a traditional operations model which examines the moderating effect of technological turbulence on the relationship between organizational process factors and operational outcomes (Tatikonda and Montoya‐Weiss, 2001). To respond to the calls for the integration of OM and behavioral research (Bendoly and Hur, 2007; Powell and Johnson, 1980), we extend this model by articulating the behavioral and mechanistic effects of team autonomy. We further view team autonomy as a bipolar factor and argue that its two effects are non‐monotonic because each mechanism has its benefits and risks, depending on the level of autonomy and technological turbulence.…”

“…Consequently, we propose a three‐step theoretically‐driven argument. First, to respond to repeated calls for the integration of behavioral and OM research (Bendoly et al, 2006; Bendoly and Hur, 2007; Croson et al, 2013; Powell and Johnson, 1980), we argue that team autonomy has both behavioral and non‐behavioral effects on operational outcomes. Consistent with Bendoly and Hur (2007), the behavioral effects can be viewed as psychological functions of the capabilities of the team and the perceived characteristics of its work environment, which may lead to team motivation or stress.…”

System Dynamics as a Structural Theory in Operations Management

“…Regularly this is the case when fuzzy or hard-to-quantify variables have a significant effect on a real-world issue and therefore need to be incorporated into a system dynamics model. Such variables are considered no matter how difficult it is to measure and to quantify them because neglecting them would certainly lead to an erroneous model and simulation (in this way, system dynamics connects to the research in behavioral OM; see Bendoly and Hur 2007, Amundson 1998, Powell and Johnson 1980. Examples are the influence that organizational factors (like work satisfaction, stress, and motivation) have on worker productivity in a model of skill formation in new production systems (Diawati et al 1994) or perceived delivery delays that affect product sales in a model of corporate growth and capacity restrictions (Forrester 1968b).…”