Theory 35

2.2.1 Stability and Change in Operating Routines

Operating routines capture the characteristic patterns with which organizations accomplish value-adding tasks. Routines drive organizational efficiency, as they economize on individuals’ cognitive resources, thereby increasing reliability and speed-of-task performances (Cohen and Bacdayan, 1994), and coordinating em- ployees’ efforts to form coherent activity patterns (Cohen, 2013; Dionysiou and Tsoukas, 2013). Operating routines have been studied in various recurring organ- izational activity patterns, including employee selection (Feldman, 2000), invoice processing (Pentland et al., 2011), drug development in pharmaceutical compa- nies (Bresman, 2013), and price-setting (Zbaracki and Bergen, 2010). They have been found to be inherently important for the perseverance and performance of any organization (Becker, 2004) and serve as efficiency drivers in many organi- zations (Stene, 1940; March and Simon, 1958; Nelson and Winter, 1982).

While researchers have traditionally depicted routines as relatively stable entities (cf. Cyert and March, 1963; Gersick and Hackman, 1990), more-recent research emphasizes that routine participants are capable of reflecting upon routine per- formances. Depending on the ideals, experiences, and resources available to rou- tine participants, they may engage in endogenous change and improve the operat- ing routine’s adaptation to environmental demands (Feldman and Pentland, 2003). For example, Feldman (2000), in her ethnographic study on university housing organizations, showed how employees initiated significant changes to

the yearly move-in routine for new residents after it was found to be economical- ly inefficient and left an undesirable and negative impression on new residents. Also employing ethnographic methods, Howard-Grenville (2005) showed how the intentions, expectations, and temporal orientation of operating-routine partic- ipants in a semiconductor company produce routine change or stability over time. These changes comprise the alteration of routine patterns, such as changing the sequence of activities or implementing novel activities within an existing operat- ing routine.

While such proactive employee behavior represents an important source of oper- ating-routine change, many operating routines “[…] partly serve the purpose of minimizing the need for such agency on a continual basis, by providing order and stability” (Katkalo et al., 2010: 1179). To foster reliability in operating routines and to further exploit what has proven successful in the past (March, 1991), or- ganizations employ mechanisms and structures to keep patterns of employee in- teraction “on track” (Schulz, 2008: 228). Examples of such structures include the strict behavioral scripts governing customer interaction in Apple stores (Kane and Sherr, 2011) and McDonald’s (Leidner, 1993). While stable operating rou- tines are likely to increase organizational efficiency through habitualization (Cohen and Bacdayan, 1994) and organizational learning (Argote, 1999), such exploitative patterns can hinder exploration of alternatives to the established op- erating routine (Leonard-Barton, 1992; Levinthal and March, 1993). Alternatives to the established operating routine are likely to be perceived as less certain in their performance, remote in time, and more distant from the current locus of ac- tion (March, 1991; Lavie et al., 2010). Therefore, mechanisms and structures de- signed to foster uniform, reliable, and efficient operating routines are likely to lead to operating-routine rigidity, a common source of inertia (Gilbert, 2005).

Inertia usually causes organizational maladaptation, implying that an organiza- tion is unable to respond to environmental opportunities or threats (Miller and Friesen, 1980; Hannan and Freeman, 1984; Collinson and Wilson, 2006). Failure to address environmental threats may not only decrease organizational perfor- mance, but also threaten organizational survival in the long run. This outcome is particularly true for small- and medium-sized organizations, as their possibilities to diversify into markets with independent and unrelated environmental threats are limited. Given the often-cited rigidity of operating routines, there is a major interest to understand the conditions leading to changes in operating routines (Gilbert, 2005; Parmigiani and Howard-Grenville, 2011; Vergne and Durand, 2011). Previous research suggests that organizations employ meta-routines—so called dynamic capabilities—to change rigid operating routines in order to ad-

dress environmental opportunities and threats and thereby avoid harmful organi- zational inertia (Teece et al., 1997). For these reasons, we argue that it is im- portant to better understand meta-routines as drivers of change in operating rou- tines.

2.2.2 Dynamic Capabilities and Change in Operating Routines

Recent literature on dynamic capabilities acknowledges meta-routines as drivers of change in a company’s operating routines (Zollo and Winter, 2002; Winter, 2003; Helfat et al., 2007). These higher-order routines are most commonly re- ferred to as sensing routines (i.e., scanning activities directed towards observing the environment and identifying relevant changes), learning routines (i.e., devel- oping new ways of responding to observed environmental changes), and recon- figuring routines (i.e., reorganizing existing resources and processes) (Teece and Pisano, 1994). Higher-order sensing routines allow organizations to quickly de- tect and evaluate opportunities and threats in their environment (Drnevich and Kriauciunas, 2011). Higher-order learning routines expand the potential actions organizations can take and thus help to generate adequate responses when envi- ronmental conditions change (Eisenhardt and Martin, 2000). Higher-order recon- figuring routines imply that companies have access to and can provide the re- quired resources if adequate solutions need to be implemented to adjust operating routines to new conditions (Teece et al., 1997).

Because of their ability to prevent the drawbacks of routine rigidity and instead keep operating routines flexible, dynamic capabilities are assumed to be especial- ly beneficial under conditions of high environmental dynamism. Some influential conceptual work even views dynamic environments as a constitutive element in the dynamic-capabilities concept (Teece et al., 1997; Teece, 2007). Accordingly, the great majority of empirical work has studied the implications of dynamic ca- pabilities in dynamic environments such as high-tech and IT industries (e.g., Deeds et al., 2000; Wu, 2007; Bruni and Verona, 2009). However, recent studies have challenged the notion that dynamic capabilities are valuable exclusively under conditions of high environmental dynamism; they thus also question high levels of environmental dynamism as a necessary condition under which higher- order routines (constituting dynamic capabilities) emerge. While earlier empirical work does not explicitly acknowledge such external organizational context (cf. Barreto, 2010), recent studies consider different environmental conditions. They hypothesize and empirically demonstrate the beneficial impact dynamic capabili- ties have on organizations under high and low levels of environmental dynamism (Drnevich and Kriauciunas, 2011; Protogerou et al., 2012). Such findings give rise to the question of whether the higher-order routines that manifest dynamic

capabilities are contingent on environmental dynamics and are thus characterized by different features and core characteristics (Eisenhardt and Martin, 2000; Romme et al., 2010; Helfat and Winter, 2011). More concretely, the conceptual work by Eisenhardt and Martin (2000) suggests that the dynamic capabilities in highly dynamic environments need to quickly create new knowledge and imple- ment novel solutions. To do so, they rely on higher-order routines that are simple and unstable. In contrast, in less-dynamic environments, dynamic capabilities operate in line with the slower pace of change in these environments and are as- sumed to be more detailed and structured.

Types of dynamic capabilities, then, may vary with regard to the level of envi- ronmental dynamism, as well as with regard to the execution frequency and codi- fication of their underlying higher-order sensing, learning, and reconfiguring rou- tines. In this study, we seek to explore the types of conditions (environmental dynamism, as well as execution frequency and codification of higher-order rou- tines) that equifinally effect change in stable operating routines. Equifinality here refers to the possibility of reaching the same final state by different and distinct causal paths (Katz and Kahn, 1978).

2.3 Sample and Method