An Information Processing Model of Organizational Control: A Computational Model of System-Level Effects CHRIS P. LONG Olin School of Business Washington University in St. Louis St. Louis, MO 63130 (314)-935-8114
[email protected] SIM B. SITKIN Fuqua School of Business Duke University Durham, NC 27708 (919)-660-7946
[email protected] LAURA B. CARDINAL Kenan-Flagler School of Business McColl Building, CB #3490 The University of North Carolina at Chapel Hill Chapel Hill, NC 27599-3490 (919) 962-4514
[email protected] RICHARD M. BURTON Fuqua School of Business Duke University Durham, NC 27708 (919) 660-7847
[email protected]
The authors wish to thank Professor Ray Leavitt, his Stanford University colleagues, and the VITE’ Corporation for the use of Vite’Project simulation software
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An Information Processing Model of Organizational Control: A Computational Model of System-Level Effects
ABSTRACT This paper investigates issues surrounding the implementation of complex control systems. This study adopts an information processing perspective to explain why effective managers use multiple forms of control and select distinct combinations of multiple controls in different control system environments. We use a computational model to build three forms of control systems (market, bureaucratic, clan) and seven control target combinations (input, process, output, input/process, process/output, input/output, input/process/output). Using this model, we examine how effectively managers operating in different control environments direct various types of organizational tasks using different combinations of control mechanisms. Results of this study demonstrate that effective managers use multiple controls to distribute decision-making responsibilities between themselves and their subordinates. The authors suggest that findings from this study should also direct scholars to incorporate the value of both an information-processing perspective and measurement models in future control research. Furthermore, the study concludes with a discussion of the contribution of computational models to control research.
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INTRODUCTION Traditional organizational research describes “control” as one of the four fundamental functions of management [the others being organizing, planning, and coordinating] (e.g., Fayol 1949) where organizational controls describe the primary mechanisms that managers use to direct attention, motivate, and encourage organizational members to act in desired ways to meet an organization’s objectives (Ouchi 1977, 1979; Eisenhardt 1985; Snell 1992). Despite the scholarly legacy, topical importance, and practical pervasiveness of control phenomena, Oliver recently (1998) observed that “the study of organizational control has a long history in administrative science and yet the need to examine the processes and implications of this phenomenon has never been greater.” While empirical control research has concentrated primarily on examining managers’ applications of single forms of control, researchers have begun to extend this work and closely evaluate the development and implementation of complex control systems comprised of multiple forms of control (Cardinal, Sitkin, and Long 2002a, 2002b; Long, Cardinal, and Burton 2002). In this paper we examine an important question regarding complex control systems: how the control system context within which managers operate affects the specific combinations of control mechanisms they choose. In order to facilitate the development of knowledge in this area, we draw upon and integrate two distinct streams of organizational control research: research on organizational control systems (market, bureaucratic, clan) and research on control targets (input, process, output). In addition, we identify recent research and draw on the principles of information processing theory (Galbraith 1977, 1973) to explore relationships between control systems and specific combinations of multiple forms of control . We use a computational model to examine whether managers operating within three
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types of control systems (market, bureaucratic, clan) use different configurations of control mechanisms (input, process, output), in response to their knowledge of transformation processes and the measurability of production outputs . Utilizing refined conceptualizations of control, we find support for both traditional control theories as well as the emerging “broader perspective” on organizational control that describes how managers can improve organizational performance by leveraging diverse elements of their control systems (Cardinal 2001; Cardinal, et al. 2002a, 2002b ; Long 2002; Long, et al. 2002). We present this research in several sections. In the first section, we introduce various control concepts as we compare traditional control theory with more recent control research. After outlining our hypotheses, we describe our study design and present our results. In the final section of the paper, we both discuss our results and evaluate the benefits of utilizing computational models for the study of organizational control phenomena. THEORY Control Theory While control researchers have developed and utilized a variety of control perspectives (e.g., cybernetic theory, critical theory, studies of power), our theoretical integration and computational analysis draw on two organizational control sub-literatures. Theory developed through this work comprises among the most cited and most influential of all control research. One sub-literature, exemplified by Ouchi (1977), Merchant (1985), Snell (1992), Kirsch (1996), and by both accounting (e.g., Simons 1995) and TQM researchers (e.g., Sutcliffe, Sitkin, and Browning 1997) examines how individual control mechanisms and clusters of mechanisms are applied to organizational production processes. This sub-literature takes a systems management approach to control and specifies how managers apply organizational controls to the inputs,
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throughputs, and outputs of the production processes they manage. The other sub-literature, exemplified by the work of Ouchi (1979, 1980) describes various types of control systems that are distinguished primarily by the fundamental social mechanisms (e.g., rules, norms) they employ to govern sets of intra-organizational transactions. Based on their common theoretical traditions and foci, research on organizational control targets (Ouchi and Maguire 1975; Ouchi 1977) and control systems ( Ouchi 1980; Lebas and Weigenstein 1986) constitute complementary perspectives that seek to identify the most efficient and effective manner by which managers can direct their subordinates (Barney and Hesterly 1996). Below we outline traditional control theory on control targets and control systems. Thereafter, we explain how information processing theory help us understand how control system context affects choices regarding the multiple control mechanisms that managers apply. Control Targets The fundamental unit of analysis in control research is the control mechanism, the specific method by which individual actions are governed. Control target research distinguishes control mechanisms by the portion of the production process they are intended to influence. For example, managers select input targets (“input control”) to direct how material and human resource elements of their production processes are qualified, chosen, and prepared through training and socialization (Arvey 1979; Van Maanen and Schein 1979; Wanous 1980), or by selecting vendors, equipment, or raw materials for use in work activities (Snell 1992, Snell and Youndt 1995). Managers choose process targets (referred to as “behavior control” or “process control”) -- such as process rules and behavioral norms -- when they want to ensure that individuals perform actions in a specific manner (Ouchi and Maguire 1975; Ouchi 1977). Finally, managers focus on output targets (“output control”) -- such as profits, customer
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satisfaction levels, and production volumes and schedules -- to align output quantity/quality with specific production standards (Ouchi 1977, 1979). Control target theorists have used distinctions between input, process, and output targets to describe how managers should focus their control efforts (Donaldson 1990; Ghoshal and Moran 1996). Researchers who examine control targets argue that to efficiently and effectively direct subordinates, managers should select controls in accordance with their knowledge of the transformation process they direct, and the measurability of production outputs (Ouchi 1977; Merchant 1985; Snell 1992; Kirsch 1996). These evaluations lead managers to direct control mechanisms to control targets (Ouchi 1977, 1979) as outlined in Figure 1. Insert Figure 1 About Here Based on the notion that simplicity allows managers to economize effort and promote efficiencies (March and Simon 1958), most research on control targets has focused on how managers use single forms of control. For example, when managers possess a high level of knowledge about the way work should be performed (i.e. the transformation process) in an organization, Ouchi (1977: p. 97) suggests that “supervisors can rationally achieve control by watching and guiding the behavior of their subordinates (i.e., by singularly using process control).” Ouchi similarly suggests that when managers are able to accurately measure the quantity or quality of products that employees produce (i.e., measurability), they will develop singularly-focused output controls that leverage their ability to measure outputs. When managers both understand how work should be performed and can effectively measure employee outputs, Ouchi does not suggest that both could be used, but rather that managers will select one or the other, thus exercising their option of using “either form of control” (Ouchi 1977, p. 98). Finally, when managers understand neither how work should be performed, nor how outputs can
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be effectively measured, it has been predicted that they will rely singularly on input controls and focus only on providing the most appropriate human and material resources for a given task (Snell and Youndt 1995). Extending Control Target Theory Beyond the Singular Empirical evidence has provided support for the relationships outlined in Figure 1 (Ouchi 1977; Eisenhardt 1985; Snell 1992). However “important questions have been raised about this set of ideas” (Barney and Hesterly 1996, p. 128). In particular, critics have claimed that the traditional emphasis in control target research on task measurement and singularly-focused controls presents “an overly rational conceptualization” of managerial attention and action (Folger, Konovsky, and Croponzano 1992, p. 130) and incorrectly assumes that managers can develop and effectively implement singularly-focused employment contracts based on accurate, reliable measurements of employee task performance (Noorderhaven 1992; Jaworski, Stathakopoulos, and Krishnan 1993; Ghoshal and Moran 1996). In addition to questioning whether the use of singular controls is possible, questions have also been raised about whether the use of singular controls is universally effective in the way depicted in Figure 1. For example, building on Ouchi’s observation that the control process is “a problem of information flows” (Ouchi 1979, p. 833), we draw upon information processing theory to hypothesize that managers are likely to be most effective when they use multiple controls contingent upon the control context. In the next several paragraphs we outline how combining an information processing perspective (e.g., Galbraith 1973, 1977) with current control theory’s emphasis on task attributes and measurement allows us to theorize about the use of multiple controls and relationships between configurations of control targets and control systems.
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Information-Processing Theory and Multiple Controls While research on single forms of control has been instrumental in explicating how controls can limit employee opportunism and promote both goal congruence and risk sharing (Barney and Hesterly, 1996), managers who over-emphasize single controls are susceptible to information-processing problems (Galbraith 1973, 1977). Because the effective application of organizational controls involves the exchange of information regarding production inputs, throughputs, and outputs, single controls may work well in routine, stable environments (Galbraith 1973). However, production problems can dramatically increase information exchanges with subordinates and can cause managers who rely on single forms of control to become overloaded with excessive amounts of certain types of performance data. Because these singularly-focused managers are able to exchange information with subordinates at only one segment of the production process (e.g., inputs), these overloads may lead them, in turn, to experience deficiencies in their ability to process information, their capacityto exchange information with subordinates, and their ability to exert effective levels of control (Galbraith 1973). In contrast, managers who rely on multiple controls are able to more easily exchange task information with subordinates over multiple production segments. As a result, they can receive a wider array of production data and maintain a greater range of opportunities to direct production efforts. In addition, because managers who utilize multiple forms of organizational controls reduce their reliance on performance information regarding individual production segments, they avoid processing delays and can exchange information when data is available and exchanges are most appropriate given situational constraints.
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While control theorists have suggested that managers might use multiple forms of control (Ouchi 1979; Bradach and Eccles 1989), control research has only recently begun to empirically examine this topic. For example, Cardinal, et al. (2002a, 2002b) report on a longitudinal case study in which they chart the development of an organization’s control system through several stages in which managers actively consider how to combine their use of multiple control mechanisms. They observed that “managers use combinations of formal and informal control mechanisms and direct them selectively at input, process, or output control targets” (Cardinal, et al. 2002b, p. 32). Complementary findings from a recent computational study by Long, et al. (2002) suggest that when managers focus on multiple control targets (i.e., employee inputs, behaviors, and outputs), they can gain greater production efficiencies compared with managers who focus on single control targets. In explaining their results, they suggest that managers who employ multiple controls are more effective because they can adjust the specific controls they use to avoid portions of the organization’s production process where production information cannot be readily acquired or effectively exchanged. Taken together, this research describes an emergent body of control-related work that points to supplementing traditional, single-mechanism control research with multiple-mechanism control studies that more accurately portray how controls are used in organizational practice. Control Mechanism Choices within Various Control Systems. While, findings suggest that managers use multiple controls, little is know about how they make choices about the specific controls they select. Recent control findings, however suggest that the combinations of multiple control mechanisms managers choose are influenced both by the nature of production tasks and the information processing attributes of the control
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systems within which those managers operate. The control system describes an organization-level concept that captures the formal and informal information-based routines that managers “use to maintain or alter patterns in organizational activities” (Simons 1995, p. 5). The control system “is comprised of various organizational design elements that affect managers’ abilities to direct subordinates in their tasks” (Long, et al. 2002, p. 198) and, while describing a distinct concept, a control system is an core element of organization’s overall design (e.g., in addition to its structure, culture, hiring and retention mechanisms, etc…) (Lewin and Stephens 1994; Daft 1998). It is important to note, however that, because it describes more than formal reporting relationships, the control system describes a concept distinct from structure (Ouchi, 1977). In addition, the control system concept is also distinct from the concept of control mechanisms which describe individual units of organizational control. Control systems constitute forms of exchange environments in which individual managers make decisions about the individual forms of control mechanisms they apply. Three types of organizational control systems have dominated attention in the control literature: market, bureaucratic, and clan (e.g., Williamson 1975; Ouchi 1977, 1979). These three control systems types are differentiated by the fundamental social mechanisms they use to govern exchanges between actors. In a market control system, managers make decisions based on price considerations (Williamson 1975; Ouchi 1979). Managers within bureaucratic control systems use primarily rules and regulations, hierarchical lines of authority, and job specifications to direct subordinates in their tasks (Weber 1946; Ouchi and Price 1978; Lebas and Weigenstein 1986). Managers within clan control systems place relatively greater emphasis on the development and actualization of common values, traditions, and beliefs (Roth, et al. 1994) and focus on
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socializing members in ways that merge individual and organizational goals. Information Processing and Organizational Control Systems Cardinal et al., (2002a, 2002b) found that, because information exchange patterns differ across market, bureaucratic, and clan control systems, managers operating within each type of control system will tend to use different combinations of control mechanisms to manage tasks. In addition, Long et al., (2002) examined relationships between control systems and control targets using a computational methodology and found that managers were differentially effective depending on the control system in which they applied specific control target combinations. Galbraith (1973, 1977) in his exposition of information processing theory provides some explanation for these observations. He shows that because both control system context and task characteristics affect how organizations process information, they lead managers to make choices regarding multiple control mechanisms. Specifically, he suggests that when levels of task programmability in bureaucratic organizations are low, managers will combine applications of process controls with input and output controls. Managers use input and output controls to effectively measure and monitor tasks, while they use process controls to ensure that they retain their capacity to exchange process-related information with subordinates at key points in the production process. Complementarity of Organizational Controls Building from these findings and from Galbraith’s (1973) observations, we argue that in order to promote efficient information processing, managers choose control mechanisms not only based on the nature of the tasks they manage (Ouchi and Maguire 1975; Ouchi 1977), but also to leverage complementarities between specific control targets and the control systems. Managers use complementarities between control systems and control targets to promote the efficient
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exchange of specific types of information with subordinates over segments of a production process. “As a general rule, communications about performance measures are most effective in producing extrinsic motivation when they are fast and frequent” (Lawler and Rhode 1976, p. 54). Because types of information are more or less influential based, in part, on when that information is exchanged (Jacques’ 1961), we suggest that managers tend to exchange certain types of information at the points in the production process when managers can most effectively use it to affect subordinate behaviors. By focusing controls on particular control targets, managers operating in particular control systems are able to gather performance information when it is most available and provide performance feedback when it is most relevant. As a result, these managers increase their chance of producing desired behavioral effects among their subordinates. Barney and Ouchi (1986), Eisenhardt (1989), and Long et al. (2002) have all identified control targets that provide managers operating in specific control systems enhanced opportunities to exchange specific types of performance information with their subordinates. Specifically, they have identified complementarities between market control systems and output control targets, bureaucratic control systems and process control targets, and clan control systems and input control targets. Because managers in market control systems make decisions based on price, they can most effectively interact with subordinates after outputs are produced and prices can be accurately assessed (Lebas and Weigenstein 1986). Hence, managers in these control systems will, regardless of task, attempt to direct subordinates by exchanging control-related information during the output phase of the production process. In contrast, managers in bureaucratic control systems rely on rules and can use these mechanisms to explicitly outline the behaviors they want
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performed. Because these rules are best applied at the point where they are utilized, managers in bureaucratic control systems will tend to apply controls to process control targets. Lastly, the traditions and norms that clan system managers use to affect subordinate behaviors are best applied before tasks are commenced. For example, if managers make efforts to select employees that already adhere to the organization’s norms and traditions and if they communicate those norms and traditions when subordinates are first socialized in the organization they will increase their chance of effectively indoctrinating those employees (Van Maanen and Schein 1979). As a result, managers in clan control systems will tend to focus their control efforts on input control targets. Building from the discussion above, we argue that managers will select control mechanisms that let them both promote effective task measurement (i.e., Figure 1) and leverage complementarities between specific control systems and control targets. By doing this they are able to both effectively measure and monitor performance and make sure they efficiently exchange performance information with subordinates. HYPOTHESES In order to develop a better understanding of how managers direct subordinates, we present three sets of hypotheses that describe how control system context and task characteristics interact to influence a manager’s selection of multiple control mechanisms. The perspective we present here combines traditional control target theory with more recent research on control system/control target configurations (Long et al. 2002; Cardinal et al. 2002a, 2002b). Specifically, we argue that managers will select specific control mechanisms based on the nature of the tasks they manage and on the complementarities between specific control targets and control systems.
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We theorize that complementarities between control systems and control targets and the nature of subordinate tasks lead managers to apply specific combinations of single and multiple controls mechanisms. Managers are hypothesized to focus on single control targets when alignment exists between the measurement requirements for the tasks subordinates’ perform and the underlying control system structures in which that manager operates. When the nature of the task and the control system structure are not aligned, managers are posited to utilize multiple control mechanisms. In this case, managers will devote some of their control efforts to the measurement of the given task and some control effort to the exchange of production information with subordinates at crucial production points. This perspective is outlined in Proposition 1. In Hypotheses 1a-3d this perspective is translated into testable hypotheses specific to each type of control system. Proposition 1: Within market, bureaucratic, and clan control systems, managers will select control mechanisms based both on the elements of their control system and the measurement requirements of subordinate tasks.
Control Mechanism Choice in a Market Control System Managers within market control systems tend to focus on output control targets because, in this environment, it is most efficient for managers to evaluate work after it is completed (Barney and Ouchi 1986). Ouchi (1979) explains the relationship between market control systems and output control targets by arguing that the former arises directly out of managers’ capacities to measure the outcomes of actors’ efforts. Lebas and Weigenstein (1986, p. 263) agree and suggest that “the most important control system components for a market approach include transfer pricing, lateral relationships and bargaining, and management compensation,” all mechanisms that managers use to focus on output control targets. This hypothesized relationship between market control systems and output control targets is specified in Hypotheses 1a-1d.
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Hypothesis 1a: Managers in a market control system will apply input and output controls when their knowledge of the organization’s transformation process is low and their ability to measure organizational outcomes is low. Hypothesis 1b: Managers in a market control system will apply process and output controls when their knowledge of the organization’s transformation process is high and their ability to measure organizational outcomes is low. Hypothesis 1c: Managers in a market control system will apply output controls when their knowledge of the organization’s transformation process is low and their ability to measure organizational outcomes is high. Hypothesis 1d: Managers in a market control system will apply process and output controls when their knowledge of the organization’s transformation process is high and their ability to measure organizational outcomes is high. Control Mechanism Choice in a Bureaucratic Control System Bureaucratic control systems contain many of the classic bureaucratic traits described by Weber (1946) and are well suited to situations where managers can more readily observe and determine the value of individual contributions to organizational tasks (Ouchi 1980). As a result, managers within bureaucratic control systems minimize risk by focusing on process control targets that allow them to proscribe behavior and to closely monitor how tasks are performed. Here, managers use rules and regulations, hierarchies, and formal (codified) communications to direct the activities of organizational actors. The relationship between bureaucratic control systems and process control affects relationships is posited in Hypotheses 2a-2d. Hypothesis 2a: Managers in a bureaucratic control system will apply input and process controls when their knowledge of the organization’s transformation process is low and their ability to measure organizational outcomes is low. Hypothesis 2b: Managers in a bureaucratic control system will apply process controls when their knowledge of the organization’s transformation process is high and their ability to measure organizational outcomes is low.
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Hypothesis 2c: Managers in a bureaucratic control system will apply process and output controls when their knowledge of the organization’s transformation process is low and their ability to measure organizational outcomes is high.. Hypothesis 2d: Managers in a bureaucratic control system will apply process and output controls when their knowledge of the organization’s transformation process is high and their ability to measure organizational outcomes is high. . Control Mechanism Choice within a Clan Control System According to Ouchi (1980), clan control systems utilize a variety of informal social mechanisms that reduce “differences between individual and organizational goals and produce a strong sense of community” (p. 136). Here, managers rely on input control mechanisms to select for shared values and norms and to inculcate subordinates with the knowledge necessary to perform organizational tasks. By focusing on instilling employees with a shared set of values, objectives, and beliefs about how to coordinate effort and reach common objectives before task activities begin, managers can create a climate of understanding that can greatly improve task completion efforts. (Pettigrew 1979; Lebas and Weigenstein 1986). The hypothesized relationship between clan control systems and input control targets leads to Hypotheses 3a-3d. Hypothesis 3a: Managers in a clan control system will apply input controls when their knowledge of the organization’s transformation process is low and their ability to measure organizational outcomes is low. Hypothesis 3b: Managers in a clan control system will apply input and process controls when their knowledge of the organization’s transformation process is high and their ability to measure organizational outcomes is low. Hypothesis 3c: Managers in a clan control system will apply input and output controls when their knowledge of the organization’s transformation process is low and their ability to measure organizational outcomes is high.. Hypothesis 3d: Managers in a clan control system will apply input, process, and output controls when their knowledge of the organization’s transformation process is high and their ability to measure organizational outcomes is high.
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COMPUTATIONAL METHODS We evaluate Proposition 1 and Hypotheses 1a through 3d using the commercial software version 2.2 of the Vite’Project (also VITE’) discrete event computational model.1 VITE’ is appropriate for this study because the program is specifically designed to model information exchanges between actors working on production tasks. This program has been validated through case studies (Jin and Levitt, 1996) and as an organizational research tool to evaluate various types of control systems (Long, et al. 2002), communication structures (Carroll and Burton, 2000) and virtual organizations (Wong and Burton 2000). Vite’Project Fundamentals We use VITE’ to create the control systems, control target combinations, and the task conditions used in the study. As Jin and Levitt (1996) outline, a modeler using VITE’ must identify a sequence of production tasks, create various types of actors, assign actors to tasks, and contstruct an organization within which work is accomplished. Below, we present a detailed description of how we accomplish these objectives to create the computational models used in this study. Actors Vite’Project actors work together to complete a multi-task project. Different actors within a VITE’ organization are distinguished by their organizational roles, the specific skills they possess, their work experience, and the other project workers with whom they communicate. An actor may serve as a project manager, team manager, and front line worker. This organizational position determines the types of decisions s/he makes and the amount of information s/he is required to process.
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The software used in our research1(Vite’Project) was developed by Professor Ray Levitt and his associates at the Center for Integrated Facility Engineering at Stanford University and is available through VITE’ Corporation.
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Project Tasks To obtain the information they require to complete production tasks, Vite’Project actors send and receive messages across established communication channels. How quickly a VITE’ organization completes its production assignment (e.g., 100 units) depends on how effectively information flows through the organization. Each actor in the organization is assigned to complete one or more tasks. If an actor encounters production problems, they will slow production and attempt to resolve these problems by obtaining missing information from the superiors, peers or subordinates with whom they are connected. When actors can efficiently process the information requests they receive, actors communicate smoothly and have their queries promptly handled. When actors cannot efficiently process information requests, they become “backlogged,” and are delayed in distributing appropriate answers to other actor’s queries. When this happens, actors in need of information must wait while others process their information requests. If delays persist in a given organization, information is not effectively distributed, and production slows. Organizational Context In VITE’ the modeler also creates an organizational structure that significantly affects patterns of intra-organizational information exchange. Communications between actors are determined by superior-subordinate, information exchange relationships that the modeler creates. In addition, the modeler can schedule and invite actors to organizational meetings where they can collectively exchange information on projects. Information flow within a given organization can be further refined using VITE’s four “organization” parameters: centralization, formalization, team experience, and matrix strength. Each of these parameters may be set to a value of low, medium or high.
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Centralization - determines the level of the organizational hierarchy where decisions are made and task problems are handled. High centralization designates project managers as decision-makers. Medium centralization designates team leaders. Low centralization (i.e., decentralization) designates front-line workers as decision-makers. Formalization - determines the frequency with which actors initiate ad hoc communications with other actors. In highly formalized organizations, actors rarely initiate adhoc communications. Instead they exchange information through hierarchical communication channels. In organizations where formalization is low, actors frequently exchange information through ad-hoc channels and gather less information through the formal organizational hierarchy. When formalization is medium, employees exchange information through both hierarchical and ad-hoc communication channels. Team Experience - reflects the total amount of project-related experience that a team possesses. Actors possessing a high level of team experience are familiar each other and the demands of a particular project. Conversely, actors with a low level of team experience are unfamiliar with each other and project demands. Matrix Strength - relates to functionalization. Actors in organizations with low matrix strength focus primarily on their functional duties and communicate less with other organizational actors. Actors in organizations with high matrix strength are more collaborative, focus less on their functional duties, and respond more to ad hoc communications initiated by other actors. Outcome Measures VITE’ calculates the cost of producing a pre-specified number of production units. We measure the cost of producing 100 production in this study.
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VIRTUAL EXPERIMENT Because the purpose of this study is to examine the most effective control target combination under a various control system/task conditions, we constructed a 2*2*3 study design and conducted twelve independent 7*1 analyses of variance (ANOVAs). We examine seven control target combinations (single input control, single process control, single output control, combined input/process control, combined process/output control, combined input/output control, combined input/process/output control), under four task conditions (low knowledge/low outcome measurability; high knowledge/low outcome measurability; low knowledge/high outcome measurability; high knowledge/high outcomes measurability), and within three types of control systems (market, bureaucratic, clan). Table 1 outlines our experimental design. Insert Table 1 About Here Modeling Actors and Tasks Similar to the approach used by Long et al. (2002) we modeled three simple control systems, consisting of one project manager, two team leaders and two teams each comprised of two workers. We differentiated the project manager, team leaders, and workers by the tasks they performed and their organizational position. While workers performed a generic production task, team leaders applied combinations of input, process, and output controls. Project managers coordinated the work of all actors and shared problem solving responsibilities with team leaders. Modeling Control Targets We used VITE’s failure dependency function to model managerial attendance to input, process, and output control targets. When failure dependencies exist between tasks, errors that
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occur in a primary task lead the actors performing that task to work with actor(s) performing, previous, “dependent” tasks to correct those errors. We modeled various types of organizational control mechanisms by connecting managers’ monitoring activities to workers’ production tasks. Specifically, when managers detected errors that occurred in workers’ production activities, managers collaborated with those workers to correct the errors and solve the identified production problems (i.e., apply controls). Specific control target combinations were determined by the position of that control in the production process and the amount of time managers devoted to their application. Similar to the approach used in Long et al. (2002), managers applied input controls before workers began working on their tasks. Errors detected by workers forced team leaders make adjustments to their input selection and preparation processes. Managers applied process controls to workers while they performed their tasks. Workers’ errors during tasks forced team leaders to spend time and effort identifying and helping workers remedy process deficiencies. Team leaders applied output controls to tasks after workers completed them. Here again, when team leaders detected problems with workers’ outputs, they spent time and effort helping those workers solve production problems. While workers devoted all their time each work day (nine hours) to completing a generic production task, managers distributed their time and attention to three control-related tasks. When applying a single form of control (e.g., a single focus on input control) both managers in an organization distributed their time each day to three of the same type of control tasks (e.g., three input control tasks). When applying a combination of two controls (e.g., input/process control), one team leader in an organization devoted 2/3 of their time to one type of control task (e.g., input control), and 1/3 of their time to the other control task (e.g., process control). The
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other team leader allocated their time differently, devoting 1/3 of their time to the first control task (e.g., input control) and 2/3 of their time to the second control task (e.g., process control). Thus, the organization distributed team leaders’ time and effort equally over two forms of control. When applying a combination of input/process/output control both team leaders in an organization distributed their time equally over three different control tasks (i.e., an input control task, a process control task, and an output control task). Our manipulations of control targets are outlined in Table 2. Insert Table 2 About Here Modeling The Production Process While managers applied various combinations of control targets, actors performed the same production process in each experimental condition. A diagram of the process used in input/process/output conditions is displayed in Figure 2. First (1), managers apply input controls and select resources for workers to use in performing tasks. Second (2a), workers perform tasks using inputs provided by managers. (2b) Insufficient inputs lead workers and managers to jointly correct errors and rework tasks. While subordinates perform tasks, managers exert process controls (3a). (3b) Insufficient behaviors displayed by subordinates during production processes lead managers to help workers correct these deficiencies. Once tasks are complete, managers evaluate work outputs (4a). Insufficient outputs lead managers to help workers correct output deficiencies. Throughout (5), the project manager supervises the process while addressing the questions and requests of team managers. Insert Figure 2 About Here Modeling Control Systems
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We differentiated control systems by manipulating organizational structures, the frequency and attendance of actors (i.e., project manager, team leaders, workers) at organizational meetings, and Vite’Project “organization” parameters. Table 3 provides a summary of control system parameters manipulated in this study. Insert Table 3 About Here Market Control System - Workers within market control systems are often employed on short-term contracts rather than as long-term employees of their organizations (Ouchi 1979). Because market control systems “may be more like a portfolio of independent contractors than a monolithic social unit” (Roth, Sitkin, and House 1994, p. 145), we chose not to connect team leaders to workers within the hierarchy. Consistent with this, we restricted daily meeting participation to project managers and team leaders, and set team experience to low. To model the relative autonomy that workers in market control systems possess, we set centralization to low. In addition, we set formalization to medium to allow workers and managers to conduct both formal and/or informal communications. Lastly, because workers are generally hired and retained for their individual contributions in market organizations, we focused them primarily on their individual tasks and set matrix strength to low. Bureaucratic Control System – Project managers, team leaders, and workers in a bureaucratic control system are all connected within a formal hierarchy. To model how actors in these control systems to exchange information through formal communication channels (Ouchi, 1980), multi-actor meetings were not scheduled. To model the characteristics of a bureaucratic control system (Weber, 1946) in which structure and formal procedure are key coordination and standardization mechanisms, formalization and centralization were set to high and matrix
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strength was set to low (Weber 1946). In addition, we modeled information exchange in mature rule systems by setting team experience to high. Clan Control System – All actors in clan control systems were connected within the organizational hierarchy. To model informal communications (Ouchi 1979, 1980), we scheduled daily meetings and required all organizational actors to attend. We set formalization to medium because actors in the clan system extensively use both informal, ad hoc communications (Roth et al. 1994) and meetings to exchange task information with individuals and groups of other employees. Centralization was also set to low and matrix strength to high to model how frontline workers are designated as primary decision-makers (Wilkins and Ouchi 1983). Modeling Organizational Tasks Manager’s Knowledge of the Transformation Process - When a managers’ production knowledge is incomplete, “managers do not fully comprehend the transformation process” (Snell 1992, p. 295). Within VITE’, we model this condition by combining the individual manager’s low levels of “Application Experience” and “Skill” with high levels of “Requirement Complexity” and “Uncertainty,” or the absence of information necessary to complete certain tasks. Application Experience describes an individual actor’s capability on a specific set of tasks. It determines how quickly and accurately actors can process project-relevant information. VITE’ allows modelers to toggle experience setting between “low” and “high.” When actors possess a “high” level of application experience, they understand how a particular task can and should be performed and can quickly process relevant information with few mistakes. Under the condition where a manager’s knowledge of the transformation process is relatively complete, that manager’s application experience will be set to “high.” When it is incomplete, that
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manager’s application experience will be set to “low.” Skill Level describes an individual actor’s capability on individual tasks and, similar to application experience, determines how rapidly and accurately actors can process project relevant information. In this model, a manager’s ability to direct subordinates in their tasks is partially determined by his/her level of “generic” skill. When managers know less about a transformation process, they are less able to direct subordinates and their skill level is set at “low.” When managers possess greater knowledge of the transformation process, they can more effectively direct organizational tasks and their skill is set at “high.” Requirement Complexity is a characteristic of production tasks both managers and subordinates perform. It describes “the number and difficulty of functional requirements that need to be satisfied to complete each activity” (VITE’ Handbook 1998, p. 24). Requirement complexity in VITE’ can be set between “low” and “high.” Tasks with higher levels are more difficult to complete. Consequently, under the condition where managers possess low levels knowledge of about transformation processes, the requirement complexity of both their tasks and the tasks subordinates perform are set to “high” Under the condition where managers possess high levels of knowledge about transformation processes, the requirement complexity of their tasks and the tasks subordinates perform are set to “low.” Outcome Measurability – We define the concept of outcome measurability in this study as the amount to which the output of individual workers in the organization is susceptible “to reliable and valid measurement” (Govindarajan and Fisher 1990, p. 261). Within VITE, we model this condition by manipulating the solution complexity, task uncertainty and interdependencies of the tasks subordinates perform.
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Solution Complexity is a characteristic of production tasks subordinates perform in this study. It describes “the extent to which an activity’s requirements affect and are affected by, the requirements of other functionally interdependent activities” (VITE’ Handbook 1998, p. 24). There are three levels of solution complexity in VITE’ low, medium and high. When tasks are functionally interdependent with other tasks, the contributions of individual workers to the production of a specific output is difficult to measure and solution complexity is set to “high.” When the measurability of task outcomes is high, the solution complexity of tasks is set to “low.” Task Uncertainty is also a characteristic of the production tasks subordinates perform in this study. It “represents the extent to which information needed to complete an activity is unavailable at the time the activity starts” (VITE’ Handbook 1998, p. 24). Similar to solution complexity, there are three levels of task uncertainty in VITE’: low, medium, and high. When outcome measurability is high, individual subordinates rely little on their co-workers for assistance and the level of uncertainty on each activity subordinates perform will be set to “low.” When it is task uncertainty is low, uncertainty on each activity will be set to “high.” Subordinate Task Interdependencies are also manipulated in outcome measurability. When subordinates perform highly interdependent tasks, managers cannot as easily determine the individual contributions of individual subordinates to production outcomes. Hence, when outcome measurability is low, the tasks subordinates perform are connected to the tasks other subordinates perform by activity successor and information exchange relationships. Because individual contributions to production outcomes are more easily measurable when task interdependencies are low, subordinate tasks are not connected under conditions of high outcome measurability.
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Table 4 outlines the manipulations for the manager’s knowledge of the transformation process and outcome measurability used in this study. Insert Table 4 About Here Procedure We used analysis of variance (ANOVA) procedures to test the efficacy of focusing on specific control combinations (single input, single process, and single output control, combined input/process, combined process/output, combined input/output, combined input/process/output) within each control system (market, bureaucratic, clan). While Vite’Project provides several measures of project output, we opted to use overall project cost (in thousands of dollars) as the performance measure for various control target combinations examined in this study. This is consistent with traditional control research that has examined the least expensive method that managers choose to complete organizational tasks (Barney and Hesterly 1996). In this study, we examine how much it costs managers operating within each control system, using various combinations of organizational control mechanisms, to produce 100 production units under four different task conditions. VITE’ calculated (in thousands of dollars) how much it cost each organization to complete a full production run. To develop statistical measures, the overall production cost of 5 complete production runs were recorded and averaged for each control system/control target/task condition. We then tested Propositions 1 and Hypotheses 1a through 3d by evaluating the cost of producing 100 units using seven control target combinations within each control system and under each task condition..
RESULTS The results for this study are summarized in Tables 5a-5c and Figures 3a-3c.
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Insert Tables 5a-5c About Here
Insert Figures 3a-3c About Here
Market Control System. Low Knowledge/Low Outcome Measurability – A 7*1 ANOVA yielded a significant effect for control combinations F(6, 28) = 3889.04, p
