An Information Processing Model of Organizational Control...

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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mailto:[email protected]

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.

1 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 ad-

hoc 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

21

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.


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

22

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.


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

23

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 front-

line 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

24

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.

25

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.

26

Table 4 outlines the manipulations for the manager’s knowledge of the transformation

process and outcome measurability used in this study.


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.

27

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 <.001. The organization performed best

when managers used single input controls (M = 56.00, sd = 2.52). No significant difference in

performance was found when managers used a combination of input and process controls.

Significant differences were found between manager’s applications of input controls and all

other control combinations (p<.001).

High Knowledge/Low Outcome Measurability - A 7*1 ANOVA yielded a significant

effect for control combinations F(6, 28) = 1105.53, p < .001. The organization performed best

when managers used single input controls (M = 46.00, sd = 1.63). No significant difference in

performance was found when managers used input and process controls. Significant differences

were found between manager’s applications of input controls and all other control combinations

(p<.05).

Low Knowledge/High Outcome Measurability - A 7*1 ANOVA yielded a significant


when managers used single input controls (M = 29.00, sd = .52). Significant differences were

found between manager’s applications of input controls and all other control combinations

(p<.05).

High Knowledge/High Outcome Measurability - A 7*1 ANOVA yielded a significant


28

when managers used input and process controls (M = 15.40, sd = .29). No significant difference

in performance was found when managers used single input controls or single process controls.

Significant differences were found between manager’s applications of input and process controls

and all other control combinations (p<.001).

Bureaucratic Control System


effect for control combinations F(6, 28) = 14.20, p<.001. The organization performed best

when managers used input and output controls (M = 51.42, sd = .37). Significant differences

were found between manager’s applications of input/output controls and all other control

combinations (p<.001).


effect for control combinations F(6, 28) = 5.62, p<.010. The organization performed best when

managers used single output controls (M = 27.60, sd = .17). Significant differences were found

between managers’ applications of output controls and all other control combinations (p<.05).



when managers used input and output controls (M = 40.40, sd = .78). No significant difference

in performance was found when managers used input, process and output controls. Significant

differences were found between managers’ applications of input and output controls and all other

control combinations (p<.05).

High Knowledge/High Outcome Measurability - A 7*1 ANOVA yielded a marginally

significant effect for control combinations F(6, 28) = 2.43, p<.050. The organization performed

best when managers used output controls (M = 15.4, sd = .29). No significant difference in

29

performance was found when managers used process controls, input and process controls,

process and output controls, input and output controls, and input, process and output controls.

Significant differences were found between managers’ applications of output controls and

manager’s applications of input controls (p<.05).

Clan Control System




in performance was found when managers used single input controls. Significant differences

were found between managers’ applications of input and process controls and all other control




when managers used input and process controls (M = 18.90, sd = 1.63). No significant

difference in performance was found when managers used single input controls. Significant

differences were found between managers’ applications of input and process controls and all

other control combinations (p<.001).




in performance was found when managers used single input controls. Significant differences

were found between managers’ applications of input and process controls and all other control


30

High Knowledge/High Outcome Measurability - A 7*1 ANOVA yielded a significant



in performance was found when managers used process controls. Significant differences were

found between managers’ applications of input and process controls and all other control


DISCUSSION

The results of this study provide partial support for both Proposition 1 and Hypotheses

2d, 3a, and 3b. More importantly, the findings of this computational study provide us important

information about relationships between control systems and control targets. First, these results

suggest that combinations of multiple controls provide managers effective mechanisms to direct

subordinates. Second, managers use different combinations of control mechanisms depending

on the control system within which they operate. Third, the results of this study suggest that

managers use multiple controls to measure the potential (1. inputs), actions (2. processes), or

performance (3. outputs) of subordinates and distribute organizational decision-making in ways

that promote efficient information processing. Lastly, this study suggests that, as managers gain

knowledge of organizational processes and outcomes, they often increase the range of control

combinations they can efficiently apply.

Multiple Controls versus Single Controls

In ten out of twelve conditions those who applied multiple forms of control were among

the most effective managers. Hence, findings from this study support the general notion that

effective managers combine combinations of multiple control mechanisms. In general, managers

who use multiple controls are able to efficiently process production information for several

31

reasons (Long et al. 2002). First, managers who utilize multiple controls monitor multiple

portions of the production process. As a result, managers are able to effectively respond to

problems that occur at single or multiple production points. In addition, because managers

monitor multiple production segments, they do not waste time waiting for problems that do not

occur. Instead, the spend their time attending to problems as they arise.

These findings also suggest, however, that single controls often do provide managers

effective control options. Furthermore, the findings generated under most of the conditions in

this study were fairly consistent with the measurement model of control presented in Figure 1

and outlined by traditional control target theory. For example, under high knowledge conditions,

managers generally employed some form of process control or input control. In addition, under

high outcome measurability conditions, the most efficient managers employed some form of

output or input control. In several cases (e.g., bureaucratic: high knowledge/low outcome

measurability), the best control combinations included control mechanisms that would not

promote effective measurement (e.g., single output control). Further research is necessary to

investigate the specifics of these conditions.

One interesting observation of this study highlights the importance of input control.

Input controls appear to be especially important factors in facilitating information processing

between managers and subordinates. In eleven out of the twelve conditions, input control was a

key element of at least one of the most effective control target combinations. This challenges

arguments presented by traditional control target theorists who position input control as a “fall-

back” strategy (Snell and Youndt 1995, p. 716) to be used only when managers do not

understand the transformation process and performance outcomes and not readily measurable.

Snell (1992), explains this by suggesting that input control “helps prevent performance

32

problems,” (p. 297) and, as a result, “it would be difficult to think of an instance where HRM

built on rigorous staffing and training (i.e., input control) would be dysfunctional” (Snell and

Youndt 1995, p. 716). Because input control is generally effective for promoting positive

organizational outcomes it is easy to understand how managers can incorporate it as an integral

component of many control target combinations.

Different Control Systems, Different Control Targets

If we build from a common assumption in control research, that managers will choose the

most efficient control option (Snell 1992), we can deduce from this study that managers in

different control contexts rely on different combinations of multiple controls. Traditional ideas

regarding complementarity between control systems and control targets (i.e., market/output;

bureaucratic/process; clan/input), however, do not appear to accurately describe managers

choices regarding multiple controls. Instead, this study’s findings appear to concur with the

relationships between market control systems and input controls, bureaucratic control systems

and output controls, and clan control systems and input controls observed by Long et al. (2002).

Furthermore, the relationships observed in this study between control systems and

multiple control targets support notions by Galbraith (1973) who suggests, that managers choose

controls to distribute decision-making responsibilities between themselves and their subordinates

in ways that promote efficient information processing. Long et al. (2002) concur and suggest

that centralization in decision-making appears to be a key factor in determining relationships

between control targets and control systems. For example, because front-line workers in both

market control systems clan control systems make many of their own decisions (i.e., low

centralization) and follow few formal rules (i.e., medium formalization), effective managers in

these control systems ensure that subordinates exhibit proper behaviors by using input controls

33

and retaining decision-making authority over production inputs. By focusing on input controls,

managers specifically maintain the important task deciding how to select and prepare workers

with the resources necessary to effectively perform their respective tasks (Barney and Ouchi

1986; Eisenhardt 1989).

Managers in bureaucratic organizations, however, respond to task uncertainties very

differently and, instead, use various combinations of input and output controls. As Galbraith

(1973) suggests and we observed, managers in bureaucratic control systems will respond to

increases in task uncertainty by shifting from using primarily process controls to using a

combination of input and output controls. Managers do this to move decision-making down the

organization’s hierarchy “to the points of action where the information originates” (Galbraith

1973, p. 12) and thereby ensuring that decision-making responsibilities for them and their

subordinates are appropriately distributed and that information processing requirements for all

actors are kept at manageable levels.

Increasing Control Options

Lastly, the results of this study suggest that, as task uncertainties diminish and managers

exhibit both a higher level of knowledge about transformation processes and a greater capacity to

measure performance outcomes, the range of control options they can use effectively increases.

The most obvious example of this occurs in the bureaucratic control system. Bureaucratic

control system managers in the high knowledge/high outcome measurability condition may

utilize six of the seven control options and produce essentially identical results. Managers in the

market organization may use three (instead of one) options, while managers in the clan

organization are able to use two options in this condition.

34

Computational Issues

While this study contributes to our understanding of organizational control in several

ways, we also want to focus this discussion on how our computational study contributes to

research on complex control systems. Within this section, we describe how the computational

nature of this study differentiates it from previous organizational control research. We then

discuss how the simulation platform we chose required us to clearly delineate and then combine

control systems and control targets. We then describe how our modeling technique allowed us to

examine various control options within a controlled experimental environment. Thereafter, we

outline how our use of outcome measures, permitted us to directly examine the performance

achieved under various control strategies. Finally, we pose suggestions for future research and

discuss several limitations of the study.

As Carley (1995) points out, computational models have been instrumental in developing

an understanding of how information processing issues affect the efficacy of specific

organization design elements. For example, Harrison and Carroll (1991) evaluated the

development of organizational cultures within several types of organizational forms while Phelan

and Lin (2001) examined the performance effects of organizational promotion systems. Carroll

and Burton (2000) have examined how communication structures affect intra-organizational

information exchange and organizational performance. In addition, Burton and Obel (1980),

Carley (1992), Mihavics and Ouksel (1996), and Ouksel and Vyhmeister (2000) have all

examined the impact of organizational structures on organizational learning and performance.

We build on this rich tradition and suggest that computational models can help

researchers also assess the performance effects of configurations of design elements. While

several authors have proposed that organizational controls should be examined as a series of

35

information flows (Ouchi and Maguire 1975; Ouchi 1979; Galbraith 1973; Long et al. 2002), few

have developed theory surrounding this perspective. By using computational modeling

techniques, we were able to construct and combine various control systems and control targets,

while we developed and tested theory on the information exchange processes of organizational

controls.

Consistent with the approach utilized by Long et al. (2002), we used the VITE’

computational platform environment to clearly delineate three “ideal” types of control systems:

market, bureaucratic, clan. Because, researchers have suggested that these ideal types of control

systems are difficult to clearly identify in field research contexts (Ouchi, 1979; Bradach and

Eccles, 1989), the control systems utilized in this study were created from largely theoretical

and anecdotal accounts of control systems (Ouchi, 1977, 1979, 1980; Lebas and Weigenstein,

1986). However, by manipulating characteristics of the VITE’ platform we were able to

construct and vary the composition of control systems along quantifiable dimensions and,

thereby, we were able to clearly distinguish control targets from control systems and from each

other.

Using computational techniques we were able to not only distinguish but also to easily

allow the simultaneous use of various configurations control systems and control targets. This

allowed us to examine the efficacy of managers using combinations of control systems and

control targets under varying task demands. While this approach differs from the cross sectional

studies that examine only one organization (e.g., Ouchi and Maguire 1975) or a category of

employees (e.g., Snell 1992) at a time, and can only identify the presence of certain the types of

control, we used experimental methodologies to compare a range of control alternatives. As a

result, we were able to test control system/control target combinations that occur but are not

36

common in managerial practice and, as a result, generate valuable descriptive and normative

implications from our research results. For example, several theorists have stressed the

importance of output controls in market control systems and process controls in bureaucratic

control systems (Ouchi 1979; Barney and Ouchi 1986; Lebas and Weigenstein 1986; Eisenhardt

1989), The results from this study and from Long et al. (2002), however, strongly suggest that

managers in market control systems should focus their efforts on implementing effective input

controls and that managers in bureaucratic control systems should develop effective output

controls.

Furthermore, our computational methodology allowed us to manipulate aspects of both

control systems and control targets and combine them while holding other external variables

constant. This controlled experimental environment provided us advantages over field research

where external variables can dramatically affect the measured dependent variables (Ouchi, 1977;

Snell, 1992). This methodological advantage allowed us to examine the efficacy of various

control system/control target configurations in situations where factors external to those we

wanted to test did not positively or negatively bias specific types of control.

Lastly, by directly examining the performance achieved by managers using various

control system/control target combinations, our computational methodology yielded us results

that previous control research could only indirectly address. While control research has

generally attempted to find the most efficient method managers can use to direct subordinates

(Eisenhardt, 1985; Barney and Hesterly, 1996), the cross-sectional nature of most studies often

required control researchers to assume the controls they observed constituted managers’ most

efficient control alternatives (Ouchi, 1977; Eisenhardt, 1985; Snell, 1992). Using our

computational methodology, however, we were able to directly measure the costs of applying

37

theoretically derived alternative control system/control target combinations in each study

condition. In addition, by comparing the performance managers achieved using various control

configurations, we were able to identify, at least within our computational environment, the most

efficient control mechanisms that managers could use to direct organizational tasks.

Limitations

The focus of this study also identifies one of its potential limitations. Building on the

work of several authors (Galbraith, 1973; Ouchi and Maguire, 1975; Ouchi, 1979) we

conceptualized and operationalized controls as information flows. We acknowledge, however,

that conceptualizing controls as flows of information between organizational actors captures only

one representation of these processes.

We also contend that VITE’ limited this research efforts in some respects. While VITE’

appears to be well equipped for research on the information processing attributes of

organizational controls, other platforms and models may be better suited to studies in this

domain. For example computational studies of control might benefit from programs and

platforms that allow actors to learn from their experiences. Learning is an important information

processing issue that should be considered in future research. For example, Ouksel and

Vyhmeister’s (2001) and Carley’s (1992) studies of how various organizational structures affect

organizational learning and performance could be used as models for future work.

Lastly, this study may be limited by the fact that we relied on the behavioral matrix that

Vite’Project programmers have created for organizational actors. While previous studies have

validated the VITE’ platform for use in organizational research (Caroll and Burton 2000; Long et

al. 2002), we acknowledge that elements of the behavioral matrix may be changed to align with

current research on decision-making and information processing.

38

Future Research

Organizational control systems are complex entities and future research is needed to

evaluate additional conditions under which managers are restricted and enabled to apply various

types of control mechanisms. For instance, additional studies could examine task or

environmental conditions such as those investigated by Snell (1992) that may affect managers’

capacities to apply different types of control mechanisms or operate across various control

systems. Results from such studies could provide scholars with an even richer understanding of

control-related issues by providing more in-depth examinations of context on control use.

Future research should conduct more complete evaluations of specific control

system/control target configurations. For example, while this study assumed that combinations

of control mechanisms were comprised of equal amounts of each control target, additional

studies could take a more nuanced approach. For example, scholars could examine situations

were the percentages of the types of controls that managers emphasize vary across time. Results

of this work would continue to extend control research by providing us a much richer

understanding of the dynamics surrounding managers’ choices between single and combinations

of multiple controls.

Lastly, future research should validate the findings of this study by replicating the

approach we have taken in field and experimental domains. Here, relevant quantitative and

qualitative analyses could permit close examinations of the trade-offs managers make between

the measurement and information processing requirements of various controls.

CONCLUSION

This paper investigates issues surrounding the implementation of complex control

systems. We use an information processing perspective to explain why effective managers use

39

multiple forms of control and will select different 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 these models,

we examine how effectively managers operating in three control environments direct various

types of organizational tasks using different control combinations. Results of this study suggest

that effective managers use multiple controls to distribute decision-making responsibilities

between themselves and their subordinates. The authors also suggest that results of this study

should direct scholars to incorporate an information-processing perspective in control research

and use computational models to effectively investigate control phenomena.

40

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Table 1

Research Design for Testing the Effectiveness of Control Target Combinations within Market, Bureaucratic, and Clan Control Systems

Control Systems 1. Market 2. Bureaucratic 3. Clan

Knowledge of the Transformation Process

Low

High

High

Control Target Combinations:

Input

Process Output

Input/Process Process/Output Input/Output

Input/Process/Output


Input

Process Output



Outcome Measurability

Low


Input

Process Output




Input

Process Output



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Table 2

Control Target Attributes for Control Target Combinations

Input

Control Targets

Process Control Targets

Output Control Targets

Input/Process

Control Targets

Process/Output

Control Targets

Input/Output

Control Targets

Input/Behavior/Output

Control Targets

Sequence in task completion effort when control is

applied

Before Tasks

During Tasks

After Tasks

Before and

During Tasks

During and After

Tasks

Before and After Tasks

Before, During, and After

Tasks

Average Time Managers in Organization Focus on Particular Control Target(s):

Time Applying Input Control

9.0 hours

0.0 hours

0.0 hours

4.5 hours

0.0 hours

4.5 hours

3.0 hours

Time Applying Process Control

0.0 hour

9.0 hours

0.0 hours

4.5 hours

4.5 hours

0.0 hours

3.0 hours

Time Applying Output Control

0.0 hours

0.0 hours

9.0 hours

0.0 hours

4.5 hours

4.5 hours

3.0 hours

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Table 3

Control System Attributes for Market, Bureaucratic, and Clan Control Systems

Market Control System

Attributes

Bureaucratic

Control System

Attributes

Clan

Control System

Attributes

VITE’ Organization Parameters:

Centralization

Low

High

Low

Formalization

Medium

High

Medium

Matrix Strength

Low

Low

High

Team Experience

Low

High

High

Structural Parameters:

Hierarchy

Incomplete: Workers and Team Leaders not

connected in hierarchy

Complete

Complete

Meetings

Daily (1-hour):

Project Manager and Team Leaders are

required to participate

None

Daily (1-hour):

Project Manager, Team Leaders, and

Workers are required to participate

Information Exchange

Coupled with each failure-dependence relationship



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Table 4

Manipulations for Knowledge of the Transformation Process and Outcome Measurability

Managers’ Level of

Knowledge about Trans.

Processes


ApplicationExperience

Generic Skill Level

Functional Complexity

Solution Complexity

Task Uncertainty

Subordinate Tasks

High High High High Low Low Low Not ConnectedLow High Low Low High Low Low Not ConnectedHigh Low High High Low High High ConnectedLow Low Low Low High High High Connected

Table 5a

Mean Project Cost (in thousands of dollars) When Managers in a Market Control System Use Specific Control Target Combinations to Manage Various Types of Tasks

Market Control System Low Knowledge/High Outcome Measurability High Knowledge/High Outcome Measurability

M sd M sd Input 29.0+ 0.5 Input 15.7+ 0.2 Process 51.2 0.9 Process 16.0+ 0.4 Output 74.1 1.1 Output 22.8 0.5 Input/Process 32.0 1.1 Input/Process 15.4+ 0.2 Process/Output 62.0 1.1 Process/Output 21.1 0.3 Input/Output 50.2 2.3 Input/Output 20.5 0.3 Input/Process/Output 46.7 1.2 Input/Process/Output 19.8 0.3 F 784.9 *** F 477.7***

Low Knowledge/Low Outcome Measurability High Knowledge/Low Outcome Measurability

M sd M sd Input 56.0+ 2.5 Input 46.0+ 1.6 Process 107.5 1.3 Process 53.0 1.8 Output 255.3 2.0 Output 161.1 3.5 Input/Process 58.1+ 0.7 Input/Process 47.4+ 1.4 Process/Output 195.9 2.8 Process/Output 146.2 4.6 Input/Output 163.4 3.0 Input/Output 83.7 3.0 Input/Process/Output 148.4 4.2 Input/Process/Output 112.2 4.8 F 3889.0*** F 1105.5 *** *** p<.001

+ - indicates lowest total project cost within a condition

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Table 5b

Mean Project Cost (in thousands of dollars) When Managers in a Bureaucratic Control System Use Specific Control Target Combinations to Manage Various Types of Tasks

Bureaucratic Control System

Low Knowledge/High Outcome Measurability High Knowledge/High Outcome Measurability M sd M sdInput 41.6 0.4 Input 19.4 0.5Process 43.9 1.0 Process 19.0+ 0.4Output 42.8 0.6 Output 18.4+ 0.2Input/Process 42.0 0.2 Input/Process 18.9+ 0.6Process/Output 42.0 0.4 Process/Output 19.2+ 0.3Input/Output 40.4+ 0.8 Input/Output 19.1+ 0.6Input/Process/Output 40.8+ 0.3 Input/Process/Output 19.0+ 0.3 F 19.5*** F 2.4*


M sd M sdInput 54.3 0.3 Input 29.7 0.3Process 53.8 0.6 Process 29.0 1.0Output 53.6 0.8 Output 27.6+ 0.2Input/Process 53.9 0.4 Input/Process 28.9 0.5Process/Output 54.2 0.8 Process/Output 28.9 0.6Input/Output 51.4+ 0.4 Input/Output 29.0 0.8Input/Process/Output 53.2 0.6 Input/Process/Output 29.0 0.5 F 14.2*** F 5.6*** * p<.05; *** p<.001


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Table 5c

Mean Project Cost (in thousands of dollars) When Managers in a Clan Control System Use Specific Control Target Combinations to Manage Various Types of Tasksh

Clan Control System Low Knowledge/High Outcome Measurability High Knowledge/High Outcome Measurability

M sd M sd Input 22.3+ 0.4 Input 13.7 0.2 Process 24.4 0.4 Process 13.2+ 0.1 Output 25.7 0.5 Output 13.9 0.3 Input/Process 22.1+ 0.4 Input/Process 13.1+ 0.3 Process/Output 26.9 0.6 Process/Output 14.7 0.2 Input/Output 37.0 0.7 Input/Output 14.5 0.1 Input/Process/Output 24.6 0.2 Input/Process/Output 14.3 0.2 F 532.2*** F 33.9***


M sd M sd Input 27.6+ 0.5 Input 19.3+ 0.2 Process 37.7 0.5 Process 20.3 0.3 Output 39.3 0.9 Output 23.1 0.3 Input/Process 27.2+ 0.4 Input/Process 18.9+ 0.2 Process/Output 42.7 1.1 Process/Output 23.3 0.3 Input/Output 33.5 0.7 Input/Output 22.0 0.4 Input/Process/Output 32.6 0.2 Input/Process/Output 21.5 0.4 F 394.4*** F 171.7*** *** p<.001


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Figure 1

Managerial Choices of Control Targets as a Function of Outcome Measurability and

Manager’s Knowledge of Cause/Effect Relations (Adapted from Ouchi, 1977, 1979) Knowledge of Cause/Effect Relations

Low High

Output Control Process or Output Control Input Control Process Control

High


Low

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Figure 2

Diagram of Model Production Process (Adapted from Long, et al. 2002)

PM

T1 SL

ST1A ST1B

ST1A ACTIVITY ST 1B ACTIVITY

11

T1 Input Control

T1 BehaviorControl

T1 OutputControl

1

1

1

1 1

2a 2a2b 2b

3a 3a

3b 3b

4b 4b

4a 4a

Applied Control

Actor-Manager Rework/ Control Adjustment

Unit Produced Unit Produced

5

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Figure 3a

Summary of Most Effective Control Target Combinations under Various Task Conditions in a Market Control System


Low

High

High

Input Control

Input/Process Control: Input Control;

Process Control


Low

Input Control; Input/Process Control

Input Control; Input/Process Control

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Figure 3b

Summary of Most Effective Control Target Combinations under Various

Task Conditions in a Bureaucratic Control System


Low

High

High

Input/Output Control; Input/Process/Output

Control

Output Control;

Input/Process Control; Process Control:

Input/Process/Output Control

Input/Output Control Process/Output Control;

Outcome

Measurability

Low

Input/Output Control

Output Control

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Figure 3c

Summary of Most Effective Control Target Combinations under Various

Task Conditions in a Clan Control System


Low

High

High

Input/Process Control; Input Control

Input/Process Control; Behavior Control

Outcome

Measurability

Low



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An Information Processing Model of Organizational Control...

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