Perspectives on technology and work organization
Annual Review of Sociology; Palo Alto; 1999; Jeffrey K LikerCarol J HaddadJennifer Karlin

Volume: 25
Start Page: 575-596
ISSN: 03600572
Subject Terms: Technology
Sociology
Abstract:
This chapter summarizes and synthesizes some major perspectives on the relationship between technology and the nature of work. Given the complexity of technology and its impacts, the chapter elucidates different perspectives on this topic rather than summaries of detailed findings in a particular area of technology.

Full Text:
Copyright Annual Reviews, Inc. 1999
[Headnote]
ABSTRACT
This chapter summarizes and synthesizes some major perspectives on the relationship between technology and the nature of work. Given the complexity of technology and its impacts, the chapter elucidates different perspectives on this topic rather than summaries of detailed findings in a particular area of technology. The central thesis of the chapter is a follows: Technology's impact on work is contingent on a broad set of factors, including the reasons for its introduction, management philosophy, the labor-management contract, the degree of a shared agreement about technology and work organization, and the process of technology development and implementation. How this is viewed varies with different theoretical paradigms. Looking through a variety of paradigms provides a richer view of the phenomenon, though integrating these perspectives remains problematic. Historically, technology was treated as a deterministic causal force with predictable impacts. More recently there is a recognition of the complexity of technology and its relationship to work which is both bi-directional and dependent on a number of contingent factors. One set of factors integral to the "impact" of technology is the dynamics of the change process and in fact the change process and "out
KEY WORDS: technology impacts, technology implementation comes" are inextricably linked. We conclude that the social reality of technology implementation is highly complex. Very different technologies are brought into very different social settings for very different reasons, often with completely opposite effects and thus complex theories that recognize the emergent and socially constructed nature of technology are needed.

INTRODUCTION

Among students of work organization, it is hard to imagine a topic that has generated as much interest, and as much ambivalence, as the impact of advanced technology. The interest comes from the fact that technology is ubiquitous. Computers have invaded our workplace, our homes, our televisions, our cars, our coffee makers, and our watches. In many ways they make our lives easier, and those of us who adapt wonder how we ever lived without them. In other ways technology complicates our lives, and simple tasks become complex. Think of the tinkering automobile owners who used to fix their own carburetor and tune-up their car; now they find when the engine won't start that the problem is in a computer chip they cannot take apart and fix. Think of the times we have struggled to get that paper out to a journal only to find the computer file won't open or the printer will not print. "My dog ate my paper" has been replaced as a student excuse with "I lost my computer file."

While these anecdotes are merely quaint, the implications of technology for work and the worker can be quite serious. On the one hand, the manager of computer-integrated manufacturing may find that s\he has a fascinating, challenging job with numerous career alternatives. Similarly, the electrician in a manufacturing plant sent to learn to repair programmable automation may find a whole new world opens up of interesting work and job opportunities. On the other hand, the skilled machinist may find the door slammed shut on his or her career when programmable controllers can operate the lathe that used to be adjusted manually.

We began work on this chapter expecting to summarize the state of knowledge on the "social impacts of technology." In reviewing our own experiences, conducting research on this topic, and reviewing the recent literature, we discovered a number of things:

There are many types of technology-Programmable controllers in factories running physical operations, transfer presses that automate an entire stamping line, Computer-Aided-Design systems that automate pieces of drafting work, word processors in offices, automatic tellers in banks, and on and on. Surely these all are very different technologies with very different implications.

There are many social contexts for technology adoption-Bringing automation into an adversarial union-management environment where management is seeking to downsize is fundamentally different from bringing that same technology into a rapidly growing company with a highly cooperative management-labor climate. Adding technology as a tool for a professional decision-maker is fundamentally different than automating clerical work where a claims adjuster becomes a clerk pushing buttons. Thus, the type of job you are starting with matters, as does the labor-management climate, and other contextual features.

There are many approaches to selecting and implementing technologyTechnology selected with a high degree of participation, for example, by those people who will use the technology, is likely to be perceived in a fundamentally different way by the receiver than technology bought and implemented without any consultation. Trying a pilot in an area and then mutually evaluating the technology by a team is fundamentally different from shutting the plant down for a complete technological renovation and reopening the doors with people now in new jobs.

There are many different perspectives on the technology and on who wins and loses. A recent survey of the field (on which we drew heavily for this chapter) identified over 20 different research paradigms on the impact of technology (Lewis 1998). These paradigms see the problem, what constitutes useful data on the problem, and how to analyze that data in fundamentally different ways. Moreover, they have different value implications for whether the technology is good, bad, or indifferent.

Given the complexity of technology and its impacts, we decided to focus on different perspectives on this topic rather than on summaries of detailed findings in a particular area of technology. The central thesis of the chapter is as follows: Technology's impact on work is contingent on a broad set of factors, including the reasons for its introduction, management philosophy, the labormanagement contract, the degree of a shared agreement about technology and work organization, and the process of technology development and implementation. How this is viewed varies with different theoretical paradigms. Looking through a variety of paradigms provides a richer view of the phenomenon, though integrating these perspectives remains problematic.

The purpose of this chapter is to summarize and synthesize some major perspectives on the relationship between technology and the nature of work. Historically, technology was treated as a deterministic causal force with predictable impacts. More recently, there is a recognition of the complexity of technology and its relationship to work that is both bi-directional and dependent on a number of contingent factors. One set of factors integral to the "impact" of technology is the dynamics of the change process, and in fact the change process and "outcomes" are probably inextricably linked.

To create some structure to our discussion, we introduce in the next section a "contingency model of technology and organization." Following that, we take a different slice and discuss the impact of technology through the lenses of a variety of different perspectives or paradigms. We conclude with a discussion of what this means for research in this area.

CONTINGENCY MODEL OF TECHNOLOGY AND ORGANIZATION

We have argued that the "impacts" of technology depend on a variety of factors (see also Liker et al 1993). Figure 1 presents a model that defines sets of variables that the impact of technology is contingent upon. This model is similar to an open-systems model that shows interactions between all sets of variables (Nadler & Tushman 1997). In fact the model can be shown as the effects of organizational context contingent upon the nature of technology. Our specific model in Figure 1 focuses on the impacts of technology because that is the focus of this chapter, not because we believe it has any absolute degree of supremacy in the model. In the next section we argue that the relative importance of each of the factors in Figure 1 and the definitions of these factors all vary across different sociological paradigms. In this section we simply provide a generic description of each.

Technology can be characterized in many different ways, from narrow notions of hardware only to broad concepts that include almost anything. We start with the definition by Tornatzky & Fleischer ( 1990:11 ) of technology as: "knowledge-derived tools, artifacts, and devices by which people extend and interact with their environment." While this definition is quite broad and includes "social technologies" like self-directed work teams and quality circles, for this paper we are focusing more narrowly on process technologies and their associated hardware and software. There are many different ways to distinguish different types of technologies, for example, distinguishing computer-integrated technologies from stand alone automation. We can also look at technologies at varying levels of analysis. Woodward's (1965) classic study focused on technology at the organizational level. For example, the classification of technology as large batch refers to the core technology of a factory. She found that associated with making large batches of product were a set of organizational characteristics. It is quite common within the same factory to see multiple technologies even at the level of Woodward's generic description, for example, a batch operation (e.g., injection molded plastics) feeding a sequential, assembly operation. To capture these differences we need to move down to the department level.

[Chart]
Caption: Figure 1

Perrow's (1967) well-known classification of the complexity of technology at the department level identifies the degree of uncertainty associated with core departmental tasks. Tasks high in uncertainty tend to be associated with more loosely structured "organic" organizational forms, compared to routine, programmable tasks that can be managed by more mechanistic bureaucracies. But core tasks at the department level obscure the fact that different individuals may use different technologies, for example, the drafter using a drawing board may sit alongside one using a CAD terminal (Liker & Fleischer 1989). Other types of classifications look at technologies as innovations when they are first brought into an organization. Rogers' (1983) well-known typology of innovations helps predict the extent of diffusion of the technology (e.g., relative advantage compared to alternatives, compatibility with existing values, trialability of the technology, etc). Another useful distinction is between autonomous and systemic technologies (Wolfe 1994, Brannen et al 1999). Autonomous technologies can be implemented as relatively independent units with relatively small impacts outside of the local area in which they are implemented. For example, if one robot is implemented in a corner of a large factory to do a specialty paint operation, it is apt to get relatively little attention except of course from the individuals who used to do the painting or those who now service the robot. On the other hand, a computer-integrated manufacturing system has a very broad scope and broad systemic impacts on almost all aspects of the business and all people in the factory. Of course, even this simple distinction is difficult to apply to an actual technology because any technology, no matter how seemingly isolated in its impact, will have broad systemic impacts on people and social organization.

The nature of the particular technology implemented obviously has a bearing on its impacts. But according to Figure 1, a set of organizational variables also play critical roles in shaping the impact, even of the same technology. For example, Shaiken (1985) observes cases in which programmable automation raises skill levels when NC programming is assigned to machinists and others, whereas skill levels are reduced when NC programming is assigned to specialist programmers. He argues this is the result of management discretion in designing jobs. These organizational variables can be characterized in many ways. We distinguish the organizational context, a static concept, from the processes of selecting the technology and the implementation process. By including the technology selection process, we are arguing that the motivations for bringing in the technology, and the process by which key decisions are made, shape both how the technology is implemented and the consequences of bringing in the technology. We show these context and process variables as moderators of the influence of technology. In sum, according to the model in Figure 1, outcomes of technology depend on characteristics of the technology itself that interact with the organizational context and process of selection and implementation.

MULTIPLE PARADIGMS ON TECHNOLOGY IMPACTS

While most, if not all, of the contemporary literature assumes the underlying structure of the contingency model in Figure 1, accounts of the same phenomenon vary dramatically across studies and researchers because of different paradigmatic views. One researcher will emphasize the nature of the technology chosen with a nod toward the implementation process. The next sees those issues as being equal in importance to culture and other pieces of the organization context. In order to make sense of the many paradigms, we constructed a 2x2 table, which categorizes the views along two axes (see Figure 2). We should state up front that the two dimensions in this table are more accurately viewed as continuous, with the cells being regions along the continuum.

The vertical axis distinguishes static views of technology impacts from dynamic views that consider a more complex interplay of technology and organization over time. On the static end of the scale, technology's impact is viewed as a billiard ball (Brannen et al 1999) in which the ball has a predictable impact on other balls on the table, though this impact may be complex to model mathematically. If context is considered in these paradigms, it is a static view of context that does not change over time or interact with the technology in complex ways. In static views the particular implementation process is largely inconsequential, and the outcomes are solely dependent on the choice of technology, existing organizational context, or both. The more dynamic paradigms look at the implementation of technology as a complex process that unfolds over time, and they highlight the social process of transformation.

The horizontal axis contrasts paradigms that focus primarily on the technology and its characteristics as having causal efficacy from those that focus primarily on the social context. A paradigm concerned with the social context rates the organizational context of the firm as equal to, or more important than, the technology itself. When these axes are combined, we have the four views depicted in Figure 2: technological determinism, management of technology, political interests, and interpretivist. We have sorted paradigms into these four categories, recognizing that some fit more neatly than others.

The functionalist, technological determinist, and traditional engineering paradigms focus largely on characteristics of the technology. Both static and acontextual, these perspectives assume that the right choice of technology will lead to desired outcomes regardless of the organizational context. Those who subscribe to the technological determinism paradigm view technology as the causal variable to which all other factors are subservient. In a societal sense, as Pacey ( 1983) notes, technological determinists "present technical advance as a process of steady development dragging human society along in its train" (p. 24). Both Woodward (1958) and Blauner (1964) saw a progression of technology, with more complex forms leading to a more humane, organic organization compared to the traditional assembly line. Some underlying propositions that apply to an industrial technology determinist view are: (a) Technology is the solution to the problems organizations face, particularly with respect to competitiveness; (b) organizations must adapt to accommodate the technology, after it is installed; (c) technology type determines the best organizational design (Woodward 1958, 1965); and (d) advanced technology tends to drive organizational change in a democratic direction toward reduced management control (Zuboff 1988, Walton & Susman 1987). In short, technology is not only a deterministic variable, but a static one as well.

As is usually the case with simplistic 2x2 tables, some paradigms do not fit neatly into one cell or another. For example, we include in the technology determinism cell the work of some contingency theorists as illustrated by some of the early work of Perrow (1967) and of Thompson (1967), even though their models consider organizational context to a degree. Their models are not unlike Woodward's argument that the best organizational form is contingent on some characteristic of the core technology. If you know the technology, you know the "right" organizational form. In Perrow's (1967) model, the key technology characteristic is the degree of uncertainty, which then determines how mechanistic versus organic the organization must be to be effective. In Thompson's (1967) model, the nature of interdependence inherent in the tasks and technology determines the degree of organizational interdependence needed for effectiveness. A more accurate representation of these theories would show them on the horizontal dimension somewhere in the middle between technology focus and contextual focus, though still toward the static end of the vertical dimension.

Management of technology, the group of paradigms that are process focused but that still concentrate to a large degree on characteristics of the technology, include sociotechnical systems (STS), human centered design, and dynamic systems (Majchrzak 1988, Liker & Majchrzak 1994). This framework assumes that the right technology design, in combination with the right implementation process, leads to the desired outcomes, provided the predictable process is followed with care. According to Bloomfield & Coombs (1992), the "deliberate interventions in the culture and therefore the understandings and practices of an organization" are the process by which the proper technology is embedded in the firm.

[Table]
Caption: Figure 2

A management-of-technology (MOT) view conceives of technology in relation to the process of change. Technology management has become a crossdisciplinary academic field of inquiry, built from the foundations of strategic management theory, systems theory, organizational behavior, sociology, economics, finance, political science, and industrial relations (Badawy & Badawy 1993). Noori (1990) has described MOT as the link between engineering and management. The management of technological change embraces two tenets: one, an integrative systems approach to change; and two, strategic planning of the technology. The systems perspective can itself be viewed through different lenses. Gaynor (1996) discusses the need to manage the system and the pieces, and to integrate "the 'pieces' into an acceptable 'whole' by focusing attention on the interdependence of the pieces" (p. 15). Sociotechnical theorists, on the other hand, believe that the way to attain systems integration is to systematically analyze the social/organizational and technological subsystems and redesign work processes as needed (Taylor & Asadorian 1986, Davis & Taylor 1979, Pasmore & Sherwood 1978). The strategic planning view most common in the MOT literature is premised on a belief that technology adoption and implementation proceed in linear, sequential fashion delineated by stages and guided by planning and strategy (D Preece 1995). A management-byobjectives approach is applicable to this paradigm of technological change (Sankar 1991:357). Again we should note that all these perspectives grouped together under MOT are not uniform. For example, arguably sociotechnical systems perspectives are in the middle between the technology focus and the contextual focus, and they try to achieve a balanced view of the two.

A third category of paradigms presented in the lower right-hand quadrant of Figure 2 is one in which the political context is considered, but in a static way. These paradigms assume people act and react according to static, predictable roles, which are generally determined by their places in the organizational hierarchy and relative power differentials. It should be noted that while their roles may be static and predictable, the outcomes are not. One example of this paradigm is labor process theory. Influenced by the writings of Karl Marx, labor process theorists believe that management introduces new workplace technology to exert control over the labor process and wrest it away from craft and production workers. Attempted management control takes many forms: the separation of work/task planning from execution (Braverman 1974), the replacement of labor-especially skilled craft labor-with machines (Marx 1906) or the use of machines to permit lesser-skilled workers to perform craft work (Yellowitz 1977), deskilling of work (Noble 1979, Wood 1982), monitoring of employee performance (Shaiken 1985), and reduction of union influence and power (Bennett 1977). At the heart of this struggle is capitalist competition, which pushes firms to seek newer, cheaper, and more predictable production methods (Edwards 1979). However, workers do not simply succumb to management attempts to control work, and they are sometimes victorious in disrupting production (Montgomery 1979).

The final view, where a dynamic process and context connect, is called interpretivist. The dynamic process and dependency on the social context of the firm in the interpretivist perspective are represented in paradigms such as social choice, structuration theory, and critical theory. Adler (1992:7) describes this as the "third generation" and what has become the dominant view of academic thought on the impact of implementing new technologies in a firm, one which "progressively veered away from the big generalizations." Dean et al (1992) call new technology a "Rorschach test for organizational leadership," where the social context of the firm is the framework through which the manager sees the choice and outcomes of technology.

The interpretivist perspective has its roots in symbolic interactionism and concepts of the social construction of reality. Barley (1986, 1988) has attempted to reconcile interactionist perspectives on technology, which suggest highly particularistic impacts of technology, with structural perspectives, which assume systematic effects, through the use of Gidden's (1979) concept of structuration. Barley (1986) argues that technology presents "an occasion for structuring." Structure in this sense is viewed as an emergent process rather than a static configuration. Technology is viewed as having a reality that can bring about patterned social interaction. Barley (1986) studied the introduction of CT scanner technologies in hospitals and found they had the effect of empowering technicians whose roles became more central to the interpretation of the data, making judgments formerly left to "professionals." But this occurred to differing degrees in the two hospitals he studied. He argues that specific features of the technology brought about new patterns of role interaction, albeit to different degrees in different contexts.

A different slant on this perspective is taken by feminist scholars who view technology as socially constructed around traditional gender roles (Wacjman 1991, Morgall 1993). The way technology is viewed and applied is shaped by the dominant male role in society. Weick emphasizes the subjective interpretations of technology and the high degree of uncertainty associated with technology through the concept of "equivoque," an experience "that admits of several possible or plausible interpretations and therefore can be esoteric, subject to misunderstandings, uncertain, complex, and recondite" (1990:2).

MULTIPLE PARADIGMS AND TECHNOLOGY IMPACTS

Management Philosophy

In Table 1, we apply these four theoretical paradigms to our model of technology and organization. Technological determinists assume that the best way of managing depends on characteristics of the technology. Long-linked, sequential technologies are best managed using Theory X (McGregor 1957) and Tayloristic approaches (Taylor 1947). Since there is little need for reciprocal interdependence on an assembly line, workers can work independently motivated by a system of rewards and punishment,with a top down planning system that is very effective (Woodward 1958, Thompson 1967). On the other hand, more complex technologies, like continuous process technologies that are more automated, break down in unpredictable ways and require more horizontal communication and empowered, skilled workers (Woodward 1958, Perrow 1967).

[Table]
Caption: Table 1

Marxist theory provides a philosophical foundation for most of the political interests paradigms. In these models capitalist enterprises are in a natural state of conflict (Edwards 1979), creating a perpetual tension of differing organizational goals and perspectives between labor and management.

The philosophical foundations of the management of technology paradigm are quite different. As discussed, there is an emphasis on managerial strategy guiding practice (Chandler 1962), but in this case applied to the adoption and implementation of new technology. A second philosophical basis for this MOT paradigm is sociotechnical systems (STS) theory, which emphasizes the interaction of system elements. Most applications of STS incorporate worker participation in planning and implementation of decisions (Walton 1985). Similarly, human-centered design assumes that certain predictable characteristics of technologies fit human cognitive and work styles, but that the particular configuration of appropriate features is best defined with the participation of users of the technology (Kidd 1988, 1992).

The interpretivist perspective leads largely to descriptive and analytic research that provides a rich picture of the process of technological change. It is less prescriptive but does lead to the philosophy of encouraging open dialogue and negotiation about new technologies, their possibilities, and their impacts. Prasad (1993) suggests that to avoid "mismanaged meanings" it is important to openly discuss users' interpretations. Simonsen ( 1997) prescribes methods for developing more contextualized designs. Garud & Kotha (1994) use the brain as a metaphor to generate insights into how firms might design flexible production systems. The brain is a self-organizing system capable of responding rapidly to a broad range of external stimuli. Using this metaphor they suggest a prescriptive model of collaborative technology design in which flexibility can be enhanced by employing practices that promote distributed processes occurring in a parallel manner. De Sanctis & Poole (1994) view "adaptive structuration" theory as a particularly powerful approach to understanding how new technologies focused on supporting collaborative group work can influence group structure and dynamics.

ORGANIZATIONAL CULTURE (IMPLIED BY PARADIGMS)

The philosophical orientations of top management clearly affect an organization's predominant culture. Zammuto & O'Connor (1992) find that firms have a wide range of success in implementing advanced manufacturing technologies and argue that organizational culture is one of the critical factors distinguishing different degrees of success.

Organizations managed using a technological determinist paradigm would tend to place great value on traditional engineering thinking. It is rooted in the belief, extending back to Andrew Ure and Charles Babbage, that application of mechanical principles to factory systems can enhance productive capacity and lower the cost of goods (Mitcham 1994). One engineering enthusiast blames "antitechnologists" for dampening the enthusiasm of engineers (Florman 1976).

The organizational culture in modern capitalist organizations suggested by a political interests perspective is one of highly authoritarian management and resisting workers, with continuing conflict between these groups. Formal power resides at the top of the organizational hierarchy, but those below earn informal and even formal power on the basis of knowledge, skill, and/or union bargaining strength. Labor-management communication and trust are low. Morale is low as well, except when front-line employees are able to exercise power through the means described above. Advanced technologies place greater power in the hands of managers and simply fuel the hierarchical and authoritarian culture.

The culture of an organization implied by a technology management perspective is very different, and in fact there is a great deal of variance within this paradigm. Great value is placed on planning for change to minimize unpredictability, and on involving cross-disciplinary teams in the process-teams that may also span hierarchical boundaries. Advanced technology is assumed to contribute to greater openness and sharing of information from this perspective (Noori 1990, Lei et al 1996, Walton & Susman 1987).

The interpretivist perspective is all about culture, but culture as an emergent phenomenon that is much more dynamic and fragile than structural perspectives would suggest (Prasad 1993). Thus, an interpretivist perspective does not presuppose a particular organizational culture associated with the implementation of new technology, but rather, tries to use overt symbols and deeper underlying assumptions to decode the culture of a particular organization.

LABOR RELATIONS

The final aspect of organization context we consider is labor relations. From a technological-determinist perspective, a traditional labor-management relationship is generally assumed. If one were to consider a unionized environment from this perspective, collective bargaining would focus on limited subjects, which do not include decisions about technology type or design, and with traditional lines of demarcation. From a political interests' perspective, labor relations play a central role as labor and management struggle to control technology (Wilkinson 1983). An organization from a technology management perspective is viewed as having the potential of participatory labormanagement cooperation in the selection and design of technology guided by a common vision and set of interests. Walton & McKersie (1991) describe how a relationship characterized by mutual commitment/cooperation can be used to effectively manage technological change. Haddad (1994; and Haddad, in press) gives examples of worker and union involvement even at the technology design stage. Yet Cohen-Rosenthal (1997) chides sociotechnical theorists and practitioners for ignoring unions in their analyses and for making naive assumptions about political interests.

An interactionist perspective can account for varied labor relations outcomes of technology, depending on specific contextual features. Barley (1988) uses the concept of "roles" and views roles as an emergent, contextual feature of organization. He notes that the traditional role relationships between radiologists, physicians specializing in diagnosing X-rays, and radiological technicians were segregated and hierarchical. CT scanners altered the tasks and roles of radiologists and technologists. In one hospital the emergent roles significantly reduced status distances and correspondingly reduced conflict between radiologists and technologists, while this effect was much smaller in the second hospital, which operated in a different context. Pichault (1995) investigated four case studies of the introduction of computer-based information systems (in a chain store, bank, teaching hospital, and a news agency) and found very different influences on the distribution of power. Management style differences seemed to account to a large degree for these different patterns.

NATURE OF TECHNOLOGY

Each paradigm also has a different perspective on the nature of technology itself. A technological determinist organization most likely considers technology as self-regulating with a minimum of human intervention. Using Bright's (1966) typology, this would be machinery that "anticipates action required and adjusts to provide it." (II:210). The presumption is of course that workers add variability to the production process, and therefore that it is better to rely solely upon the technology. Butera & Thurman (1984), however, point out that even under conditions of full automation, significant operator involvement is needed because of frequent disturbances. A political interests organization would view the technology in terms of its potential for controlling and eliminating labor, with the added objective of deskilling the remaining jobs. Political interest theorists search for feedback systems, which they presume employers will use in seeking to monitor and control employee performance.

A firm adhering to the technology management perspective would see different types of technology as fitting with different strategic business objectives that can be identified a priori (Cleland & Bursic 1992, Preece 1995). In fact, technological change may come after organizational innovation (Haddad 1996), which is opposite the direction assumed under technological determinism. When the sociotechnical systems philosophy is factored in, additional criteria are likely to be considered, namely the potential of particular technology design features to support ease of use, organizational learning, human resource policies, and organizational structure and behavior. These include issues of the types of implementation approaches that best fit with the technology, such as training-how much and what type will be needed (Flynn 1988, Majchrzak 1988), the compensation and reward system, and the likelihood of middle management resistance (Noori 1990).

The interactionist perspective takes into account differences in meaning of the technology through design and implementation. The nature of technology is socially constructed. Turkle (1984:13) distinguishes between the "instrumental computer" and the "subjective computer." She observes how computers evoke a great deal of emotion and hold very different meanings for different people. In his study of the computerization of administrative services in an HMO, Prasad (1993) found a number of different and distinct conceptions of the computer. Some associated it with increased professionalism, making the hospital a more "professional place to work." Others used anthropomorphic imagery to talk about it, identifying its superhuman characteristics as if it were a living thing. By probing deeper Prasad (1993) was able to uncover some of the underlying reasons for these different images. For example, the "professionalism" perspective was linked in some employees' minds with an underlying anxiety that this relatively "small" hospital was not a professional place to work; for them the computer was a symbol of a more modern, sophisticated facility. Let us contrast this with a small, family-oriented factory in the Midwest where programmable automation was brought in, threatening to eliminate the jobs of some employees and enhance the positions of others (Liker et al 1987). There were sharp differences in the meaning of the technology in these two groups of employees, which corresponded directly with their position in the social structure. Threatened employees spoke passionately about the technology in terms of feelings of loss, while those whose jobs were enhanced spoke more dispassionately about what the technology would mean for their new challenging roles and the competitiveness of the firm. One does not see the threatened feelings of a portion of the shopfloor workers among the hospital "professionals." Similarly, even expert systems were not perceived as threatening to accountants who knew that only an inconsequential portion of their jobs could be automated by the expert systems and who had a choice of whether or not to use them (Liker & Sindi 1997).

TECHNOLOGY SELECTION AND IMPLEMENTATION

The views of technology selection and implementation processes also vary across the four paradigms. The technological determinism framework has generally started with the assumption that firms have already adopted the technology. It is taken as a given. But one would assume that the technology is selected by the managers, generally with the advice of the engineering unit, based on rational, technical criteria. The implications for organizational design can then be worked out afterward. Though this perspective recognizes one has to change the organization to fit the technology, it is a matter of design, almost like an architect designing a physical structure. As for the implementation process, new technology precedes organizational or human resource changes, and the organization is left to adapt. There is no conception of managing a change process.

From a political interests perspective, management will attempt to exert control over technology selection and implementation decisions by centralizing decision-making-in the hands of upper-management during the selection process, and with middle managers and technologists during the implementation process (Zammuto & O'Connor 1992). However, Thomas (1994) among others recognizes that upper-management control cannot be assumed, for "choices of technology could be influenced as much by efforts to alter structure and power relations as they could by efforts to reinforce or reproduce existing relations" (p. 229).

In a firm following the prescriptive tenets of technology management, the selection and implementation processes are planned according to a rational, predictable logic, and a great deal of research and thought have gone into how to make these decisions and how to plan the change process to increase the probability of successful outcomes. During the selection phase, data about technology needs are systematically collected and analyzed (Bancroft 1992), and choices are made about which technology types best meet the strategic needs of the organization. Cost-benefit analyses are also performed as part of the planning and justification process (Gerwin & Kolodny 1992, Gaynor 1990, Noori 1990), and these procedures, too, influence technology choice. As part of the implementation process, plans are developed to prepare the site for the technology, provide user support, manage training and operations, and maintain the new technology to reduce the chances of system failure (Bancroft 1992, Majchrzak & Gasser 1992). Ramamurthy (1995), based on survey data, finds evidence that manufacturing firms using a high-quality planning system, measured in terms of scope/comprehensiveness and adaptability, achieved higher performance from their advanced manufacturing systems, and managers were more satisfied with the outcomes than companies without a strong planning system.

As a different variant of the management of technology, STS proponents advocate technology selection and design as a participative process (Taylor & Asadorian 1986, Davis & Taylor 1979, Pasmore & Sherwood 1978). STS starts with an open-systems model and emphasizes interaction with the environment and that organizations are living, dynamic systems. Yet the classical tools of STS born out of the Tavistock Institute are primarily static, mapping variances in the technical system and relating them to static descriptions of roles and functions (Pasmore & Sherwood 1978). A broad cross-section of participants are brought through a visioning exercise to develop a vision of the future social and technical systems and how they can work together and then identify a detailed implementation plan. There is some recognition of the dynamic, unpredictable nature of the social and technical change, but the assumption is that the change can be managed much more smoothly by a crossfunctional team systematically planning and executing according to plan.

The human-centered design perspective, rooted in human factors engineering, is even more focused on rational prediction and control of user reactions to the technology (Liker & Majchrzak 1994). At one extreme, Majchrzak & Gasser (1992) have attempted to develop an expert system that takes as inputs characteristics of the organizational structure, people, and culture and advises the user on the optimal design of the "human infrastructure" for technology and how to implement the technology. It is assumed that technology is a fixed thing to be used by an organism whose reactions can be predicted and controlled. (More recent versions of this technology allow for user interaction with the system as technology implementation progresses.)

While the interpretivist perspective does not focus on prediction and control, it has the potential of greatly enhancing our understanding of how technology decisions are really made and how the human side of technological change can be intelligently managed through the change process. It is recognized that there is no one reason for technology selection, and the social construction of the technology and the organization it will lead to are negotiated processes among many actors. The entire "enacted" process of selection and implementation is viewed as unfolding without clear dividing lines between a selection phase and an implementation phase. By understanding some of the general metaphors people use to understand the technology, how these meanings vary across individuals and groups in the organization, and analyzing the process as it unfolds, there would seem to be an opportunity for jointly developing the people and the technology toward desirable ends. The brain metaphor (Garud & Kotha 1994) discussed earlier provides what many interactionists might consider an effective design paradigm. This metaphor assumes a self-organizing, emergent process of design and implementation.

CONCLUSIONS

As we began the arduous task of synthesizing a voluminous literature on technology and work organization, we noticed its diversity and the contradictory claims and observations. Thus, to "review" the literature it seemed more useful to organize the diversity of views than try to identify common findings and yet "unanswered" questions. In identifying a range of perspectives on the implications of technology for work organization, we have not tried to develop our own prescriptions or to synthesize these disparate paradigms. We in fact do not believe they can be synthesized. As described by Morgan (1997), these paradigms are different metaphors that provide different windows on the same phenomenon. Morgan has emphasized repeatedly that multiple metaphors can be used to enhance our understanding of social reality so long as we recognize that they are metaphors and do not mistake the metaphor for the realities.

In the case of technology the social reality is quite complex. Very different technologies are brought into very different social settings for very different reasons, often with completely opposing effects. Statistical studies have attempted to generalize predictable effects of the technology and found statistically significant correlates (Dean et al 1992, Dean & Snell 1991, Hull & Collins 1987). But for each study finding that the computer centralizes power, another will find that computer technology decentralizes and democratizes the workplace. For example, Dean et al (1992) use a survey of 185 firms to see if the use of advanced manufacturing technology is associated with centralization of power, as Marxist theories would suggest, or decentralization, as the "idealist" perspective would suggest. They find mixed evidence, partly supporting both perspectives. Are such observations useful? One gets the picture of a dog endlessly chasing its tail, always out of reach. Bloomfield & Coombs (1992) argue that the question of whether technology centralizes or decentralizes power is the wrong question. The right question, they argue, is: What are the symbolic dimensions of the development of information systems, and how do these vary by professional discipline? This is certainly an interesting question, but is it sufficient to understand differences in meanings assigned to technology?

Figure 2 sorted paradigms into four quadrants. Admittedly, this is an oversimplification. Not all paradigms fit perfectly. For example, contingency theory could fit into the determinist or frozen roles' quadrant. Lewis (1998) identified over 20 paradigms and mapped them in a more complex way with many on the boundaries between quadrants. But the question is: Does this represent progress?

Lewis (1998) argues that by deconstructing views on technology impacts and then reconstructing them into a conceptual framework we make progress. The point is not to synthesize the paradigms but to "employ their distinctions, maintaining mutual co-existence within a multidimensional understanding" (p. 27). If one believes the model we presented in Figure 1, it seems clear that there are some benefits, and weaknesses, to each of the quadrants. All but the interpretivist perspective oversimplify reality, either by ignoring context or by taking a static perspective, or both. The interpretivist perspective accounts for process and context in a richer way, and as Adler (1992:8) poignantly observes: "The dominant image of the future of work in this research is that of a kaleidoscope of complex patterns, constantly shifting and forming no overall tendency." But with no ability to generalize we are left with highly particularistic accounts of the implementation of technology under a myriad of circumstances.

Adler (1992) provides some hope that a fourth generation of research, superceding the interactionist aversion to generalization, can take into account process and context in a dynamic way, yet leave room for the role of systematic social patterns. Adler (1992) argues this work moves beyond both class conflict and local variability. He argues this new generation allows for a "soft" technological determinism, "one that neither underestimates the causal roles of variables nor telescopes the time frame required for technological constraints to manifest themselves" (p. 9). Adler's edited volume does indeed present a richer and more complex collection of research than many of the simplistic views summarized in this chapter. Nonetheless, one does not get the sense of a synthesis or an agreement on general trends in the impacts of technology. Nor is there a consensus on the critical contextual factors that condition its impacts, nor of general trends in social processes likely to unfold.

Thus, we are left with many differing, competing perspectives painting different portaits of the impacts of technology. Perhaps diversity is a good thing, as Lewis (1998) argues. Perhaps the intellectual debate is all we can hope for. In the meantime, the speed of technological change is quickening and its complexity grows. It is rare that technological change is "autonomous" with localized effects; more likely, technology influences complex social networks and acts sometimes as an integrative force and other times as a disintegrative force that separates people. If complex phenomena call for complex theories, we will need even more complex theories to account for the impact of technological change.

ACKNOWLEDGMENTS

The authors gratefully acknowledge support given by the Air Force Office of Scientific Research through the Japan Technology Management Program at the University of Michigan, as well as support provided by a Spring-Summer Faculty Research Award granted by Eastern Michigan University.

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[Author note]
Jeffrey K Liker

[Author note]
Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109-2117; e-mail: liker@umich.edu
Carol J. Haddad

[Author note]
Department of Interdisciplinary Technology, Eastern Michigan University, Ypsilanti, Michigan; e-mail: idt_haddad@online.emich.edu
Jennifer Karlin
Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109-2117; e-mail: jennifer.karlin@swe.org



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