Computer-integrated manufacturing and competitive performance Moderating effects of organization-wide integration

ELSEVIER

J. Eng. Technol. Manage. 13 (1996) 83-110

Journal of ENGINEERINGAND TECHNOLOGY MANAGEMENT JET-M

Computer-integrated manufacturing and competitive performance Moderating effects of organization-wide integration Raghavan Parthasarthy *, Jason Z. Yin W. Paul Stillman School of Business, Seton Hall Unicersio'. South Orange. NJ 07079. USA

Abstract The research reported here examined the conditions under which computer-integrated manufacturing (CIM's) machine-related benefits can be effectively exploited in competition. Using the integration theme that many have implicitly proposed to analyze CIM implementation, this research sought to test whether a CIM user with a higher level of tool integration enjoyed a higher competitive performance when there was a corresponding degree of organizational integration. Data from l l0 CIM users belonging to the automotive, aerospace, medical instruments, and consumer appliance businesses provide strong support to this extended integration hypothesis. Data indicate that integration in operational jobs, competitive criteria, and relationship with customers/suppliers significantly moderate CIM's impact on competitive performance. Task integration, commonly recommended in this context, has only main effects. It is necessary, but not sufficient, to transform CIM benefits into competitive benefits. We discuss our findings and their implications to technology management theory and practice. Keywords: Computer-integrated manufacturing; Job and task integration: Integrated competitive strategy: Firm-environment integration

1. Introduction

Manufacturing, as a source of competitive advantage, has received scholarly attention for quite some time now (Cohen and Zysman, 1987; Hayes and Wheelwright, 1984; Skinner, 1969). The introduction of computers in manufacturing has added new dimensions to this line of inquiry. Early research focused on the cost advantage arising out of

* Corresponding author. Tel.: 201-761-9133; Fax: 201-761-9217. 0923-4748/96/$15.00 Copyright © 1996 Elsevier Scie,.Te B.V. All rights reserved. PII S092 3-4748(96)00006-9

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the efficient use of machines due to computer control. Recent interest has turned toward the flexibility benefit that computer integrated manufacturing (CIM) provides for the firm's operations and its exploitation in competition (Nemetz and Fry, 1988; Parthasarthy and Sethi, 1993). CIM comprises design and manufacturing tools individually programmed and centrally integrated by a supervisory computer to perform as a unified system (Groover, 1987). Programming features enable CIM to offer economical production of volume/variety, while integration of operations enables it to handle design changes speedily (Meredith, 1987). As a result, efficiency and flexibility can be operationally optimized contemporaneously (Schonberger, 1986). These benefits are greater when successive stages of the manufacturing process are hierarchically integrated, i.e., from design to production to storage, etc. (American Machinist, 1985). Increasing competitive pressures and the need to innovate are inducing manufacturing firms to invest in CIM. Achieving higher levels of integration has emerged as a manufacturing strategy due to the differential advantage it provides for the firm's operations (Duimering et al., 1993). However, scholars contend that, to translate CIM's machine-related benefits into tangible measures of competitive performance, complementary organization-wide changes must be made (Nemetz and Fry, 1988; Parthasarthy and Sethi, 1992). Firms that progressively invested in integrating several manufacturing stages, but did not make incremental organizational adjustments, failed to realize the competitive benefits associated with CIM (Jaikumar, 1986). The general belief is that a higher level of manufacturing integration increases operational flexibility but a corresponding higher level of integration in other areas within the firm and in the firm's external relationship is necessary to exploit this flexibility in competition (Duimering et al., 1993). Field research that would indicate the conditions under which CIM's intrinsic benefits are effectively exploited in competition is sparse. At the same time, whatever little research that has been done in this area (e.g., Parthasarthy and Sethi, 1993) has produced only partially conclusive results. Two problems can be identified as causes for the lukewarm results. First, CIM was measured using the ratio of investments in computer automation to total manufacturing outlay. Such a measure captures only crudely the differences in the level of computer integration among firms and, as a result, distorts CIM's true performance impact. Ideally, future research in this area should obtain finer distinctions, preferably by using a graded scale. There is a second, more important reason why the above cited research could have produced only partially conclusive results. It analyzed specific strategy and structure choices, deemed complementary to CIM, to determine whether CIM's effect on competitive performance is higher when those choices are present. Related research on manufacturing integration and performance has, however, focused on the existence of a corresponding integration in the firm's operational goals and structure and between the firm and its operative environment (Ettlie and Reza, 1992; Dean and Snell, 1991). Broad job description, tight cross-functional linkages, a strategy that emphasizes both cost and differentiation, and close relationship with suppliers and customers are viewed as indicative of such an integration. This latter approach, centering around an integration theme, seems methodologically more cogent for several reasons: (1) It fits operationally

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with theoretical statements in this area which claim that integrated manufacturing will be effective only when similar integration exists in other organizational systems and processes (Child, 1987; Garud and Kotha, 1994; Voss, 1986); (2) CIM is an evolutionary system with room for further tool integration, An integration-based perspective in CIM implementation research should permit us to examine whether higher levels of tool integration require an isomorphic level of integration in other pertinent areas to have an effect on performance; and (3) It offers a parsimonious and unifying theme for organizing theory and research on CIM implementation. Given the need for better empirical understanding in this area, the research reported here sought to investigate the conditions under which CIM's intrinsic benefits can be effectively exploited in competition. The focus of this research, therefore, is on CIM users only. For reasons mentioned above, we approached the issue from an integration perspective: Whether a CIM user, with a higher level of tool integration and a corresponding level of organization-wide and firm-environment integration enjoyed a higher competitive performance? Toward this end, we analyzed a sample of 110 firms belonging to the automotive, aerospace, medical instrument, and consumer appliance businesses (where CIM use is indicated to be high - U.S. Department of Commerce, 1988) regarding their CIM level, level of integration in operational jobs, tasks, goals, supplier-customer relations, and competitive performance. The central thesis examined was whether integration in the above areas moderated CIM's impact on competitive performance. We discuss our findings and their implications to technology management theory and practice. 2. Relevant literature 2.1. Computer-integrated manufacturing

Fragmented markets have lately shifted the manufacturing requirements in several industries from an economical production of standard products in high volume to an economical production of differentiated products in mid-volume. Shortened product life cycles require that firms have an ability in their manufacturing technology to entertain frequent design changes speedily and cost effectively. To meet these challenges, firms have been reorganizing their manufacturing operations by using a computer. Depending on the area of computer application, several manufacturing sub-technologies have emerged (Helfgott, 1988; Majchrzak, 1988; Parthasarthy and Sethi, 1995; Susman, 1990). The most important of these are: 1. Computer-aided design system (CAD) which uses computer hardware and software to develop designs, display or store them for future reference, and to select appropriate parts. CAD reduces the time and effort needed to develop new designs, custom designs, or to quickly modify an existing design to improve product producibility. 2. Computer-aided engineering system (CAE) which uses computer hardware and software to test designs graphically without building expensive prototypes. CAE reduces the lead time and cost in new product development. 3. Computer-aided manufacturing system (CAM) comprises machine tools and robots that are programmed to manufacture the product as per design instructions. A

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supervisory computer prepares route sheets and controls machining operations, material flow, and testing. Scope production, high machine utilization, and scrap minimization are the advantages of CAM. 4. Automatic storage and retrieL'al system (AS/RS) comprises tbrklifts, loaders, and transfer lines which are programmed to deliver a batch of parts to a location or pick up processed parts for storage. Savings in time, labor costs, and efficient use of storage space are its advantages. While these individual technologies have their merits, capabilities are suggested to be far greater when they are centrally integrated and controlled by a supervisory computer (Goldhar and Jelinek, 1983; Gunn, 1987). The resulting system is referred to as CIM. Researchers indicate that electronic integration of several operations along the value-adding sequence enables CIM to offer benefits heretofore considered economically infeasible to attain in manufacturing. For example, manufacturing can now participate in design decisions in real time and thus enhance product producibility while reducing defects. The traditional manufacturing system, with its mechanical orientation and sequential processing of tasks, has been unable to offer this benefit. Consequently, frequent design changes and achievement of total quality have been inhibited. Similarly, manufacturing can now involve itself in marketing decisions so as to quickly respond to changing market needs. Stated differently, electronic integration of manufacturing tools facilitates a parallel information flow, thereby permitting both speed and efficiency in product operations (Ettlie, 1988). As a result, frequent changes in design, volume and delivery schedules can be economically achieved (Goldhar and Jelinek, 1983). These are in addition to the scope production benefit that stems from the programming features embodied in computer-aided manufacturing. In conceptual terms, operational flexibility and efficiency, that have been traditionally viewed as trade-offs, are now reconcilable under CIM (Schonberger, 1986). However, researchers observe that the key to maximally realizing the above benefits lies in integrating several disparate manufacturing operations (Salzman, 1981). The higher the level of such integration, the broader becomes the system's knowledge base and the more effectively it is able to respond to diverse market contingencies (Parthasarthy, 1993). Additionally, the parallel processing capability that results due to electronic integration of several tools adds speed and efficiency to the system's response. Metaphorically, as Garud and Kotha (1994) suggest, CIM is analogous to the human brain which responds rapidly and effectively to diverse external stimuli because of a rich interconnectivity in its anatomical structure. Integration of parts at each level, and several such levels connected to each other, enables the brain to process information both distributively and in parallel to achieve scope and speed in its response. Integration of tools within each operation, and a hierarchial integration of several such operations, engenders similar competencies to CIM. Inferentially, the higher the level of such integration, the higher should be the scope and speed competencies of CIM. 2.2. Integration in strategy, structure, and firm-enuironment relationship

The foregoing strengths of CIM are apparently intrinsic to the technology. To exploit these strengths in competition, scholars contend that the firm's strategy-making process,

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content, and implementation mechanisms should have characteristics similar to those of CIM (Dean and Susman, 1989; Goldhar and Jelinek, 1985). In other words, the firm's choices in these areas, and the manner by which they are linked to one another, should result in traits that are isomorphic with CIM's. Stated in CIM terms, the choices should be more comprehensive than narrow, represent more inclusion (i.e. this and that) than exclusion (this or that) in their contents, and must have multiple linkages in their organizational relationships. Suggestions tbr developing this arrangement recommend that the firm concentrate more on integration than on differentiation when designing its strategic goals, tasks, and processes (Dean and Snell, 1991; Ettlie and Reza, 1992; Hirschhorn, 1984). Traditionally, organizations focused primarily on division and compartmentalization of work (Kanter, 1983). The goal in organizing was to gain competency on a single competitive criterion (e.g., quality, low price, or market focus), because pursuit of more than one goal was considered to leave the firm "stuck in the middle" (Porter, 1980). Toward this end, jobs and tasks were narrowly divided and grouped around a specialized function to promote competency on a single competitive criterion (Ansoff, 1987). Differentiated tasks engendered this competency by allowing for concentrated actions on each of them. Each completed subtask was then handed "over the wall" (Wheelwright, 1985) to the next stage to complete the total task in a logical progression and according to predetermined plans, i.e.. tasks were sequentially linked from research to product design to production to marketing in more or less a relay race fashion (Van de Ven, 1986). In summary, a single-function orientation, a differentiated task structure, and a sequential flow of work would describe this organization. Evidently, these organizational traits are conflicting with the diversified and parallel processing traits of CIM; as such, they are inappropriate for competitively exploiting CIM capabilities. An organization that has a range of functional competencies and has interactively linked its tasks and processes at several levels, similar to a neurological network (Ashby, 1960), is commonly recommended for a user of CIM (Boddy and Buchanan, 1986; Garud and Kotha, 1994). Such an organization can process diversified information effectively, as well as rapidly, and would have features isomorphic with CIM's (Bessant and Buckingham, 1989). Designing this organization would require reformulating the job content, task coordination, authority, and operational criteria to achieve the necessary comprehensiveness. Most writings on CIM implementation consequently recommend an integration-based approach (as opposed to differentiation) in the knowledge, authority, work goal, and workflow components of the organization. At the job level, the suggestion is for grouping interdependent operational activities that require diverse skills and providing more authority to the job incumbent (Hirschhorn, 1984; Walton and Susman, 1987). In other words, jobs must be horizontally and vertically integrated or loaded (cf. Hackman and Oldham, 1980). At the task-group level, the suggestion is for teaming diverse functional units that would make them operate as almost one unit (Galbraith, 1982; Majchrzak, 1988; Susman, 1990; Zuboff, 1988). That is, task-groups must be integrated through committees and joint projects in order to achieve unity (Lawrence and Lorsch, 1967). Job and task-group integration is claimed to bring scope and speed to organizational activities, similar to CIM (Garud and Kotha, 1994). Job integration widens an individ-

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ual's response repertoire (i.e., creates breadth in knowledge) by providing opportunities to work under diversified circumstances (Morgan and Ramirez, 1983). Task integration widens the firm's knowledge base when it makes several groups act cooperatively (Cohen and Levinthal, 1990). Job integration offers speedy processing by letting communication among the integrated stages of work occur intrapersonally (Galbraith, 1982). Task integration engenders speed by facilitating frequent and interactive communication among specialized groups leading to a parallel processing of work (Garud and Kotha, 1994). Incidentally, it is pertinent to point out here that many have proposed job and task integration for all firms (rather than only to those that employ CIM) to deal with increasing environmental change and complexity that organizations presently face. For example, Morgan and Ramirez (1983) suggest that organizations should build redundancies within each job unit (job integration, in our terms) that would help individuals learn to be versatile. Nonaka (1990) argues for task integration that is claimed to promote cross-functional cooperation leading to speedy organizational response. Apparently, these mechanisms, when employed by a firm, are likely to produce an impact on its performance that is independent of the manufacturing technology it uses. Job and task integration, however, require corresponding changes in the firm's operational goals (Dean and Snell, 1991). The multifunctional orientation that emerges due to job and task integration should logically demand multiple performance criteria (e.g., manufacturing efficiency, design quality, timely delivery, etc.) to be employed in operations to maintain a balance. A corollary argument made in this context is that firms should have a corresponding degree of integration in their competitive strategy content: competing on a combined strategy of cost, product quality, and market focus (Dean and Susman, 1989; Parthasarthy, 1993). That is, they should compete by maximizing both product quality and customer preferences for variety, innovation, etc. without any increase in price or even by minimizing price (Dean and Susman, 1989). ~ Observations from the automotive industry (Abernathy et al., 1983), where CIM use is considered high, indicate a trend toward this type of competition. Use of a combined competitive strategy, while acting as a match for the integrated structure described above, aligns a firm's strategic intentions with its manufacturing competencies and should pave the way for exploiting the latter in the market place. An external integration that is directed toward customers and suppliers is an added suggestion for a user of CIM technology. A considerable body of research indicates that products fail because they are designed without regard to customer needs (Feigenbaum, 1983; lshikawa, 1985). Traditional manufacturing, dedicated largely to a single product design, has been unable to adapt to changing user needs in a timely manner. The flexibility strengths of CIM permit this change to be undertaken frequently and on short notice. However, to exploit this strength, mechanisms that would rapidly bring informa-

I It is thus differentfrom pure low cost and pure differentiationstrategies that focus solely on volume production to minimize cost or discrete productionto maximizeproduct change. Since discrete lot size at minimal cost is an inherentCIM competency,a combinationof cost and flexibilityis the appropriatestrategy to pursue. For a descriptionof C1M firms that pursue such strategies, see Dean and Susman (1989).

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tion from the customer to several value centers within the firm are needed (Garud and Kotha, 1994). Similar mechanisms on the supply side enable the firm to quickly respond to design change needs or meet new delivery deadlines (Ettlie, 1988). Moreover, suppliers have often been found to possess information that is crucial for developing new technologies or exploiting existing ones (Flynn and Cole, 1988; Rubenstein and Ettlie, 1979). Many, therefore, advocate a close integration of the firm's operations with customer and supplier operations (Clark, 1989; Ettlie, 1988; Nemetz and Fry, 1988; Von Hippel, 1988). Alliances with customers/suppliers, consulting them in product design decisions, and co-opting suppliers early-on in product development activities are some of the suggested ways for achieving this type of integration (Ettlie and Reza, 1992). The integration suggested here is different from the traditional vertical integration mechanisms that add value in a product sequentially, from beginning to end; rather, it involves adding value through parallel processing of work. This is achieved by establishing multiple data transfer linkages between the firm and its customers/suppliers that promote flexible interaction and cooperative completion of tasks. In summary, integration is a central theme that runs across literary discussions pertaining to the competitive effects of CIM. The rationale is that a convergent technology must have a corresponding convergence in elements within the firm, and in relationships between the firm and its operative environment, if its strengths are to be competitively exploited.

3. Variables and hypotheses The hypotheses for this study are formulated based on the following logic: 1. CIM offers flexibility to a finn's operations without increasing cost. To exploit this strength in competition, organization-wide integration is required. 2. Higher levels of CIM offer higher flexibility to operations. To exploit this added strength in competition, incremental adjustments in organization-wide integration are necessary. Therefore, for the purposes of this research, CIM (as measured by the level of toot integration) is the independent variable, competitive performance is the dependent variable, and integrations in the specified organizational areas (jobs, task, etc.) are the moderating variables. Clearly, the analysis is endogenic in nature. Environmental factors (e.g., competitive intensity) are not part of the analysis because they are assumed to be common to all the firms in the industry groups studied. Besides, since CIM is indicated to be most prevalent in the industry groups studied, the unit of analysis is restricted to this group only.

3.1. Independent L,ariable: CIM CIM refers to an operational system comprising programmable tools in design, manufacturing, storage, and transfer lines that are linked by a central computer (Groover, 1987). A system that has extensive linkages between stages (e.g., between design and component manufacturing) and that has linked several such stages (e.g., from design to

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component manufacturing to assembly to materials handling, etc.; see Appendix A) is viewed as one with a higher CIM level. The flexibility competencies of CIM are higher when the level of integration is higher (Parthasarthy and Sethi, 1992; Parthasarthy and Kotha, 1995). Because firms are known to automate in stages and in a piecemeal fashion, differences in their level of manufacturing integration have to be assumed. These differences are crucial for the analysis undertaken here because of their implications to the firm's operational flexibility and consequent performance effects. We, therefore, describe and measure CIM as a dimension consisting of high and low tool integration. 3.2. Moderator t,ariables: strategic and organizational integration

Generally defined, integration is the process of combining distinct parts or elements to form into a harmonious whole (See Webster's New World Dictionary, any recent edition). Organization theorists (e.g., Lawrence and Lorsch, 1967) define it as the process of bringing together diverse subsystems to achieve unity of effort. Two types of integration are conceptualized here: (1) Combining two or more parts into a single unit the resulting system is wholistic, or acts as such, because it has the combined property of the individual parts; and (2) Connecting two or more parts in a way that they act wholistically and yet remain distinct (cf.loosely coupled systems, Weick, 1979); redundant connections (Morgan and Ramirez, 1983) among them permit rapid information sharing thereby enabling them to act in unison. Using this classification, integration in the following strategic and organizational areas are identified: 1. Job integration. Combining fragmented operational jobs and necessary authority for their completion in order to create a whole job (i.e., horizontally and vertically loading jobs, Hackman and Oldham, 1980). Degree of integration will depend on the breadth of job scope, skill variety needed for task completion, and level of autonomy. 2. Task integration. Connecting several operational task groups (i.e., functions) through common goals and assignments to make them work in unison (Clark and Fujomoto, 1991 ; Nonaka, 1990). Degree of integration will depend on the type of coordination mechanisms used and the extent to which they promote frequency of interaction and information sharing among task-groups. 3. Strategy integration. Using a comprehensive strategy of cost, quality and flexibility in competition (Dean and Susman, 1989). Degree of integration will depend on the extent to which an equally higher importance is attached to all the three choices in competition. 4. Firm-ent,ironment integration. Connecting the organization's operational activities with customer and supplier activities through the use of teams and collaborative assignments (Nonaka, 1990; Von Hippel, 1988). Degree of integration will depend on the extent to which collaborative mechanisms promote frequency of interaction and information sharing among the firm and the external groups. -

3.3. Dependent t~ariable: competitit~e performance

Past research on computer automation (Parthasarthy and Sethi, 1993) used growth and profitability as performance variables. However, product quality, innovativeness,

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and frequency of new products introduced have also been cited by scholars as competitive benefits arising out of CIM (e.g., Meredith, 1987). Accordingly, we developed a composite scale that would measure the firm's performance in relation to its competition on product quality, technological innovativeness, new product frequency, customer satisfaction, sales growth, and profitability. The aggregate of these was treated as the performance measure. 3.4. Hypotheses

The following hypotheses summarize the relationship among the foregoing variables:

Hypothesis 1. A higher CIM lezel will haze a positize and greater impact on competitize performance when job integration is high than when it is low.

Hypothesis 2. A higher CIM lezel will haze a positize and greater impact on competitize performance when task integration is high than when it is low.

Hypothesis 3. A higher CIM lezel will haze a positire and greater impact on competitize performance when strategy integration is high than when it is low or non-existent (i.e., when a.firm attaches an equally high importance to cost, quali~', and .flexibility choices in competition than when it attaches low or unet,en importance to them).

Hypothesis 4. A higher CIM lezel will haze a positize and greater impact on competitize performance when.firm-ent~ironment integration is high than when it is low (i.e., when the firm attaches greater importance to customer and supplier inzoh:ement in product dezelopment / design tasks and when the frequency of interaction is high).

4. Methodology 4.1. Sample and instrument

The sampling universe for this research was defined as single product U.S. manufacturers belonging to industry groups where CIM usage would be high. The environments in which they operated were described as, more or less, equal in competitive intensity, requiring frequent product innovations and use of multiple competitive criteria. Four industry groups that fitted these conditions, based on a U.S. Department of Commerce (1988) survey, were selected: (1) automotive (parts only), (2) aircraft (parts only), (3) medical instruments, and (4) consumer appliance businesses. Only medium and large-size firms (500 employees or more) were selected from these industry groups as sample companies, for two reasons: (1) The items used in this research to measure variables, especially organizational structure, could not apply to small firms, and (2) Owing to high capital cost, only medium and large companies are known to invest in CIM (U.S. Department of Commerce, 1988). Two hundred and ninety firms were selected from

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Table 1 Sample profile ( N = 110) SIC

Industry

No. o f cases

M e a n sales ($ millions)

M e a n sales g r o w t h (%)

M e a n CIM level *

363 371 372 384

C o n s u m e r appl. A u t o parts Aircraft parts Med. instmts.

22 37 26 25

240 655 472 311

10.00 6.50 5.00 9.20

2.00 3.37 3.20 2.90

* Seven items on a 1 to 5 Likert scale m e a s u r e d the level o f integration in m a n u f a c t u r i n g stages (from design to processing to inspection to storage), 1 = not integrated, 3 = moderately integrated, and 5 = highly integrated. Scores on the seven items were averaged to obtain a composite score.

Ward's Industrial Directory (Ward, 1993, vol. IV), using the SIC codes of the above mentioned businesses and employee strength as criteria. A questionnaire was the only instrument used in data collection. Questionnaire items were selected from previous research and further refined through discussions with colleagues, professional managers, and a pilot test. Core questions used numerically anchored multi-item rating scales. (Copy of the questionnaire can be obtained from the first author). Questionnaires were initially mailed to the chief executive only. If unable to personally respond, the CEO was requested to delegate the responsibility to a senior manager who has a clear knowledge of the firm's competitive strategy, structure, and manufacturing technology. One hundred and twenty four completed questionnaires were received, of which fourteen had to be discarded because of missing data or non-use of integrated manufacturing. The remaining 110 questionnaires, or 38 percent surveyed (110/290), were available for analysis. A concern for the validity of the responses and problems associated with common method variance (Campbell and Fiske, 1959) prompted us to seek a second response from another executive of the same firm before undertaking analysis. We therefore called those who had responded and with their help, identified and mailed a second questionnaire to their colleague. After a reminder or two, 69 responses were received from this mailing. Four of them had to be discarded because of incomplete data and or

Table 2 Descriptive statistics and zero order correlations Variable

Mean

S.D.

IRR ~

1

2

3

4

5

1. 2. 3. 4. 5. 6.

3.0 3.0 3.0 3.5 3.7 3.4

0.79 0.80 0.77 0.68 0.50 0.74

0.70 0.91 0.57 0.65 0.84 0.78

(0.89) 0.49 * 0.27 * 0.44 * * 0.48 * * 0.28 *

(0.81) 0.16 0.42 * * 0.44 * * 0.50 * *

(0.89) 0.36 * * 0.26 0.34 * *

(0.75) 0.28 * 0.51 * *

(0.82)

Perf CIM Job Task Strgy Env

0.53 * *

6

(0.74)

a Interrater reliability was c o m p u t e d using the " s t r i c t p a r a l l e l " model described in the SPSS M a n u a l (1993, p. B193). ÷ p < 0.10, * p < 0.05, * * p < 0.01. C r o n b a c h alpha in the diagonals.

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Table 3 Factor analysis for the composite scores of each variable Bartlett's test of sphericity 2

Factors with eigenvalue > 1

Variable

KMO i

Value

Sig.

Factor

Eigenvalue

Performance CIM Job Task Strategy Environment

0.83 0.79 0.80 0.77 0.70 0.78

588 326 257 168 155 274

0.000 0.000 0.000 0.000 0.000 0.000

l 1 1 1 1 1

3.71 3.55 3.05 2.45 1.82 2.44

i KMO, the Kaiser-Meyer-Olkin measure of sampling adequacy, is an index for comparing the magnitudes of the observed correlation of the composites to the magnitudes of the partial correlation coefficients. If KMO values are close to 1, variables are considered to be measuring a single construct. Values ranging from 0.70 to 0.99 are considered ideal. ~-Bartlett's sphericity test is used to test whether the correlation matrix of the composites is an identity matrix, i.e.. all diagonal items are 1 and all off-diagonal terms are 0. The large value of the test statistic and the small significance level would indicate that the underlying population correlation matrix of the respective composites is an identity.

respondent identity was missing and could not, therefore, be connected to the first set of completed questionnaires. W e used the r e m a i n i n g 65 responses to compute interrater reliability. For data analysis, we took the average of the two scores; however, we used all of the 110 responses for performing regression and for testing the hypotheses. Table 1 shows the characteristics of the sample. The n u m b e r of cases are somewhat evenly distributed across the four industry groups, indicating data representativeness. However, non-response bias could not be calculated. Table 2 provides descriptive statistics, zero order correlations, alpha values for the research variables, and interrater reliabilities. Alpha values are high, indicating m e a s u r e m e n t reliability. Interrater reliabilities are positive and high, indicating the validity of the data set.

4.2. Measures

A p p e n d i x A describes the items, scales, and response formats used in measuring the variables. To test whether the items measure more than one construct, we conducted a factor analysis and report the results in Table 3. Data indicate that the items measure only the construct of relevance, and there are no other underlying factors in the research variables. CIM. A 5-point Likert scale with seven items asked the respondents the extent to which operations were computer integrated (e.g., from product design to production planning, production p l a n n i n g to c o m p o n e n t manufacturing, c o m p o n e n t m a n u f a c t u r i n g to assembly, etc.). A high composite score was assumed to indicate high integration. All items were borrowed from previous research (Snell and Dean, 1992). Job integration. A 5-point semantic differential scale with six items queried respondents on worker j o b design, j o b description, worker skills, training, autonomy, and work

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procedures to determine job complexity, variety, and interdependence. These items have been used before to measure job integration (Dean and Snell, 1991). Task integration. A 5-point semantic differential scale with ten items queried respondents on the task and authority dimensions, and on the cross functional coordination mechanisms used in the firm's operational structure. Items were borrowed from previous research (Miller, 1987; Miller and Droge, 1986). Extreme points on the scale indicated high differentiation or high integration. Strategy integration. A 5-point Likert scale with seventeen items asked respondents the importance their firm attached to competitive pricing, unique product design, fast market response, etc., in comparison to competition. All items were selected from previous research (Dess and Davis, 1984; Swamidass and Newell, 1987). Four items measured importance attached to cost strategy, five measured quality, and eight measured flexibility. A uniformly high response (4 or 5) on all three strategies was viewed as a firm that vigorously pursued an integrated cost-quality-flexibility strategy compared to one with a uniformly low response. Uneven responses (e.g., 5,4,3 or 5,4,2 or other combinations) were viewed as firms pursuing any one of these strategies, i.e., they were not pursuing an integrated strategy. Responses were coded to capture this distinction for 2 analytical purposes. It is pertinent to reiterate here that the cost strategy, as defined and measured in this research, is different from the traditional low cost strategy described by Porter (1980). The high volume standardized production that the traditional low cost strategy demands in its implementation has been indicated to be inappropriate for CIM (Jaikumar, 1986). Consequently, we took sufficient care to avoid items such as "aggressive pursuit of scale economies" or "stocking finished goods for off-the-shelf availability" that are commonly used to measure the traditional low cost competition (see, e.g., Dess and Davis, 1984). Instead, we used items that sought to measure the firm's concern for offering differentiated products at a competitive price. Firm-em~ironment integration. Respondents were asked the importance their firm attached to cooperation with suppliers/customers, and the structural mechanisms they used to implement them. A Likert scale and a semantic differential scale with eleven items were used. All items were borrowed from previous research (Ettlie and Reza, 1992; Parthasarthy and Sethi, 1993). Performance. Using a 5-point Likert scale, respondents were asked to subjectively rate their firm in comparison to competition on product quality, technological innovativeness, new product frequency, customer satisfaction, sales growth, and profitability. Subjective assessments have been recommended where they are appropriate and when hard data are difficult to obtain (Dess and Robinson, 1984). However, we were able to assess the validity of the data provided, to some extent, by cross referencing respondent sales data against published sales data. Data only for fifty four firms could be externally located. While discrepancies were found in most cases between the two, they were not significant.

2 The authors thank one of the reviewers for suggesting this procedure.

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4.3. Data analysis The hypotheses offered in this research postulate that higher levels of CIM should be accompanied by higher organization-wide and environment-directed integration to have an incremental effect on performance. In other words, the level of integration in the hypothesized areas will have a moderating influence in the CIM-performance relationship. The issue, therefore, is to examine whether CIM positively and significantly interacts with the integrator variables to cause a differential impact on performance. We, therefore, chose moderated regression (Arnold, 1982) as the statistical scheme for data analysis. We first entered all the main effect variables as a block, followed by the interaction variables entered one by one. To detect interaction, we computed linear equations for each moderator (see Appendix B) and graphed them. In this analysis, the hypotheses would be supported if the interaction coefficients are positive and statistically significant, indicating that higher levels of CIM would require corresponding levels of integration in other areas to produce a greater impact on performance.

4.4. Subgroup analysis To test the robustness of the overall findings, we decided to perform a subgroup analysis based on high-low performance. If the hypothesized predictions are true, the coefficients for the interaction variables should be higher and statistically significant for high performers than for low performers. To test this logic, we divided the sample into three parts by computing the cumulative distribution of performance. The upper-third and lower-third-percentile firms were defined as high and low performers respectively and regression was performed on these two groups. This analysis would provide additional support to the hypotheses tested in this research if the interaction coefficients are comparatively higher and significant in the case of high performers. We also planned to perform an industry-subgroup analysis, purely to answer an academic question: Whether the findings of this research are stable across the four industry groups that provided data for this study, or are they industry specific?

5. Results

Table 4 provides the results of moderated regression performed on the total sample. Equations shown in Appendix C were used for computing regression. Column 1 shows the results of regression performed with only the main effect variables. The coefficients of CIM and most integration variables are significant and explain 48 percent of the variation in performance. Columns 2 to 5 show regression results of each interaction variable. The positive and significant results of the coefficients indicate that performance of a CIM user will increase when the hypothesized integrators are in place. The industry subgroup analysis (results not shown here, but may be obtained from the first author) showed no industry specific differences, However, the interaction coefficients varied in size across the subgroups indicating that the CIM-integrators relation-

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Table 4 Moderated regression results ( N = 110) Variables

Mean

S.D.

R2

1. C I M

3.0

0.80

0.26

2. Job

3.0

0.78

0.14

3. Task

3.5

0.70

0.30

4. Strgy

3.5

0.56

0.25

5. Env

3.4

0.73

0.10

1 0.35 * * (0.10) 0.09 (0.38) 0.44 * " (0.04) 0.24 * (0.11) -0.19 (0.34)

6. CIM × J o b

2

3

4

5

0.33 * * (0.17) 0.40 * (0.24) 0.42 " * (0.07) 0.26 * (0.14) -0.17 (0.39) 0.80 *

0.11 (0.45) 0.10 (0.34) 0.46 * (0.04) 0.26 * (0.12) 0.12 (0.41)

0.36 * * (0.21) 0.12 (0.36) 0.43 * * (0.05) 0.46 * (0.09) -0.16 (0.38)

0.34 * " (0.23) 0.08 (0.39) 0.45 * * (0.10) 0.19 + (0.17) -0.20 (0.32)

(o.11) 7. C I M × T a s k

0.28 * (0.14)

8. C1M × Strgy

1.34

* *

(0.05) 9. C I M × Env Constant R z (multiple regression)

- 0.65 0.48

0.59 0.49

0.05 0.5 !

3.00 0.55

0.83 + (0.18) 0.50 0.51

+ p < 0.10, * p < 0.05, * " p < 0.01. All coefficients are standardized with standard errors in parenthese: 18 17 16 15 14 13 12

71

®

si 4

o.

1 0 -1 -2 -3 ..4 -5 -6 -7 -8 -9 -10

to., tra~rat~ n

2

4

6 CIM Job Integration

Fig. 1. J o b integration.

8

10

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Table 5 Linear equations s h o w i n g the relationship between CIM and p e r f o r m a n c e at high and low m o d e r a t i n g situations Moderator

H i g h integration

1. 2. 3. 4.

Y= Y= Y= Y=

Job integration T a s k integration Strgy integration Env integration

L o w integration

0.95(CIM) + 0.31(CIM)+ 1.11 (CIM) + 0.95(CIM)+

1.59 1.32 4.58 1.47

Y= Y= Y= Y=

- 0.29(CIM) + - 0.09(CIM)+ - 0.41 (CIM) + -0.27(CIM)+

0.97 0.58 4.06 1.79

The first n u m b e r in e a c h equation is the beta coefficient (standardized). The second n u m b e r is the constant.

ship is stronger in some industries than in others and would, therefore, produce a comparatively higher impact on performance. Linear equations that were computed using Table 4 data are shown in Table 5. Graphs drawn on the basis of these equations to visually examine interaction are shown in Figs. 1-4. Results of the subgroup analysis involving high-low performers are provided in Table 6.

5.1. CIM and job integration: Hypothesis 1 The main effect of job integration on performance (b =0.09) is meager and statistically insignificant, suggesting that there is no one-to-one relationship between job

18

17 16 15 14 13 12

11 10 9 8

7 6

High integration Q- 1 0 -1 -2 -3 -4

Low Integration

-5

-6 -7 -8 -9 -10

4

6 CIM Task Integration

Fig. 2. Task integration.

10

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-1 -2 -3 .-.4 -5 -6 -7 ~9 -9 -10 2

4

6

8

10

CIM Strategy Integration

Fig. 3. Strategyintegration integration and performance. However, its interaction effects are higher and significant ( b = 0.80, p < 0.05), indicating its moderating influence in the CIM-performance relationship. That is, a higher CIM level (recall that CIM was measured on a high-low dimension) will have a greater impact on competitive performance when there is a correspondingly higher level of job integration. The linear equation in Table 5 and the graph in Fig. 1 provide visual evidence to the claims made in Hypothesis 1. Additional support to Hypothesis 1 is available from the high-low performers analysis. The CIM X job interaction coefficient for high performers is higher and significant as compared to low performers, suggesting that higher performance results when CIM level and job integration level are both high. The t-test score in Table 6 indicates that the differences in coefficients between the high and low performers are statistically significant and, therefore, valid. 5.2. CIM and task integration." Hypothesis 2 Task integration has a high and significant main effect on performance (b = 0.44, p <0.01). However, its interaction effects are comparatively low ( b = 0.28, p < 0.05)indicating that task integration has a lesser moderating influence in CIM-performance relationship. The linear equation data for high and low integration situations (Table 5; see, also, the graph in Fig. 2) are close to each other, thereby providing visual

R. Parthasarthy, J.Z Yin / J . Eng. Technol. Manage. 13 (1996) 83-110 2O 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 i 2 1 0 -1 -2 -3 -4 -5 -6 -7

99

Low I n ~ g r ~ i ~

-9 -10 2

4

6

8

10

CIM Firm-Environment Integration

Fig. 4. Firm-environment integration.

evidence to this inference. Additional confirmation to this effect is available from Table 6 data on high-low performers where the t score indicates that there is no difference in the task integration levels of high and low performers. Therefore, Hypothesis 2 receives only a moderate support from this research. The interpretation here is that a high task integration is necessary for all users of computer automation irrespective of the level of integration in their tools. That is, the effect of task integration on performance is not contingent upon the level of CIM. This

Table 6 Regression results for high-low performers L o w p e r l b r m e r s ( N = 36)

H i g h p e r f o r m e r s ( N = 34)

Variables

Beta

S.D.

Re

Beta

S.D.

R2

t

CIMxJob

0.95 +

0.60

0.34

0.35

0.61

0.10

4.15 * ~ *

CIM X Task

0.32 +

0.72

0.37

0.27

0.67

0.27

0.30

C I M X Strgy

1.68 * ~

0.70

0.48

0.74

0.22

0.25

7.53 * *

CIM x Env

0.90 +

0.51

0.36

0.30

0.62

0.06

4.43 * ~ *

+ p < 0.10, * p < 0 . 0 5 ,

* * p<0.01,

* * * p < 0.001.

Betas are standardized coefficients. S.D. is the standard deviation of beta. t is the t-test score for the difference in betas for high and low performers.

1oo

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statement is in agreement with previous observations made on the CIM user population (Parthasarthy and Sethi, 1993). It is also in agreement with suggestions made by others in a general way that a team orientation is imperative for all firms in today's competitive environment (Imai et al., 1985; Nonaka, 1990). 5.3. CIM and strategy integration: Hypothesis 3

Strategy integration has a significant main effect on performance (b = 0.24, p < 0.05) which suggests that for users of CIM, in general, competing on a combination of cost, quality, and flexibility would positively influence performance. However, the interaction effects of this variable are much higher and significant (b = 1.34, p < 0.01) indicating that CIM users with a higher level of tool integration would realize higher performance when they attach greater importance to all these competitive criteria. Once again, the linear equations and associated graph provide visual proof to the claims made in Hypothesis 3. Additional evidence about the moderating effects of the strategy variable in CIM-performance relationship is available from the high-low performers analysis. For high performers, the interaction coefficients is significantly higher, suggesting that superior performance resulted because a high CIM level was implemented with a highly integrated competitive strategy of cost, quality, and flexibility. 5.4. CIM and firm-environment integration: Hypothesis 4

This variable has a negative main effect on performance (b = - 0 . 1 9 ) suggesting that, with all else remaining the same, cooperation with critical groups in the firm's operational environment will lead to lower performance. However, the coefficient here is not significant. More importantly, its interaction with CIM is in the hypothesized direction and is also statistically significant (b = 0.83, p < 0.10). The linear equations and associated graph provide visual proof for the claims made by Hypothesis 4, whereas the high-low performance analysis provides additional support. However, the comparatively low level of statistical significance here inhibits categorical generalizations. Hypothesis 4, therefore, receives only a moderate support.

6. Discussion

Previous research on CIM implementation examined strategic and organizational choices that should be in place based on their compatibility with CIM's competencies (Parthasarthy and Sethi, 1993). Results of that research were inconclusive in some areas. In the present study, we proposed that to be conceptually and methodologically consistent, CIM implementation should be researched by investigating the extent to which a complementary integration exists organization-wide and between the firm and its operational environment. We suggested an integration-based research perspective

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because, besides providing a unifying theme, it permits us to examine whether isomorphic organization-wide linkages are necessary to implement CIM, especially when CIM is described to be evolving hierarchically. Clearly, the focus of this research is on CIM users only. Reports indicate that these firms continuously upgrade their level of tool integration as part of an overall manufacturing strategy but have not realized commensurate competitive benefits. A general contention is that higher level of tool integration enhances operational flexibility but to capture it in competition, a corresponding higher level of integration must exist, organization-wide and in firm-environment relationship. Hypotheses to test this central proposition were framed such that higher CIM levels were predicted to generate higher competitive performance when operational level jobs, task-groups, business strategy content, and the firm's association with the suppliers/customers were also highly integrated. Findings provide general support to this extended integration hypothesis. Statistically significant results for all the research variables suggest that an integration based framework is a much better scheme to organize research issues on CIM implementation. It is pertinent to call the reader's attention to the linear equations in Table 5 which basically summarize the results of the study. These equations predict competitive performance as a function of CIM. The CIM-performance relationship is positive under all the high integration situations, indicating that performance will be higher when CIM level and integration in other areas are both higher. The CIM-performance relationship is, however, negative under all four low integration situations. The interpretation is that progressively increasing the level of tool integration without corresponding integrationbased adjustments in other areas would lead to lower performance. This is because an expensive asset base is under-utilized or wrongly utilized resulting in lower income due to high costs or unexplored opportunities. Analysis of high-low performers provides additional support to the integration hypotheses. A tangential analysis performed to examine whether these findings are industry specific (recall that the data for this research were provided by four different industry groups) reveals that differences exist across industry settings only in the strength of CIM-integrator relationship but not in the form of this relationship. That is, CIM's interaction with the integration variables is stronger in some industry groups than in others, thereby generating a higher level of performance in them. More specifically, job integration results suggest that the performance of a CIM user would hinge on significant design changes made at an individual level. The Taylorian (Taylor, 1967)job design that firms have traditionally followed - creating specialized jobs through fragmentation of work - is inappropriate for a CIM user. Instead, jobs must demonstrate a functional diversity and a built-in autonomy to rapidly respond to unforeseen contingencies. Toward this end, jobs must incorporate a body of work skills together with planning, analytical, and judgmental responsibilities. More importantly, as the CIM level is enhanced, the above features of a job have to be correspondingly enhanced. Therefore, flexibility as opposed to standardization must be the theme for designing jobs. However, organizational inertia and managerial resistance to job enrichment (Child et al., 1987; Dean and Snell, 1991) may pose serious hurdles in implementing such a job design strategy. Inasmuch as it is challenging, the performance effects of

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designing and installing highly integrated jobs for a user of CIM are significant enough that any perfunctory attempt made toward it could prove to be costly. The high-low performers data (Table 5) which show a vast difference in their interaction coefficients support this inference. A significant finding of this research is that task integration is not contingent upon CIM level. That is, a high task integration is necessary for all firms notwithstanding the extent of tool integration present in their operations. By implication, high performing firms have achieved their success by appropriately matching CIM with integration in other areas while maintaining a high task integration profile. This point deserves repeated emphasis because current writings leave the impression that a cross functional integration is a peremptory requirement for successfully implementing CIM. On the contrary, it seems to be a necessary, but insufficient, condition. Various factors could be causing high task integration, an imperative for all firms notwithstanding the level of CIM, such as: (1) markets have become increasingly complex requiring teams consisting of several specialists to make sounder decisions; (2) elimination of middle management and flatter organizational structures that have presently emerged require cross-functional teams to achieve coordination of efforts; and (3) the implementation of the total quality principle demands integration of tasks along the value-adding chain. With the widespread adoption of task integration, the differential advantage enjoyed by the "haves" versus the "have nots" is no longer strikingly apparent. However, with the independent effect of task integration on performance intact, higher performance for a CIM firm should be a function of task integration plus integration in other hypothesized areas. Of the variables analyzed in this research, strategy integration appears to exert the greatest moderating influence. In general, intensifying competition due to globalization has presently compelled firms to add more value for the customer by offering differentiated products at a low price. Added to this competitive pressure are the unique demands that the CIM technology places on its user. The characteristics of CIM are such that using it to maximize only a single manufacturing objective (e.g., cost, quality, or speedy delivery) underutilizes its potential and thus results in higher operating costs. A firm should, thus, not only set multiple operational goals but also use corresponding number of criteria to compete. The high interaction coefficient for strategy integration (b = 1.34) suggests that performance of successful CIM firms has come largely from the pursuit of a combined cost-quality-flexibility strategy. Difficulties involved in implementing such a strategy such as, for example, the need to radically reorient the existing structure or alter control mechanisms could be restraining the low performers from adopting such a strategy on a large scale. While the competitive advantage that high performers have here should soon dissolve when others catch up, the various combinations available to a firm in this area such as competing on multiple dimensions in a broad market or in multiple niche market segments (Susman and Dean, 1989) should still enable it to create unique advantages for itself. Thus, a judiciously formulated combination strategy seems to offer the highest competitive performance potential for a CIM user. Contrary to previous research results (Parthasarthy and Sethi, 1993), firm-environment integration receives support from this research, though it is only moderate. Given

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the fact that U.S. manufacturing firms now recognize the need to cooperate with critical stakeholders or have already implemented such a strategy (Kanter, 1989; McMillan, 1990), a strong analytical support for this hypothesis was expected. However, what is surprising is the comparatively low significance level of the interaction coefficient here ( p < 0.10). This could possibly have occurred because stakeholder cooperation is still a nebulous concept and items that would precisely measure it are not yet in place. Exactly what types of external integration mechanisms impact performance and how much effect they exert are not clear (Ettlie and Reza, 1992). Consequently, some of the measurement items used here could be less rigorous and some of the responses obtained could have been contaminated thereby minimizing the robustness of results. However, an alternative explanation is that stakeholder cooperation is not as yet popular with U.S. firms as is being currently theorized. More research in this area is needed before categorical generalizations can be made.

7. Implications and directions for future research

7.1. Theoretical and practical implications The theoretical implications of this study pertain to the development of a new framework for organizing CIM implementation research. The framework should be based on the premise that integration of parts in one subsystem must have a corresponding level of integration in other subsystems if the overall system is to remain in balance. Variations in subsystem design inhibit communication and coordination of activities among them thereby causing ineffectiveness of the major system. Viewed from this perspective, the framework for analyzing implementation of an integrated manufacturing system must model various subsystems of the firm that have interdependent relationships with the manufacturing process in integrational terms. In this new framework, integration within subsystems and between subsystems (cf. redundancies within and between parts, Morgan and Ramirez, 1983) must be both emphasized suggesting that integration is a hierarchically layered phenomenon. Methodologically, this would mean investigating the level of integration within subsystems to determine isomorphism. The more that subsystems are isomorphic, the more the overall system is in balance and the more it is effective. There is also a need to develop a construct that would describe an integrated competitive strategy as used in this research and to propose schemes to measure it in dimensional terms of high and low integration. Items and methods used in this research to measure strategy integration are exploratory and may have inherent biases. Similarly, external integration seems less robust as is evident from the comparatively low significance level of the regression coefficient. Refinement in both these areas is necessary. For CIM users, the findings of this research suggest that task-group integration is necessary, but not sufficient to competitively exploit CIM. Diversified operational-level jobs, competing on several dimensions (e.g., price, quality, fasts delivery, etc.) and

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cooperative relationships with customers and suppliers are additionally required. These areas must be enhanced when the CIM level is enhanced. For management practice, the implications suggest the need to redesign managerial roles by integrating tasks that are interdependent. Managing an organization that has integration in its technology and social systems requires managerial responsibilities that are equally integrative in nature. To prepare a manager to be effective in this new role, job rotation techniques such as those employed by many Japanese firms (Song and Parry, 1993) may be necessary. Additionally, use of appropriate reward mechanisms that would encourage managers to diversify their formal background may be appropriate.

7.2. Limitations This study focuses exclusively on the users of CIM technology. Therefore, the findings are limited to firms that have integrated their tool operations in varying degrees through the use of a computer. As such, they may not apply to firms that use programmable tools in design, processing, or other operational areas but have not electronically integrated them in some meaningful way. That is, firms that have only "islands of automation" are not affected by the findings of this study. Within this limited group of CIM users, the findings may be biased toward those who are embarking on upgrading their level of tool integration. Suggestions that call for a corresponding integration-based change within the firm, and between the firm and its environment, are for this group only. As mentioned earlier, some authors have recommended job and task integration for all firms so as to effectively deal with increasing competitive pressures. It is possible that some CIM firms have responded to this suggestion and are enjoying superior competitive performance as a result, even though their level of CIM is low. Some industry conditions and product categories may demand higher integration in organizational areas only, though not a higher integration in manufacturing tools. It is appropriate to note this significant exception to the findings of this study. Also, the findings may not apply to firms that compete in a somewhat staid environment and whose products are not differentiable (i.e., commodity products). Moreover, the fact that non-response bias could not be calculated by this research, could to some extent limit the generalizability of its findings. Use of caution is, therefore, in order while attempting to interpret the results of this research.

7.3. Directions for future research The "faceless factory" is a phrase that writers often use to suggest that organizations are using the computer to integrate every stage of the manufacturing process, from product planning to production, testing, and storage. Would such hierarchical integration of manufacturing require a corresponding degree of hierarchical integration in organizational processes pertaining to decision-making and implementation? Can a firm be conceptualized as having a layer of integration, starting from the individual job unit, to task groups, to organizational and environmental levels that can be described as

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analogous to an operational system with a high CIM level? Future research must provide theoretical frameworks and operational mechanisms for facilitating investigations along these research streams. Computer technology has evolved from the vacuum tube stage, to the transistor, integrated circuit, large scale integration, and parallel architecture stages (Savage, 1984). Computer-integrated manufacturing has evolved along with these changes, acquiring in the process a higher level of flexibility. Organizational structures have also evolved in more or less a corresponding manner, from simple structures to functional/divisional and matrix forms. However, the matrix structure is only at the integrated circuit stage and cannot, therefore, effectively implement more recent manufacturing technologies. Developing an organizational analog for the parallel architecture stage is necessary to implement today's CIM.

Acknowledgements

We wish to thank Jan Hammond, Dennis Scotti, and Constance Willett for their helpful comments and suggestions. The comments of this Journal's anonymous reviewers are gratefully acknowledged.

Appendix A

Job integration. A 5-point semantic differential scale was used with the following items and dimensions: ( 1 ) j o b design (narrow job scope - broad job scope), ( 2 ) j o b description (planned/structured - informal/flexible), (3) employee skills (routine problem solving/innovative), (4) employee training (specialized - diversified), (5) employee autonomy (low - high), and (6) work procedures (standardized - flexible). Task integration. A 5-point semantic differential scale was used with the following items and dimensions: (1) coordination among R&D, manufacturing and marketing (planned/structured - information/team based), (2) communication among operational functions (formal - informal/oral), (3) cross functional teams for new product/process development (rarely used - often used), (4) task forces for special projects (rarely used - often used), (5) autonomous work teams (rare - often encouraged), (6) line and staff functions (distinct - blurred), (7) job rotation of design and manufacturing engineering personnel (rare - common), (8) operations level decision-making (hierarchical - committee based), (9) hierarchy (many levels - minimal levels), and (10) functional units directly reporting to the general manager (too many/over ten - average/five or less). CIM. A 5-point Likert scale measured the extent to which transactions between operational processes were computer integrated. The dimensions were: 1 = low integration, 3 = moderate integration, and 5 = high integration. The items were: (1) product design to production planning, (2) production planning to component manufacturing, (3) component manufacturing to assembly, (4) assembly to production scheduling, (5)

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production scheduling to maintenance, (6) maintenance to materials handling, and (7) materials handling to quality control. Strategy integration. A 5-point Likert scale asked the importance the firm attached to seventeen competitive methods to measure the pursuit of cost, quality, and flexibility strategies. Cost strategy: competitive pricing, operating efficiency, manufacturing process innovation, and industry price leadership. Quali~. strategy: unique product design, enhancing product features, product R&D, high value added products, and brand identification. Flexibili~ strategy: product variety, volume flexibility, entering new markets, speed in innovation, fast delivery, fast market response, frequent new product introductions, and jut-in-time manufacturing. Firm-enuironment integration. A 5-point Likert scale and a 5-point semantic differential scale were used. Items on the Likert scale asked the importance the firm attached to: (1) seeking customer input in product development/design, (2) supplier involvement in product development/design, (3) seeking industry alliances for developing new technologies, (4) cooperation with academic/other research institutions for developing new technologies, and (5) sharing technical information with suppliers. Items on the semantic differential scale measured the use of mechanisms to promote firm-environment integration: (1) supplier representation in product/process development teams (never - frequently), (2) customer representation in product/process development teams (never frequently), (3) external research group representation in product/process development teams (never - frequently), (4) business transactions with suppliers (formal - informal), and (5) business transactions with customers (formal - informal).

Appendix B The equations in Appendix C were used for computing regression. The independent variable CIM and the four moderator variables were entered as a block to compute their main effects (Eq. (C.1)). Then, the cross product terms were entered individually to compute interaction effects (Eqs. (C.2)-(C.5)). After all the equations were computed, linear equations were derived for each equation starting from Eq. (C.2). The idea was to arrive at regression equations that would predict performance as a function of CIM across different moderating contexts. Using mean values for all the predictors except for CIM, the moderator, and the CIM-moderator cross product, Eqs. (C.2)-(C.5) were reduced. For example, in Eq. (C.2), mean values were substituted for all predictors except for CIM, Job, and CIM X Job. In the resulting situation, the regression equation contained the coefficients for CIM, Job, and CIM X Job along with a numerical constant that represented the main effects of other variables. Next, the standard deviation score for the moderator variable was selected and using one standard deviation over and below zero, a high and a low moderating situations were created. For Job, these scores are 0.78 and -0.78. Using these standard deviation scores, the regression equation was further reduced to be stated as a function of CIM. Two linear equations were derived for each moderator variable, one showing the impact of CIM on performance under a high

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moderator situation and the second, a low moderator situation. These equations were then used to graphically show the change in the slope of the relationship between CIM and performance under different moderating situations. 3

Appendix D. Equations for moderated regression Y = blCIM + b 2Job + b3Task + bsStrgy + b6Env + A.

(C. 1)

Y= bICIM + b2Job + b3Task + b 5 + b6Env + bv(CIM × Job) + A .

(C.2)

Y = bjCIM + b2Job + b3Task + bsStrgy + b6Env + bT(CIM × Task) + A. (C.3) Y = blCIM + b2Job + b3Task + bsStrgy + b6Env + bT(CIM × Strgy) + A.

Y= blCIM + b2Job + b3Task + bsStrgy + b6Env + bT(CIM × Env) + A . CIM Job Task Strgy Env A Y

= = = = = =

(C.4) (C.5)

Computer Integrated Manufacturing Job integration Task integration Strategy integration Firm-environment integration Constant (Y intercept) Performance

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3 Note. It is possible to arrive at, more or less, similar results by using existing standard deviation scores as the base for low performers and one standard deviation over it for high performers. If this procedure is used the linear equations (See Table 5) for job integration will be: Y = 0.58(CIM) + 2.4 (High integration), Y = 0.50(CIM) + 2.0 (Low integration). Evidently, a higher CIM increases performance in both instances but when job integration is high, the performance impact of CIM is comparatively higher. (We are grateful to one of the reviewers for pointing this out to us).

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