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The relationship Relationship between between strategy operations strategy and size in and firm size engineering consulting firms

Daniel Arias Aranda

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Universidad de Granada, Granada, Spain Keywords Service operations, Strategy, Size, Engineering, Consultants Abstract The relationship between strategy and firm size has been broadly considered and studied in strategic management literature. However, this topic has not been paid as much attention in the operations management field in manufacturing studies. The aim of this study is to analyse the relationship between operations strategy and firm size in a sample of engineering consulting firms. According to the results, there is a significant relationship between operations strategy and size in consulting engineering firms. In this context, small firms tend to follow customer-oriented operations strategies, medium sized firms tend to follow process-oriented operations strategies and larger firms tend to follow service-oriented operations strategies.

Introduction The relationship between strategy and firm size has been broadly considered and studied in strategic management literature (see for example Andrews, 1971; Argyris, 1985; Dess and Davis, 1984; Herbert, 1984; Miller, 1981; Rich, 1992). However, this topic has not been paid as much attention in the operations management discipline (Swink and Way, 1995). Moreover, when considering service firms, studies directly relating to strategy and size are even scarcer (Bozarth and McDermott, 1998). Most of them just do not consider size as a moderating variable (see among others Ettlie, 1995; Mills et al., 1998; Morita and Flynn, 1997; Smith and Reece, 1999). The aim of this study is to analyse the relationship between operations strategy and firms’ size in a sample of engineering consulting firms. Operations strategy is measured through a set of items configuring nine dimensions. Size is measured through firm turnover. Our main goal is to verify whether service firms pursue different operations strategies according to different turnover levels. Multivariate regression analysis is the statistical tool used for this study. First we will review the concept of operations strategy and its possible relationship with firms’ size in the context of service operations management. Operations strategy and size Operations strategy has received intense treatment for more than three decades (Nieto AntolõÂn et al., 1999). Such interest has not excluded incorrect assumptions about the environment. Moreover, many studies have neglected environmental The author wishes to thank Professor Antonio RodrõÂguez Duarte (Universidad Complutense de Madrid) for his inestimable help with the statistical processing of this paper. Nevertheless, the author is the only person responsible for possible mistakes and omissions.

International Journal of Service Industry Management, Vol. 13 No. 3, 2002, pp. 263-285. # MCB UP Limited, 0956-4233 DOI 10.1108/09564230210431974

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factors in operations strategy research (Flynn et al., 1995; Hayes and Schmenner, 1978; Skinner, 1969; Wheelwright, 1984). Historically, operations strategy was not considered as a source of competitive advantage until 1956 when Miller and Rogers (1956) distinguished between operations and business strategy. The notion of operations strategy as part of the business unit is relatively recent (Skinner, 1978; Hayes and Wheelwright, 1984). In fact, the operations function was relegated in the past to the mere accomplishment of efficiency standards through time, resources and space optimization throughout the development of the scientific work management principles (Abernathy and Corcoran, 1983; Chandler, 1991). The concept of operations management (OM) considers that there is one only right approach to manage production activities. Skinner (1969) was the first to set the basic principles for elaborating an operations strategy: Different firms have different strengths and weaknesses so they can choose their own way to be competitive. In a similar manner, different production systems have different operations features so there is not necessarily a unique standard production system. The main operations function goal is to develop a production system that reflects the firm’s implicit priorities and tradeoffs related to its specific competitive situation and strategy, all of that through interrelated and internally consistent decisions. OM literature identifies two main elements allowing the definition of operations strategy. Those are established from a functional point of view. The first element is related to those goals that the OM function must achieve (Skinner, 1978). This element is known as the operations task, which is built from those capabilities that the OM function must develop in order to create a competitive advantage for the firm. Some of those tasks are quality, cost, reliability and flexibility (Heizer and Render, 1996). Hill (1989) defines operations strategy considering the development of those tasks that allow the firm to focus on the customer instead of focusing on the production process. As a result, operations strategy is defined by the group of decisions related to the structure of the production system including the systems and policies that define the infrastructure of the firm (Clark, 1996, p. 45). Hence, the operations function confronts different alternative decisions, which configure the OM performance (Hayes and Wheelwright, 1984). However, the operations strategy must be consistent with all strategy levels (Anderson et al., 1989; Buffa, 1984; Miller and Roth, 1994; Roth and Miller, 1990, 1992; Swamidas and Newell, 1987) in order to support and be part of the whole firm’s strategy (Hayes and Wheelwright, 1984). In the long term, the operations strategy success depends on the capability to generate abilities in order to achieve a competitive advantage for the firm in a proactive way (Ferdows and De Meyer, 1990; Hayes and Wheelwright, 1984; Hill, 1989). Consequently, operations strategy can be defined as a vision of the operations function that depends on the corporate management for decision

making. This vision must be integrated with the firm’s strategy and is frequently The relationship reflected in a formal plan. Output of the operations strategy should be a consistent between strategy standard for the decision-making process in order to achieve a competitive and firm size advantage for the firm (Schroeder, 1992, p. 2). Operations strategy also feeds back the firm’s corporate strategy (Hayes, 1985). Once the operations strategy concept has been defined, the different types of 265 operations strategies are to be determined. Strategic management as well as organizational design academicians have analyzed this topic on many occasions (Hambrick, 1983; Fahey and Christensen, 1986; McGee and Thomas, 1986). There is a limited number of feasible strategies for each productive configuration (Miller and Friesen, 1984; Miller, 1986), so strategic models based on productive configurations are generally classified into taxonomies and typologies (Miller and Friesen, 1984; Meyer et al., 1993). Typologies describe ideal models, each one representing a unique combination of organizational attributes (Doty and Glick, 1994). Hence, there might not be any organization that fits perfectly in a determined ideal model. Anyway, a firm’s identification with one of the ideal models could imply significant improvements in the organizational performance (Venkatraman, 1989; Venkatraman and Prescott, 1990). On the other hand, taxonomies do not define ideal models, but they classify organizations in mutually exclusive and exhaustive groups (Doty and Glick, 1994). Taxonomies are derived either from multivariate statistical techniques or from mere observation (Wheelwright and Hayes, 1985). Bozarth and McDermott (1998) review different taxonomies and typologies for productive configurations (see Tables I and II). Level of analysis

Authors

Development

Stobaugh and Telesio (1983)

Conceptual; from case study

Firm/strategic unit

Wheelwright and Hayes (1985)

Conceptual from field work

Strategic unit

Miller and Roth (1994)

Empirical, from 164 firms clusters

Production strategic unit

Grouping

Variables

Three strategic types: low cost, technological and marketing intensive Four stages that describe the strategic role of OM: internally and externally neutral and internal and external support Three types of strategy: risk evaders, market oriented and innovators

Eight dimensions based on decisions about plant and technology management Strategic focus toward OM; level of involvement in strategic decisions

Source: Adapted from Bozarth and McDermott (1998, p. 432)

11 competitive priorities Table I. Taxonomies of strategic configurations

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Development

Hayes and Wheelwright (1979, 1984)

Conceptual from case study

Production plant

Richardson et al. (1985)

Empirical from a 64-firm sample

Firm

Hill (1989)

Conceptual, based on plant research and literature review Conceptual, partially based on Porter (1980)

Production plant

Strategic unit

Eight types of strategy based on combinations of three dimensions

Conceptual, based on literature review

Firm/strategic unit

Four configurations: niche, market scope, low cost and lean production

Kotha and Orne (1989)

Ward et al. (1994)

Table II. Typologies of strategic configurations

Level of analysis

Authors

Grouping

Variables

Four types of processes: shop, batch, line and flow Six types of strategy: three of them based on technology, two based on product customization and one based on costs Five types of processes: project, job shop, line and continuous

Process flow, product volume, and standardization Three dimensions: volume, product variety and degree of innovation

More than 20 aspects about products, markets, production, investment and infrastructure Three dimensions: complexity of the process structure, product line and organizational scope 16 dimensions measuring four areas: strategy, environment and production capabilities

Source: Adapted from Bozarth and McDermott (1998, p. 433)

Different studies relate operations strategy to other management variables. However, firm size is not even considered in many empirical works (Berry et al., 1991). Moreover, empirical models are tested and validated for manufacturing firms of significantly different sizes without further analyses (see Minor et al., 1994). For service industries and due to service heterogeneity, firm size turns into a complex variable to consider in service operations management studies. Hence, the size variable can be more effectively controlled in single sector studies. The relationship between operations strategy and firm’s size is supported by the contingency theory (Lawrence and Lorch, 1967) according to which environmental and structural contingencies make some strategies more effective than others. Therefore, if firm’s size is a clear structural contingency, it should influence operations strategy in some way. Nowadays, firm’s size as a

contingent variable is specially considered in studies related to finance and The relationship industrial economics. Recent research shows how resources availability limits between strategy R&D investments and acquisition of technology (Poyago-Theotoky, 1998; and firm size Garvey, 1994). Therefore, firm growth emerges as the key factor to reach new and larger markets (Schutjens and Wever, 2000; Van Wissen, 2000). Process technologies allow firms to produce and serve focused on higher volume 267 demands. Hence, larger firms display low degrees of asymmetry in their risk across recession and expansion states, which makes them less sensitive to credit market conditions (Perez-Quiros and Timmermann, 2000). Substitution of workforce by technology is especially relevant for medium and large firms because of over employment of smaller firms (Smith, 1998). In this context, we deduce and suggest the following pattern of behaviour for engineering consulting firms in order to state our hypothesis. Small engineering consulting firms usually tend to focus on a few segments of customers in such a way that service delivery systems are designed to customize most service-products by combining general use technologies and intensive workforce. These small firms specialize in delivering specific services with a high customer orientation. Medium sized firms have larger capacities to serve a wider range of customer segments. However, acquisition of specialized technology is still not available to these firms. Such technologies are profitable only to satisfy larger demands, for which these firms lack capacity. On the other hand, the combination of general technologies and intensive workforce does not allow these firms to customize services in the same way smaller firms do. Therefore, medium sized firms focus on segments of customers with similar needs, so service process optimisation can be achieved. Finally, larger firms are able to combine both customisation and process optimisation through the combination of general use and specialized technologies and workforce. These larger firms try to offer customers integral services by standardizing early stages of service delivery and customizing final specifications. Consequently, the main hypothesis to be tested is: H1. Operations strategy is closely related to firm size in engineering consulting firms. This main hypothesis can be split into the following sub-hypotheses. H1a. Small firms tend to follow customer-oriented operations strategies. H1b. Medium firms tend to follow process-oriented operations strategies. H1c. Larger firms tend to follow service-oriented operations strategies. Dimensions in service operations strategy Literature on service operations management identifies three basic operations strategies according to the firm’s focus of activities. Therefore, service industries can pursue process, service or customer-oriented operations strategies (see among others Johnston, 1994; Haynes and Du Vall, 1992; Bowen and Youngdahl, 1998; Hart, 1995; Desatnik, 1994; Berry and Parasuraman,

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1997; Lusch et al., 1996; McCutcheon et al., 1994; Tersine and Harvey, 1998; Collier, 1994, 1996; Sampson, 1996). From a reflective analysis of these studies, nine dimensions configuring the basic service operations strategies were extracted. These are: (1) type of operations layout;

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(2) push/pull orientation of the service delivery process; (3) degree of process standardisation; (4) number of different services offered; (5) use of information technologies (cost reduction vs service improvement); (6) back and front office activities relationship; (7) human resources specialisation; (8) degree of customer participation; and (9) new service design and development. Type of operations layout directly influences the way operations are configured in the service delivery process. A process layout tends to organise service delivery as a sequential activities process (Bowen and Youngdahl, 1998). On the opposite side, product layout does not imply task sequentiality. This leads to task development with no pre-established order (Johnston, 1994). Mixed layouts in which only a part of the service delivery process is sequential while other parts are developed according to service specific characteristics are also considered (Haynes and Du Vall, 1992). Push/pull orientation of the process determines the production philosophy of the service delivery. Pull oriented service firms initially consider customer needs when developing service activities. Activities do not end until the service firm has satisfied perceived customer expectations (Bitran and Hoech, 1990; Hart, 1995). Push oriented service firms undertake important investments in production capacity in order to satisfy demand. Demand is fostered through strong marketing efforts (Tersine and Harvey, 1998; Hart, 1995). Again, mixed push/pull configurations are considered. Degree of service standardisation is referred to as the extent to which task procedures are pre-established. Therefore, it also influences employees’ empowerment (Bowen and Schneider, 1985; Mills and Morris, 1992). Standardisation intends to minimise variability in the service delivery process, so procedures of developing each task are limited (Hart, 1996). The number of different services offers measures the degree of diversification of the firm according to the final products/services delivered (Desatnik, 1994). This dimension shows how the firm is oriented towards many or few customer segments (Lewis and Klein, 1984). It also regards how related the final products/services are, so a firm offering two products/services lines with few similarities between them is considered to retain a higher degree of

product/service amplitude than a firm offering many related products/services The relationship lines. between strategy Use of information technology (IT) is considered according to two and firm size parameters. On one side, IT can be used in order to reduce costs through, for instance, substitution of workforce by technology (Berry, 1995). On the other side, IT investment can be made for final service improvement, for instance, 269 through simulation technology to verify service quality and reliability. The relationship between front and back office activities is referred to as physical location as well as to workforce information exchange. Such a relationship directly affects customer perception of service delivery. When both activities are physically separated, customer effort to obtain information about back office activities is higher and will be moderated by the mechanisms of information exchange between both front and back office activities (Price et al., 1995; Lusch et al., 1996). However, physical closeness of both activities increases information effectiveness and reliability for the customer (Chase, 1981). Degree of workforce specialization intends to determine personnel versatility when accomplishing various and different activities. Hence, the staff can be prepared either to undertake one or few specific tasks, or else, to carry out any activity totally or partially (George, 1990; McCutcheon et al., 1994; Tersine and Harvey, 1998). A more versatile workforce responds more quickly and efficiently to environmental changes, while highly specialized personnel tend to be more rigid (Ashford and Humphrey, 1993; Schneider and Bowen, 1993; Bowen and Lawler III, 1995). This fact is especially relevant for those service firms that have IT with a high degree of obsolescence at the basis of their activity. Degree of customer contact and participation relates to the level of interaction between customer and service delivery process. Such interaction can be utilised either to transfer some activities to customers in order to reduce process costs or to customise service delivery (Bolton and Drew, 1991; Cadotte and Turgeon, 1988). In the first case, the customer acts as staff by developing tasks of the service delivery process (Lampel and Mintzberg, 1996). In the second case, the customer exchanges information with the service delivery activities, which will be developed in the firm (Collier, 1994, 1996; Gouillart and Sturdivant, 1994). Finally, intensity of design and development of new services refers to whether or not the firm sets new service delivery procedures through new task organisations and investments in specific resources. Therefore, it is possible to know, through this dimension, the firm’s intention to innovate in new processes and services (Bowen and Youngdahl, 1998; Berry et al., 1991; Sampson, 1996). Methodology Sample and the sampling procedure This study was conducted in the context of engineering consulting firms in Spain. The previously stated dimensions of operations strategy are of

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particular importance in this service sector. Three firm types (civil, industrial and environmental) were considered, covering most activities of engineering consulting firms. Table III shows the main activities of every type. According to the Spanish Ministry of Industry (1998), the nature of the work undertaken such firms in Spain is determined by the following intermediaries’ patterns: Operations are design to primarily satisfy internal demand. Only 10 per cent of average turnover of the sector comes from outside markets. More than 55 per cent of turnover derives from public administration projects. Intermediate demand plays a fundamental role as it allows constructors to act as intermediate suppliers for final demands of infrastructures and equipment. It is a knowledge-intensive sector. Fixed workforce costs represent about 65 per cent of all fixed costs of the sector due to the need to hire professional staff. Most projects performed are prototypes. Hence, production processes are not easily industrialised. Investments are written off in short periods of time, especially for computer equipment that has to be continually renewed in order to remain competitive. These firms tend to centralise resources for service delivery. Only multinational firms have offices abroad for commercial purposes, this is why no distinction was made between overall firm size and average office size (Table IV shows the operations patterns of these firms according to the Spanish Ministry of Industry (1998)). Initially, a copy of the questionnaire was sent to ten firms representing every turnover and activity group as a pre-test. They were asked not to answer the questionnaire but to remark on all doubts or possible mistakes detected. Only Civil

Table III. Main activities of engineering consulting firms

Transportation and communications Hydrology and hydraulics Geology and geodetics Agronomy, fishing and cattle Town planning and architecture

Main activities of engineering consulting firms Industrial Environmental Energy Mining Industrial plants Chemical plants

Source: Spanish Ministry of Industry (1998)

Environment protection Management and use of natural resources

small syntactic changes were made but none of the firms remarked on The relationship difficulties for concept understanding or misuse. between strategy The data for the empirical investigation of the model were obtained through and firm size a field study in Spain. Data were collected from participating firms predominantly via e-mail to the operations managers/executives or equivalent having a high level of responsibility in their companies. The Spanish 271 Association of Spanish Engineering Consulting Firms (Tecniberia) provided all information about addresses and firm names. Initially, and in order to attract the maximum number of participating firms, an e-mail was sent to all firms registered in Tecniberia soliciting their participation while stressing the importance of the study. The researchers considered a total of 129 firms with a turnover higher than 150,000 euros. As a second step, a copy of the questionnaire was sent to all of them. A total of 12 firms requested the questionnaire to be sent via ordinary mail with a 100 per cent response rate. Non-respondents were contacted as much as three times in order to get them to participate in the study. Of these, usable data were collected from a total of 71 firms (55 per cent). The questionnaire’s original language was Spanish. Table V shows a description of the sample according to the five turnover categories. Comparing the sample distribution with the sector as a whole, no significant discrepancies were observed. Most of the firms’ turnover ranges from 300,000 to 3,000,000 euros (60 per cent approximately of the total sample). On the other hand, civil engineering firms represent the higher percentage of the sample (49 (1) (2) (3) (4) (5) (6) (7) (8)

Customer needs and wishes detection for project configuration Feasibility and environmental impact studies Information exchange with customer for final technical and technological specifications Plans and budgets elaborations Project contract development with final specifications and project termination dates Project development Project delivery to customer Post-sale services

Source: Spanish Ministry of Industry (1998)

Cat.

Turnover (euros)

1 2 3 4 5

< 300,000 300,000-600,000 600,001-3.000,000 3,000,001-6,000,000 > 6,000,000 Total

Source: Own processing

Civil Firms Per cent 7 11 11 3 3 35

20.0 31.4 31.4 8.6 8.6 100.0

Group of activity Industrial Firms Per cent 3 3 4 0 2 12

25.0 25.0 33.3 0.0 16.7 100.0

Table IV. Operations patterns

Environmental Firms Per cent 7 7 8 2 0 24

29.2 29.2 33.3 8.3 0.0 100.0

Table V. Sample distribution (turnover and group activity)

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per cent) compared to 17 per cent of industrial engineering and 34 per cent of environmental engineering. Table VI shows the turnover distribution of the firms according to Spanish Ministry of Industry (1998). Measures Questions related to operations strategy are based on a five-point Likert scale. Every one of the nine dimensions of operations strategy was clearly represented in differentiated blocks in the questionnaire. Control questions were included in order to verify internal consistency of the questionnaire. For every dimension, a set of items was included in the questionnaire. Questions related to service strategies were developed after an extensive literature review and inputs from a panel of service managers. For every item a Likert scale ranging from 1 (completely agree) to 5 (completely disagree) was used to measure agreement of the operations managers/executives with such items (see Appendix). Partial indicators were developed in order to identify the firm positioning for every operations strategy dimension. Such indicators combine the different items corresponding to each dimension in order to measure the firms’ trends. A global indicator was developed to measure operations strategy according to such trends, taking into account that the indicator’s rank should flow between 1 and 5 values in order to be consistent with the Likert scale previously used. So, it was designed as follows: P P P P 5‰… biˆa Ain ¡ diˆc Ain † ‡ j… diˆc Ain ¡ 5 biˆa Ain j ‡ 1Š Ebn ˆ P P P P ‰j…5 biˆa Ain ¡ diˆc Ain †j ‡ j… diˆc Ain ¡ 5 biˆa Ain j ‡ 1Š

where: Ebn = the indicator. Ain = the score obtained in question i of block n in the questionnaire. Rank [a,b] represents questions scoring towards one of the trends in each block. Rank [c,d] represents questions scoring towards opposite extremes of rank [a,b] in each block. P P Hence, … diˆc Ain ¡ 5 biˆa Ain † represents the smallest reachable value, supposing that one firm scores the highest (score 5) in all questions for one of the trends and the P lowest (score P 1) in all questions of the opposite trend. On the other hand, …5 biˆa Ain ¡ diˆc Ain † represents the smallest reachable value for a firm positioned at one extreme, scoring the lowest (score 1) and the highest Table VI. Distribution in percentage of engineering consulting companies in Spain

Turnover (euros) Percentage of firms

6,000,000

27.3

32.3

27.2

6

7.2

Source: Spanish Ministry of Industry (1998)

(score 5) for the opposite trends. Once the extremes and possible intermediate The relationship values have been obtained, the indicator transforms this rank in a scale from 0 between strategy to 5 by adding to the value obtained, the smallest reachable value plus 1. The and firm size value obtained is finally divided by the highest reachable value adding the lowest value plus 1 in order make the scale positive. Finally, the obtained value is multiplied by 5 to transform it to the 0 to 5 scale. 273 Partial indicators of the nine dimensions of operations strategy were obtained, so combining these partial indicators into a global indicator; firms are classified according to the operations strategy they pursue. Such indicator intends to resume the multidimensional nature of operations strategy. Therefore, it is possible to know every firm’s positioning in or near one of the three basic strategies previously defined. Inter-item analysis was used to check scales for internal consistency or reliability. Specifically, Cronbach’s reliability coefficient (alpha) is calculated for each scale (dimension), as recommended by empirical research in operations by many researchers (Flynn et al., 1995; Swamidass and Newell, 1987; Smith and Reece, 1999). Cronbach’s alphas and trends for every dimension according to the indicator values are shown in Table VII. Usually, a value of 0.7 in the Cronbach’s alpha is considered as adequate in order to ensure reliability of the internal consistency of the questionnaire (Nunnally, 1978). However, a margin of 0.5 to 0.6 is generally considered adequate for exploratory work (Nunnally, 1978; Srinivasan, 1985). Construct validation is a process of demonstrating that an empirical measure corresponds to the conceptual definition of a construct (Schwab, 1980). Consequently, three types of validity can be established: nomological or theoretical validity, vertical validity and horizontal or criterion-related validity. We can argue that the measurement instrument establishes the basis for nomological or theoretical validity since all items are developed through an extensive review of the Operations strategy dimension I. II

Type of operations layout Push and/or pull orientation of the service delivery process III. Degree of process standardisation IV. Number of different services offered V. Use of information technologies (cost reduction vs service improvement) VI. Back office and front office interrelationship VII. Human resources specialisation VIII. Degree of customer participation IX.

New service design and development

Source: Own processing

Cronbach’s alpha Value near 0

Value near 5

0.5981

Fix

Moving

0.6530 0.6844 0.6240 0.6775

Pull Low Narrow Service improving

Push High Broad Cost reduction

0.8826 0.6310 0.7580

Close Versatile Cost reduction

Separate Rigid Service adaptation

0.9331

Low

High

Table VII. Operations strategy dimensions

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service operations strategy body of research. Factor analysis was used to check unidimensionality of scales, which provides evidence of a single latent construct (Flynn et al., 1995). Cronbach’s alpha values address vertical validity, which describes the extent to which a scale represents its construct. Evidence of criterion-related validity is presented through the Browne and Cudeck (1993) cross-validation index for covariance structure modelling. Index value for this research is 0.642, which indicates a high probability that the model results are consistent with population parameters. Table VIII shows the definition of the service operations strategies according to the nine basic dimensions. Results An initial scatterplot (see Figure 1) shows the spread of firms along the five operations strategies considered. Strategies are shown in a continuum along the possible values of the global indicator. This continuum lets us observe how close firms are, according to the operations strategy pursued, so firms included in a determined category with high values are closer to those firms with low values in the next category. Three groups can be identified at a first glance. First, a group of seven firms score values from 0 to 1 in operations strategy, which means they pursue a customer oriented strategy or similar according to the previous nine strategy dimensions and the value of the final indicator. A total 26 firms pursue service oriented or similar strategies, scoring values from 1 to 3. Finally, a group of 28 firms are closely pursuing a process oriented or similar strategy by scoring from 3 to 5. An X-Y plot of operations strategy vs firm’s turnover lets us see how firms are distributed along the different strategies according to size. As we can observe, firms with the highest and lowest turnover tend to score between the values 1 and 3 while medium sized firms tend to score between 4 and 5. After an initial approximation to data distribution, a multiple regression analysis was performed in order to test the main hypothesis and each of the sub-hypotheses. Table IX shows the P-value in the previous ANOVA analysis to be less than 0.01, so there is a statistically significant relationship between the variables at the 99 per cent confidence level. The output shows the results of fitting a multiple linear regression model to describe the relationship between operations strategy and two independent variables. The equation of the fitted model is Operations strategy= 0.0604618 + 2.26107*Turnover – 0.420298*Squared_Turnover The R-squared statistic indicates that the model as fitted explains 31.646 per cent of the variability for the operations strategy variable (see Table IX). The adjusted R-squared statistic is 29.6356 per cent. The standard error of the estimate shows the standard deviation of the residuals to be 0.979206. The mean absolute error (MAE) of 0.837237 is the average value of the residuals. The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in the data.

Dimension

Process oriented

Customer oriented

Service oriented

I

Process layout. Service process activities are mainly sequential. Service location is usually not movable. Main process goal is space optimisation. Workforce is highly specialised

Product (service) layout. Service delivery tasks are neither sequential nor fixed located. Tasks allocation is flexible

Layout is hybrid, although usually process oriented. Service delivery tasks tend to be sequential, though task variability leads to a significant degree of customisation through changes in location

II

High investments in capacity satisfy large demands supported by strong marketing efforts. Process is push oriented

Service delivery process is pull oriented. Customer satisfaction drives service delivery process

Operations are pull oriented. Process capacity tends to be low. Only small demands can be satisfied

III

Most activities are standardized. There is one or few ways to achieve service delivery tasks. Task variability is to be minimised. Work procedures are preestablished

Most service delivery activities are customised. There are few pre-established procedures to develop service delivery tasks

Most process activities are customized, although customisation range is small. There are many different ways to accomplish tasks. Pre-defined general procedures drive service delivery

IV

Range of different services offered is short and services are usually closely related

Differentiation of the services provided is high. Every service delivered can be considered as unique

There are few different services offered, all of them being closely related. Diversification is low

V

New technologies investments are accomplished in order to reduce costs. Workforce tends to be replaced by technology

Use of and investment in new technologies has as the main goal to increase customer satisfaction

Use of and investment in new technologies tends to balance cost reduction and customisation

VI

Back and front office activities are physically separated in order to increase efficiency

Back and front office activities are physically integrated by sharing personnel. Customer gets on line information about service delivery

Back and front office activities tend to be physically separated, although they share personnel. Such separation is usually due to space optimisation (continued)

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Table VIII. Definition of the service operations strategies according to the nine basic dimensions

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Dimension

Process oriented

Customer oriented

Service oriented

VII

Workforce is highly specialized. Versatility is low. Every worker accomplishes one of few very specific tasks

Personnel are not highly specialised but trained for versatility. Anybody must be able to develop any task totally or partially

Personnel are very specialized. However, they are trained for versatility and fast adaptation to organisational and technology change

VIII

Low customer contact. Customer participates in the service process only to reduce costs for the firm

High degree of customer contact in order to customise service

Degree of customer contact is high. Customer participation in the service delivery process is high in order to customize service

IX

Design and development of new services and processes is not strongly supported

High intensity in design and development of new service. New services and processes are being developed continually

Low intensity in design and development of new services and processes

276

Table VIII.

Source: Own processing

Figure 1. Plot of operations strategy with predicted values

Since the DW value is less than 1.4, there may be some indication of serial correlation. However, after plotting the residuals versus row order no pattern could be determined. In determining whether the model could be simplified, the highest P-value on the independent variables is 0.0000, belonging to the turnover variable. Since the P-value is less than 0.01, the highest order term is statistically significant at the 99 per cent confidence level. Figure 1 shows also the fitted line of this model.

Parameter Constant Turnover Turnover^2

Dependent variable: operations strategy Estimate Standard error T statistic 0.0604618 2.26107 –0.420298

0.535823 0.423928 0.0751008

0.0112839 5.33362 –5.59645

P-value 0.9105 0.0000 0.0000

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Source Model Residual Total (corr.)

Sum of squares 30.1864 65.2014 95.3878

Analysis of variance Df Mean square 2 68 70

15.0932 0.958844

F-ratio

P-value

15.74

0.0000

Notes: R-squared = 31.646 percent R-squared (adjusted of d.f.) = 29.6356 percent Standard error of est. = 0.979206 Mean absolute error = 0.837237 Durbin-Watson statistic = 0.569112 Source: Own processing

As it can be observed, an inverted U form configures the fitted model line according to the quadratic equation. Conclusions According to the results, there is a significant relationship between operations strategy and size in consulting engineering firms. Small firms tend to follow customer-oriented operations strategies, medium firms tend to follow processoriented operations strategies and larger firms tend to follow service-oriented operations strategies. So, the main hypothesis and the three sub-hypotheses are positively contrasted for engineering consulting firms. Hence, we believe that the results presented in this study provide valuable information related to the management of service operations. Even though the current research was exploratory in nature, it presented a better understanding of management issues related to a determined service industries size. Also, a pattern for the life cycle of consulting engineering firms can be extracted from the results. Consequently, increases in firms’ capacity, use of technology and customer segments seem to be the three key factors for operations strategy changes and flexibility in this type of service industry. Small engineering consulting firms perform customized and flexible operations strategies. When they grow, standardised and more rigid operations strategies are implemented. Finally, larger firms balance both flexibility and standardisation in the service delivery system through higher investments in technology and human resources.

Table IX. Multiple regression analysis

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The conclusions of this study are also relevant to practitioners, not only for operative decisions such as staffing, training and scheduling, but also for those strategic decisions that position the firm in a determined service/market. Hence, decisions related to firm’s growth should be closely attached to those related to process technology investments in order to be competitive. Practitioners should also consider that the firm’s operations strategy defines the way firms are going to manage the service delivery process. So, acquisition of new process technology is going to modify the way the firm serves customers. Moreover, it can also change focusing patterns on customer segments. Therefore, target segments can differ according to firm size or elsewhere; the same customer segments may be served in a different way by firms of different sizes. A competitive advantage can be obtained by identifying the preferred service delivery system for customers. Even though this paper presents interesting results related to service management, the study contains limitations, which should be dealt with in future research projects. Now we discuss some of those limitations and provide directions for future research projects. The current study implicitly assumes that the service, customer and processoriented strategies are a precise classification. Another related issue involves the selection of the nine dimensions as classification scheme for analysis. As mentioned earlier in the paper, service management literature contains a number of typologies and taxonomies. However, there is not enough empirical support for the proposed concepts. Therefore future research should be directed towards empirically testing/validating the proposed ideas in different service sectors. With respect to the current study itself, a few issues are of concern. For example, since we developed the 53-item questionnaire based on service operations literature, it is possible that certain other important operations management issues were ignored. Direction for future research The findings of this study answer some of the questions about the relationship between service operations strategy and size. It has been observed that firm size affects operations strategies significantly. This research also suggests the importance of concentrating on a few appropriate strategies rather than implementing all the available ones. One of the areas of future research is the investigation of the appropriateness of an individual strategy or a combination of strategies that may benefit a particular service industry. Recommendations can be made to implement a group of strategies categorized by different classes and sizes of industry; these will be a significant contribution to the literature on operations strategy. Additionally, significant control variables should be identified in order to develop new models that moderate the relationship between size and operations strategy. In addition, the application of this model to different service sectors remains to be tested.

As mentioned before, the current study contains several limitations, but at The relationship the same time provides empirical analysis of some important service operations between strategy management issues. We hope that this study, although exploratory in nature, and firm size would encourage others to reconsider generally accepted concepts and hopefully motivate them to undertake empirical service management research projects in different service sectors.

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Service delivery activities are performed in a pre-established and fixed place.

(2)

Production resources are sequentially located.

(3)

Resources for service delivery are located in order to optimise space and maximise efficiency.

(4)

Downstream tasks are never performed until upstream tasks are over.

(5)

Every worker is assigned to an exclusive task.

(6)

System efficiency goals have priority when designing service delivery process.

Aspects of a movable layout: (7)

Service delivery activities are performed where it is more convenient for the customer.

(8)

Production resources can move to those places where service is delivered.

(9)

Resources for service delivery are located in order to optimise customer satisfaction and final service delivery.

(10)

Workers assignation is made on a rotation basis.

(11)

Workers perform different tasks in the same shift.

(12)

Customer satisfaction goals are to have priority when designing service delivery process.

Block A.II. Push/pull orientation Push orientation: (13)

Important marketing efforts are made in order to attract new customers.

(14)

A crucial marketing goal is that customer is delivered as much services as possible.

(15)

Production output is always maximised.

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Pull orientation: (16)

Important service delivery efforts for improvement are made in order to increase customers’ satisfaction.

(17)

A crucial marketing goal is that customer is satisfied.

(18)

Customer satisfaction is more important than output optimisation.

Block A.III. Level of standardisation (19)

Service delivery system is designed so there is one or a few ways to perform every task.

(20)

Variability is continually decreased along the service delivery process

(21)

Most work procedures are pre-established and cannot be modified.

(22)

Empowerment degree is very low.

(23)

All incidents not prevented in the work procedures must be communicated to a superior for resolution.

(24)

There is a procedures book, which is known by all workers.

(25)

Most service delivery activities are oriented towards service customisation.

Block A.IV. Different services offered (26)

The firm offers a wide range of different services.

(27)

All offered services are customised.

(28)

New services are continually offered to customers.

(29)

The firm delivers one of few very specialised services.

(30)

Services are delivered to satisfy one or a few small customer segments.

Block A.V. Use of information technologies (31)

Acquisition of information technologies is oriented towards costs reduction.

(32)

Workforce is replaced by new technologies when possible.

(33)

Customers can send or receive information about service delivery through information technologies such as Internet, EDI, WAP etc.

(34)

Acquisition of information technologies is oriented towards customer satisfaction.

(35)

Decisions about information technologies adoption are made on the basis of tasks improvements from the worker’s point of view.

(36)

Decisions about information technologies adoption are made on the basis of service customisation.

Block A.VI. Back and front office activities (37)

Front office activities are physically separated and differentiated from the back office activities.

(38)

The customers cannot access those service activities in which they are not required.

(39)

Personnel of front office activities works exclusively there and never in back office activities.

Block A.VII. Human resources (40

Personnel are highly specialised.

(41)

Personnel are able to perform various and different tasks.

(42)

Job rotation is commonly used.

(43)

More than half of our personnel are university graduates.

(44)

Training is given crucial importance in the firms budgets.

Block A.VIII. Customer participation (45)

Service delivery process is designed so customer performs by him/herself those activities he/she is qualified for.

(46)

Customer performs part of the service delivery activities in order to reduce costs.

(47)

Customer is informed in detail about all previous activities he/she has to perform before service delivery.

(48)

Customer knows about cost reductions due to his/her participation in the service delivery process.

(49)

Customer participates in the service delivery process in order to customise service.

Block A.IX. Design and development of new products (50)

New procedures for service delivery are continually developed.

(51)

New services are continually developed.

(52)

Customer opinions are indeed considered when designing new services.

(53)

There is an exclusive team for service design and development.

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