HIGH PERFORMANCE WORK SYSTEMS AND FIRM’S OPERATIONAL

PERFORMANCE: THE MODERATING ROLE OF TECHNOLOGY

MARTIN LARRAZA KINTANA Departamento de Gestión de Empresas

Universidad Pública de Navarra Campus de Arrosadía, 31006, Pamplona (Navarra) - Spain

Tel. 34-948-168931 / Fax: 34-948-169404 martin.larraza@unavarra.es

AINHOA URTASUN ALONSO

Departamento de Gestión de Empresas Universidad Pública de Navarra

Campus de Arrosadía, 31006, Pamplona (Navarra) - Spain Tel. 34-948-169384 / Fax: 34-948-169404

ainhoa.urtasun@unavarra.es

Mª CARMEN GARCÍA OLAVERRI Departamento de Estadística e Investigación Operativa

Universidad Pública de Navarra Campus de Arrosadía, 31006, Pamplona (Navarra) - Spain

Tel. 34-948-169209 / Fax: 34-948-169204 mameng@unavarra.es

(November, 2003)

We would like to acknowledge the comments and suggestions of Prof. Emilio Huerta Arribas and Prof. Vicente Salas Fumás to preliminary versions of this paper, and the financial aid obtained from “Fundación BBVA” and the

“Spanish Ministry of Education” (project PB 98/0550).

HIGH PERFORMANCE WORK SYSTEMS AND FIRM’S OPERATIONAL

PERFORMANCE: THE MODERATING ROLE OF TECHNOLOGY

Abstract The paper analyses the moderating effect of technology on the potential impact of High Performance Work Systems (HPWS) on firm’s operational performance. The paper distinguishes between production technology and the technological intensity of the industry. This potentially moderating effect is analysed in a sample of 965 Spanish manufacturing firms. Results support the moderating role of the technological intensity of the industry, while qualifying the hypothesised moderating effect of production technology.

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INTRODUCTION

Should managers consider technology when deciding how to manage workers? Human resources

management has acquired greater relevance since the early 90’s. Both academics and

practitioners have stated that human capital is a key resource, with the capacity to help firms gain

the competitive advantage that they pursue (Pfeffer, 1994, 1998; Becker and Huselid, 1998).

Agreement over the strategic importance of people to firm success has come hand in hand with

acceptance of resource-based claims concerning the relevance of internal resources as sources of

competitive advantage (Wright et al., 2001). While there is consensus about the relevance of

human resources, the debate persists over the way these human resources should be managed in

order to maximise their contribution to a firm’s results, and how this contribution can be

maintained through time. This debate is polarised between two normative models: the

universalistic or “best-practice” model and the contingent or “best-fit” model (Boxall and Purcell,

2000).

The core of the debate over these two models lies in different assumptions concerning the

relationship between the human resources and the other resources of the organization and

between the organization itself and its environment. Advocates of the universalistic approach

consider employees to be above any other resource of the firm or environmental factor, and that

the sole commitment of workers with the objective of value creation is enough to improve the

collective result (e.g. Pfeffer, 1994, 1998; Kochan and Osterman, 1994). The contingent view,

meanwhile, challenges this view and contends that what is good for one firm may not necessarily

be good for another. Therefore a particular set of human resource management policies may not

always have the same impact on firm performance, since its effect may change with the firm’s

strategy, organizational culture, employee value or environmental factors (Schuler and Jackson,

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1987; Baird and Meshoulam, 1988; Youndt et al., 1996; Lepak and Snell, 1999; Baron and

Kreps, 1999).

In the context of this theoretical and empirical debate, HPWS are suggested as a set of

best practices, with the potential to boost firm performance by developing a more talented and

committed workforce (Pfeffer, 1994, 1998; Kochan and Osterman, 1994; MacDuffie, 1995;

Becker and Huselid, 1998). Although there is no definitive consensus about the practices that

make up these HPWS (Boxall and Purcell, 2000), some are widely accepted (Way, 2002). These

include selective and exhaustive staffing procedures, stability in the employment relation,

compensation schemes linked to group performance, above average compensation, flexible job

assignments (job rotation), self-directed work teams, extensive training, and a high level of

communication and fair treatment through all organizational levels. The evidence reported in the

literature concerning the universalistic or contingent character of the relationship between HPWS

and firm performance is not conclusive. In-depth critical reviews of the studies that have this

relationship can be found, for example, in Becker and Huselid (1998), Whitfield and Poole

(1997), Boxall and Purcell (2000) or Wright and Boswell (2002).

Analysis of the contingent factors has focused mainly on the influence of firm strategy

(Wood, 1999; Boxall and Purcell, 2000; Guthrie et al., 2002). One factor that we believe may be

of great importance in explaining the role of HPWS in determining firm performance, and that

has been somewhat neglected in the past, is technology. Technology is a key environmental

factor, closely linked with the organization of work and work processes (Baron and Kreps, 1999),

and hence something to keep in mind when managing human resources. Its potential relevance

was already suggested by Youndt et al (1996) who called for more research into the relationship

between human resources management and firm performance, through examination of the

moderating role of organizational characteristics such as technology. However, little attention has

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been paid to this suggestion in the literature investigating the role of human resource

management, particularly HPWS, in firm performance. Only recently, Lepak et al., (2003) have

reported that the relationship between different employment modes, specifically, knowledge-

based employment and job-based employment, and firm (financial) performance vary across

levels of technological intensity.

We contribute to the literature on human resource management by analysing the role of

technology and argue, in line with the contingent line of research on the effectiveness of HPWS,

that technology imposes certain requirements that influence the optimal mix of human resource

policies to be applied, and hence the potential impact of the use of HPWS on firm’s operational

performance.1 In other words, technology moderates the impact of HPWS on firm’s operational

performance. The word technology has been used in the literature to refer to different, albeit

related concepts. On the one hand, for authors such as Snell and Dean (1992), Baron and Kreps

(1999) or Lepak et al., (2003) it refers to the technology employed by the firm in the production

of goods and services. For this, we use the term production technology. On the other hand, it has

been used to refer to the relative level of R&D effort and the pace of change in relevant

knowledge and technology, with which the firm and its competitors have to contend daily (e.g.

Hambrick et al., 1995; Balkin et al., 2000; Frías and Guerediaga, 2000). We denote it with the

term technological intensity of the industry. In this paper we make a distinction between these

two related notions of technology and explore their implications for human resource

management. In particular we argue that the positive influence of HPWS on firm’s operational

performance will strengthen with increases in the complexity of production technology and the

technological intensity of the industry. To analyse this moderating effect we employ a sample of

965 Spanish manufacturing firms.

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The paper is organized as follows. The first section explores the suitability of HPWS as

technology changes, and presents the hypotheses to be tested. The second section describes the

sample, and the measures employed to approach the variables included in the study. The results

are presented in section three and discussed in section four.

HYPOTHESES DEVELOPMENT

Production technology

We hypothesise here that the set of human resource management policies that are part of

the HPWS are more accurate as the complexity of production technology employed in the plant

increases. Such accurateness should ultimately be reflected in a stronger HPWS-firm’s

operational performance relationship, as complexity of production technology increases. Snell

and Dean (1992) noted that the use of advanced production technology creates opportunities for

employees to increase their potential contribution to the firm, and suggest that this contribution

can be nurtured by investing in human capital. The human resource policies embedded in HPWS

are directed towards the development of a more skilled, knowledgeable and committed workforce

(e.g. Pfeffer, 1994, 1998; Becker and Huselid, 1998). That is, HPWS represent the investment in

human capital that, according to Snell and Dean (1992), can enhance employees’ contribution to

the firm in the presence of advanced production technologies. A more skilled and knowledgeable

workforce is necessary to take advantage of the initial increase in productivity associated with the

use of advanced production technologies. Osterman (1994) also recognizes the greater skills,

knowledge and commitment demands required from employees in organizations with advanced

manufacturing technologies. Further, the use of more advanced manufacturing technologies

increases the variability of the transformation process, such that the contribution required from

the employee becomes more difficult to specify in advance (Lepak et al., 2003). The flexibility

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that HPWS can provide to the workforce via its emphasis on human capital investment might be

of great help in dealing with this uncertainty. In summing up, the more use is made of advanced

production technologies, the more appropriate it is to apply HPWS. The choice of appropriate

policies will be reflected in firm’s operational performance, such that the expected positive

impact of HPWS will be greater when advanced production technologies are employed. This idea

is captured by our first hypothesis.

Hypothesis 1: The relationship between HPWS and firm operational performance is

moderated by production technology; the relationship is stronger with advanced

production technology

Technological intensity of the industry

HPWS will be more accurate as the technological intensity of the industry in which the

firm operates increases. As noted, technologically intense industries are characterized by high

investment in R&D and a remarkable pace of innovation and technological change that demands

a continuous research effort and a solid technological base (Judge and Miller, 1991; Chabot,

1995; Frías and Guerediaga, 2000). Industries of great technological intensity have received

separate attention by management researchers who have labelled them high-tech, and described

them as more complex and dynamic (e.g. Hambrick et al., 1995; Balkin et al., 2000). As

technological intensity increases, the environment for firms becomes more knowledge-intensive

(Hambrick et al, 1995). Further, with growing technological intensity, firms confront the need to

foster cooperation and to promote creativity in their workforce in order to deal with competition

and demand shocks (Balkin et al., 2000). All this requires a highly skilled workforce, greater

decentralisation in the decision-making process and reliance on group structures as a vehicle for

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pooling and sharing knowledge. This higher degree of technological development and autonomy

not only requires a workforce with the necessary technical skills and knowledge, but also with the

right attitude and personality traits, able to bear an extra load of responsibility and uncertainty.

Decentralisation, and the greater autonomy and freedom this brings, allows committed workers to

put in more effort and submit new ideas, which would increase their contribution to the firm

relative to their cost. Investment in training, use of incentive schemes linked to group or firm

performance, or acceptance of employee suggestions, all practices that form part of HPWS, may

be particularly useful for firms that need to encourage creativity, exchange of ideas, and increase

their capacity to attract and retain talent (Gómez-Mejía et al., 2001).

Conversely, in an industry of low technological intensity, firms require different types of

skills, knowledge, and attitudes from their employees. As technological intensity decreases,

stability is greater, technological progress and innovation is less frequent and changes tend to be

more incremental than radical (e.g. Judge and Miller, 1991). This constitutes a less knowledge-

intensive environment in which relative stability facilitates the design of more standardized jobs

with limited autonomy, especially in the case of blue-collar workers. The use of groups tends to

be more the exception than the norm, and a generally less skilled workforce is needed. Partly

owing to relatively stable conditions, these firms will not demand as much creativity, flexibility

and risk (and responsibility) sharing from their workforce as firms in technologically more

intense environments. In an industry of lower technological intensity the knowledge and

commitment enhancing properties attributed to HPWS are less relevant than in industries of

greater technological intensity.

Lepak and Snell (1999, 2002) provide additional arguments to support the notion that

HPWS are more appropriate as the technological intensity of the industry increases. These

authors argue that human resource policies must match the value and uniqueness of the worker.

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As the value and uniqueness of the worker increase, worker’s strategic value for the firm

increases and the optimal set of human resource policies moves towards the ideal of HPWS.

Because of the knowledge, creativity, cooperation and flexibility demands faced by firms

competing in industries of greater technological intensity, the strategic relevance of workers is

generally greater in these environments. Therefore HPWS would make more sense in

technologically intense industries. From a contingent perspective, selecting the right set of human

resource practices should lead to an improvement in firm’s operational performance. Hence, if

HPWS have the capacity to improve firm’s operational performance, the improvement should be

greater as technological intensity increases. The above arguments lead us to formulate the

following hypothesis,

Hypothesis 2: The relationship between HPWS and firm operational performance is

moderated by the technological intensity of the industry; the relationship is stronger

in more technologically intense industries.

Our hypotheses argue for a positive influence of HPWS on performance but state this to be

contingent on production technology (hypothesis 1) and on technological intensity of the industry

(hypothesis 2). If the human resource policies advocated by HPWS endow the firm with a more

knowledgeable, skilful and committed workforce, and this workforce enhances its operational

performance under the requirements imposed by the presence of advanced production

technologies or technologically speaking intense industries, HPWS will also be the right course

when both factors are at their height. A more advanced production technology imposes additional

knowledge and commitment demands in an environment that is already asking for higher levels,

compared with less technologically intense industries, of those variables. We have been clearly

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stated in our previous arguments that the human resource policies that comprise HPWS are the

best providers of such knowledge and commitment. Hence we should expect improvements in

firm performance when the human resource policies applied approach the standard of HPWS in

firms that using advanced production technology also operate in a technologically intense

industry. This interaction effect between the two concepts of technology analysed in the paper is

captured in our last hypothesis.

Hypothesis 3: The relationship between HPWS and firm operational performance

is stronger when advanced production technologies are used in more

technologically intense industries.

METHODS

Sample and Data Collection

The information was obtained from a series of in-depth personal interviews conducted

between March 1997 and December 1997 as part of a research project directed towards the

analysis of the organizational practices of Spanish manufacturing firms with more than fifty

workers (see Huerta Arribas et al. [2003] for more details). Since policies are applied at plant

level, the plant was chosen as the unit of analysis. The units finally interviewed were selected

from a total population of 6,013 firms, after a stratification process based on industry and size, to

guarantee the representativeness of the sample. Either the plant manager or the production

manager was invited for interview, because of their close knowledge of the human resource

practices applied at the plant and the firm's operational performance record. Interviews were

arranged by telephone well in advance, so that the respondents would have time to gather all the

information they needed to answer the questionnaire. The interviewer had knowledge on

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management. The interview process resulted in 965 valid questionnaires (16.05% of the total

population).

Measures and Analyses

Our dependent variable is improvement in firm operational performance. We construct an

indicator of firm operational performance as the average value of the responses given to a set of

questions concerning the improvement experienced by the firm in several dimensions over the

last three years. These dimensions are: working time utilisation rate (productive hours relative to

total number of hours available), completion of delivery time, percentage of products returned by

customers, percentage of defective finished products, and percentage of scraps. The answers were

given on a 5 point Likert scale (1= the situation is much worse, 5= the situation is much better).

This indicator of improvement in operational performance gives a Cronbach’s alpha of 0.84.

According to the literature on HPWS (e.g. MacDuffie, 1995; Pfeffer, 1998; Becker and

Huselid, 1998; Way, 2002), HPWS includes human resource policies in the areas of: 1) staffing,

2) compensation, 3) employment security, 4) flexible job assignments, 5) self-directed teams, 6)

training, and 7) communication. All the questions in the survey that are employed to measure the

human resource policies applied in these areas refer to the blue-collar workers at the plant.

Good staffing practices require a real effort to find the best candidate. The criteria

employed in selecting new workers is a key element in the staffing process, since it provides

information on how the firm is managed, and reveals the kind of skills, behaviours and attitudes

that are being sought after in candidates for jobs in the firm. The literature on HPWS stresses that

a good staffing process should clearly specify in advance the required critical skills, behaviours

and attitudes, as well as paying attention to the candidate’s cultural and attitude fit (Pfeffer,

1998). Candidates’ knowledge, capacity to learn, interpersonal characteristics, or even

personality would be among the criteria to be considered in the selection process for firms

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wishing to maximise their chances of creating the committed, flexible, knowledge-based and long

term oriented workforce pursued by HPWS (Pfeffer, 1998 Lepak and Snell, 1999). Basing

staffing decisions on employee potential rather than simply on experience and current

performance may be more likely to achieve the commitment required of the firm’s employees

(Lepak and Snell, 1999). With this in mind, we approach the firm’s staffing policy by examining

its selection criteria. The questionnaire showed a list of six criteria that may be considered in the

selection process: age, experience, training, personality, capacity to work in teams, and capacity

to learn, and asked which of these occupy first and second place in the firm's priorities. Training,

personality, capacity to work in teams and ability to learn are related with the potential of the

candidate, rather than with his/her current productivity. On the other hand, age or experience

appear to be more closely tied to current, short-term, performance. When staffing processes

follow the lines suggested in the literature on HPWS, the essential criteria relate to the

candidate’s potential rather than his/her current productivity. The variable staffing, therefore,

takes its maximum value (3) if the two criteria employed are personality, training, capacity to

work in teams, or ability to learn. It takes value 2 when the first criterion relates to employee

potential and the second is tied to current performance. Conversely, staffing takes a value of 1 if

the top criterion relates to age or experience, but the second has to do with training, personality,

capacity to work in teams, or ability to learn. Finally, it takes its minimum value (0) if the criteria

used to select blue-collar workers are age and experience.

Human resource practices in other areas are measured as follows. Pay level and incentives

are measured to capture the firm’s compensation policy. Pay Level is a dummy variable that takes

a value of 1 if the pay level is above that of competitors and 0 otherwise. Incentives is a dummy

variable that takes a value of 1 if the plant uses incentives linked to quality standards or to group

or firm performance, and 0 when the plant uses either incentives based on individual performance

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or none at all. Employment Security is measured as the percentage of total employees that are not

temporary. Job Rotation is a discrete variable that takes one of four values; 0 when there is no job

rotation, 1 when workers are able to perform different jobs but do not usually change jobs, 2

when job rotation is applied with some frequency but always between jobs in the same section,

and 3 when job rotation is quite frequent and may even involve moving between sections. Self-

Directed Teams is the percentage of employees involved in self-managed teams. Training is

measured as the yearly average number of hours of formal training provided by the firm to one

employee. Finally, communication is the percentage of employees involved in improvement

groups (Way, 2002). Improvement groups are defined as groups of workers involved in regularly

scheduled meetings to identify, select, analyse, discuss, and propose solutions to work-related

issues. A well-known example of an improvement group is the quality circle. Higher values of

these human resource policy variables are indicative of the adoption of policies that are more

likely to create a more skilful and committed workforce, as intended by HPWS (Pfeffer, 1998;

Way, 2002).

Following Way (2002), the information concerning the use of the different human

resource practices just described is combined in a single index that captures the extent to which

the firm tends to make use of HPWS when managing its human resources. In particular we use

scaled values for each human resource policy so each value varies between 0 and 1. The value of

the index for a firm is the sum of the values of the human resource variables described above.

Therefore, the value of the index varies between 0 and 8. High (low) values of the index would

be indicative of a firm with a system of human resource practices close to (far from) the

theoretical ideal of HPWS. This index, to be used in regression analyses, is consistent with

Becker and Huselid’s conclusion that the literature on HPWS has a preference “for a unitary

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index that contains a set (though not always the same set) of theoretically appropriate HRM

practices derived from prior work” (1998: 63).2

With respect to the measurement of production technology, Gaither and Fraizer (1999:

161) argue that “advanced production technology means applying the latest scientific or

engineering discoveries to the design of production processes”. Information technologies have

been the driving force in the evolution of production technology. Computer-Integrated

Manufacturing Systems (CIM) would currently represent the most advanced stage in this

evolution (Krajewski and Ritzman, 1996; Gaither and Fraizer, 1999). CIM systems use

computers to connect all the different stages involved in the provision of goods into a coherent

and integrated whole. CIM represents the latest step in the evolution of production technology

because it interconnects a broad range of previously known and used technologies in a

meaningful set. In this vein, a fully integrated CIM system combines Computer Aided Design

(CAD), with a manufacturing process in which robots and Computer Aided Manufacturing

(CAM) are used, and with a distribution section employing Automated Storage and Retrieval

Systems (ASRS) and Automated Guided Vehicles (AGV). In this way a firm’s production

technology can incorporate all the elements of a fully scaled CIM system or just some of its

constituent elements, which may even be applied only in certain parts of its production process

(Krajewski and Ritzman, 1996). Consequently, we measure production technology in terms of

the use of a fully integrated CIM system. Firms that completed the survey were asked to reply,

using a 10 point Likert scale, to a question concerning how far the firm was to making full-scale

use of a CIM system. A mark of 10 meant that all the elements and characteristics of a CIM

system were fully installed in the plant, that is, the greatest possible use of advanced production

technology. Scores at the other end of the scale indicated the less use of production technology.

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Technological intensity of the industry is measured by three dummy variables that reflect

whether the plant belongs to an industry of high, medium, or low technological intensity. The

high-tech dummy takes a value of 1 if the plant belongs to an industry of high technological

intensity and 0 otherwise. Mid-tech industries represent a second group of plants with an

environment of lower technological intensity relative to the high-tech group. The mid-tech

dummy takes a value of 1 if the plant belongs to a medium technology industry and 0 otherwise.

Finally, plants operating in an industry of low technological intensity make up the third group.

The dummy variable low-tech takes a value of 1 if the plant belongs to this group and 0

otherwise. Industrial sectors were classified as high-tech, mid-tech or low-tech according to the

classification of the Organization for Economic Co-operation and Development (OECD) and the

“Instituto Nacional de Estadística” (INE).3 This classification is based on objective indicators of

the technological activities that take place in each industry such as R&D expenditures relative to

value added or R&D expenditures relative to production (Frías and Guerediaga, 2000). All three

groups were of a similar size in terms of the number of plants (302 high-tech firms, 272 mid-tech

and 391 low-tech firms).4 The average level of production technology was significantly higher

among firms belonging to the high-tech group (4.31) than among those in the other two groups,

where it was very similar (3.02 for mid-tech and 3.07 for low-tech). However, the production

technology indicator is highly variable, with high and low levels in all three groups. Therefore,

this evidence shows that despite the correspondence between technological intensity at the

industry level and the use of advanced production technology, each represents a different concept

with potentially different implications for human resource management.

To test the three hypotheses formulated in this paper, we perform regression analyses with

interaction terms. In these analyses, we control for the effect of a set of plant characteristics that

may influence operational performance. These control variables account for the effects of plant

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size (size), presence in foreign markets (export), and the extent to which the plant belongs to a

larger organizational structure, either through ownership (multinational) or through close trade

relationships (network). Size is measured as the logarithm of sales (in millions of pesetas).

Multinational is a dummy variable that takes a value of 1 if the plant belongs to a multinational

and 0 otherwise. Export measures the plant's presence in a foreign market as the percentage of

total sales in foreign markets. Network is an index that measures the extent to which the firm has

developed formal cooperation mechanisms with clients and suppliers. The index was created as

the sum of the responses to questions relating to firm's use of systematic audits, technical

collaboration, JIT deliveries and pre-agreed quality standards with both with suppliers and

customers. Replies were given using a 5 point Likert scale (1 = never, 5 = in every case;

Cronbach’s alpha = 0.78).

RESULTS

Table 1 shows the mean, standard deviation and Pearson’s correlations for the variables

included in the study. Pearson correlations in Table 1 show some significant correlations between

pairs of human resource variables. The relatively low correlations meant that factor analytic

techniques could not be used to reduce the information relative to the system of human resource

practices to a few factors, as reported in previous studies, mainly using USA samples, (e.g.

Huselid, 1995; MacDuffie, 1995). These differences may be due in part to the specific

characteristics of our sample (i.e. Spanish industrial firms) and may therefore show a specific

pattern not captured previously. Low correlations may be a sign that Spanish industrial firms are

at an early stage in their effort to develop a standard set of internally consistent human resource

practices. In the absence of such a standard, our research gains relevance, in as much as it may

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provide some guidance to firms about the appropriateness of HPWS as the standard for managing

human resources.

Insert Table 1 about here

Hypothesis 1 is tested through the interaction effect estimated in Model 2 on Table 2. This

model includes all the control variables, the main effects of the HPWS index and the technology

variables, and the interaction effect of HPWS and production technology. The interaction is

computed using centred variables (Aiken and West, 1991) while high technology is taken as the

reference group to analyse the main impact of the technological intensity of the industry. As an

additional check of the significance of the interaction effect, Model 1 only includes control

variables and main effects. In all models firm performance is the dependent variable. The main

effects of HPWS and production technology are significant in both models but their interaction,

contrary to Hypothesis 1, is non-significant. This somewhat unexpected result suggests that

advanced production technologies are sufficient in themselves to improve operational

performance and that it is not necessary to invest in HPWS to fully experience the advantages

associated with them.

Insert Table 2 about here

Hypothesis 2 established the moderating role of the technological intensity of the

industry, while Hypothesis 3 stated a stronger HPWS-firm operational performance relationship

when advanced production technology is employed in more technologically intense industries.

Despite the use of centred variables the introduction of interaction terms involving dummy

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variables produces multicollinearity problems. In order to minimise the multicollinearity problem

to obtain reliable tests for Hypotheses 2 and 3 we estimate two regression models in three

different sub-samples: high, medium and low technology industries. The results are shown in

Table 3. Similar to models in Table 2, the first model in each sub-sample of Table 3 estimates the

effect of control variables and the main effect of HPWS index and production technology on

firm’s operational performance. The second model adds the interaction between HPWS and

production technology to model 1. The sample size in the three sub-samples is large enough to

yield reliable coefficients. Further, descriptive analyses (not reported in this paper but available

from the authors) identify differences in the degree of adoption of HPWS in the three sub-

samples, indicating a different structure in each sub-sample.

Results on Table 3 show that the main effect of HPWS is significant in the high-tech sub-

sample, and slightly significant in the mid-tech sub-sample. It is not significant in the low-tech

sub-sample. This analysis provides support for Hypothesis 2, as it shows that fuller application of

HPWS leads to improvements in firm operational performance as the technological intensity of

the industry increases The sub-sample analysis summarized on Table 3 also reveals the positive

and significant effect (p-value = .084) of the interaction between HPWS and production

technology in the high-tech sub-sample, but not in the other two sub-samples. This result is

consistent with Hypothesis 3 as it shows that the application of human resource policies that are

closer to those indicated by the advocates of HPWS presents a stronger relationship with firm

performance when advanced manufacturing technologies are employed in industries of high

technological intensity.

Insert Table 3 about here

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DISCUSSION

Our analysis of the moderating role of technology has shown that, while the technological

intensity of the industry moderates the HPWS – operational results relationship, production

technology seems not to play the relevant role we advanced in Hypothesis 1. These results show

that the two interpretations of technology effect distinguished in the paper are effectively separate

ideas and hold different implications from a strategic human resources perspective.

According to our results, firms should pay more attention to the technological intensity of

the industry than to production technology when managing their human resources. Specifically,

more emphasis on HPWS appears to be more effective in high technology industries than in mid-

tech, and particularly in low-tech ones. More precisely, the adoption of HPWS practices in a low

technology industry seems to have no significant effect (either positive or negative) on the

improvement of operational performance. This result may indicate either that the application of

HPWS in firms operating in low technology industries does not improve workers’ knowledge and

commitment, or that, assuming HPWS positively influence knowledge and commitment, a highly

skilled and committed workforce might not realize its full potential in a more stable and less

knowledge intensive industry. Unfortunately with the data at our disposal, we are unable to

perform a direct test of these competing ideas. Research on human resource management

suggests that human resource practices like the ones described in HPWS positively influence

employee commitment (Agarwala, 2003). This evidence points towards the second alternative,

but clearly more research is needed. For example it would be interesting to determine whether the

commitment enhancing properties we may associate to HPWS are contingent on the

technological intensity of the industry.

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The results of our analyses do not support the global moderating effect of production

technology that we expected, led by our argument that the higher level of knowledge and skills

provided by HPWS should be fully exploited in the presence of advanced production

technologies. The lack of empirical support may lead us to think that this argument was

erroneous. However, a closer look at the results reveals that it is not a null effect as such. Results

indicate that the moderating effect of production technology depends on the technological

intensity of the industry. Production technology enhances the influence of HPWS in

technologically intense industries, which suggests that a combination of production technology

with environment technology, rather than production technology in isolation, is needed to create

the right conditions for the potential of HPWS to work to full advantage. As noted, high-tech

industries demand cooperation and creativity from the workforce (Balkin et al., 2000), and

provide the right setting in which to implement human resource policies that allow exchange of

ideas, employee participation and reinforce employee commitment. Continuing in the line of our

argument in the paragraph above, these results may suggest that a knowledgeable workforce,

using complex production technology in an uncertain environment, increases its value when the

appropriate mechanisms are put in place to promote employee participation, flexibility and

commitment. It is in these circumstances that a highly trained and skilled employee can make a

real difference. In more stable conditions, where there is less need for innovation and the

flourishing of new ideas, though production technology demands skilled employees, conditions

prevent them from delivering a performance that is differentiable from that of less engaged and

more routine oriented workers. In short, the technological intensity of the industry appears as a

key factor because it determines the conditions under which the skilled and committed workforce

created by the applying HPWS can deliver its full potential.

21

All the above comments stress the idea that HPWS seem to be particularly appropriate in

technologically intense industries. According to our results, their usefulness is doubtful in

technologically more stable industries. The question is, could we describe a set of human

resource policies particularly suited to industries of low technological intensity? For example, are

cost reducing human resource policies more appropriate in these industries than commitment-

oriented ones? Is monitoring a more efficient alternative than commitment? Does training really

make a difference? We leave these questions open for future studies.

The limitations of the study demand some cautiousness while pointing to several issues

for future research. First, this study only examines cross-sectional differences among

manufacturing plants. For a clearer understanding of the causal relationship between the use of

different human resource practices, industry’s technological level and firm performance a

longitudinal analysis would be required. This would involve gaining the cooperation of a

representative set of firms, willing to respond to the same basic questionnaire at various moments

in time (e.g. Becker and Huselid, 1998). It would also provide a better understanding of

causation. It would even allow researchers to observe how long the influence of the adoption of

HPWS on firm performance persists in time. It would also allow us to observe how the human

resource management practices adopted by firms evolve over time. A second drawback of this

study, and one that is quite common in survey based research, is that results may be influenced

by a single respondent bias. The exhaustive data-gathering process described earlier was intended

to minimise that risk. The different relationships observed among the variables under study in the

three sub-samples suggest the absence of any significant bias of this nature. Finally, our

dependent variable measures the improvement in operational performance experienced by the

firm in the last three years. It allowed us to test whether firms that are more intense in their

application of HPWS were able to achieve greater improvements in their operational

22

performance. The drawback of our survey-based measure of firm operational performance is that

we may have missed the improvement in operational performance resulting from the application

of HPWS-type policies prior to our three-year observation period (1995-1998), as well as those

that will take place after it. The failure to capture improvements in firm operational performance

outside the time window covered by the questionnaire may be the reason behind some of the

unanticipated non-significant effects. Future research should also look at the moderating effect of

technology employing other performance measures. For example innovation activity could be a

very important outcome for firms in high technology industries (Storey et al., 2002). In any case

this paper should be viewed as a first step towards assessing the moderating role of technology.

There is a clear need for further empirical evidence from different databases in order to

strengthen the evidence and define the guidelines presented here.

To close, this paper reports on an empirical analysis of the moderating role of technology in

the effectiveness of HPWS. Although many research studies have already suggested, and even

proved, that human resource systems have considerable economic potential, there is as yet little

consensus on how to achieve that potential. The present paper sheds some light on the debate and

provides some guidance for managers seeking to manage their human resources in the way best

suited to their technological environment and the production technology they use.

23

FOOTNOTES

1 Following the line of previous authors, such as MacDuffie (1995), Huselid (1995) or Way

(2002), we will focus our attention on operational performance, that is, firm performance in

aspects related with the production of goods and services. By leaving aside the financial

dimension of performance we are focusing on those key operational success factors that might

lead to financial performance (Venkatraman and Ramanujan, 1986). We focus consciously on

operational performance since the consequences of the application of certain human resource

policies would be reflected primarily in the operational dimensions of performance, such as

workers' productivity, or reduction of defective products, rather than in financial performance

(Huselid, 1995). Further, financial performance may be subject to factors outside the firm’s

management control, such as economic downturns or fiscal policies that may hide or exaggerate

the influence exerted by certain human resource policies, particularly those included in HPWS.

2 The HPWS index is multidimensional by definition. It covers 8 different aspects of human

resource management. Multidimensional constructs do not require high internal consistency (i.e.

high values of Crombach’s alpha) since they do not reflect a unified underlying construct but

together form the construct by representing different aspects or dimensions of it (Cattell and

Tsujioka, 1964; Bollen and Lennox, 1991).

3 INE is the Spanish National Institute of Statistics.

4 The Chemical industry (70), Machinery and mechanical equipment (72), Electrical material and

equipment, electronics and optics (70), and Transport material (90) are high technology

industries. Numbers in parenthesis represent the number of plants of each industry in the sample.

24

Rubber and plastic (58), Manufacturing of non metallic mineral products (66), Metallurgy (55),

Manufacturing of metallic products except machinery and equipment (63), and Other

manufacturing industries (30) are the ones classified as medium technology industries. Finally

Food, drinks and tobacco (146), Wood, cork and furniture (56), Textile, clothing and leather

(118), and Paper, edition and printing (71) are the ones classified as low technology industries.

25

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Table 1. Mean, Standard Deviation and Pearson Correlations

Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 Performance 3.768 .6172 Size 7.833 1.239 .105**

3 Export 32.09 28.660

.103** .206**4 Multinational

.31 .461 .085* .368** .116**

5 Network

2.904 .899 .118** .246** .104** .255**6 Staffing 2.044 .969 .062 .181** .030 .171** .165**7 Incentives

.239 .427 .059 .057 .081* .032 .073* .106**

8 Pay Level

.435 .496 .014 .212** -.007 .078* .088* .068* .0579 Security .791 .212 -.020 .202** .018 .187** .082* .183** -.037 .090**

10 Job Rotation

1.379 .857 .051 .150** .046 .130** .168** .083* .077* .089** .06311 S-D. Teams

17.38 29.441 .018 .072 -.008 .108** .117** .031 -.032 -.001 .005 .110**

12 Training 21.15 28.466 .044 .141** .097** .086* .111** .121** .094** .081* .079* .133** .03813 Communication

11.223 23.267 .131** .178** .051 .155** .223** .103** .112** .066 .031 .137** .106** .181**

14 HPWS index 3.102 1.093 .126** .363** .090* .240** .241** .482** .485** .543** .317** .458** .309** .363** .419** 15 Production Tech 3.448 3.712 .135**

.223** .036 .118** .221** .129** .078* .035 .066* -.007 .018 .078* .138** .120**

16 High Tech .312 .463 .033 .198** .228** .228** .168** .072* .006 -.043 .157**

.179** .147** .124** .133** .178** .158** 17 Mid Tech .282 .450 .025 -.092* .064* -.035 .002 .036 .014 -.009 .022 -.070* -.043 .038 .028 -.032 -.072* -.422** 18 Low Tech .405 .491 -.054 -.104** -.274** -.183** -.162** -.101** -.018 .049 -.170** -.105** -.100** -.151** -.151** -.142** -.083* -.557** -.518**

*: p<.05; **: p<.01

29

Table 2. HPWS’s impact on firm performance

Model 1

Model 2

Independent variables

Standard Beta

t-value

Standard Beta

t-value

Size -.016 -.273 -.018 -.305Export

.056 1.069 .056 1.063Multinational .049 .868 .049 .871Network .037 .700 .036 .681HPWS Index .111* 2.057 .112* 2.065Production Technology

.116* 2.255 .117* 2.260

MidTech .074 1.249 .073 1.245LowTech .054 .883 .053 .865HPWS × ProdTech .013 .247F Change 2.060* .061 N 965 965

+: p<.10; *: p<.05; **: p<.01; ***: p<.001

30

Table 3. HPWS’s impact on firm performance by sub-samples High Technology Environment Medium Technology Environment Low Technology Environment

Model 1

Model 2

Model 1

Model 2

Model 1

Model 2

Independent variables

Standard

Beta

t-value

Standard

Beta

t-value

Standard

Beta

t-value

Standard

Beta

t-value

Standard

Beta

t-value Standard

Beta

t-value

Size -.081 -.739 -.092 -.845 .001 .005 -.023 -.214 .097 1.039 .115 1.217 Export

.048 .493 .050 .520 .096 .943 .095 .932 .046 .564 .049 .601 Multinational

-.009 -.091 -.010 -.096 .140 1.406 .143 1.433 .020 .230 .013 .153

Network .008 .082 -.023 -.234 -.035 -.347 -.035 -.349 .125 1.514 .125 1.513 HPWS Index .192* 2.067 .207* 2.238 .157 1.536 .176+ 1.692 -.019 -.206 -.011 -.121 Production Technology .060

.672 .076 .855 .106 1.062 .105 1.049 .168* 2.029 .168* 2.029

HPWS × ProdTech .153+ 1.739 .097 .958 -.107 -1.317

F Change .935 3.024+ 1.448 .918 1.756 1.736N 302 302 272 272 391 391

+: p<.10; *: p<.05; **: p<.01; ***: p<.001

31