D O C U M E N T O D E T R A B A J O
       W O R K I N G P A P E R S S E R I E S


•••••••••••••••••••••••••••••••••••••••...
E-mail: working.papers.dge@unavarra.es




                  2
Human Resource Management and Operational Performance in Spanish
                     Manufacturing Industry


           ...
Abstract
In recent years companies have begun to implement a number of human resource
management practices that are referr...
1. Introduction


      The belief that the people working for a firm are one of its main assets and one of
the decisive f...
manufacturing plants we will try to discover whether findings in similar sectors in other
parts of the world are also appl...
This is possible because of the series of requirements that they fulfil [42,43]. The
first of these is the value added to ...
Other investigations have attempted to examine the individual impact of not one
but several of these practices. These woul...
overall performance in this area. These would include Liouville and Bayad [27], which
incorporates aspects such as the def...
through the reduction of the waste and allows the factory to give good value to
customers.


       Traditionally quality ...
impact of high-performance work practices on operational results. We understand that,
according to the above mentioned lit...
practices that make up our study. These practices are adopted in the plant, and therefore,
it is at this level where probl...
After making 3246 telephone calls to make the necessary appointments, 965 valid
interviews were undertaken. This number re...
on the shop floor. The PROMOTION variable assumes a value of one when more than
half the supervisors are in this position ...
Several of the aspects included refer to the nature of the job. On the one hand,
there is the existence of autonomous work...
which are applied by only twelve percent of the plants in the sample. Another two
practices with a limited spread of aroun...
it fails to register even a single positive correlation significantly distinct from zero with
any of the remaining sixteen...
The final result of this process was two groups. Table 2 shows the number of
plants making up each group, the mean value i...
The different manufacturing performance, which are measured in an absolute way,
depend a great deal on the technology and ...
5.2.3. Control Variables


      The first control variable is the size of the plant measured by the natural logarithm
of ...
Table 3 shows the average and standard deviation on the dependent variables,
control variables and HRMG and NUMPRAC. Table...
5.3. Estimating framework


      The question of whether human resource management leads to differences in
operational re...
improving the efficiency of its productive processes. This likelihood also increases with
size of the plant. The drop in s...
explain the evolution of the plant's quality performance. In the case in hand, results
remain conclusive whatever method i...
management leads not only to an increase in the percentage of on-time deliveries but
also to a reduction of the amount of ...
practices comprised in personnel management. Although we have looked on the system
as a whole and not on individual practi...
[7] Becker, BE, Huselid MA. High performance work systems and firm performance In:
  Ferris G, editor. Research in Personn...
[18] Huselid MA. The impact of human resource management practices on turnover,
    productivity, and corporate financial ...
[31] Morishima M. Information sharing and firm performance in Japan. Industrial
    Relations 1991;30:37-61.
[32] Neely A,...
[44] Youndt MA., Snell SA, Dean JWJr, Lepak DP. Human resource management,
   manufacturing strategy, and firm performance...
Table 1
Mean, standard deviation and correlationa matrix for HRM practices (N=758)


Variable          Mean s.d.      1   ...
Table 2
Association between HRM practices and groups resulting from cluster analysis


Variable          Mean      Mean   ...
Table 3
Mean, standard deviation and correlationsa between results variable, control variables, HRMG and NUMPRAC (N=621)

...
Table 4
Logit analysis for efficiency performancea


                                 Model 1                    Model 2  ...
Table 5
Logit analysis for quality performancea


                                                  QUAL1                 ...
Table 6
Logit analysis for based-time performancea


                                                    TIME1            ...
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HUMAN RESOURCE MANAGEMENT AND OPERATIONAL PERFORMANCE IN ...

  1. 1. D O C U M E N T O D E T R A B A J O W O R K I N G P A P E R S S E R I E S •••••••••••••••••••••••••••••••••••••••••• HUMAN RESOURCE MANAGEMENT AND OPERATIONAL PERFORMANCE IN SPANISH MANUFACTURING INDUSTRY Alberto Bayo Moriones Javier Merino Díaz de Cerio DT 43/00 •••••••••••••••••••••••••••••••••••••••••• Universidad Pública de Navarra Nafarroako Unibersitate Publikoa Campus de Arrosadía, 31006 Pamplona, Spain Tel/Phone: (+34)948169400 Fax: (+34)948169404
  2. 2. E-mail: working.papers.dge@unavarra.es 2
  3. 3. Human Resource Management and Operational Performance in Spanish Manufacturing Industry Alberto Bayo Moriones Departamento de Gestión de Empresas Universidad Pública de Navarra Campus de Arrosadía 31006 Pamplona – Spain Fax number: 34 948 169404 E-mail address: abayom@unavarra.es Javier Merino Díaz de Cerio Departamento de Gestión de Empresas Universidad Pública de Navarra Campus de Arrosadía 31006 Pamplona – Spain Fax number: 34 948 169404 E-mail address: jmerino@unavarra.es 3
  4. 4. Abstract In recent years companies have begun to implement a number of human resource management practices that are referred to in the literature as high performance or innovative. One of the aims of this study is to provide descriptive details of the use of these practices in Spanish industrial factories. In addition, we will attempt to determine how and to what extent the adoption of this type of practices affects the firm's performance record. We focus specifically on the impact HRM has on operational performance. We examine whether there is any difference in the relationship between HRM and the different kinds of operational results (efficiency, quality and time). We try to assess if the effect of high-performance work practices is positive and significant for all the operational results measures included. For this we will use a database covering an initial sample of 965 factories each with a workforce of over 50 employees. We begin with a review of the literature published so far, before going on to present the descriptive statistics for the variables to be used and, finally, testing the impact of HRM on operational performance by estimation of several logit models. Our results reveal the presence of a positive, statistically significant correlation between the adoption of these practices and improvements in performance, quality and flexibility. Keywords: Human resource management, empirical, manufacturing, European Community 4
  5. 5. 1. Introduction The belief that the people working for a firm are one of its main assets and one of the decisive factors in determining its progress, is one that leaves little room for argument. There is no question regarding the fact that workers' qualities, attitudes and behaviour in the workplace go a long way to determining a company's success or lack of it. While this type of resource is one over which companies do not have complete control, there do exist certain instruments to enable them to exert their influence on the quality and performance of the human capital on which they rely. The human resource management practices that they adopt will have a vital influence in this area and thereby on the performance achieved by the firm. In recent years references have begun to appear in the literature regarding a series of human resource management practices that are dubbed high-performance, high- commitment or innovative, and are said to help firms to achieve significant improvements in performance. The aim of these practices is to achieve a more valuable workforce, by selecting and retaining the more highly skilled individuals, and to increase motivation in the existing workforce by getting its members to adopt the firm's objectives as their own. This is accompanied at the same time by structuring and organising tasks in such a way as to obtain the maximum benefit from the increased capacity and motivation thus achieved among the workers. These practices include, for example, the provision of training for the workers, broader job definition and meticulous personnel selection processes. This study will attempt to test the hypothesis that the way a firm manages personnel influences its operational performance. Thus it will be in those firms where the above mentioned practices are implemented that more outstanding improvement in results will take place. The environment in which we intend to test this hypothesis is the Spanish industrial sector. Using an initial sample of close to a thousand Spanish 5
  6. 6. manufacturing plants we will try to discover whether findings in similar sectors in other parts of the world are also applicable to Spain. This study aims to offer, first, a stock of useful descriptive data concerning the implementation of a wide range of human resource management practices in a large sample of Spanish industrial firms covering all sectors. This, in itself, marks an important step forward in research into personnel management in our environment, since, until now, very little empirical data of this nature has been published relating to Spain. The analysis of the relationship between the general use of human resource management practices in the operational context and the various performance measurements (efficiency, quality and time-based) is also a significant contribution, in view of the general scarcity of empirical studies with this sort of focus, the few that exist applying mainly to the US context. In the second section we will carry out a review of existing empirical findings regarding the connection between combinations of human resources practices and business results while section three we will be devoted to briefly explain the importance of operational results in factory management. In section four we define the research objectives of the article. Section five will begin with a few remarks regarding data collection methods before going on to define the variables to be used in our subsequent empirical analysis and defining the estimating framework. In section six we will estimate the effect of personnel management on a plant's performance and analyse the results obtained. We will finish with a discussion of the main conclusions to be drawn from our study. 2. Human resource management and its impact on results Workers are a key element in the running of a firm and as such play an important part in the firm's success in reaching its fixed objectives. Human resources, taken to be "the pool of human capital under the firm’s control in a direct employment relationship" [43,p. 304] can provide the firm with a source of competitive advantage with respect to its rivals. 6
  7. 7. This is possible because of the series of requirements that they fulfil [42,43]. The first of these is the value added to the company's production processes, the contribution made by each individual having its effect on the results obtained by the organisation as a whole. Also, since individuals are not all the same, their characteristics are in limited supply in the market. In addition, these resources are difficult to imitate, since it is not easy to identify the exact source of the competitive advantage and reproduce the basic conditions necessary for it to occur. Finally, this type of resources is not easily replaced; though short-term substitutes may be found, it is unlikely that they will result in a sustainable competitive advantage anything like that provided by human resources. For a firm to find the human capital to provide it with a sustained competitive advantage is not just a matter of luck. It depends rather on the action the firm is prepared to undertake towards that end. It is through personnel management practices that firms try to obtain the human resources that will give them the advantage when it comes to holding their own against other companies. It is the human resources themselves, however, and not the mere practices, that make up the source of competitive advantage. Personnel management practices do not qualify as sources of sustainable competitive advantage, since they are perfectly replaceable and quite likely to be copied by other firms. They are, nevertheless, necessary, not only for the firm's human capital to develop but also to enable it to be employed in such a way as to ensure improvements in the company's performance. If human resource management practices help to create better human capital in the firm and, thereby, a sustained competitive advantage, it is reasonable to assume some sort of connection between the way in which personnel is managed and the results obtained by the firm. There are numerous empirical studies that deal with the influence of individual personnel management practices on different performance measurements. Among these we would find quality circles [26], recruitment [16], worker training [5], profit-sharing schemes [38] and information sharing [31]. 7
  8. 8. Other investigations have attempted to examine the individual impact of not one but several of these practices. These would include investigations carried out by Kalleberg and Moody [24], Delaney and Huselid [10], Black and Lynch [3,4], Cappelli and Neumark [8], Harel and Tzafrir [15] and Fey et al. [12]. Finally, there are some studies that take a global perspective on the relationship between personnel management and performance, taking the view that practices are not applied separately but in conjunction with one another. Instead of looking at the impact of one specific practice, they study the total joint effect of the way in which the different aspects of personnel management are dealt with, particularly the adoption of high performance working practices1. Studies such as those of Huselid [18] and MacDuffie [28] are among these. This type of studies, and here we would include our own, analyse a wide range of measurements. Among them we find, for example, such measurements of human resource management performance as the ability to attract and retain employees [24], management-worker relations [41], turnover [7,18,41], absenteeism [17,41], workers' commitment to the company [17], job satisfaction among workers [17] and indices that capture several of these [27]. In addition to these, we also see measurements of financial results, which are an indication of the firm's overall performance. Among others we might mention productivity [18,20], profitability [19], customer satisfaction [24], Tobin's q [18,20] or the firm's market value [7]. Finally, we find studies which, like ours, are concerned with analysing the effect of human resource management on aspects of operational performance, such as the hours of labour required to manufacture a particular product [2,28], the percentage of programmed time that the production line is in operation [22,23], the defects rate [2,28] or the percentage of production that meets the required quality standards [22,23]. There are also works that take as their dependent variable indices that capture the firm's 1 There are several works that offer an in-depth critical review of this literature. Among them we must mention those of Dyer and Reeves [11], Becker and Gerhart [6], Huselid and Becker [19], Ichniowski et al. [21], Guest [14], Whitfield and Poole [39] and Becker and Huselid [7]. 8
  9. 9. overall performance in this area. These would include Liouville and Bayad [27], which incorporates aspects such as the defects costs, or Youndt et al. [44], that includes the degree of utilisation of equipment, minimisation of spoilage, product quality and in-time delivery. Broadly speaking, all the studies that address this issue, irrespective of the type of operational result on which they are focused, show that the impact on the plant's productive system, brought about by introducing high performance human resource management practices, also has a beneficial effect on the business as whole. 3. Operational performance The subject of the measurement, evaluation and conceptualising of operational performance in a company is a recurrent theme in the different sections of the academic literature. One of the first general classifications that has been widely used, is that of Venkatraman and Ramanujan [36]. This adopts a Strategic Management perspective and focuses on the measurement to establish a division between financial and operational performance, with the emphasis on the latter. Following a similar line, Kaplan and Norton [25] believe that the traditional measurements of financial performance are no longer valid for today’s business demands. Therefore, they consider that operational measurements for management are needed in relation to customer satisfaction, internal processes and the activities concerning improvement and innovation in the organisation, which lead to future financial returns. Manufacturing performance, which includes part of the operational performance previously mentioned, is commonly used in the field of Operations Management. This type of results takes into account the company’s performance in reaching its basic objectives, that is, productivity, quality and service. There are several studies which aim to establish a classification of this kind of results [9,13,32]. For example, Corbett and Van Wassenhove’s model [9] consider the measurements of the performance in three dimensions: cost or efficiency, quality and time. Efficiency refers to the best possible use of all available resources in order to obtain the maximum output of them. This leads to the attainment of low cost products 9
  10. 10. through the reduction of the waste and allows the factory to give good value to customers. Traditionally quality has been defined in terms of conformance to specification and hence quality-based measures of performance have focused on issues such as the number of defects produced and the cost of quality. With the advent of total quality management (TQM) the emphasis has shifted away from conformance to specification and toward customer satisfaction. In any case, the firms must obtain high levels of quality performance in order to improve or, at least, maintain their competitiveness. The first dimension of time-based performance is reliability. It represents fulfilling delivering promises. On-time deliveries may have a significant impact on customer satisfaction, which makes it an issue to be seriously taken into account in operations management. The second dimension related to time is that referred to the speed of production processes. Time elapsed between materials reception and delivery of product to the customer is frequently used as a measure of it. The development of “just in time” (JIT) and other systems of production planning and control (e.g. OPT) have as one of their main goals to improve the fluidity of production processes in order to be able to give a faster response to customers demands. 4. Research objectives As we have explained above, both the theoretical and empirical literature suggest that the way in which the employees of a plant are managed has a significant impact on its performance. The human resource management practises adopted contribute to a great extent to explain organisational success. Those companies that have implemented a bundle of high-performance work practices, that is, a human resource management system aimed at improving the capabilities and motivation of people, outperform those rivals that have not do so. We have also stated that operational performance measures are the most adequate to assess how a factory has been managed. In this article we intend to analyse the 10
  11. 11. impact of high-performance work practices on operational results. We understand that, according to the above mentioned literature, the most immediate and direct effects of human resource management in a factory should be on manufacturing performance, as financial results can be affected by other variables that cannot be controlled from an operations management point of view. This article aims to contribute to the literature in some respects. First, it attempts to ascertain whether the impact found for human resource management on firm performance in other countries also holds in Spain. We investigate if high-performance work practices lead to better performance. Second, we focus specifically on the impact HRM has on operational performance using a large sample of manufacturing establishments. Third, we examine whether there is any difference in the relationship between HRM and the different kinds of operational results. We try to assess if high- performance work practices are useful for factory managers in their efforts to achieve each and every of their manufacturing goals or if their effect is not uniform for all the operational results measures. 5. Methodology 5.1. Data The Spanish manufacturing industry constitutes the scope of our study. The concept of manufacturing industry is clearly defined in the National Classification of Economic Activity (NACE), which includes all the manufacturing industries (from code 15 to code 37) with the exception of oil refining and the treatment of nuclear fuel (code 23). The sector of production and distribution of electricity, gas and water (codes 40 and 41), as well as the mining industries (from 10 to 14) are also excluded. Fixing the unit of analysis was an important matter to settle. Two possibilities were initially open to us: to choose the company or plant as the organisation to be considered under study. We opted for the latter. In the industrial sector, the plant constitutes the business unit that is of strategic importance for the implementation of the 11
  12. 12. practices that make up our study. These practices are adopted in the plant, and therefore, it is at this level where problems arise and where the results must be analysed. Moreover, the answers to the different questions raised are expected to be more reliable when taken from the plant; since the knowledge of these issues is greater even if only because of greater proximity. Another aspect of the field of application to determine was the size of the plants. The industrial plants included in our sample employ fifty or more workers. This limit has been used in other studies relating to this area (see [33]) and it serves to cover a wide spectrum of the population employed in Spanish industry, what is more it simplifies the fieldwork. With these criteria the reference universe was formed by 6013 units, a thousand units being the aim of the sample, these were stratified according to sector and size. In order to carry out the investigation a questionnaire was made up and after the corresponding pre-test, it was modified in different ways to form the final questionnaire. The questions concerning human resource management refer to blue-collar workers. The fact that we refer to a specific group of workers creates problems, as far as the generalisation of the results to other professions is concerned. However, limiting the occupation under study means that it is easier to compare the different units we have studied, as within the company there are possibly several internal labour markets with substantial differences between them. As we had foreseen at the beginning of the study, most of the questionnaires (more than three-quarters of them) were filled in by either the plant manager of the production manager. The questionnaire covered different issues, all linked to production. It was meant to be filled in by a person with a broad understanding of the organisational aspects of the plant as well as, though to a lesser degree, the technical ones. Nevertheless, the questionnaire’s complexity did not mean it could not be understood by any of the plant managers with knowledge of the areas under study. 12
  13. 13. After making 3246 telephone calls to make the necessary appointments, 965 valid interviews were undertaken. This number represents a response rate of 29,72 percent and constitutes the initial sample about which we have information. The sample was size- and sector- stratified in order to create adequate samples within size and sector categories. 5.2. Measures of variables 5.2.1. Measuring high commitment HRM practices A look at the different empirical studies to be found in the literature dealing with high-commitment HRM practices soon reveals a lack of unanimity surrounding the type of practices that ought to be included under this general heading. It sometimes even happens that practices considered by some authors to be synonymous with innovative HRM systems, are taken by others to be more closely related to traditional personnel management systems. For the purpose of the present study, the choice of practices to be included in the category of high-commitment management was made with various points in mind. We first turned our attention to those practices most often included in investigations into innovations in HRM. A second consideration were the restrictions imposed upon us by the questionnaire we were using; we could obviously only include in our study those practices about which we had data. Taking these details into account, we included the practices most often mentioned in the literature and which figured in our database. We then added a number of other variables which, though not included in other studies, we feel to fit into the management philosophy that we wish to investigate. The use of employees already working for the organisation to fill vacancies further up in the hierarchy is a variable common to all the different studies of high performance HRM practices. Respondents were asked to show on a scale of one to five how many of their current supervisors and skilled technicians had previously had jobs 13
  14. 14. on the shop floor. The PROMOTION variable assumes a value of one when more than half the supervisors are in this position and zero if this is not the case. Another factor that encourages workers' identification with the firm is their confidence in being able to keep their jobs. The SECURITY variable assumes a value of one when less than a five percent of the workforce is on a fixed-term contract of employment and zero otherwise. The criteria applied in the selection and recruitment of new workers give some indication of how the firm is being run, since they reveal what type of qualities and what kind of behaviour is being sought after in candidates for jobs in the firm. The SELECTION variable tells us whether one of the two main factors considered when selecting and taking on new workers gives priority to personality characteristics or the ability to work as part of a team and learn new skills, rather than focussing on higher qualifications and a closer match between the technical requirements of the job and the abilities and technical skills possessed by the candidate. The effort made by the firm to train its workers also serves as an indication of its concern for the future of its workforce. TRAINING is equal to one if the workers have received any formal training within the past year. As for the content of that training, GROUPTRAIN assumes a value of one if the workers have received any training in teamwork and problem solving techniques. There are several issues relating to wage systems that must be included. WAGELEVEL is a binary variable that indicates whether the workers' wage level is above average for the same type of workers' in the same sector and the same region. PLANTINC shows whether or not the plant uses incentive schemes based in any way on its operations or financial performance. KNOWPAY indicates whether the main factor in determining workers' wages is their level of professional skill, as opposed to other criteria, such as the nature of their job or their length of service in the firm. 14
  15. 15. Several of the aspects included refer to the nature of the job. On the one hand, there is the existence of autonomous working teams, which is measured by the variable TEAM. The use of task rotation among shop-floor workers is captured by the binary variable ROTATION. Self-inspection by workers as a quality management technique is represented by the SELFINS variable. Respondents were asked in the interview to indicate on a scale of nought to ten the degree to which their workers were allowed to plan and organise their own work. For all those observations in which this assessment was between five and ten, both inclusive, AUTONOMY assumes a value of one. Specific action designed to increase the participation of workers in the running of the firm may take the form of schemes to collect individual suggestions or the creation of groups of workers to meet periodically in order to identify problems related with their work and propose possible solutions. These two instruments are reflected in the binary variables SUGGESTION and GROUPS, respectively. The use of methods to allow communication to take place between workers and company management is an issue that needs to be taken into account when dealing with the attempt to get workers to identify with the firm. While periodical meetings with employees in order to inform them about company issues creates a downward flow of communication, conducting surveys in which employees are asked about their degree of job satisfaction sets up a flow of communication in the opposite direction. This type of action is captured by the variables MEETINGS and SURVEYS. OPENDOOR, meanwhile, indicates whether or not the firm has organised open-door days in an attempt to involve employees and the surrounding community and to create links between the workers' private environment and the firm that employs them. INSERT TABLE 1 Table 1 contains the most relevant descriptive statistics of the seventeen human resource management practices that we have considered as high performance. This table shows that the incidence of the different practices in Spanish industry varies considerably. As far as the spread is concerned, at one extreme we find plant incentives, 15
  16. 16. which are applied by only twelve percent of the plants in the sample. Another two practices with a limited spread of around twenty percent are related to increasing worker involvement. These are, namely, surveys to detect job satisfaction and the organisation of open days. In addition, only a quarter of the plants interviewed attach special importance to the workers' level of knowledge and skills when fixing wages. At the other extreme, we have workers' self-inspection of the quality of the product they are manufacturing. This occurs in ninety percent of the plants. There is also very widespread provision by the firms of some kind of formal training for its production workers (eighty percent of the survey). Though to a more moderate extent, we should also mention the widespread application of personality-based criteria in personnel selection processes, as well as frequent use of internal promotion in order to fill posts as job vacancies occur within the organisation. Associations between the different practices and how they interrelate is an issue of great relevance. A close look at the correlation table will provide some very interesting information in this respect. As was to be expected, the correlation coefficients are overwhelmingly positive, and among the negative only one is significantly distinct from zero. In all, of the 136 coefficients that make up the matrix, only seventeen are negative. It can be seen how some of the practices considered possess an individual diffusion profile that falls far wide of the trend followed by most of the rest. This casts serious doubts on the theory that human resource management practices are implemented in such as way as to remain consistent with one another. However, this phenomenon may also be interpreted as an indication of error in the theoretical definition of the practices that make up the subject of our investigation. In other words, it may be that some of the practices included fail to complement the rest. In relation to this issue, there is evidence to suggest that paying above the going rate, providing job security, remuneration for knowledge and the use of internal promotion are practices with very little connection with other aspects of human resource management. The case of the PROMOTION variable is particularly remarkable, in that 16
  17. 17. it fails to register even a single positive correlation significantly distinct from zero with any of the remaining sixteen practices analysed. This echoes the results obtained in Becker and Huselid [7] and points to the need for further theoretical investigation into the relationship of this feature of internal labour markets and the rest of the human resource management system. When there is a great number of practices to be analysed, as in the case in hand, the investigator is faced with two possible ways of evaluating the degree to which HRM can be considered to be based on high commitment. The first of these involves taking the variables that indicate the presence of individual practices and creating an index that measures the extent to which high-performance employment practices are being applied (e.g. [30,34,40]). The second option is to classify firms according to their level of application of the whole set of practices under consideration [1]. It is thus possible to opt for a more or less continuous measurement or for using a nominal variable. For the purposes of the present study, we will apply both options, since this will help in ascertaining whether the results obtained are robust in relation to the procedure used to evaluate the way in which human resources are managed. The index we intend to use for the present study is the total number of high- performance practices taking place in the establishment, which we will call NUMPRAC. In order to organise the plants into groups according to the degree to which they have introduced high-performance practices into their management of human resources, we have classified them by cluster analysis. We began by carrying out an agglomerative analysis using a Ward hierarchical procedure and the square of the Euclidean distance. From this we were able to deduce that the optimum number of groups to be formed was two. Taking the results of this first analysis as our initial centroids, we then performed a non-hierarchical cluster analysis using two groups. INSERT TABLE 2 17
  18. 18. The final result of this process was two groups. Table 2 shows the number of plants making up each group, the mean value in each group for the variables, the chi- square statistic and the statistical significance of the correlation between each of the variables and membership of one group or the other. Table 2 enables us to appreciate that the two groups resulting from the cluster analysis are similar in size, since there are only four observations of difference between them. Judging from the frequencies of the different HRM practices examined, the first of the groups is the one that can be classed as containing the firms where high performance HRM practices have been introduced. The second group, meanwhile, is made up of plants that continue to practise traditional methods of personnel management in which the emphasis is placed on the strict control of everything done by the workers. HRMG is a dichotomous variable which has a value of one when the plant belongs to the first group taken from the cluster analysis, that is, those plants that include high performance practices in their human resource management, and a value of zero otherwise. It can be clearly seen how the first group contains a greater percentage of plants where the seventeen practices considered have been introduced. However, it must also be emphasised that differences between the two groups are not always significant. This is the case for two of the variables; on the one hand, no major differences can be observed in the use of internal promotion of manual workers to fill vacancies for jobs as supervisors and skilled technicians. Neither are there any differences significantly distinct from zero to be seen in the scores on our employment security measurement. These findings coincide reasonably closely with the conclusions drawn from our previous analysis of correlation in the implementation of these practices. On all the remaining variables, however, there is absolutely no question regarding the fact that averages for the plants in the second group are substantially higher. 5.2.2. Operational performance measures It is important to explain the two characteristics of the measurements of performance we have used in this study. First of all, they are relative measurements of the improvement in the results of the plant in relation to the situation three years earlier. 18
  19. 19. The different manufacturing performance, which are measured in an absolute way, depend a great deal on the technology and type of process found in the plant. Therefore, it becomes difficult to establish comparisons when the data is obtained from a group of heterogeneous plants, even when the sector variable is introduced as a control variable. The other noteworthy characteristic is the subjectivity of the information used. Results of a subjective kind are often used in research on organisation. Some studies have shown that there is an important relationship between the objective financial performance and the same measured in a subjective way. This may serve as a justification for the use of this kind of performance [35,37]. The indicator for efficiency (cost performance) we use is EFIC, that refers to the percentage of productive hours in relation to the total number of hours the workforce is directly present. It reflects the waste and inefficiency of the productive system and states the unproductive moments owing to organisational problems (lack of material, breakdowns, problems with quality, etc.). The two indicators of improvement in quality performance correspond to a concept of product quality as conforming to specifications and they are defined as the percentage of defective products. We have included aspects related to finished products (QUAL1) as well as unfinished products (QUAL2). We also use two indicators for time performance. TIME1 indicates the improvement in the percentage of delivery dates complied with is a typical measurement of punctuality, considered a basic aspect of customer attention. TIME 2 indicates the improvement in the reduction in the time taken from the moment the material is received to the moment the product is delivered to the customer. That serves as an indicator of the speed of the processes (lead time). The five performance variables are dichotomous variables. They have the value of 0 for those plants whose manufacturing results have not improved in the last three years, they takes the value 1 for those plants whose results have improved. 19
  20. 20. 5.2.3. Control Variables The first control variable is the size of the plant measured by the natural logarithm of the number of employees that work in the factory (LNSIZE). It is a usual way of measuring establishment size. Technology has a great significance when trying to explain the operational results achieved by the plant. Technical features are measured by the variable AUTOMAT, which aims to capture the degree of automation in the plant. The questionnaire enquired after four technical features that we felt to be directly related to the degree of automation: namely, robots or programmable automatons, automatic materials storage and retrieval systems (AS/RSs), computer integrated manufacturing (CIM) and computer networks for the processing of the plant's production data. By applying factor analysis to these four variables, a single factor is obtained with an eigenvalue greater than one, which accounts for just over 47% of the variance. The factor loadings on these variables are greater than 0.44. AUTOMAT is defined as the average of the four variables mentioned. Competitive pressure can force firms to make special efforts in trying to obtain better operational performance if they wish to survive in the market or maintain and improve their financial results. The level of competition being faced by the firm is captured by the variable COMPET. This assesses on a scale of one to five the evolution of competition levels over the last three years in the sector in which the plant operates. A score of one on this scale indicates a large decrease, whereas a score of five represents a large increase. QUALASSUR is a binary variable that assesses if the plant has set up a quality assurance system. This tells us if the factory is following a quality strategy by establishing organisational routines whose aim is to avoid defective products arriving to the customer. 20
  21. 21. Table 3 shows the average and standard deviation on the dependent variables, control variables and HRMG and NUMPRAC. Table 3 also shows the correlation between HRMG, NUMPRAC, performance variables and control variables. INSERT TABLE 3 This table enables us to see how the plants included in the survey remain at intermediate levels of automation, though slightly below the exact mid-point. However, the plants that participated in the study claimed that the competition they had to face had increased over the three years previous to the interview. It is also worth mentioning the effort that is going into increasing quality assurance at the plants, since almost seventy percent of the manufacturers claim to have set up systems to deal with this. As was to be expected, the vast majority of the plants report improvements in the different plant performance areas considered, that is, efficiency in the use of resources, quality, and the speed at which they complete the different stages in the productive cycle. Averages for the variables that measure these aspects fall between 0,64 for EFFIC and 0,72 for TIME2. Analysis of the correlation matrix brings us to the conclusion that no incompatibilities are present in the performance attaining processes in the different areas of production management at the plant. Plants that have improved their management in one of the three areas considered are more likely to have improved in the remaining areas also. Table 3 also offers a simple profile of the plants that have opted to introduce innovative practices in their personnel management style. The larger the size of the plant, the greater the likelihood of its introducing this type of practices. Likewise, in the area of technology, there is clear evidence to show that these tend to be plants with more highly automated processes and where it will be more usual to find quality assurance schemes in progress. 21
  22. 22. 5.3. Estimating framework The question of whether human resource management leads to differences in operational results will be tested, because of the dichotomous character of our dependent variables, by estimating logit models [29]. For each dependent variable (performance measurement) three different models are constructed. The first of these includes only control explanatory variables, the second incorporates the HRMG variable and the third substitutes HRMG with the NUMPRAC variable which is another measurement of the prevalence of high performance human resource management practices. In this way we aim to obtain a clearer picture of the impact of the practices under consideration on plant performance. Although this will be checked in all cases by activity sector (eleven dummy variables), the coefficients do not appear in the corresponding tables. 6. Results Tables 4, 5 and 6 show results for the logit models estimated to examine the determinants of plant performance. Table 4 is an analysis of the efficiency of the productive system, table 5 deals with quality and table 6 time-related performance. 6.1. Efficiency Table 4 shows the results of the logit model estimated for improvement in efficiency, measured in terms of changes in the proportion of productive hours over the total number of hours of direct labour. Though the model proves significant, results are not entirely satisfactory, owing to the low pseudo-R2 value. INSERT TABLE 4 Out of all the control variables included, both the level of automation and the installation of quality assurance systems register coefficients significantly distinct from zero. These, therefore, are factors that increase the manufacturing plant's chances of 22
  23. 23. improving the efficiency of its productive processes. This likelihood also increases with size of the plant. The drop in significance of these three variables in the second and third models was to be expected in view of their strong correlation with HRMG and NUMPRAC. In the second model it can be seen how the fact of belonging to the high performance HRM practitioners group enhances a factory's efficiency results, though not to a significant degree. Results obtained on model 3, in contrast, show that if the level of application is measured in terms of the number of practices installed (NUMPRAC) we see the emergence of a positive and significant impact on improvements in efficiency. For the case in hand, therefore, we are unable to draw any definite conclusions regarding the relationship analysed. 6.2. Quality Table 5 shows the results of the logit models estimated for the two variables that capture the plants' quality performance. The first three models refer to QUAL1, changes in the percentage of product defects, while the remaining three refer to QUAL2, changes in the percentage of processing defects. All six models are statistically significant and give a substantially better overall impression than those obtained when attempting to explain manufacturing efficiency. INSERT TABLE 5 As in the previous table, both the level of automation and the installation of quality systems have a strong influence on the firm's progress in the pursuit of quality. The two factors have a similar type of effect on the firm's capacity to improve the quality of its products. In the case of QUAL1, however, a significant impact is also brought about by the evolution of the firm's competitive position in the market. It would appear that plants feeling themselves exposed to increasing competition are forced to improve the quality of the final products that they take to market. For both product and processing defect rates, it is apparent that an understanding of the impact of the human resource management policies being implemented helps to 23
  24. 24. explain the evolution of the plant's quality performance. In the case in hand, results remain conclusive whatever method is used to measure the implementation of these practices. The explanatory capacity of the two models increases substantially by introducing the HRMG variable (for QUAL1, the pseudo-R2 increases by 51,8% and for QUAL2 the increase is 39,5%) a similar effect also takes place with the NUMPRAC variable. The implementation of high performance HRM practices in a factory has a beneficial effect on reducing the defect rate. 6.3. Time-based performance Table 6 shows the results of the logit models estimated to explain trends over the last three years in the two indicators used to evaluate speed of action, which are the percentage of on-time deliveries and the time that elapses between receiving the materials and delivering to the client. It can be seen that all six models are significant and help to explain the tendencies of the variables being analysed. INSERT TABLE 6 Once again we find that quality assurance systems and high levels of automation in product processing help firms to achieve improvements in time-based performances. In the case of TIME2 it is also possible to see the effect of competition on the results obtained by the firms in this respect. Plants that come under higher levels of competitive pressure make a greater effort to reduce the time that elapses between receiving materials at the start of the production cycle and the moment when the product finally reaches the client, irrespective of whether or not they accompany this with action in the technological area. The conclusion that emerges yet again is that the way workers are managed has its effect on a plant's performance. Correlation between HRMG and NUMPRAC and both dependent variables is highly significant and, as with the quality results, the explanatory capacity of the models increases noticeably with the introduction of these variables. The implementation of a framework of innovative practices in the area of human resource 24
  25. 25. management leads not only to an increase in the percentage of on-time deliveries but also to a reduction of the amount of time the firm takes to manufacture the product and deliver it to the client. 7. Conclusions This study enables us to confirm that the way in which human resources are managed influences a company's performance. Generally speaking, our findings support the theory that by using high performance HRM management systems, organisations can improve their chances of reaching objectives as long as they do not lose sight of other aspects, mainly of a technological nature, the explanatory capacity of which is considerable. These findings coincide largely with those of researchers into this issue in other contexts. It can not be said, however, that the impact of the type personnel management practices used has a significant impact in all of the three measurements of manufacturing performance analysed. When it comes to efficiency, for example, we can not be absolutely certain that the development of high performance human resource management practices in the plant noticeably increases the likelihood of improvements, since results differ depending on how the implementation of such practices is measured. In the case of quality and time-based performance, however, there is conclusive evidence that high performance work practices can bring about substantial improvements in company performance. One of the limitations of this study is a result of the context in which the empirical analysis was carried out. The fact that we have focused on the manufacturing industry, with the wide range of activities which that implies, and have concentrated on only one of these, albeit the most frequent, means that our conclusions can not be made applicable to all professions and sectors. In subsequent studies we look further into issues that have been neglected in this one. One of these is the possibility of a complementary relationship between the 25
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  31. 31. Table 1 Mean, standard deviation and correlationa matrix for HRM practices (N=758) Variable Mean s.d. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1. Promotion 0,66 0,48 2. Training 0,80 0,40 0,01 3. Grouptrain 0,36 0,48 0,04 0,38*** 4. Wagelevel 0,42 0,49 0,04 0,05 0,12*** 5. Plantinc 0,12 0,32 -0,01 0,08** 0,11*** 0,04 6. Knowpay 0,25 0,43 0,00 0,09*** 0,04 0,03 -0,05 7. Team 0,47 0,50 -0,05 0,11*** 0,11*** -0,01 0,02 -0,04 8. Rotation 0,46 0,50 0,03 0,12*** 0,13*** 0,05 0,10*** 0,01 0,02 9. Selfins 0,90 0,31 -0,01 0,06* 0,06* -0,05 0,07* 0,01 0,05 -0,01 10. Autonomy 0,27 0,44 -0,02 0,04 0,05 0,00 0,07** -0,01 0,19*** 0,02 0,03 11. Groups 0,39 0,49 0,00 0,28*** 0,30*** 0,09** 0,11*** 0,03 0,18*** 0,07* 0,03 0,15*** 12. Suggestion 0,57 0,50 0,00 0,29*** 0,19*** 0,01 0,05 0,01 0,09*** 0,00 0,10*** 0,12*** 0,29*** 13. Meeting 0,59 0,49 0,01 0,33*** 0,18*** 0,10*** 0,10*** 0,10*** 0,14*** 0,08** 0,09*** 0,09** 0,34*** 0,34*** 14. Survey 0,21 0,41 0,00 0,17*** 0,16*** 0,09** 0,12*** 0,05 0,15*** 0,07** 0,10*** 0,13*** 0,32*** 0,24*** 0,28*** 15. Opendoor 0,19 0,39 0,01 0,15*** 0,18*** 0,03 0,06* 0,01 0,14*** 0,03 0,07** 0,06* 0,23*** 0,17*** 0,22*** 0,28*** 16. Selection 0,67 0,47 0,02 0,07** 0,13*** 0,01 0,01 -0,04 0,03 -0,01 -0,01 0,02 0,03 0,07** 0,09*** 0,10*** 0,04 17. Security 0,28 0,45 -0,06* 0,06* 0,08** 0,01 -0,04 0,02 0,00 0,08** 0,00 0,04 0,01 -0,00 0,00 -0,00 0,00 0,09*** a *** p<0,01, ** p<0,05, * p<0,10 (N=758) 31
  32. 32. Table 2 Association between HRM practices and groups resulting from cluster analysis Variable Mean Mean Mean chi-2 Group 1 Group 2 p-value 1. Promotion 0,66 0,67 0,65 0,573 2. Training 0,80 0,97 0,62 0,000 3. Grouptrain 0,36 0,58 0,14 0,000 4. Wagelevel 0,42 0,48 0,36 0,001 5. Plantinc 0,12 0,17 0,06 0,000 6. Knowpay 0,25 0,29 0,20 0,003 7. Team 0,47 0,62 0,31 0,000 8. Rotation 0,46 0,53 0,40 0,000 9. Selfins 0,90 0,93 0,86 0,001 10. Autonomy 0,27 0,36 0,18 0,000 11. Groups 0,39 0,68 0,08 0,000 12. Suggestion 0,57 0,84 0,29 0,000 13. Meeting 0,59 0,88 0,29 0,000 14. Survey 0,21 0,38 0,04 0,000 15. Opendoor 0,19 0,32 0,06 0,000 16. Selection 0,67 0,75 0,60 0,000 17. Security 0,28 0,29 0,26 0,422 N 758 381 377 32
  33. 33. Table 3 Mean, standard deviation and correlationsa between results variable, control variables, HRMG and NUMPRAC (N=621) Variable Mean s.d. 1 2 3 4 5 6 7 8 9 10 1. Lnsize 4,88 0,85 2. Automat 4,18 2,35 0,315*** 3. Compet 3,48 0,89 -0,032 0,040 4. Qualassur 0,71 0,45 0,283*** 0,291*** 0,016 5. Efic 0,64 0,48 0,103** 0,129*** 0,014 0,146*** 6. Qual1 0,65 0,48 0,072* 0,155*** 0,054 0,237*** 0,400*** 7. Qual2 0,65 0,48 0,109*** 0,207*** 0,026 0,214*** 0,416*** 0,777*** 8. Time1 0,68 0,47 0,079** 0,130*** 0,022 0,165*** 0,498*** 0,445*** 0,483*** 9. Time2 0,73 0,45 0,095** 0,228*** 0,047 0,184*** 0,224*** 0,263*** 0,323*** 0,296*** 10. HRMG 0,51 0,50 0,237*** 0,311*** 0,014 0,336*** 0,121*** 0,227*** 0,224*** 0,183*** 0,201*** 11. NUMPRAC 7,64 2,80 0,316*** 0,347*** -0,020 0,351*** 0,139*** 0,181*** 0,208*** 0,157*** 0,219*** 0,765*** a *** p<0,01, ** p<0,05, * p<0,10 33
  34. 34. Table 4 Logit analysis for efficiency performancea Model 1 Model 2 Model 3 b s.d. b s.d. b s.d. Constant -1,356** 0,634 -1,70** 0,731 -1,921*** 0,738 Lnsize 0,210** 0,103 0,222* 0,120 0,202** 0,122 Automat 0,062* 0,034 0,070* 0,040 0,065* 0,040 Compet 0,008 0,086 0,019 0,097 0,025 0,097 Qualassur 0,444* 0,179 0,338* 0,206 0,323 0,206 HRMG 0,247 0,186 NUMPRAC 0,063** 0,035 Pseudo-R2 7 8,8 9,1 Chi-square 43,33*** 44,10*** 45,56*** Log L -514,18 -409,70 -408,96 N 666 666 666 a *** p<0,01, ** p<0,05, * p<0,10 34
  35. 35. Table 5 Logit analysis for quality performancea QUAL1 QUAL2 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 b s.d. b s.d. b s.d. b s.d. b s.d. b s.d. Constant -1,230* 0,727 -1,172 0,736 -1,552** 0,741 -1,335* 0,740 -1,228* 0,123 -1,667** 0,752 Lnsize -0,047 0,119 -0,093 0,120 -0,099 0,121 0,126 0,121 0,080 0,123 0,063 0,123 Automat 0,114*** 0,041 0,089** 0,042 0,094** 0,042 0,161*** 0,041 0,137*** 0,042 0,140*** 0,042 Compet 0,235** 0,101 0,245** 0,102 0,246** 0,101 0,122 0,100 0,131 0,101 0,134 0,101 Qualassur 0,836*** 0,208 0,698*** 0,213 0,745*** 0,211 0,639*** 0,204 0,494** 0,210 0,523** 0,208 HRMG 0,739*** 0,195 0,731*** 0,193 NUMPRAC 0,095*** 0,037 0,113*** 0,037 Pseudo-R2 18,4 21,1 19,7 15,4 18 17,1 Chi-square 94,48*** 109,05*** 101,22*** 78,19*** 92,65*** 87,68*** Log L -377,12 -369,83 -373,75 -387,37 -380,13 -382,63 N 659 659 659 664 664 664 a *** p<0,01, ** p<0,05, * p<0,10 35
  36. 36. Table 6 Logit analysis for based-time performancea TIME1 TIME2 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 b s.d. b s.d. b s.d. b s.d. b s.d. b s.d. CONSTANT -0,837 0,725 -0,786 0,731 -1,128 0,737 -0,978 0,755 -0,884 0,759 -1,362* 0,769 LNSIZE 0,067 0,120 0,036 0,121 0,021 0,121 -0,053 0,126 0,088 0,127 -0,109 0,128 AUTOMAT 0,101** 0,041 0,081* 0,041 0,082** 0,041 0,237*** 0,046 0,217*** 0,047 0,213*** 0,047 COMPET 0,100 0,099 0,106 0,100 0,108 0,100 0,176* 0,105 0,174 0,106 0,185* 0,106 QUALASSUR 0,595*** 0,204 0,466** 0,209 0,499** 0,208 0,584*** 0,208 0,461** 0,213 0,464** 0,212 HRMG 0,598*** 0,192 0,603*** 0,201 NUMPRAC 0,090** 0,036 0,115*** 0,139 Pseudo-R2 12,4 14,2 13,5 18,5 20,1 20,1 Chi-square 62,69*** 72,55*** 69,01*** 96,28*** 105,32*** 105,12*** Log L -392,17 -387,23 -389,01 -364,33 -359,81 -359,90 N 680 680 680 707 707 707 a *** p<0,01, ** p<0,05, * p<0,10 36

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