The determinants of ICT competencies among employees

New Technology, Work and Employment 20:1 ISSN 0268-1072

The determinants of ICT competencies among employees Kea Tijdens and Bram Steijn This study aims to explain employees’ adaptability to information and communications technology (ICT), using a representative sample of 713 employees in the Netherlands. The willingness to acquire ICT competencies and the mastery of equipment and software are primarily affected by intensity of ICT use, an informated ICT strategy of the organisation and an intensive personnel policy.

Introduction In February 2000, the European Commission (EC) launched an ambitious plan to enhance educational levels and employment opportunities within the European Union (EU) given the rise of the information society. One such measure involves awarding all employees an opportunity to procure the requisite level of qualification. The EC has therefore appealed to the national governments to develop activities directed at achieving this, insofar as such activities have not yet already been initiated. Concerns about the competitive position of the member states in relation to the US are an important factor underlying this move (e.g. European Union, 2000). At the next EU Summit in Portugal, various decisions were taken in the interests of increasing the information and communications technology (ICT) competencies of employees. The Dutch government has also taken steps to this end. For example, various departments have established committees to chart the departmental impact of the information society. However, to date, scientific insight into the processes that affect employees’ adaptability to new ICT developments has been fragmented. This article, which is based on the ‘ICT competencies 2002’ project, aims to consolidate and expand such insight.1 It focuses on the following key question: ‘What factors explain if, and, if so, to what extent, employees build up ICT competencies?’ These factors are sought in characteristics of the employees themselves, such as educational background, gender, job rating and so on, and in characteristics of the workplace, such as organisational form and personnel policy. In so doing, concurrence is established with the discussion of so-

❒ Kea Tijdens is Associate Professor and Research Coordinator at AIAS, the Amsterdam Institute of Advanced Labour Studies, University of Amsterdam. Bram Steijn is Associate Professor at the Department of Public Administration, Erasmus University Rotterdam. © Blackwell Publishing Ltd 2005, 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

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New Technology, Work and Employment

called new production concepts (cf. ILO, 2002), inasmuch as it can be assumed that these new production concepts offer employees ample opportunities for learning and adaptation. This article first addresses existing research into this field. Section 3 then focuses on the hypotheses, data and research method. The manner in which the most important concepts are put into practice follows in Section 4, and the analysis results are presented in Section 5. The final section contains the conclusions.

Previous studies The evolution of production processes in times of rapid technological change leads to a new challenge to education and training. Positions gained according to years of education may not work as systematically and steadily as before (Soete, 2001). Rates of returns to a given degree or course may change and will thus influence individual behaviour as regards their willingness for information technology (IT)-related education. Moreover, it seems obvious to assume that individual employee characteristics will influence this willingness. For example, research into employability shows that women, older people and less well-educated people have greater difficulty complying with supplementary educational requirements than men, younger people and well-educated people (Gaspersz and Ott, 1996: 28; Webster, 1996). Concerning women, it should be noted that establishing a balance between work and care obligations makes it particularly difficult to comply with supplementary educational requirements. In practice, however, there are few differences between men and women when it comes down to actually pursuing an education (Gaspersz and Ott, 1996: 29; ROA, 1998). Differences in age and educational background therefore appear to be more important determinants of differences in willingness for education and following supplementary education programmes (ROA, 1998: 25–26). Without detracting from the significance of individual factors as determinants for willingness for education, we are especially interested in the importance of workplace characteristics. In recent times, the influence of these characteristics has been emphasised in both educational (Eraut, 1999; Onstenk, 1997) and organisational research (cf. ILO, 2002). Additionally, there is an important link with the literature on new production concepts. (Steijn, 2003). Onstenk (1997) points out that these concepts can contribute significantly to opportunities for employee education. For example, working in teams has a positive impact on learning, because ‘employees cooperate intensively, exchange information and discuss the distribution of work’ (p. 351). Assuming that such opportunities will indeed be utilised, we can expect that these are linked to a higher level of willingness among employees to utilise such opportunities with respect to new production concepts. A broad survey carried out recently by the International Labour Organisation (ILO) (2002) also points out that high performance work organisations offer increasing opportunities for education to the employees concerned. Additionally, reference is made to numerous studies that reveal the actual educational level and training options as being genuinely higher within this type of organisation (cf. Osterman, 1995; Lynch and Black, 1998). A similar link is also highlighted in a study carried out by Appelbaum et al. (2000) that surmises that employees in high performance work systems not only require higher qualifications, but are also awarded greater opportunities to gain such an education. Finally, reference can be made to the views of Zuboff (1988), who states that two possible strategies can in principle be pursued when introducing IT. In the first approach, only the functions are automated, ruling out the possibility of genuinely new production concepts. The second involves an information strategy. Only in the latter case are the opportunities presented by IT utilised fully and can employee competencies develop further—this implies a de facto increase in educational opportunities. Rather than focusing on the determinants of the willingness for education or educational opportunities in general, this article is directed at gaining greater insight into the determinants of ICT skills and the willingness that exists to learn such skills. As © Blackwell Publishing Ltd 2005

Determinants of ICT competencies 61

far as we can ascertain, there has been no previous research into this area. As such, this article will certainly reveal more about the determinants of ICT competencies and the willingness to learn them.

Hypotheses, data and research method We consider the variables referred to above to be indicators for the adaptability of employees to ICT developments. The concept of adaptability is here operationalised as (1) employees’ willingness to master new ICT competencies, and (2 and 3) their past performance with regard to ICT qualifications. This past performance is operationalised as the respondent’s own assessment of his or her level of mastery of the device most used by him or her (computer, cash register and so on) and the programs used most frequently (word processor, statistics package and so on). The respondents make both assessments by giving themselves scores out of 10 for their mastery of these two aspects. A high score indicates the level to which employees have succeeded—at least in their own minds—in adapting to recent ICT developments. The willingness to acquire new competencies indicates a level of willingness to adapt to new ICT developments. The central question in our survey is: which factors are determining the adaptability of employees to ICT developments? The discussion in Section 2 shows that a large number of factors can influence this adaptability. We divided these factors into four clusters of explanatory variables. The four clusters are personal characteristics, job characteristics, characteristics of the ICT with which employees work and characteristics of the workplace. Therefore, in this study four hypotheses will be tested: (1) We have assumed that adaptability is lower for employees with a lower education, who do not have a vocational education, who are older, who are female and who take care of the children at home. (2) We have assumed that adaptability is lower if job performance creates obstacles so that these employees cannot acquire new competencies. The study ‘Skill-Biased Technological Change’ (Autor et al., 1998), for example, suggests that employees with a weak position in the job market lag behind in the development of ICT competencies. Likewise, it is likely that employees in a busy job will have less time to adapt to new developments. This is why we have assumed that adaptability is lower if the job level and job security are lower, the workload is higher, in the case of a temporary contract, part-time work or a non-managerial position. (3) We have assumed that adaptability is lower the lower the intensity with which the employee works with ICT. We have also assumed that the nature of the technology will have an influence. In this respect, we have drawn a distinction between embedded and programmable technology. Embedded technology is built into the equipment used (as with cash registers), whereas with programmable technology, the equipment offers more possibilities for operation. Our hypothesis is that adaptability to ICT is lower if embedded technology is used, because the ‘automatic’ nature of this technology by definition offers fewer learning opportunities. (4) We have assumed that the adaptability is lower in a Tayloristic workplace design than in a non-Tayloristic design. Second, we have also assumed that a more intensive personnel policy will be accompanied by greater adaptability. Finally, we will assess what the effect is of the ICT strategy implemented in the organisation. Following on from Zuboff (1988), we have distinguished between an automation strategy and an information strategy. We expect an information strategy to be accompanied by greater adaptability. In order to test the hypotheses for a representative sample of Dutch employees, we used the Telepanel, which is a database with more than 2,000 households that are questioned weekly with the aid of computers. This panel is managed by CenERdata Panel at the University of Tilburg, the Netherlands. As the respondents form part of the computer panel, the fact that their computer skills are higher than the ‘average’ employee 62


© Blackwell Publishing Ltd 2005

cannot for that matter be excluded. This means that it would be advisable to present the questions under a different setting too. Although Telepanel itself emphasises the representative character of the panel, the average educational level and job rating of the respondents also appears to be relatively high (cf. Tijdens and Steijn, 2002: 6). Nonetheless, this has no impact on determining the scope of the effects of different independent variables on their adaptability. The survey on ICT competencies was taken in January 2002. The respondents— people in paid employment aged between 15 and 64—were asked 50 questions. A total of 938 people (597 men and 341 women) answered questions about their ICT use and competencies, their jobs, the human resources management policy and other characteristics of the organisation in which they work. The average age of the men in the dataset was 43.0, whereas it was 39.7 for the women. Almost three-quarters live with a partner, slightly more men than women (77 per cent versus 71 per cent). Divided into family phase, relatively more men live in a family with young children, and more women live in a family in which the children have already left home. On average, the men work 37.8 hours per week and the women, 28.5 hours. Table 1 presents the descriptive statistics on both the variables to be explained and explanatory variables. The data revealed that the level of computer use in the Netherlands is extremely high. In 2002, 89 per cent of employees indicated that they use one or more automated devices in their work. This is higher than in previous measurements (Steijn, 2001a; Wetzels and Tijdens, 2001). In 1994, the same panel showed that 71 per cent of employees used automated equipment. By 2000, this had risen to 80 per cent, and a year later 84 per cent of employees were using automated equipment. In other words, computer use has grown dramatically in recent years. The use of computers in sectors such as financial and business services, the civil service and education has risen to between 96 per cent and 97 per cent. On the other hand, sectors such as utilities, construction and garages have the lowest percentage at 77 per cent, followed by the health-care and welfare sector at 81 per cent. The automated equipment is used intensively. For example, 43 per cent of computer users spend more than three-quarters of their time at work using the equipment. In this respect, there is almost no difference between men and women. The more intensively employees used the automated equipment, the more their colleagues also worked with this equipment. Therefore, computer use takes place in a computerised organisation context. Satisfaction with the most frequently used device is high, and employees felt that it is easy to use. However, they did not have a lot of say regarding the purchase of the equipment. The majority of computer users feel that the equipment in their department is modern, half of them feel that the equipment is used optimally and just under half of them stated that new equipment had been purchased in the past year. There are notable differences between computer users and nonusers. For example, computer users are more optimistic than nonusers that their work will become more interesting. This also applies to the expectation that the content of the work will change and to the chance that the work will incorporate more automated tasks in the future. However, the groups do not differ in their expectations regarding whether their work will come to an end. The answers to one of the survey questions are also worth noting in the context of the subject of this article: how did the respondents acquire their current ICT competencies? It turns out that there are major differences in the manner in which respondents learned to master the device or software they use most frequently. With regard to mastery of equipment, respondents indicated that they taught themselves (70 per cent). Courses (43 per cent) and colleagues (39 per cent) also turned out to be highly important. Note that the respondents were able to name more than a single method of procurement. In terms of software mastery, these three learning methods were again the most important, although self-learning (39 per cent) plays a much smaller role in this case. It is notable that schools play a relatively minor role in the acquisition of both competencies (17 per cent and seven per cent respectively). However, age does play a significant role: 32 per cent of those under 30 years of age said that their schools © Blackwell Publishing Ltd 2005


Table 1: Descriptive statistics of the dependent and independent variables

Variables to be explained Willingness to acquire ICT competencies Equipment mastery for the most frequently used device (mark 1–10) Software mastery for the most frequently program (mark 1–10) Individual characteristics Male Age 20–29 Age 30–39 Age 40–49 Vocational training diploma High educational level Low educational level Job characteristics Managerial position Sufficient job security High workload Permanent contract Full-time Job level (1 = low . . . 5 = high) IT characteristics Embedded technology Intensity ICT use Workplace characteristics Informated ICT strategy (3 = very weak . . . 12 = very strong) Tayloristic production concept Intensity personnel policy (1 = low . . . 5 = high)

N

Minimum

Maximum

Mean

Standard deviation

733

1.00

20.00

13.92

3.38

733

1.00

10.00

7.46

1.35

702

1.00

10.00

7.48

1.30

733 733 733 733 733 733 733

0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.00 1.00 1.00 1.00 1.00 1.00 1.00

0.65 0.08 0.32 0.33 0.62 0.48 0.17

0.48 0.28 0.47 0.47 0.49 0.50 0.38

733 733 733 733 728 716

0.00 0.00 0.00 0.00 0.00 1.00

1.00 1.00 1.00 1.00 1.00 5.00

0.40 0.90 0.72 0.97 0.69 3.44

0.49 0.30 0.45 0.18 0.46 0.95

733 720

0.00 0.02

1.00 1.00

0.14 0.62

0.35 0.33

692

3.00

12.00

9.30

2.18

733 733

0.00 0.00

1.00 5.00

0.15 3.44

0.36 1.55

had contributed to their mastery of the most frequently used device. This is 20 per cent for the most frequently used program. These percentages are much lower for those over 30. Therefore, school is a relatively important institution for young people in the acquisition of ICT competencies. Similar findings from 239 computer end-users in 50 manufacturing firms and service organisations in the US indicate much higher levels of in-house training (Culpan, 1995). When asked how they had learned to use computers, 80 per cent had received in-house training, 13 per cent indicated that they taught themselves, whilst seven per cent said their colleagues helped them. Crossnational varieties, as well as IT evolution between 1995 and 2002, may have affected these differences. Which factors determine whether, and to what extent, employees adapt their competencies to ICT developments? After we have looked at the operationalisation of the key variables in the next section, Section 5 will examine this central concept. Using OLS regression analyses, an assessment will be made for each cluster of independent 64



variables of the influence of these variables on adaptability. Each analysis incorporates the variables from previous clusters, so that by the fourth and final cluster, it becomes clear which cluster(s) of variables play(s) a key role. In these analyses, we will limit ourselves to the 713 respondents who (1) use automated equipment in their work and (2) have been employed by the company they work at for at least one year. The latter limitation is necessary because there is a large chance that employees who have been employed for a shorter period will provide unreliable and incomplete information on their job characteristics and, in particular, the organisation characteristics. An initial analysis, for example, shows that many of these employees either failed to answer questions related to personnel policy or answered them inadequately.

Operationalisation Adaptability has been operationalised in Section 3 as (1) the willingness of employees to acquire new ICT competencies, (2) the respondent’s own opinion regarding his or her level of mastery of the device used most frequently by him or her (computer, cash register and so on), and (3) the respondent’s own opinion regarding his or her level of mastery of the software used most frequently (word processor, spreadsheet and so on). In this section, the operationalisation will be described. Means and standard deviations are presented in Table 1. In the next section, we will assess the extent to which these variables are determining the employee’s adaptability to ICT. Willingness to acquire ICT competencies was measured by using four Likert items, forming a scale with an alpha of 0.74. An example of an item of this nature is: ‘I enjoy learning how computers or programs operate’ (cf. Tijdens and Steijn, 2002: 23). Respondents’ scores were between 4 (very low willingness, applies to 0 per cent of respondents) and 20 (extremely high willingness, applies to three per cent of respondents). Of the respondents, 77 per cent scored above the median (12) of the scale. Along with the mean scale score of 13.9, this indicates that the respondents have a generally positive attitude towards the acquisition of new ICT competencies. The opinions of employees regarding their mastery of both equipment and software were measured by awarding marks from 1 to 10. This showed that the respondents had a high opinion of their abilities. The mean score for equipment mastery was 7.46 and for software mastery, 7.48. Only seven per cent rated themselves as unsatisfactory, and 52 per cent gave themselves 8 out of 10 or higher. The respondents were equally positive about their mastery of the software: only six per cent rated themselves as unsatisfactory, and 54 per cent gave themselves 8 out of 10 or higher. Four clusters of explanatory variables are distinguished in Section 3. The operationalisation of the personal characteristics of gender and age speak for themselves. Three educational levels are distinguished (low, medium and high).2 Another variable indicates whether the respondent’s highest education was a vocational one or a general one. Care for children was operationalised by using a dummy variable to indicate whether the respondent had responsibility at home for children below 12 years of age. In order to measure the job characteristics, the job level was determined by using the SBC92 job classification of Statistics Netherlands, which distinguishes five levels: primary, lower, medium, higher and academic occupations. A distinction was also drawn between employees on a permanent contract (including those on temporary contracts with prospects of a permanent contract) on the one hand and temporary, agency and casual staff, and staff with a verbal agreement on the other. Questions about part-time work, a managerial position, sufficient job security in the past year and a high workload were answered by the respondents with either ‘Yes’ or ‘No’. The ICT intensity characteristic was determined by dividing the number of hours per week that respondents worked with ICT by the total number of hours worked per week. The distinction between embedded and programmable equipment was made by defining—for the device used most frequently by the respondent—PCs, laptops, palmtops, terminals and CAD-CAM equipment as programmable and other devices (cash registers, scanning equipment, robots, industrial equipment and copiers/fax © Blackwell Publishing Ltd 2005


machines) as embedded.3 Operationalised in this manner, 86 per cent of the respondents work primarily with programmable equipment. The three workplace characteristics included in this analysis were operationalised as follows. The workplace organisation was dichotomised into two types, using the operationalisation in Steijn (2001b): Tayloristic and non-Tayloristic. In order to measure the intensity of the personnel policy, an assessment was made for five aspects relevant to personnel policy as to whether these were discussed between the employee and a manager, i.e. career opportunities, job performance, salary increases, training and performance of manager. The context within which this meeting between manager and employee took place is therefore of no consequence. It might be during a formal performance review, during a formal meeting of a different nature, during an informal meeting or during work discussions. These aspects together formed a scale (alpha 0.74) that indicated the intensity of the personnel policy. On average, 3.44 of these five aspects were discussed between the employee and their manager; seven per cent of respondents said that not a single subject was discussed with them; 32 per cent said that all five aspects had been discussed. The third workplace characteristic is its ICT strategy. Using the analogy of the distinction made by Zuboff (1988), this was measured by using a scale consisting of three Likert items. The key issue with these items is that the respondents were asked whether they could independently operate the automated equipment used. The questions also related to the level of proficiency with respect to the programs used independently. Although including two related items improves the scale (alpha = 0.83, cf. Tijdens and Steijn, 2002: 30), these have not been included here because 31 respondents do use a device in order to carry out their work, but do not use a program. The three items form a reasonable scale (alpha = 0.71). The answers to these items are added together, creating a scale with values ranging from 3 (highly automated) to 12 (highly informated). The average scale score was 9.30. The fact that the median is 7.5 implies that on average the respondents were working in an informated situation. This is further emphasised by the fact that only two per cent of the respondents had the lowest scale score of three (= the most automated) and 20 per cent the highest score of 15 (= the most informated). Furthermore, only 19 per cent of respondents had a scale score not exceeding 7.

The determinants of employee adaptability to ICT As indicated above, adaptability to ICT is operationalised by means of a scale that measures willingness to acquire ICT competencies, and the scores awarded by respondents to themselves for their mastery of the device and software used most frequently by them. The results of the analyses are shown in Tables 2, 3 and 4 respectively. First, we evaluate the relationship between the personal characteristics and these three dependent variables (see the first _ column in Tables 2, 3 and 4). In terms of willingness to acquire ICT competencies, gender and educational level are important factors: females and employees with a lower education level were less prepared to acquire competencies than men and people with a medium and higher education level. At the same time, the absence of other significant effects is notable. This means, for example, that—contrary to earlier findings regarding their willingness to retrain— older people are certainly not less prepared to acquire ICT competencies than young people are. The personal variables appear primarily to affect the estimation of the mastery of the equipment: males and employees in their twenties and thirties gave themselves a higher score for mastery of the equipment than others did, whilst employees with a vocational education and with a high or low level of education gave themselves a lower score than employees with a general education or with a medium educational level. Age is also an important factor with regard to mastery of software: people in their thirties gave themselves a higher score, whilst employees with a vocational education and a low educational level gave themselves a lower score for mastery of software. In none of the three analyses was the obligation to care for (young) children shown to play a significant role. For this reason, this variable was left out of the 66





0.160 697

* ns

0.22

0.32

0.016

* ns ns ns ns

0.21 0.39 0.27 0.26 0.21

0.026

**

Significance

0.31

Standard error

Source: ICT competenties, 2002. ** p < 0.01, * p < 0.05, ns = not significant.

R, R2, adj R2 N

(Constant) 13.98 Individual characteristics Male 0.43 Age 20–29 -0.15 Age 30–39 0.11 Age 40–49 0.40 Vocational training 0.40 diploma High educational -0.50 level Low educational -0.14 level Job characteristics Managerial position Sufficient job security High workload Permanent contract Full-time Job level (1 = low . . . 5 = high) IT characteristics Embedded technology Intensity ICT use Workplace characteristics Informated ICT strategy (3 = very weak . . . 12 = very strong) Tayloristic production concept Intensity personnel policy (1 = low . . . 5 = high)

B

0.032

0.23 0.58 0.25 0.14

0.38 0.82 0.03 -0.15

0.179 680

ns ns

0.21 0.35

-0.05 -0.08

0.013

ns ns ns ns

ns

ns

ns ns ns ns ns

**

Significance

0.33

0.26

0.25 0.40 0.27 0.26 0.22

0.82

Standard error

-0.20

-0.37

0.45 0.03 0.15 0.46 0.29

13.43

B

0.065

0.32

1.40

0.255 671

0.34

0.23 0.57 0.25 0.14

0.21 0.34

0.32

0.26

0.24 0.40 0.27 0.26 0.22

0.87

Standard error

-0.37

0.46 0.90 0.06 -0.14

0.00 -0.10

-0.20

-0.40

0.59 -0.05 0.13 0.44 0.49

12.24

B

0.044

**

ns

* ns ns ns

ns ns

ns

ns

* ns ns ns *

**

Significance

0.081

0.07

0.14

0.285 663

ns

0.30 -0.01

0.056

*

*

0.05

0.10

**

ns

* ns ns ns

ns ns

ns

ns

* ns ns ns *

**

Significance

0.32

0.36

0.23 0.57 0.25 0.15

0.21 0.34

0.32

0.26

0.25 0.40 0.27 0.26 0.22

0.96

Standard error

1.29

-0.28

0.45 0.76 0.02 -0.21

-0.07 -0.09

-0.11

-0.37

0.54 -0.12 0.07 0.44 0.50

11.34

B

Table 2: Explaining the willingness to acquire ICT competencies from individual characteristics, job characteristics, IT characteristics and workplace characteristics

68



0.246 731

* *

0.11

0.15

0.051

ns ** ** ns **

0.10 0.20 0.13 0.13 0.10

0.060

**

Significance

0.15

Standard Error


R, R2, adj R2 N

(Constant) 7.42 Individual characteristics Male 0.19 Age 20–29 0.71 Age 30–39 0.56 Age 40–49 0.22 Vocational training -0.37 diploma High educational -0.25 level Low educational -0.34 level Job characteristics Managerial position Sufficient job security High workload Permanent contract Full-time Job level (1 = low . . . 5 = high) IT characteristics Embedded technology Intensity of ICT use Workplace characteristics Informated ICT strategy (3 = very weak . . . 12 = very strong) Tayloristic production concept Intensity personnel policy (1 = low . . . 5 = high)

B

0.073

0.056

* ns ns ns

0.11 0.28 0.13 0.07

-0.22 -0.39 0.14 0.01

0.270 714

ns ns

0.11 0.16

0.06 -0.12

*

*

ns ** ** ns **

**

Signifance

0.15

0.13

0.12 0.20 0.13 0.13 0.11

0.39

Standard Error

-0.34

-0.26

0.09 0.73 0.54 0.20 -0.36

8.00

B

0.14

1.20

0.183

0.14

0.76

0.428 703

0.10 0.25 0.12 0.07

0.10 0.15

0.14

0.12

0.11 0.18 0.12 0.12 0.10

0.38

Standard Error

-0.21 -0.27 0.13 0.04

0.09 -0.02

-0.29

-0.12

0.16 0.50 0.38 0.15 -0.24

6.75

B

0.166

**

**

* ns ns ns

ns ns

*

ns

ns ** ** ns *

**

Significance

0.03

0.09

0.239

0.14

0.17

0.489 666

0.02

0.14

0.16

0.10 0.26 0.11 0.07

0.10 0.15

0.14

0.12

0.11 0.18 0.12 0.12 0.10

0.43

Standard Error

0.12

1.17

0.94

-0.24 -0.40 0.11 -0.01

0.05 -0.08

-0.17

-0.06

0.07 0.43 0.32 0.13 -0.17

5.70

B

0.218

**

ns

**

**

**

* ns ns ns

ns ns

ns

ns

ns * ** ns ns

**

Significance

Table 3: Explaining the equipment mastery self-assessment (mark 1–10) from individual characteristics, job characteristics, IT characteristics and workplace characteristics



0.203 700

ns *

0.11

0.15

0.032

ns ns ** ns **

0.10 0.19 0.13 0.13 0.10

0.041

**

Significance

0.15

Standard Error


R, R2, adj R2 N

(Constant) 7.60 Individual characteristics Male -0.13 Age 20–29 0.36 Age 30–39 0.45 Age 40–49 0.21 Vocational training -0.31 diploma High educational -0.10 level Low educational -0.32 level Job characteristics Managerial position Sufficient job security High workload Permanent contract Full-time Job level (1 = low . . . 5 = high) IT characteristics Embedded technology Intensity of ICT use Workplace characteristics Informated ICT strategy (3 = very weak . . . 12 = very strong) Tayloristic production concept Intensity personnel policy (1 = low . . .5 = high)

B

0.036

ns ns ns *

0.11 0.28 0.12 0.07

-0.07 -0.29 0.00 0.18

0.054

ns ns

0.10 0.17

-0.05 -0.03

0.233 683

ns

*

ns ** ** ns *

**

Significance

0.16

0.13

0.12 0.19 0.13 0.13 0.11

0.39

Standard error

-0.25

-0.29

-0.13 0.50 0.44 0.16 -0.22

7.42

B

0.110

0.15

0.86

0.331 674

0.16

0.11 0.27 0.12 0.07

0.10 0.16

0.15

0.12

0.11 0.19 0.12 0.12 0.10

0.41

Standard error

-0.31

-0.05 -0.23 -0.02 0.14

-0.03 -0.02

-0.23

-0.23

-0.04 0.46 0.46 0.18 -0.09

6.77

B

0.090

**

ns

ns ns ns *

ns ns

ns

ns

ns * * ns ns

**

Significance

0.141

0.03

0.08

0.375 666

ns

0.14 -0.02

0.117

*

**

0.02

0.09

**

ns

ns ns ns ns

ns ns

ns

ns

ns * ** ns ns

**

Significance

0.15

0.16

0.10 0.26 0.12 0.07

0.10 0.16

0.15

0.12

0.11 0.19 0.12 0.12 0.10

0.44

Standard error

0.78

-0.13

-0.05 -0.34 -0.05 0.10

-0.10 0.03

-0.13

-0.19

-0.10 0.41 0.41 0.18 -0.06

5.93

B

Table 4: Explaining the software mastery self-assessment (mark 1–10) from individual characteristics, job characteristics, IT characteristics and workplace characteristics

analysis. Therefore, based on the overall picture, it appears that personal variables do not play a very important role in ICT adaptability. The next step is to evaluate which explanatory effect the job characteristics of the respondents have (see the second B column in Tables 2, 3 and 4). It appears that none of the job variables included had a significant effect on willingness to acquire competencies. The explanation of differences in equipment and software mastery as a result of job characteristics also appears to be small. A high workload turns out to be linked to a lower level of equipment mastery, whilst a higher professional level is linked to better software mastery. There were no other significant effects. The link with the two variables which characterise ICT paint a different picture (see the third B column in Tables 2, 3 and 4). These two ICT variables are important in the explanation of ICT adaptability. The intensity of technology use affects significantly the willingness to learn ICT competencies, the mastery of equipment and the mastery of software. The use of embedded technology affects significantly the mastery of the equipment. Finally, we assess the explanatory effect of the workplace characteristics (see the fourth B column in Tables 2, 3 and 4). Of the three characteristics included, the ICT strategy and intensity of personnel policy significantly affect the three dependent variables. Willingness to acquire ICT competencies and equipment and software mastery are higher the more intensive the personnel policy is and the more informated the ICT strategy is. This is in line with what was expected on the basis of the literature. The assumption that adaptability would be lower for employees working in a Tayloristic setting does not come true. The workplace setting does not have an influence on the three dependent variables. If we compare the influence of the four explanatory clusters for the employee adaptability to ICT (see the fourth B column in Tables 1, 2 and 3), it then becomes clear that of the personal characteristics, only age has an effect with regard to mastery of the equipment and software. Employees in their twenties and thirties rate their own mastery of their equipment and software higher than do other age groups. However, age does not play a role in the willingness to acquire ICT competencies. The education characteristics initially appeared to be of importance, but the effect of these was negated in the subsequent models by the added variables. Taking all the aspects into account, we find that mastery of equipment and software are not affected by education. Equally, willingness to acquire competencies is also not affected by the education level. Yet, employees with a vocational education have a greater willingness to acquire competencies. Finally, there did not appear to be any difference between men and women with regard to equipment and software mastery, although men are more often willing to acquire ICT competencies. The job characteristics turned out to have little effect on the adaptability to ICT. They do not contribute to the explanation of mastery of the software. Pressure of work was the only aspect that affected equipment mastery and the willingness to acquire competencies. Employees under greater work pressure indicated a low level of mastery of the equipment and a higher willingness to acquire competencies. One interpretation of this could be that employees have insufficient time to gain a thorough knowledge of the equipment they work with, although they do have a need and willingness to do so. The ICT characteristics have a relatively greater influence on the ICT adaptability. Employees who work with embedded technology have a greater mastery of the equipment. The intensity of ICT use plays a particular role. Employees who work more intensively with ICT indicated a higher level of mastery of equipment and software, and also a greater willingness to acquire ICT competencies. Finally, two out of three workplace characteristics made a relatively important contribution to the explanation of ICT adaptability. An informated ICT strategy leads to a higher level of mastery of equipment and software, and also to a greater willingness to acquire ICT competencies. An intensive personnel policy has the same effect. However, in all the analyses, the variance eventually explained was relatively low; it was only above 20 per cent with regard to equipment mastery. The level of expla70



nation of willingness to acquire ICT competencies, in particular, was low. This level of willingness may be due to an attitude that can scarcely be influenced by structural characteristics, and we probably need to look for more socio-psychological factors to explain this attitude. Nevertheless, the analyses show that the workplace characteristics—particularly an informated ICT strategy and an intensive personnel policy—play a significant role in the explanation of willingness to acquire ICT competencies. This finding in particular provides points of departure for organisations when they aim to increase the ICT adaptability of employees.

Conclusions If the results of this analysis are representative of the Dutch working population, then it would appear that there are few problems in terms of adaptability to ICT. According to the employees surveyed, their mastery of the equipment and software is high, although a more ‘objective’ measurement of this is needed in order to confirm it. The willingness to acquire additional ICT competencies appears predominantly high. In this respect, there are differences between employees. However, these differences are only linked to a limited extent to personal and job characteristics. This largely disproves the expectations formulated in Section 3. The fact that gender is linked to willingness to acquire competencies indicates that it might be worthwhile—if one is trying to achieve greater adaptability to ICT—paying extra attention to women, although it should be remembered in that case that whilst women may appear to have a lower willingness to undergo training, in practice they are at the same level as the men in actual educational activities (compare with Section 2). The fact that older employees indicate a lower level of mastery of equipment and software but are no less willing to acquire competencies indicates that it is worthwhile focusing educational efforts on older people. The idea that they ‘would not want to’ is disproved by our analysis results. Job characteristics are shown to be barely relevant in terms of adaptability: pressure of work is the only factor that plays a modest role. This role does raise the question of how employees who are already very/too busy can be given the opportunity to acquire the necessary additional competencies. The modest effect of the job characteristics has a primarily theoretical relevance. In connection with the lack of effects of educational level, this implies that at first sight we cannot support the findings of the American Skill-Biased Technological Change survey (Autor et al., 1998), in which it is suggested that employees with a weak position in the labour market will fall further and further behind in the development of ICT competencies. Further investigation is required to investigate whether the Netherlands faces similar developments, because it may, for example, well be that the important role of intensive personnel policy is absorbing the effects of education and job characteristics. In view of the results of the analysis, our expectations regarding the effects of technological and organisation characteristics have been confirmed to a greater extent than our expectations regarding the personal and job characteristics. Therefore, they also appear to be more important as a determinant of adaptability. One relevant finding is that more intensive ICT use is linked to higher mastery and greater willingness to acquire ICT competencies. This suggests that ‘learning by doing’ would be a feasible strategy for influencing adaptability—to a degree, employees adapt by themselves when they start working with, or work more with, ICT. In this context, however, organisation characteristics also play an important role. A more intensive personnel policy and applying an informated ICT strategy both result in a higher adaptability. One remarkable aspect, however, appears to be that the organisational concept—contrary to the literature on the subject—does not have a (direct) influence on adaptability. It should be noted here that a link does exist between the organisational concept, the intensity of personnel policy and the ICT strategy pursued: in a Tayloristic production concept, personnel policy is less intensive and a strategy of automation is more often pursued. In this sense, the organisational concept does tie indirectly with adaptability. © Blackwell Publishing Ltd 2005


In view of the fact that of all the variables discussed here, the intensity of personnel policy and the ICT strategy implemented are the most readily manipulated, investment in these two variables would seem to be the most effective if the intention is to increase employees’ adaptability to ICT. Acknowledgements The Netherlands Research Organisation (NWO-Small Grant no. 014-43-604) has supported this research. An earlier version of this paper was presented at the 2002 3rd Flemish-Dutch Labour Market Congress, held in Rotterdam, the Netherlands. Kevin Martley is acknowledged for the language revision of the text. The dataset is available from the NIWI/KNAW Steinmetz-data archive, no. P1566. Notes 1. This article is based on a study of both authors into the competencies of employees in the information society (Tijdens and Steijn, 2002). It follows on from previous research conducted by the authors in this area (Steijn and de Witte 1996; Tijdens and van Klaveren, 1997; Tijdens 1999, 2002; Wetzels and Tijdens, 2001; Steijn 2001a, 2002; van Klaveren et al., 2000; de Witte and Steijn, 2000). 2. Primary education, lower secondary vocational education (LBO) and lower level general secondary education (MAVO) are considered lower-level education; higher level general secondary education (HAVO), pre-university education (VWO) and upper secondary vocational education (MBO) are considered mid-level education; and higher vocational education (HBO) and university education (WO) are considered as higher-level education. 3. At http://www.jasa.or.jp/et/english/faq_e.html embedded technology is defined as follows: ‘At present it commonly means a system that includes hardware and software that is embedded in a single package and that operates independently. Mobile devices used to take orders at places such as restaurants, mobile phones which play a key role in computer and home electronics, car navigators, set-top boxes for digital TVs, and digital cameras are examples of modern embedded systems. Embedded systems are improving not only to operate autonomously, but also to be connected on wired or wireless networks’.

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