Training opportunities, technology acceptance and job satisfaction

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Training opportunities, technology acceptance and job satisfaction A study of Italian organizations Marco Giovanni Mariani, Matteo Curcuruto and Ivan Gaetani Department of the Education Sciences, University of Bologna, Bologna, Italy

Training and job satisfaction

455 Received 2 December 2011 Revised 4 July 2012 2 October 2012 28 December 2012 Accepted 19 March 2013

Abstract Purpose – The purpose of this research is to study the role of the opportunity to receive job training in enhancing people’s willingness to accept information technologies (IT) and in achieving employee satisfaction. The study aims to consider training opportunities as a predictor of IT self-competence, TAM model constructs and job satisfaction. Design/methodology/approach – Structural equation models are used with a sample of 497 Italian workers who filled in a questionnaire. Findings – The results show a good fit between hypothesis and empirical data. Moderator roles of individual and contextual variables on training opportunities effects were studied. Practical implications – The practical implications of this study relate to the role played by training opportunities in the IT acceptance process and in creating job satisfaction. Originality/value – The most innovative finding pertains to moderator roles of individual and contextual variables on training opportunities effects. Keywords Training, Technology acceptance model, IT self-competence, Job satisfaction Paper type Research paper

1. Introduction The adoption by a company of new technology instruments can undoubtedly improve its competitiveness, but it can also render one’s work easier and less repetitive, thus producing significant benefits for employees. The use of IT, however, can also give rise to problems (Brynjolfsson and Hitt, 2000): many firms experience adversity in the IT implementation process (Rizzuto and Reeves, 2007). As several studies have demonstrated (see Venkatesh and Bala, 2008), human variables play a fundamental role in this process, particularly in regard to the way in which users perceive IT. While IT adds value that is abundantly clear to the organizations, the consequences of using technology are not so clear to the end-users in terms of their individual experiences and satisfaction with their workplace. Each year organizations invest a significant amount of money in personnel training (Towler and Dipboye, 2006), thereby demonstrating that they view training as an important tool to improve workers’ skills, performance, their future competitiveness and the organizational learning climate in general. However, we are interested in considering the implementation of IT processes from the workers’ point-of-view, posing the following questions: does training facilitate the acceptance of IT? Can training influence worker satisfaction? Do some particular IT features, such as ease of use or

Journal of Workplace Learning Vol. 25 No. 7, 2013 pp. 455-475 q Emerald Group Publishing Limited 1366-5626 DOI 10.1108/JWL-12-2011-0071

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usefulness, affect job satisfaction? Do self-competence and self-efficacy of employees play a role in this process? Moreover, what can moderate the effect of training opportunities? These are the questions that direct the present research, in which we investigated the links between perceptions of IT, training, self-competence, self-efficacy and job satisfaction using data collected via a questionnaire. Although a significant amount of literature analyses some of these relationships, as shown in the following chapters, what makes the current paper different from previous works is the use of an integrated model which stands out for the fact of covering both organizational outcome, concerning the use of software useful to business results, and individual outcome, regarding employees’ work satisfaction. With these questions and aims as our starting point, the following sections present the theoretical background and our proposed research model relating to the variables which can explain the intention to use IT and subsequent levels of job satisfaction. Then the methodology is described, followed by the presentation of our results. The paper concludes with a discussion comprising the limits and practical implications of our research. 2. Theoretical background and hypotheses 2.1 Conditions that favor the intention to use IT The use of IT in the workplace may contribute to greater efficiency by making communication easier, faster and less expensive and providing the ability to link disparate employees and divisions (Dewett and Jones, 2001). Given the range of benefits that IT can offer, it is not surprising that research in this area has produced a large quantity of literature and models relating to the psychological aspects which can facilitate the acceptance and adoption of IT. The Technology Acceptance Model (TAM), devised by Davis (1989), is one of the most validated and used models in the IT field, although more recent frameworks have been presented (i.e. UTAUT of Venkatesh et al., 2003). Davis’s Model is based on Martin Fishbein and Icek Ajzen’s Theory of Reasoned Action (Fishbein and Ajzen, 1974). TAM explains that the Perceived usefulness (PU) and Perceived ease of use (EOU) of IT are predictors of the user’s attitude towards its implementation. This attitude then, in turn, affects the user’s intention to introduce IT and subsequent behaviors. Intention is a proximal predictor of behavior in Fishbein and Ajzen’s Theory of Reasoned Action. Perceived usefulness is the extent to which a person believes that using a system will enhance his performance and Perceived ease of use is the extent to which a person believes that using the system will be relatively free of effort (Premkumar and Bhattacherjee, 2008). Moreover, TAM considers the effect of EOU on PU: an increase in EOU contributes to improved performance and so EOU has a direct effect on PU. However, these variables are also contemplated in the Unified Theory of Acceptance and Use of Technology (UTAUT) model, which was defined by Venkatesh et al. (2003) on the basis of comparisons and integration of eight different models. UTAUT extends to three the constructs which affect IT usage intention and behavior: (1) performance expectancy, as an individual believes that using the system will help him to attain gains in job performance; (2) effort expectancy, the degree of ease associated with the use of the system;

(3) social influence, the degree to which an individual perceives that important others believe he/she should use the new system; and (4) facilitating conditions, the degree to which an employee believes that an organizational and technical infrastructure exists to support use of the system. UTAUT also considers the role of some moderators, which have been introduced in the second part of the present research. However, the focus of this study doesn’t consider social influence, because its effect has often resulted low (e.g. Van Raaij and Schepers, 2008; Venkatesh et al., 2003) and Facilitating conditions, because they affect user behavior only, which is not included in this research, and not Intention to use IT, which is a dependent variable of ours. For this reason we have adopted the more parsimonious perspective of the TAM model and, in accordance with it, we have devised the following hypotheses: H1. Perceived ease of use of IT positively affects the intention to use IT (e.g. Venkatesh and Bala, 2008). H2. Perceived usefulness of IT positively influences the intention to use IT (e.g. Venkatesh and Bala, 2008). H3. Perceived ease of use of IT positively affects Perceived usefulness of IT (e.g. Venkatesh and Bala, 2008). In addition to Ease of use and Perceived usefulness, it is important to also consider the worker’s professional resources: this is why computer self-efficacy has also been analyzed. The results of several researches (see Venkatesh, 2000) show the importance of computer self-efficacy, beyond the variables of the original TAM model. Therefore, we have assumed that a person who considers himself effective in the use of a computer will probably perceive the use of IT easier than a person who considers himself unable to use IT. Accordingly, we advance the following hypothesis: H4. IT self-efficacy positively affects ease of use of IT (e.g. Venkatesh, 2000). Being effective implies being competent. Self-efficacy is related to the concept of self-competence (Bandura, 1977) given that both concern the perception of being able (“I can do it”) and being a good performer (“I am effective”) (Markus et al., 1990; Williams and Lillibridge, 1990). Generally, self-competence is defined as an evaluation of one’s ability to successfully bring about desired outcomes (Bosson and Swann, 1999). Self-competence is one of two components (the second is Self-liking) of global Self-esteem. Self-competence is defined as the sense of one’s capability. Self-liking, on the other hand, is defined as a subjective evaluation of personal worth (Mar et al., 2006). IT self-competence is a particular type of self-competence, given that it is related to specifically technological tasks or settings. H5. IT self-competence positively influences IT self-efficacy. When an organization wants to introduce IT, or improve its employees’ use of IT, a practice that can be used is training, which has been increasingly used of late. A meta-analysis (Arthur et al., 2003) indicates that the effectiveness of organizational training interventions is higher than effects previously observed for other interventions, such as performance appraisal, feedback and management by


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objectives. Given the failure of some IT implementations (Brynjolfsson and Hitt, 1996, 2000), computer skills training is increasingly seen as essential and, accordingly, is a frequent type of training provided in organizations (Davis and Yi, 2004). Recently Escobar-Rodriguez and Monge-Lozano (2012) hypothesize that experience of training influences both usefulness and perceived ease of use of TAM model. Results confirm effect of training on usefulness. If we consider the case of a person who begins to use a particular computer program, or indeed even begins to use a computer, and who finds the implementation and use of the technology particularly difficult, it is possible that training may facilitate the use of the computer as well as the way in which the employee perceives its usefulness. In line with this, our hypotheses are: H6. Opportunity for training positively influences perceived ease of use of IT (e.g. Escobar-Rodriguez and Monge-Lozano, 2012). H7. Opportunity for training positively affects perceived usefulness of IT (e.g. Escobar-Rodriguez and Monge-Lozano, 2012). Similarly, a person who has already undergone training and has improved his IT competence, is likely to perceive an improvement in his competence. Accordingly, our final hypothesis is that: H8. Training has a positive influence on IT self-competence (e.g. Bandura, 1977). 2.2 Conditions that influence job satisfaction The implementation of IT produces changes in working practices, organizational processes, job characteristics and relationships between colleagues. As a consequence of these changes, job satisfaction may be influenced. Job satisfaction is a historically popular variable in studies of work psychology. Satisfaction, like other employee reactions, can be influenced by the nature of work tasks and aspects of the work environment, on a par with the IT devices. Haines et al. (1999) studied the antecedents of successful human resource information systems. They demonstrated that system conditions are the most important ones in affecting the user’s levels of satisfaction, across both the organizational conditions (size, user support, computer experience) and the individual/task characteristics (age, gender, work experience, computer knowledge, etc.). System conditions are made up of several elements, in particular: user involvement, training, management support, usefulness and ease of use of the system. The results of the research show high correlations between (training and user satisfaction, ease of use and user satisfaction, and usefulness and user satisfaction. Taking into account this research, we consider job satisfaction, and not user satisfaction, as the dependent variable and we hypothesize that: H9. Perceived ease of use of IT has a positive effect on job satisfaction (e.g. Haines et al., 1999). Birdi et al. (1997, p. 855) have demonstrated that there is a relevant link between training and job satisfaction, whereby “job satisfaction was found to be greater among employees who had previously taken part in more required training courses and work-based development activities”. In line with these results we propose that:

H10. Training opportunities has a positive influence on job satisfaction (e.g. Birdi et al., 1997). Last, our final consideration takes into account the relationship between self-efficacy and job satisfaction. If an employee is efficient in IT adoption, he likes his job and thus could have a high level of job satisfaction; for this reason, empirical studies show that self-efficacy at work effects job satisfaction (e.g. Klassen and Chiu, 2010). Accordingly, we advance the following hypothesis: H11. IT self-competence positively affects job satisfaction. 2.3 Conditions that moderate the effects of training opportunities In the second step of our research we wanted to test the invariance of model in sub-samples, focus on the effects of training opportunities and analyze the possible moderation role of some individual and contextual variables. The moderation variables have been chosen on the basis of: two models that describe the acceptance of technology, like TAM 3 (Venkatesh and Bala, 2008) and UTAUT (Venkatesh et al., 2003); a model which defines the learning strategies in using information and communication technology systems (Korpelainen and Kira, 2010); and Perrow’s schema that allows us to classify tasks according to the knowledge required to operate (Perrow, 1967). If we conjointly consider TAM 3 and UTAUT, we find four moderators – gender, age, experience and voluntariness of use – which can facilitate IT adoption. Regarding gender, longitudinal research by Venkatesh et al. (2000) showed that women were more strongly influenced by perceived control in using new technology. Regarding age, Morris and Venkatesh (2000) found that perceived control is more salient for older workers. An organization which offers more training opportunities should permit a higher level of IT mastery and control, so we think that the effects of training opportunities role are advantageous for women and older workers. Along these lines, we believe that training opportunities play a principal role in employees who do not have experience in IT use too. Moreover, in line with this, we believe that employees with a university degree need low training opportunities for an exhaustive adoption of an IT. A meta-analysis of Wu and Lederer (2009) has verified that the degree to which use of IT is perceived as being voluntary is an important contextual variable to analyze the results of IT adoption in the workplace. For this reason we want to check if the effects of Training opportunities are moderated by this Voluntariness. The second group of moderators relates to learning strategies, that is, general ways of going about the learning. Korpelainen and Kira (2010) proposed a simplified model to classify and analyze learning strategies according to contexts. The model defines learning strategy by its organizational context (e.g. the inherent degree of formality or informality) and its social context (whether they focus on learning alone, from others or with others). In accordance with Korpelainen and Kira (2010), an employee may learn in formal organizational contexts: for instance, by taking a self-study course guiding IT use (formal, alone), by attending a user training session (formal, from others) or by carrying out a training exercise with colleagues in a user training session (formal, with others). There are informal organizational contexts where an employee may learn too: for instance, a worker may learn by reading a manual on IT use (informal, alone), by


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asking for help from other people (informal, from others) or trying out things with others (informal, with others). The aim of our research is to study if the adoption of some of these learning strategies, in particular informal/from other and informal/with other, can improve or worsen the effects of training opportunities. The third group of moderators concerns the nature of tasks in the light of knowledge required to operate technology. Perrows’s model uses two dimensions which define the tasks: variability, which refers to the number of exceptional cases that a worker encounters in his workday, and analyzability, which describes the degree of search activity that is required to solve a problem when exceptional cases appear. The crossing of Perrow’s dimensions allows us to define four clusters of tasks which require different technologies: (1) routine (low variability – high analyzability), new situations are few, there is a lack of exceptions and search activity is not required; (2) craft (low variability-low analyzability), exceptions rarely occur, tasks are characterized by lack of exceptions but new situations are difficult to analyze and take time to resolve by adapting existing procedures to new situations; (3) engineering (high variability - high analyzability), tasks are characterized by many exceptions and their depth of comprehension by developed problem-solving procedures; and (4) Non-routine (high variability-low analyzability), is the most complex and least routine in the classification characterized by many exceptions and poor comprehension; problems appear frequently with no existing solutions. The question is: do the effects of training opportunities improve or worsen on the basis of the task characteristics? Finally, we wanted to verify if there is a moderation role of an organizational area where employees work. The question is: does the training opportunities index affect its dependent variables in line areas (e.g. production and sales) as well as in staff areas (e.g. administration and human resources)? The entire hypothesized model is shown in Figure 1. 3. Method 3.1 Sample The survey was conducted in eight different organizations based in Italy. These organizations were selected on the basis that they were from different employment sectors (ranging from public administration to information systems companies, in addition to a pharmaceutical company and a publisher’s graphics firm) and if they have adopted new IT tools in the last 12 months. A total of 497 participants took part in the survey (the response rate was 86 percent); 53 percent were women, the average age was 38 years ðmin: ¼ 21 years, max: ¼ 65 years; SD ¼ 8:52Þ: Of the participants, 50 percent had completed secondary school (41 percent had a university degree); 33.4 percent are employed in administration and human resources; 28.6 percent in technical divisions of their companies. The participants had used a computer for an average of 11.9 years ðSD ¼ 6:5Þ for a daily average of 6.3 hours ðSD ¼ 1:7Þ: 49.8 percent had used, to some extent, IT office applications, and browser and e-mail software.


461

Figure 1. Research model

The majority of the employees had been working in their organization for less than seven years (47.8 percent). The rest of the participants consisted of employees who had worked for their company for a period of time greater than seven years. 3.2 Measure and data collection A cross-sectional survey was used with an anonymous self-administered questionnaire. The questionnaire was constructed on the basis of 24 interviews with long tenure employees, three for each company: the interviews allowed us to define the research areas and to construct the first version of the questionnaire. This draft version was presented to two focus group (15 participants), to evaluate the appropriateness of the items in regards to the different jobs and organizational aspects. This is the process which allowed us to construct the questionnaire. The instrument was used in order to detect, extensively, users’ perceptions regarding the various features of information technology. The questionnaire starts with some preliminary questions regarding the use of computers and software in accomplishing work tasks. It then presents the item scales regarding the measurement of the different psychological constructs considered in this survey. The questionnaire ends with the gathering of socio-demographic data (gender, age, educational level) and information regarding the employee’s relationship with the organization (tenure and position in the organization). The Appendix shows the items of scales which were used in the present research. . TAM scales. The original scales devised by Davis (1989) were used to assess the two TAM constructs: Perceived Usefulness and Perceived Ease of Use. These measures showed high Cronbach alpha reliability levels (the indexes usually exceed 0.90) and a high degree of convergent, discriminant, and nomological validity in numerous studies (for example, Davis and Venkatesh, 1996). Both Perceived Usefulness and Perceived Ease of Use scales are made up of six items on which respondents express an opinion on a five-point Likert scale representing levels of agreement. The mono-factorial structure of both scales

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.

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Table I. Descriptive statistics and correlations

was verified by parallel analysis (Table I). Factor analysis showed loadings higher than 0.72. These scales have alpha coefficients of 0.91 and 0.90 (Table I) respectively, with this sample. Intention to use IT. This construct was tested with the Italian version (Curcuruto et al., 2009) of Venkatesh’s (2000) Intention to Use scale. Parallel analysis suggested a mono-factorial structure of the scale (Table I). Factor analysis showed loadings higher than 0.80; the alpha coefficient for the Intention of Use IT scale was 0.88 (Table I). Job satisfaction. Overall job satisfaction was measured with a single-item measure on a ten-point Likert-scale, ranging from 1 (strongly disagree) to 10 (strongly agree). Wanous et al. (1997) verified that single-item measures of satisfaction may be easier and may contain more face validity. Dolbier et al. (2005) found that reliability of the item measuring satisfaction with one’s job as a whole is estimated between rxx ¼ 0:73 and rxx ¼ 0:90: IT self-competence. Two questions assessed IT Self-competence. The scale uses a ten-point Likert-scale, ranging from 1 (no competence) to 10 (extremely competent). Parallel analysis suggested a mono-factorial structure of the scale (Table I). Factor analysis showed loadings higher than 0.78. With the present sample, the Cronbach a was 0.75. IT self-efficacy. This construct was measured with a revised form of Sherer’s (1982) Self-Efficacy Scale. Four items were adapted to IT use (e.g. “When I set important goals for myself, I rarely achieve them.” One was adapted to “When I set important goals with software X, I rarely achieve them”). The mono-factorial structure of the scale was verified by parallel analysis (Table I). Factor analysis showed loadings higher than 0.68. The alpha coefficient for the IT Self-efficacy measure was 0.80 with this sample. Training opportunities. The training opportunities offered by the company were measured by a scale of two Likert-type items. The scale uses a five-point, strongly low to strongly high, response format. Parallel analysis suggested a mono-factorial structure of the scale (Table I), with the present sample. Factor analysis showed loadings higher than 0.75. This measure showed an alpha coefficient of 0.74.

1. Job satisfaction 2.Intention to use IT 3. Usefulness 4. Ease of use 5. IT self-efficacy 6. IT self-competence 7 Training opportunities

NI

M

SD

1 3 6 6 4 2 2

6.48 4.22 4.20 3.60 4.06 6.87 2.51

2.01 0.78 0.70 0.83 0.83 1.59 0.82

PA EFA Alpha – 1 1 1 1 1 1

– 70 62 61 51 61 56

– 0.88 0.91 0.90 0.75 0.80 0.74

2

3

4

5

0.16 0.21 0.21 0.11 0.52 0.35 0.29 0.35 0.23 0.34

6

7

0.22 0.20 0.17 0.21 0.21

0.49 0.19 0.26 0.19 0.19 0.25

Notes: n ¼ 497; NI ¼ Number of item; PA ¼ Number of factors suggested by Parallel Analysis; EFA e:v: ¼ % of explained variance of explorative factor analysis

.

Moderator variables. Gender, age, educational level and organizational area variables were measured by four single items like experience in using the IT.

Voluntariness of use was assessed by the three items that were used by Venkatesh, and Bala (2008) in the validation of TAM3. Parallel analysis suggested a mono-factorial structure of the scale which explained 53 percent of variance, in this study. Loadings of Factor analysis were higher than 0.67. This scale showed an alpha coefficient of 0.77. For the verifying moderator effect, the sample was split into two groups by median value of Voluntariness scale distribution. Regarding learning strategies, two single Likert-type items were adopted: one concerned the learning strategies classified as informal/from other (“Do you ask for help from other people when you have a problem with software X?”) and one on the learning strategies defined as informal/with other (“When you have a problem with software X, do you try to solve it with other colleagues?”). For the verifying moderator effect, we split the sample into two groups by median value of items. The dimensions of Perrow’s model were assessed by items from scales of Withey et al. (1983). Three items measured variability and three items assessed analyzability. The mono-factorial structure of both scales was verified by parallel analysis. Factor analysis showed loadings higher than 0.76. The first scale had an alpha of 0.84 and the second scale an alpha of 0.86. For the verifying moderator effect, we split the sample into two groups by median values of two scale distributions. So we constructed four groups of participants, one for each of Perrow’s clusters. The procedure to translate the scales from their original English into Italian was as follows: . translation of the scales into Italian by two experts familiar with both the constructs and the English language; . comparison between the experts’ two versions to produce a single version for every scale; . translation of this version by a native English speaker: this translation was done “blind”, e.g. without knowing the original versions of the scales; and . definition of the final version in light of the indications yielded by the entire translation process. 3.3 Statistical analysis We used a Structural Equation Model to test the hypotheses, through the deployment of an AMOS 18 statistical package. Since data screening showed deviations from normality (e.g. kurtosis and skewness), the Asymptotically Distribution-Free method (ADF) was performed. As indicated by the literature (Byrne, 2001), GFI, AGFI, CFI and RMSEA were adopted to define the fit of the models. We considered GFI, AGFI and CFI values of 0.90 as acceptable and values of 0.95 or higher as indicative of excellent fit (Hu and Bentler, 1999). For the RMSEA, values up to 0.08 represent reasonable errors of approximation (Browne and Cudeck, 1993). For each hypothetical moderator variable, an AMOS multi-group analysis was performed in order to examine the configural invariance which assumed that the pattern of fixed and free parameters is the same across sub-samples. This initial


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baseline model has no between-group invariance constraints on estimated parameters. Successively, we used the baseline model to assess four constrained models: (1) the first model assumed that the effect between Training opportunities and Ease of use was invariant across groups (as defined by the specific moderator variable); (2) the second model hypothesized that the effect between Training opportunities and Perceived usefulness was invariant across sub-sample (as defined by the specific moderator variable); (3) the third model assumed that the effect between Training opportunities and IT self-competence was invariant across groups (as defined by the specific moderator variable); and (4) the fourth model hypothesized that the effect between Training opportunities and Job satisfaction was invariant across subsamples (as defined by the specific moderator variable). To compare the models, the x2 difference test was used (Byrne, 2001). If the difference between the x2 statistics is not statistically significant, then the statistical evidence points toward no cross-group differences between the constrained parameter. If the x2 difference is statistically significant, then evidence of cross-group inequality exists. 4. Results 4.1 Preliminary analysis Table I presents the means, standard deviations and inter-correlations of the indexes included in the model. Two were the highest correlations indexes, about 0.50: one between Intention to Use technology and Usefulness, and one between Job Satisfaction and Training Opportunities. The lower correlations indexes were found between Job Satisfaction and Intention to Use and between Job Satisfaction and IT Self-efficacy. Moreover, because we used self-reports to measure variables, we considered the degree to which common-method variance could be a threat to our analyses. We performed Harman’s single-factor test by Confirmative Factor Analysis (CFA) to verify the hypothesis that a single factor can account for all of the variance in our data (Podsakoff et al., 2003). The CFA showed that fit indexes are not adequate for an only factor model ðCFI ¼ 0:72; RMSEA ¼ 0:16Þ: Therefore, we rejected the idea that common method variance explained a substantial amount of covariance among variables. 4.2 Testing hypotheses When the hypothesized model was tested, the fit indexes were mainly at a good level: Chi square ¼ 22:58; DF=Chi2 ¼ 2:25; GFI ¼ 0:98; AGFI ¼ 0:96; CFI ¼ 0:96; RMSEA ¼ 0:05 (Table II). The standardized regression weights were all significant with one exception: the coefficient of the IT Self-efficacy on Job Satisfaction. In the light of these results, we built an optimized model with only the significant effects: Chi square ¼ 22:90; DF=Chi2 ¼ 2:08; GFI ¼ 0:98; AGFI ¼ 0:96; CFI ¼ 0:96; RMSEA ¼ 0:05: The difference between the two models is 0.32 for Chi square, and 1 for degrees of freedom: it is not significant ð p ¼ 0:57Þ: Since the models have a similar fit, we chose

Estimates H1. EOU ! INT H2. PU ! INT H3. EOU ! PU H4. ISE ! EOU H5. ISC ! ISE H6. TO ! EOU H7. TO ! PU H8. TO ! ISC H9. EOU ! JS H10. TO ! JS H11. ISE ! JS Model fit Chi2 DF/Chi2 GFI AGFI CFI RMSEA

1 - Model

2 - Model

Hypothesis verification

0.21 * * 0.46 * * 0.31 * * 0.37 * * 0.42 * * 0.16 * * 0.21 * * 0.23 * * 0.10 * 0.45 * * 0.03

0.21 * * 0.45 * * 0.31 * * 0.37 * * 0.42 * * 0.16 * * 0.21 * * 0.23 * * 0.11 * 0.46 * * –

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No

22.58 2.25 0.98 0.96 0.96 0.05

22.90 2.08 0.98 0.96 0.96 0.05

Notes: n ¼ 497: JS ¼ Job satisfaction; INT ¼ Intention to use IT; PU ¼ Perceived usefulness; EOU ¼ Perceived ease of use; ISC ¼ IT self-competence; ISE ¼ IT self-efficacy; TO ¼ Training opportunities. *p , 0.05; * * p ,0.01

the second one which is more parsimonious than the first one that we hypothesized. The explained variances within this model are fair for Intention to Use (32 percent) and sufficient for Job Satisfaction (24 percent). Otherwise, there were very low explained variances for Usefulness (17 percent), Ease of Use (17 percent), IT Self-efficacy (18 percent) and IT Competence (6 percent). The specific direct, indirect and total effects are shown in Table III. The higher effects related to Training Opportunities that influenced Job Satisfaction, and IT Self-competence that influenced IT Self-efficacy. Lower significant effects were Ease of Use on Job Satisfaction, and Training Opportunities on Ease of Use. Moreover, we analyzed critical ratios for differences between some parameters: there is not a significant difference (p , 0.01) between the effect that Training Opportunities has on Ease of Use and the effect that Training Opportunities has on Usefulness ðt ¼ 0:35Þ: On the other hand, the effect of Usefulness on Intention to Use IT is higher than the effect of Ease of Use on the same dependent variable ðt ¼ 3:90Þ: Finally, the effect of Training Opportunities on Intention to Use is wholly mediated by Perceived Usefulness and Ease of Use, and it is essentially low (standardized indirect effects ¼ 0:16Þ: 4.3 Moderator effects Fit indexes of multi-group analysis (Table IV and Table V) tested the configural invariance of the model across the sub-samples as defined on the basis of: Gender, Age, Gender for Age, Educational level, Experience in using IT, Voluntariness of IT use,


465

Table II. Results: hypothesis verification and model fit indexes

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Table III. Standardized effects

Table IV. Multi-group analysis

Direct effects IT self-competence IT self-efficacy Perceived usefulness Perceived ease of use Intention to use IT Job satisfaction Indirect effects IT self-competence IT self-efficacy Perceived usefulness Perceived ease of use Intention to use IT Job satisfaction Total effects IT self-competence IT self-efficacy Perceived usefulness Perceived ease of use Intention to use IT Job satisfaction

Training opportunities

IT selfcompetence

IT selfefficacy

Perceived usefulness

Perceived ease of use

0.23 0.00 0.21 0.16 0.00 0.46

0.00 0.42 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.37 0.00 0.00

0.00 0.00 0.00 0.00 0.45 0.00

0.00 0.00 0.31 0.00 0.21 0.11

0.00 0.10 0.06 0.04 0.16 0.02

0.00 0.00 0.05 0.16 0.05 0.02

0.00 0.00 0.11 0.00 0.13 0.04

0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.14 0.00

0.23 0.10 0.27 0.20 0.16 0.48

0.00 0.42 0.05 0.16 0.05 0.02

0.00 0.00 0.11 0.37 0.13 0.04

0.00 0.00 0.00 0.00 0.45 0.00

0.00 0.00 0.31 0.00 0.45 0.11

Note: n ¼ 497

Multi-group analysis by

Chi2

DF

Chi2/DF

GFI

AGFI

CFI

RMSEA

Gender Age Gender £ age Educational level Experience in using IT Voluntariness of IT use Learning strategies (informal - with) Learning strategies (informal - from) .03 Perrow’s clusters Organizational area

33.94 32.92 73.38 28.49 30.56 33.19 36.71 32.55

22 22 44 22 22 22 22 22

1.54 1.50 1.67 1.30 1.39 1.51 1.67 1.48

0.98 0.98 0.96 0.98 0.98 0.98 0.98 0.98

0.94 0.95 0.90 0.96 0.95 0.94 0.94 0.95

0.96 0.95 0.90 0.96 0.97 0.96 0.95 0.96

0.04 0.03 0.04 0.03 0.03 0.03 0.04

48.22 44.38

44 22

1.09 2.02

0.97 0.97

0.93 0.93

0.99 0.94

0.01 0.05

Note: n ¼ 497

Informal-with learning strategy, Informal-from Learning strategy, Perrow’s clusters and Organizational area. The second step of analysis allowed us to test if the path coefficients for the relationships between Training opportunities and its dependent variables (PU, EOU, ISC and JS) were equal across groups as defined by the hypothetical moderator indicators.

Results showed that the effect of Training opportunities on Ease of use was moderated by participant age: the standardized effect was 0.05 for the younger participants and 0.22 for the older ones (comparison of parameters, t ¼ 2:00Þ: Substantially, the data shows that Training opportunities has a role in definition of Ease of use only for older employees. The effect of Training opportunities on Perceived Usefulness was the lowest stable parameter. The standardized effect was 0.05 for participants with short IT experience and 0.26 for participants with long IT experience (comparison of parameters, t ¼ 2:52Þ: The standardized effect was 0.05 for participants with short IT experience and 0.26 for participants with long IT experience (comparison of parameters, t ¼ 2:52Þ: Results show a combined moderation action of gender and age: the lowest effect of Training opportunities on Perceived Usefulness was in younger males (standardized effect ¼ 0:02Þ; substantially not effect, and the highest effect was in older females (standardized effect ¼ 0:35Þ: The comparison of these two parameters shows a t-value of 2.70. Learning strategies, informal and in company with colleagues, has a moderator role: when this typology of learning strategies is less widespread the Training opportunities show a standardized effect on Perceived Usefulness of 0.26 while when they are more widespread the standardized effect is 0.12 (comparison of parameters, t ¼ 1:92Þ: As regards the effect of Training opportunities on IT Self-competence, the standardized effect was 0.07 for the graduate participants and 0.25 for participants with lower educational levels (comparison of parameters, t ¼ 2:48Þ: The combined moderation action of gender and age was found too: the lowest effect of Training opportunities on Self-competence was in younger males (standardized effect ¼ 0:08Þ; substantially not effect, and the highest effect was in older females (standardized effect ¼ 0:36Þ: The comparison of these two parameters shows a t-value of 2.61. Finally, as regards the effect of Training opportunities on Job satisfaction, there is an only low moderation effect (comparison of parameters, t ¼ 1:68Þ : training opportunities affect Job satisfaction at a higher level in staff areas (standardized regression ¼ 0:55Þ than in line area (standardized regression ¼ 0:45Þ (see Figure 2).


467

5. Discussion We worked on a model regarding a set of important variables from a theoretical and practical point-of-view, which has not appeared in the literature (e.g. in the PsycINFO

Moderator TO ( TO ( TO ( TO (

EOU PU ISC JS

I

II

III

Age * IT Experience * * Educational level * * Organizational area *

Gender £ Age * Gender £ Age *

LS-IW *

Note: JS ¼ Job satisfaction; PU ¼ Perceived usefulness; EOU ¼ Perceived ease of use; ISC ¼ IT Self-competence; TO ¼ Training opportunities. LS IW ¼ Learning strategies (informal - with). *p , 0.10; * *p , 0.05. n ¼ 497

Table V. Multi-group analysis

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Figure 2. Research results

and ERIC databases) until the present research. This model explains outcomes both for employees and companies when new IT is introduced into the workplace. Indeed, the main purpose of the present study was to explain the Intention to use technology and Job satisfaction, making use of Training opportunities and the TAM model constructs. A broad sample of Italian employees allowed us to verify our hypotheses. A procedure which combined qualitative and quantitative methods was used. The results, which should be considered in the light of the study limitations too, show that Intention to Use as a technology is influenced by Ease of Use (H1) and Usefulness (H2); moreover, Ease of Use (H1) affects Usefulness (H3) in the same way as the TAM model explains. IT Self-efficacy influences Ease of Use (H4) and it is affected by IT Self-competence (H5). Moreover, evidence is in favor of Training Opportunities effects on Ease of Use (H6), Usefulness (H7) and on IT Self-competence (H8). Finally, our results prove that Job Satisfaction is influenced by Ease of Use (H9) and by Training Opportunities (H10), but not by IT Self-efficacy (H11). The configural invariance of the model was analyzed across different sub-samples as defined by: Gender, Age, Gender for Age, Educational level, Experience in using IT, Voluntariness of IT use, Informal-with learning strategy, Informal-from Learning strategy, Perrow’s clusters and Organizational area. Finally, we focused on the invariance of Training opportunities effects, which is the most original aspect of this study. Results show that they are not constant but that Training opportunities effects can change on the basis of gender, age, learning strategies, educational level, IT experience, and organizational area. Training opportunities plays an important role in defining Ease of use, mainly for older employees, IT Self-competence, particularly for employees with lower educational levels, Perceived usefulness, especially for participants who have more extensive experience with the specific IT, for older females and for those who do not adopt learning strategies informal-with, and Job satisfaction, principally for those who work in staff organizational areas.

This evidence highlights the important role played by the opportunities for training that companies can offer to their employees, both for Job Satisfaction and for the intention to use a technology, even if, in the last case, the effect is indirect. Our results are consistent with past research into technology acceptance models (the primary effect of perceptions of usefulness on intentions to use IT, and the parallel influence of ease of use on users’ intentions), but they also present specific new evidence on the influence of training opportunities on the TAM model and, finally, on psychological outcomes such as worker satisfaction. In regards to the opportunity for IT training, past studies have assumed this to be one external facilitation variable to the intention to use IT, via the Ease of use (Venkatesh, 2000), and generally to promote the active use of IT innovations adopted in organizations (Moore and Benbasat, 1991). The present study is in favor of the idea that there are two different ways in which IT training opportunities are directly influent in IT adoption: one by Ease of use and one by Perceived usefulness. We can hypothesize that the more IT training opportunities are offered to workers, the more they develop specific knowledge and perceptions about its relative advantages, in particular if these opportunities offer results that allow the worker to observe the ways in which IT functionalities directly relate to carrying out the specific work tasks which they were assumed to support. Second, we found that the IT training opportunities variable was shown to be linked to the perception of IT self-competence. Even if this result may be considered as an expected consequence of IT training in the workplace, the research model describes this theoretical link as a distinct path of influence mediation of training, which could be considered as another distinct antecedent of the development of usefulness and ease of use perceptions. While these theoretical links with these two latter variables express two forms of direct influence of training on user acceptance of IT (the first via expectations of high level of performance results, and the second via expectations of general lack of effort), we may also assume that the more IT users have access to, and opportunities for active IT training experiences, the more they develop feelings of confidence in their abilities and subsequently self-efficacy. This perceived subjective competence impact on TAM variables by IT self-efficacy. However, our study has highlighted related concepts such as IT self-competence and self-efficacy: in the previous literature, the only IT self-efficacy has been shown only as an antecedent to ease of use perceptions (e.g. Wang et al., 2012), in terms of an internal facilitating condition in carrying out specific work-tasks (Venkatesh and Davis, 1996; Venkatesh, 2000). In summary, the present results show that levels of training opportunities are linked to both technology perception (as in the TAM model) and to differential subjective individual competence which works as a mediatory variable on the level of TAM model technology perceptions. However, only the levels of technological perception affect the intentions to use and future usage of IT, mediating the effects of training rather than subjective competences and self-efficacy. This last consideration allows us to consider two appropriate aims for IT training programs within organizations: (1) to develop specific levels of technology perception (advantages and lack of complexity) as direct factors of acceptance; and (2) to exercise the competences as a second way of leading to the acceptance of technology.


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As far as the impact of training on work satisfaction is concerned, the results are in favor of two different ways in which training opportunities influence levels of general satisfaction amongst workers. The main path of our model shows a significant and positive direct effect on job satisfaction. The second path shows a lesser effect: ease of use on satisfaction. Taken together, we suggest that these results indicate the importance of the technical and organizational elements of training opportunities in improving the quality of the individual work experience in two ways. First, there are direct and relevant effects of training on worker satisfaction, evidenced by the general impact and meaning of IT technologies to the day-to-day work experience. So, beyond the previously discussed effects of TAM variables on enacting technology usage, we can assume a second broad function of IT training activities: improving the experience of technology by users and, further, contributing to their general job satisfaction. The second, previously unacknowledged effect on job satisfaction, is the way in which perceived ease of use could be interpreted as an extension in which technology contributes to reduce work overload and other stressful factors which may be detrimental to the workers’ general satisfaction. However, this last effect was low. This could be due to the specificity of the technologies used in the sample if compared to the overall work experience, or to the general construct of work satisfaction used in this study. Moreover, it is important to consider that our measure of job-satisfaction shows limitations: results from the single item measure tend to paint a more positive picture of job satisfaction as research by Oshagbemi found (Oshagbemi, 1999). Future research will have to consider the specific effect of our general “training and acceptance model” on specific facets of work satisfaction, also considering aspects like role performance satisfaction, as well as variables related to specific aspects of training, which consider the degree of training transferability and technology trialibility in the training context. However, training opportunities have been considered as a proxy variable too. They show that if a company adopts human resource processes in favor of development of employees’ skills and of human capital. Of course the present study has limitations which have to be considered to interpret the results. Even if the questionnaire of this research was constructed on the basis of qualitative methods, what the results of this study have in common with other research is the limitations of the quantitative approach. However, unlike many quantitative studies conducted on these issues, we sought to take into account the contextual dimension that is controlled through moderation effects. In this sense, therefore, although a quantitative methodology was used, we have taken account of the importance that the environment and context can play. In addition to general limitations of quantitative methods, there are limitations in research design and participants. This is cross-sectional research, it was impossible to consider a longitudinal design because the companies were not available for a second collection of data. Regarding participants, our research adopts a non-probability sampling technique (a convenience sample), the geographic region is not particularly diversified, and respondents are only from Italy. Furthermore, we used a single-item approach indicator of job satisfaction which, even if it permits a global measure and there is research which has verified its validity (e.g. Dolbier et al., 2005), it cannot show the different components of job satisfaction. We have not studied which training opportunities companies offered to participants of this research, or the prior individual

competence and experience of training in IT; in particular, we did not consider every learning strategy which the Korpelainen and Kira (2010) model presents. In concluding, we offer some suggestions on the further practical consequences of these findings. Our results confirm the strategic role played by training, not only in creating job satisfaction, but also in the promotion of the use of IT. Offering training opportunities to employees would be more useful in improving organizational effectiveness than that resulting from IT implementation alone, and it would also lead to greater job satisfaction. Firms have to focus their attention on the different roles that training opportunities can play with employees of different gender, age, learning strategies, educational level, IT experience and organizational areas where they work. Results suggest the need to provide older female employees more opportunities to access training programs (i.e. course projects should consider the needs of this group). Firms can encourage their employees to try out IT software/devices with others (learning strategies: informal, with others), and can support ICT learning by providing time and space for informal learning; this finding stresses the point that support and stewardship among employees is a surplus value for firms. Finally, since offering training improves job satisfaction, particularly in some organizational areas like the staff area, firms could consider this if they want to reduce turnover and absenteeism, which can also be caused by low level of satisfaction (Oshagbemi, 1999).

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Rizzuto, T.E. and Reeves, J. (2007), “A multidisciplinary meta-analysis of human barriers to technology implementation”, Consulting Psychology Journal: Practice and Research, Vol. 59 No. 3, pp. 226-240. Sherer, M. (1982), “The self-efficacy scale: construction and validation”, Psychological Reports, Vol. 51 No. 2, pp. 663-671. Towler, A. and Dipboye, R.L. (2006), “Effects of trainer reputation and trainees’ need for cognition on training outcomes”, Journal of Psychology: Interdisciplinary and Applied, Vol. 140 No. 6, pp. 549-564. Van Raaij, E.M. and Schepers, J.L. (2008), “The acceptance and use of a virtual learning environment in China”, Computers & Education, Vol. 50 No. 3, pp. 838-852. Venkatesh, V. (2000), “Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model”, Information Systems Research, Vol. 4 No. 11, pp. 342-365. Venkatesh, V. and Bala, H. (2008), “Technology acceptance model 3 and a research agenda on interventions”, Decision Sciences, Vol. 39 No. 2, pp. 273-315. Venkatesh, V. and Davis, F.D. (1996), “A model of the antecedents of perceived ease of use: development and test”, Decision Sciences, Vol. 27 No. 3, pp. 451-481. Venkatesh, V., Morris, M.G. and Ackerman, P.L. (2000), “A longitudinal field investigation of gender differences in individual technology adoption decision-making processes”, Organizational Behavior and Human Decision Processes, Vol. 83 No. 1, pp. 33-60. Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003), “User acceptance of information technology: toward a unified view”, MIS Quarterly, Vol. 27 No. 3, pp. 425-478. Wang, H., Chung, J.E., Park, N., McLaughlin, M.L. and Fulk, J. (2012), “Understanding online community participation: a technology acceptance perspective”, Communication Research, Vol. 39 No. 6, pp. 781-801. Wanous, J.P., Reichers, A.E. and Hudy, M.J. (1997), “Overall job satisfaction: how good are single-item measures?”, Journal of Applied Psychology, Vol. 82 No. 2, pp. 247-252. Williams, K.J. and Lillibridge, J.R. (1990), “The identification of managerial talent: a proactive view”, in Murphy, K.R., Saal, F.E., Murphy, K.R. and Saal, F.E. (Eds), Psychology in Organizations: Integrating Science and Practice, Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 69-94. Withey, M., Daft, R.L. and Cooper, W.H. (1983), “Measures of Perrow’s work unit technology: an empirical assessment and a new scale”, Academy of Management Journal, Vol. 26 No. 1, pp. 45-63. Wu, J. and Lederer, A. (2009), “A meta-analysis of the role of environment-based voluntariness in information technology acceptance”, MIS Quarterly, Vol. 33 No. 2, pp. 419-432.

Further reading Downey, J.P. and Zeltmann, S. (2009), “The role of competence level in the self-efficacy-skills relationship: an empirical examination of the skill acquisition process and its implications for information technology training”, International Journal of Training and Development, Vol. 13 No. 2, pp. 96-110. McHenry, J.H. and Strønen, F.H. (2008), “The trickiness of IT enhanced competence management”, Journal of Workplace Learning, Vol. 20 No. 2, pp. 114-132. Mariani, M. and Zappala`, S. (2004), “Percezione del rischio e possibilita` di proteggersi da eventi negativi negli acquisti on line”, Rassegna di Psicologia, Vol. 21 No. 2, pp. 11-32.


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Nagy, M.S. (2002), “Using a single-item approach to measure facet job satisfaction”, Journal of Occupational and Organizational Psychology, Vol. 75 No. 1, pp. 77-86. Venkatesh, V., Thong, J.Y.L. and Xin, X. (2012), “Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology”, MIS Quarterly, Vol. 36 No. 1, pp. 157-178. Appendix. Items for model constructs Perceived usefulness scale: Using software X in my job enables me to accomplish tasks more quickly. Using software X improves my job performance. Using software X increases my productivity on the job. Using software X enhances my effectiveness on the job. Using software X makes it easier to do my job. I find software X useful in my job. Perceived ease of use scale: Learning to operate Software X is easy for me. I find it easy to get software X to do what I want to do. My interaction with software X is clear and understandable. I find software X to be flexible to interact with. It is easy for me to become skillful at using software X. I find software X easy to use. Intention to use scale: Assuming I had access to the software X, I would intend to use it. Given that I had access to the software X, I predict that I would use it. I plan to use software X in the next few months. Job satisfaction item: Overall, how satisfied are you with your job? IT self-competence scale: What is your level of competency when using a computer? What is your level of competency when using the Internet? IT self-efficacy scale: When I set important goals with software X, I rarely achieve them. When unexpected problems occur with software X, I don’t handle them well. I feel insecure about my ability to do things with software X. I do not seem capable of dealing with most problems that come up with software X. Training opportunities scale: Opportunities for training and professional development are offered by my company. I can access specific training courses on the IT that I use. Voluntariness scale: My use of the software X is voluntary. My supervisor does not require me to use software X.

Although it might be helpful, using software X is certainly not compulsory in my job. Variability scale: To what extent would you say your work is routine? People in this unit do roughly the same job in the same way most of the time. Basically, unit members perform repetitive activities in doing their jobs. Analyzability scale: To what extent is there a clear known way to do major types of work your normally encounter? To what extent is there an understandable sequence of steps that can be followed in doing your work? To what extent is there a clear defined body of knowledge of subject matter which can guide you in doing your job? Corresponding author Marco Giovanni Mariani can be contacted at: [email protected]

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