turning work into play: implications for microcomputer software training

Journal of Management 1993, Vol. 19, No. I, 127-146

Turning Work into Play: Imp/h tions for Microcomputer

Software Training Jane Webster

The Pennsylvania State University

Joseph J. Martocchio University of Illinois at Urbana-Champaign

This study examined the effects of task labelling (asplay or work) and trainee age on learning outcomes. Results indicate an interaction between task labelling and age: Younger employees who received training labelled as ‘play’ showed higher motivation to learn and performed better in an objective test of software knowledge than older employees. In contrast, no dtfferences were found between younger and older employees receiving training labelled as ‘work t Implications for training are discussed.

Studying age-related differences in training performance is an important human resource management imperative. By the end of this decade, the composition of the work force will include more older employees (Schuler & Huber, 1990). Human resource practitioners will need to become increasingly sensitive to developing human resource programs such as training older employees. Any training that places older employees at a disadvantage relative to younger employees may constitute a violation of the Age Discrimination in Employment Act (AREA).

An important training topic in organizations is the use of microcomputer software. A growing body of research has emerged which examines factors associated with successful use of microcomputers in the workplace (e.g., Bostrom, Olfman, & Sein, 1990; Elias, Elias, & Robbin, 1987; Gist, Rosen, & Schwoerer, 1988; Gist, Schwoerer, & Rosen, 1989; Turnage, 1990), yet adoption of computer technology in the workplace is not always successful (Turnage, 1990). Research on the causes of computer failures has demonstrated that the majority of causes are behavioral, such as a lack of training, rather than technical (Turnage, 1990). Thus, further research into the successful use of microcomputers is warranted.

Direct all correspondence to: Jane Webster, The Pennsylvania State University, The Smeal College of Business Administration, University Park,Pennsylvania, 16802.

Copyright Q 1993 by JAI Press Inc. 0149-2063

128 WEBSTER AND MARTOCCHIO

Label 1 ing of Training Approach (O=work, 1 =play)

Age H2 (-1

I’ H3 t-1

Hl (+I

Training Outcomes: c * motivation

to learn * test

performance

Figure 1. Model of Relationships between Task Labelling and Age with Training Outcomes

Older individuals have been shown to perform less well on objective tests of software mastery than younger individuals (Elias et al., 1987; Gist et al., 1988). Gist et al. (1988) hypothesized there would be an interaction between employee age and training design characteristics (behavior modeling versus tutorial) such that older trainees (compared to younger trainees) would benefit significantly more from behavioral modelling than tutorial training. Although Gist et al. (1988) did not find a significant interaction effect, their efforts were spurred by ADEA, which highlights the importance of examining the impact of training design characteristics on training outcomes for younger and older employees.

We argue that labelling the task as play or work will affect the evaluation of a task, which in turn, will have an impact on learning in training (Sansome, Sachau, & Weir, 1989; Tang & Baumeister, 1984). Figure 1 diagrams an exploratory model specifying the anticipated influences of task labelling and age on motivation to learn and learning. Further, the model responds to calls for research exploring the moderating effects of individual differences on play and work labelling (e.g., Cellar & Barrett, 1987; Tang & Baumeister, 1984) by examining the moderating effect of age. After developing the model, we present a study that extends previous empirical research in work and play labelling (e.g., Sandelands, 1988) in several ways: from contrived tasks in laboratory settings to real tasks in a field setting, from students to employees, and from affective outcomes to learning outcomes.

Theoretical Basis for Training Approaches of Work and Play

Our approach to training uses labels to categorize training sessions as ‘play’ or ‘work’. It draws on the literatures on social information processing, cognitive categorization, and play to propose that the labelling of training sessions as ‘play’ will enhance microcomputer software training. More specifically, the research of Salancik and Pfeffer (1977, 1978) on Social Information Processing

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(SIP) provides a basis for the labelling of microcomputer training. Salancik and Pfeffer’s theory suggests that attitudes towards tasks may be influenced merely by the labelling of tasks by others (Staw, 1984). The social context provides socially acceptable attitudes and it makes certain information more salient to the employee (Salancik & Pfeffer, 1978). For example, in one study, managers used social information to change employees’ perceptions of the job and the organization; for a control group of employees in the same job and organization, these perceptions were not changed (Griffin, 1983). Lieberman (1977) has extended this argument to play settings, suggesting that social expectations will influence an individual’s manifestation of play.

Organizational research supports the influence of social information for tasks labelled as ‘work’ or ‘play’ (e.g., Cellar & Barrett, 1987; Glynn, 1988; Sandelands, 1988; Webster, Heian, & Michelman, 1990). Researchers found differences in such outcomes as intrinsic motivation, positive affect, and learning between the two conditions, favoring the play condition. Further, extrapolating from the literature on play (Csikszentmihalyi, 1975; Ellis, 1973; Glynn, 1988; Levy, 1983; Lieberman, 1977; Malone, 1981; McGrath & Kelly, 1986; Miller, 1973; Sandelands, 1988; Sandelands, Ashford, & Dutton, 1983), increased playfulness in computer interactions should result in employees who put more effort into learning new systems, learn more effectively, are more self-directed in their learning, and experience more control.

The use of labels in SIP rests on the assumption that labels initiate cognitive categories. Cognitive categorization theory (Rosch, 1975, 1978) provides a basis for understanding concept formation. Categorization theory maintains that individuals form categories (i.e., schemas, Fiske & Taylor, 1984) to make sense of their world based on their observations of the features or attributes of objects or issues. Categorization theory has been applied to organizational behavior research on leadership (Lord, Foti, & Phillips, 1982), performance appraisal (Feldman, 1981; Nathan & Lord, 1983), training (Martocchio, 1992), and strategic decision making (Jackson & Dutton, 1988; Thomas & McDaniel, 1990). Cognitive categories provide expectations that guide the understanding of new information (Fiske & Taylor, 1984): they affect subsequent affect, attitudes, cognitions, motivations, and behaviors (Dutton & Jackson, 1987; Glynn, 1988). For instance, compared with the category of work, play involves more emphasis on means than on ends (Sandelands, 1988) and feelings of both pleasure and involvement (Sandelands & Buckner, 1989). Since research has demonstrated that adults have separate cognitive categories for work and play (Glynn, 1988) this paper proposes that labelling microcomputer training sessions as ‘play’ should encourage employees to view training as a game, rather than as work.

Trainees in the play condition should learn more than those in the work condition. Researchers studying play (e.g., Miller, 1973) argue that during more playful interactions with tasks, people exercise and develop skills through exploratory behaviors, resulting in enhanced task performance. For instance, Piaget (1962) argued that playfulness provides children with the opportunity to practice social, physical, cognitive, and emotional behaviors. Malone (1981)

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proposed that students will spend more time and effort in task performance when at play, will enjoy what they are doing more, will be more likely to use what they have learned, and will learn more effectively. Studies of children using computers have supported these results (e.g., Papert, 1980; Turkle, 1984). In addition, Webster et al. (1990) found that university students experienced higher mood and involvement and learned more in computer training classes labelled as play rather than as work. However, there is a lack of research examining these phenomena in older adults.

Prior research (Lieberman, 1977) provides some support for the hypothesis that play encourages task performance in all ages. Carroll and Mack (1984), who conducted a protocol analysis of office temporaries who were naive users of computers, concluded that the capacity to treat work as play characterizes successful adult learners and problem solvers. Further, Schuck (1985) suggested that traditional training programs do not allow for play. She proposed that training programs encouraging play would result in enhanced employee learning. Therefore, based on the prior discussion, we suggest that employees in training sessions labelled as play will demonstrate higher training outcomes (such as motivation to learn and learning) than those in training sessions labelled as work.

Hypothesis 1: Employees in training sessions.labelled as play will demonstrate higher training outcomes than those in training sessions labelled as work.

Theoretical Approach to Age Effects

There is a widespread belief that job performance declines with advancing age (Cascio, 1986; Rhodes, 1983). Motivational and ability explanations have been advanced. Labouvie-Vief and Chandler (1978) use a “contextualism” explanation to suggest how motivation to perform tasks varies with age. The contextual factors include prolonged job boredom, meaninglessness of work, and lack of intellectual stimulation which may be associated with lower job performance for older employees than for younger employees. Expectancy theory provides a basis to explain this influence of contextual factors. Fossum, Harvey, Paradise, and Robbins (1986) used expectancy theory and human capital theory as a basis to suggest that older workers, compared to their younger counterparts, may not take the initiative to learn new skills. They argue that older workers may generally believe either that successful skill command is not likely attainable or they may be less willing to invest in updating because of a perceived shorter stream of payoffs.

Giniger, Dispenzieri, and Eisenberg (1983) provide a “decremental theory of aging” explanation. Specifically, memory and learning abilities have been shown to decrease with age (Elias, Elias, & Elias, 1977; Welford, 1984). The literature on learning, memory, comprehension, and problem-solving suggests that older individuals are less well-equipped than younger individuals to acquire the skills necessary for performing microcomputer-based word processing tasks (Elias et al., 1987; Erber & Botwinick, 1983).

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There is tentative empirical support for this expectation that older individuals may learn less well than younger individuals. For example, Elias et al. (1987) showed that older workers not only took significantly longer to complete microcomputer software training, but also performed less well on an objective test of software mastery than younger individuals. Gist et al. (1988) found that older trainees in both a modeling training condition and in a nonmodeling training condition exhibited significantly lower performance than did younger trainees on an objective test of software knowledge. Thus:

Hypothesis 2: Younger employees will demonstrate higher training outcomes than older employees.

Moderating Effect of Age on Training Approach

As noted earlier, examining age-related differences in employee training, and particularly microcomputer training, is important. In light of ADEA, it is especially relevant to examine contingency relationships that involve age and training design characteristics. Several reasons for an interaction may exist: the increasing segregation with age between work and play, and the increased work experiences of older employees.

First, work and play become more segregated with age. The classic definition of play is:

a free activity standing quite consciously outside “ordinary” life as being “not serious” . . . It is an activity connected with no material interest, and no profit can be gained by it. It proceeds within its own proper boundaries of time and space (Huizinga, 1950: 13).

This characterization of play as occurring within fixed limits of time and space reflects a social distinction between work and play. Erikson (1972) suggested that the working adult views play as recreation, and as a separation from limitations occurring at work. Kabanoff (1980: 60) offered industrialization as one of the factors strengthening this demarcation in the past:

Industrialization resulted in a major segregation of the roles between the economic and noneconomic. Work became distinct spatially, and to some extent, socially . . . Thus the previously highly integrated system of personal roles became disrupted, and this separation resulted in a clear recognition of the dualism of work and leisure. . . . work often has had the connotation of a chore and nonwork the connotation of play.

Because of this segregation between work and play, we may be ridiculing adults when we suggest that they play at work (Millar, 1968). “Play has been viewed . . . as only marginally legitimate . . . reserved for the young and understood as antithetical to work” (Blancard, 1986, p. 79). Some argue that adults view play as hedonistic and decadent (Csikszentmihalyi, 1975), that they repress childhood pursuits (Erikson, 1972), that socializing agents such as

schools have inhibited curiosity and play (Miller, 1973; Voss, 1987), or that our puritan society has viewed play as inferior to work (Ellis, 1973).

Since play acts such as games or sports (Stevens, 1980) usually take place in play settings, adults may frown on play during work time. Exceptions to this segregation occur in the temporary transformation of a work setting into a play setting, for instance for office parties (Dandridge, 1986), or for play acts (such as computer games) during lunch breaks at work. However, adults would probably discourage these play acts during working hours. Therefore, since older employees are further from their childhood days of playing, they may view the training approach of play more negatively than younger employees.

A second reason to expect an interaction between age and training approach concerns the increased work experiences of older employees. Research has shown that more ambiguous activities are more amenable to labelling (Rommetveit, 1968). By labelling ambiguous settings as “play”, we may encourage individuals to initiate the cognitive category of play in their minds, resulting in more playful, exploratory behaviors leading to enhanced learning (Glynn, 1988; Sandelands, 1988). However, to the extent that older employees have had more work experiences, tasks or situations at work will be less ambiguous to them. Therefore, utilizing the training approach of play will probably be less positive for older employees than for younger employees. Based on the prior discussion, we suggest that younger employees will react more positively than older employees to the training approach of play.

Hypothesis 3: There will be an interaction between age and training condition such that training outcomes will be signljkantly greaterfor younger employees than for older employees in the play training sessions.

Method

Sample

Participants in this study were sixty-eight clerical and administrative full- time employees of a large public university. This sample comprises part of a larger series of microcomputer software training studies (Martocchio & Webster, 1992). The data for the present study were drawn from a different point in time than the data in the Martocchio and Webster paper. All participants were enrolled in a university-sponsored microcomputer training course to learn the merging feature of WordPerfect 5.0, a popular wordprocessing program. Eighty-nine percent of the sample was female. Average age was 41.27 years (SD = 11.6 years). Approximately 32 percent of the sample had a high school diploma or equivalent and an additional 47 percent had some college or technical training beyond high school.

Procedure The training course was taught as an integral part of microcomputer

training activities that are offered regularly to university employees_ The course

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announcement specified that this course was being offered in conjunction with research on microcomputer training sponsored by two public universities. Employees received a substantial discount on the training fees, and were guaranteed confidentiality. The training covered the use of the mail merge feature of WordPerfect. The training was conducted by one of the researchers in the microcomputer training laboratories of the university. The researchers designed the training program to teach the principles of mail merge as well as to provide trainees with the opportunity to interact with the program to practice mail merge principles.

Separate sessions were conducted to accommodate all course registrants. Each session was randomly labelled as either play or work. Thirty-six trainees received play labelling; 32 trainees received work labelling. Employee anonymity and confidentiality were promised.

The trainer began the session with a general introduction and administered the pre-training measures (including demographics and control and antecedent measures). After collecting the pre-training measures, the trainer labelled the class as ‘play’ or ‘work’. He then taught identical concepts and WordPerfect merging features in each one-hour session. The training approach consisted of lecture and practice (Gist et al., 1989). After a key issue was communicated, participants were asked to practice based on examples provided by the instructor. Participants were encouraged to elaborate on the examples if desired. Immediately following the lecture and practice, participants completed an objective test on the material contained in the training, and a questionnaire measuring motivation to learn and a manipulation check on the labelling.

Training Approach: Labeling as Work or Play

How do we label training classes as work or play? As argued above, we can initiate the appropriate cognitive category in trainees’minds by simply using these words. We developed ‘play’ and ‘work’ scripts. The appropriate words were generated in two ways. First, we built upon scripts developed for adults by Sandelands (1988, p. 1036-1037) and Glynn (1988, p. 113). Second, we a conducted a pretest study to generate more words relating to ‘play’ and ‘work’.

Pretest study. Our objective was to determine whether individuals identify particular words or issues as either play or work. That is, are some words associated only with an individual’s interpretation of work while there are other words that are associated only with an individual’s interpretation of play? The words chosen for our study were based on the research of several investigators who examined playfulness in adults spanning a variety of ages (i.e., Glynn, 1988; Jackson, 1984; Sandelands, 1988; Webster, 1989), as well as terminology that we felt relates to microcomputer usage. To address the question of whether some words are associated only with play or work, we surveyed 73 undergraduate students from a large, public, midwestern university who rated two parallel lists on the extent to which they interpreted the words as (a) play, and (b) work. Paired t-tests were conducted to determine whether there were systematic differences in individuals’ interpretations of words as either work or play.

The study revealed that individuals systematically identify particular words as associated with the concept of play, and particular words with the concept of work. Thus, the following scripts were developed based on both the pretest words and previous scripts created by Sandelands (1988) and Glynn (1988) (the scripts were piloted by Webster et al. (1990)). We established the context with the following script communicated to trainees at the beginning of the training. Italicized words reflect ‘play’ terms; bracketed words reflect ‘work’ terms. In order to retain uniformity in establishing the context as play or work, the trainer read the script across the training conditions.

In today’s training game (exercise), we would like (expect) you to think of yourselves as players of a game (employees of an organization). Please use this time imaginatively (efficiently) to explore (expand your knowledge of) the merging feature of WordPerfect. We think (fully expect) that you will have fun with this game (accomplish a lot in this exercise). Think of yourself as pluying with a puzzle (solving a real problem for your employer). Please don’t worry about making mistakes (try to keep mistakes to a minimum). For those of you already familiar with WordPerfect, we encourage you to (it is important for you to) use the time today freely (productively) to explore (investigate) WordPerfect Merging commands further. Please play around with (work away at) the WordPerfect commands, while the rest of us create (produce) the following document: fan letters to popular comedians (an employee benefits update form). Please be inventive (set ambitious goals) during this game (exercise). Beflexible and relax (purposeful and industrious). Enjoy yourself! (Work hard!)

In addition to the initial script, the trainer used condition-appropriate terminology at equal intervals throughout the training.

As a manipulation check on the training approaches of play and work, we evaluated whether participants perceived the training as similar to play or work. This was measured with Glynn’s (1988) item that states: “I thought that interacting with WordPerfect was more like playing a game than working on a test”. Responses were measured using a 5-point Likert scale ranging from strongly disagree to strongly agree.

Measures

The Appendix describes the control and antecedent measures (computer anxiety, computer attitudes, software efficacy, pretraining motivation to learn, training expectation, computer experience, and pretraining WordPerfect knowledge), independent measures (training approach and age), and training outcome measures (post-training motivation to learn and test performance).

Analyses

As a manipulation check on the play and work training approaches, we conducted a l-test (two-tailed) on the manipulation measure between training

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conditions. Trainees did differ in the expected direction in terms of perceptions of the training as work or play (t (64) = 2.37, p < .05).

To determine whether trainees differed based on their assignment to the experimental conditions (play or work), we performed t-tests: there were no differences between individuals in the play and work groups based on the pretraining measures of age, gender, education, length of service, occupation, WordPerfect knowledge, motivation to learn, computer anxiety, computer attitudes, computer experience, or software efficacy. Trainees did differ in the two groups on pretraining expectations of training as work or play (t (64) = 2.37, p < .05). Therefore, analyses controlled for this variable.

Further, many studies have demonstrated a negative relationship between age and both computer experience and computer knowledge (e.g., Gist et al., 1988). Because computer experience and knowledge should influence the training outcomes (Gist et al., 1989) we also controlled for computer experience and knowledge in the analyses. Thus, three covariates (training expectation, computer experience, and pretraining WordPerfect knowledge) were utilized in the tests of the hypotheses reported below.

To test the model outlined in Figure 1 (Hypotheses 1 through 3) we performed the following analyses. First, because the training outcomes of motivation to learn and test performance were correlated (r = .37, p < .OOl), scores on these measures were standardized to z-scores and summed to create an overall training outcome measure. This practice is consistent with prior training research (Noe & Schmitt, 1986). Second, we performed an hierarchical regression analysis. After controlling for the covariates (training expectations, computer experience, and pretraining WordPerfect knowledge) in the first steps, training approach (O=work, l=play), age, and the interaction term (training approach by age) were entered in subsequent steps.

Results

Table 1 presents correlations between study variables, including control and antecedent measures, independent measures, and training outcome measures.

Table 2 shows the results of the hypothesis tests. The covariate, training expectation, was not significant, but as suggested by past research, computer experience (Pchange = 7.56, p < .Ol) and pretraining WordPerfect knowledge (F change = 11.44, p < .Ol) were significant.

Hypothesis 1 proposing that ‘play’ training sessions would be associated with higher training outcomes than in ‘work’ training sessions, was not supported. Similarly, Hypothesis 2, which proposed that lower training outcomes would be associated with older employees than with younger employees, was not supported.

Hypothesis 3 proposing an interaction effect of age and training approach was supported (Fchange = 10.29, p < .Ol). In the play condition, the correlation between age and training outcome was negative (r = -.54,p < .OOl). In contrast, in the work condition, the correlation was not significant (r = .04). Figure 2

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Table 2. Summary of Results for Hypothesis Tests

Hypothesis Predictors R2 R’ change F change

Covariate Covariate Covariate HI HZ H.1

Training expectation Computer experience Pretraining WordPerfect knowledge Training approach

Age Interaction (Training approach by age)

.Ol .Ol 0.76

.12 .I1 7.56**

.26 .14 11.44**

.27 .Ol 0.89

.28 .Ol 1.24

.39 .ll 10.29**

Note: N = 66. ““p<.Ol

Training Outcomes

Training Approach

Figure 2. Interaction of Age and Training Approach

diagrams this interaction based on a split of younger employees versus older employees (< 40 versus S 40 from ADEA). Training outcomes were higher for younger employees (M = 0.64) than older employees (M = -0.57) in the play condition (t =‘2.12, p < .05). However, there were no training outcome differences in the work condition between younger employees (M = .32) and older employees (M = -0.03).

Discussion and Conclusions

Study results indicate that younger employees who received training labelled as ‘play’ scored higher on the training outcomes measure than older employees. In contrast, there were no differences between younger and older employees for training labelled as ‘work’. As argued above, one explanation for the interaction between age and training approach may be the increased work experiences of older employees. That is, older employees may see less ambiguity in their jobs. This relationship between age and work experience is borne out in the present study: the correlation between age and number of years of full-time work experience was strong (r = .51, p < .OOl).

Past research with students has demonstrated positive effects for play labelling as compared with work labelling in computer training (e.g., Webster et al., 1990). However, study results suggest that such labelling may have a negative effect on training outcomes for older employees. That is, with the increasing segregation with age between work and play, older employees may view the training approach of play more negatively than younger employees. In contrast, younger employees who are closer to their childhood days may still view the training approach of play positively because play involves feelings of pleasure and involvement. Thus, employee training may represent a situation that may be positively influenced by play labelling only for younger employees.

The absence of a main effect for age in the present study might be attributed to a self-selection bias related to ability. The significant effects for computer experience and knowledge might reflect individual differences in ability. That is, only the most able registered for the course (as reflected by relatively higher computer experience). Thus, it is possible that experience or ability, not age per se (Rhodes, 1983) influenced performance and motivation in training.

A strength of this study rests in the use of full-time employees in regular training sessions at work, and in the random assignment of employees to conditions. It extends previous empirical research in work and play labelling (e.g., Sandelands, 1988) by utilizing real tasks with employees in a field setting, and by extending measures from affective outcomes to learning outcomes. In addition, it responds to calls for empirical research exploring the moderating effects of individual differences on play and work labelling (e.g., Cellar & Barrett, 1987; Tang & Baumeister, 1984). Further, we found no differences between employees in the two conditions on pretraining measures of age, gender, education, length of service, occupation, WordPerfect knowledge, motivation to learn, computer anxiety, computer attitudes, computer experience, or software efficacy. Pretraining expectations of training as work

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or play were controlled for statistically. Therefore, although this study was conducted in the field, we believe that we achieved good control over alternative explanations for our findings.

Although we are confident about the validity of our results, we note several limitations of our research. First, employees received a substantial reduction in training costs, limiting somewhat the generalizability of our findings. Second, one of the researchers served as the trainer throughout the study. From a design perspective, it would have been desirable to have the trainer blind to the experimental manipulation (i.e., whether the condition was play or work) to avoid possible differences in trainer behavior as an alternative explanation for the findings. One solution would have been to use videotape presentations, or to have a trainer blind to the research question conduct the training. However, microcomputer training courses are designed to have trainees interact with the expert trainer. Given that the availability of experts is limited and that videotape presentations do not permit interaction with an expert during the training process, trainer awareness of the manipulation was a limitation of this field research.

We indirectly assessed whether the trainer’s behavior may have differed significantly between the play and the work conditions. Several single-item measures were used to assess trainees’ perceptions of the trainer’s performance as well as trainees’ satisfaction with and affective reaction to the trainer’s performance. If the trainer were behaving differently in the play and work conditions based on his knowledge of the research hypotheses, one would expect him to have behaved in ways that trainees perceived more favorably in the play condition than in the work condition. No differences were found between the play and work conditions on either trainees’ perceptions of the trainer’s performance or trainees’ satisfaction with the trainer’s performance.

A final limitation may relate to motivation. That is, since employees self- selected into the training, those who attended the training may have been highly motivated, again limiting the generalizability of the results. However, we examined the distributional properties of pretraining motivation to learn and found: mean (40.60), median (41), skewness (-0.38), kurtosis (-0.28), and range (21). Because a measure demonstrates good distributional properties when its mean and median are similar, skewness is less than two, and kurtosis is less than five (Ghiselli et al., 1981; Kendall, 1958), and since participants demonstrated a wide range of motivational scores, pre-training motivation does not seem to represent a limiting factor in the generalizability of the findings for employees who self-select into training. Of course, we can not generalize these results to computer training for employees who are required to attend.

Practical Implications Training designers and instructors must be sensitive that they do not create

situations where older employees perform less well than younger employees. Under the ADEA, older employees (at least 40 years of age) are considered protected class individuals with respect to employment practices such as training. Our interaction effect showed that providing uniform training (play

labelling) to younger and older workers resulted in lower training performance for older employees than for younger employees. Thus, it is important for trainers to know the potential effects of training techniques on older employees as compared to younger ones.

Two theoretical perspectives provided a basis to expect age differences in microcomputer training outcomes. These explanations suggest implications for human resource managers who sponsor training within organizations. The first explanation is motivational in nature. Older employees may be less likely to exert the effort necessary in training contexts because they believe that there will be shorter stream of payoffs than younger employees (Fossum et al., 1986). Thus, training designers will be faced with the challenge of linking successful transfer of training skills with more meaningful rewards for older employees (Schuler & Huber, 1990) with a shorter-term focus. The second explanation is ability-based (Giniger et al., 1983). Specifically, there tends to be a decline in memory and learning abilities among older employees than younger employees. The existing evidence regarding age-related differences in microcomputer training performance suggests that older employees can reach approximately the same proficiency as younger employees; however, doing so takes longer (Elias et al., 1987). Thus, perhaps developing training programs that incorporate self-paced progression should be considered.

The research presented here suggests that labelling situations as play can have practical significance for training younger employees. However, managers have tended to overlook the power of social information, such as labelling, on employees’ behaviors (Thomas & Griffin, 1989). With the high costs of training to organizations, it is critical that training enhance learning and motivation to learn (Huber, 1985). Labelling training as play or work is virtually a costless intervention that can impact positively trainee learning and motivation for younger employees.

We have suggested that turning work into play will not be universally beneficial for all ages of employees in organizations. In addition, we would expect more playful employees to take longer to interact with computers (Sandelands, 1988). Therefore, the tradeoff between increased time to task completion and enhanced learning for younger employees, for example, must be made for particular tasks and occupations. Notwithstanding the drawbacks of play, however, playfulness on computers represents a significant topic for organizations.

Research Implications Our research has focused on the interaction between age and training

approach on training outcomes. Having established an effect in the training context, future research should examine the extent to which the effects in the training context transfer to an individual’s job. Further, future research should replicate these findings in other contexts and organizational cultures. More generally, researchers should continue to explore the effects of other individual characteristics on training outcomes.

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Future research needs to assess trainees’ preconceptions about training which may have a subsequent impact on their learning and motivation. Although we found no effects of training expectations in the present study, this might not be the case in other organizations. Employees in other organizations may have much stronger expectations for the training sessions. Future research should investigate the strength of organizational cultures in terms of support for play at work. That is, organizational cultures may provide strong suggestions to employees regarding expectations of play or work that may override any effects of labelling or of age. For example, if the organization has a strong work ethic, labelling an activity as play may have detrimental effects even for younger employees. In contrast, some organizations have cultures that are very supportive of play (Godfrey, 1989), and in these organizations, labelling situations as play may have positive effects for both younger and older employees.

Although we did not find positive effects for play labelling with older employees, there may exist other methods for encouraging playfulness in these employees. For instance, these employees may learn new software by playing a game that is embedded in the software. Or, computer technologies may permit blurring of the work-nonwork distinction by allowing work-at-home. One prevalent alternative work arrangement is the electronic cottage (Kroll, 1984) in which employees work at home by means of computer terminals linked to their employer. The electronic cottage may offer workers more freedom to perform their jobs based on their own choices. Further, research comparing characteristics of spreadsheet software (Webster, 1989) and computer games (Malone, 198 1) has demonstrated the influence of technology characteristics on playfulness. Therefore, future research may want to examine the relationships between task, technology, and situational characteristics and playfulness in older employees.

Encouraging playfulness in employees may have more general implications for productivity in organizations. More effective learning and motivation should lead to higher quality outputs or products. Further, this learning and motivation should result in employees who are better able to react to new situations or tasks. Consequently, higher playfulness may result in higher individual and organizational creativity and flexibility. Therefore, organizations that can encourage playfulness in employees should be more adaptable to changing environments (Glynn, 1988; Levy, 1983; Miller, 1973; Starbuck & Webster, 1991).

In conclusion, findings from the present research will enable organizations to design more effective training programs that will enhance learning and motivation to learn. Using a relatively cost-free labelling intervention can have an impact on performance for some individuals within organizations. In particular, designers of effective microcomputer software training programs must consider differences in ages of the employees not only for legal reasons, but also to meet the challenges of competitive business environments.

Acknowledgment: The authors contributed equally to the research; authorship is in reverse-alphabetical order. The authors gratefully acknowledge the comments of Susan E. Jackson and Lance E. Sandelands on an earlier version of this article. This research received financial support from the Center for Interdisciplinary Research in Information Systems at The Pennsylvania State University, and training support from the Computer Education Center at the University of Illinois. Special thanks go to Julia McFali and Margaret O’Brien of the University of Illinois for research assistance, and to Jon Werner and Wayne Cooley for assistance in pretest data collection.

Appendix

Measures

Control and Antecedent Measures

1. Computer anxiety. A 19-item, self-report inventory (Heinssen et al., 1987) measured computer anxiety. Participants responded on 5-point scales (from strongly disagree to strongly agree) to nine positively- worded items, such as “I feel I will be able to keep up with the advances happening in the computer field”, and to ten negatively- worded items, such as “I feel apprehensive about working at a computer terminal.” Scores range from 19 (low computer anxiety) to 95 (high computer anxiety). Heinssen et al. (1987) reported an internal consistency reliability of .97, and a test- retest reliability of .70 over four weeks. Several studies have provided evidence of the validity of the measure (e.g., Chu & Spires, 1991; Webster et al., 1990). In the present study, internal consistency reliability was .95.

2. Computer attitudes. Zoltan and Chapanis’ (1982) General Attitudes Scale measured general attitudes toward microcomputers. It is based on 41 pairs of adjectives in a semantic differential format, such as efficient- inefficient. Their factor analysis of the scale resulted in six factors; here, we used the 11 pairs of adjectives making up the first factor. Possible scores range from 11 to 77, where higher scores indicate positive attitudes. Zoltan and Chapanis found that experienced users were more likely to stress positive adjectives than were inexperienced users. This measure has demonstrated high validity and reliability (Webster et al., 1990). In the present study, internal consistency reliability was .88.

3. Software efficacy. Software efficacy was measured using a six-item scale adapted from Hollenbeck and Brief (1987). We chose to use this scale rather than a scale that requires estimation of one’s confidence (e.g., the scale utilized by Gist, 1989; Gist et al., 1989). We believe that trainees would experience difficulty in estimating confidence levels when complex and abstract features of a word processing program would be encountered for the first time. The format of the scale is consistent with other research examining self-

JOURNAL OF MANAGEMENT, VOL. 19, NO. I. 1993

WORK INTO PLAY 143

efficacy (e.g., Hill, Smith, & Mann, 1987; Hollenbeck & Brief, 1987). Responses were measured using a 7-point Likert scale ranging from strongly disagree to strongly agree. Sample items include “I believe that WordPerfect Merging is a task on which I can perform well,” and “It is just not possible for me to use WordPerfect Merging as well as I would like” (reverse scored). Coefficient alpha was .95.

4. Pretraining motivation to learn. A seven-item measure was adapted from Baldwin and Karl (1987). Responses were measured using a 7- point Likert scale ranging from strongly disagree to strongly agree. Sample items include “I expect to learn more than the average participant in today’s WordPerfect course” and “I expect to become very proficient in the use of the WordPerfect merging feature.” Internal consistency reliability was .89.

5. Training expectations. Participants indicated their expectations toward the training as play or work. Participants were asked: “Of the activities listed below, please choose the activity that you expect will be the most similar to the training session today.” The activities listed were Glynn’s (1988, p. 205) six items (three work-like and three play-like) that she developed to determine an individual’s perception of an activity. For example, one item is “playing a chess game.” We utilized her items plus four additional items that we developed. Based on the choice, a dichotomous variable (0 = work, 1 = play) was created.

6. Computer experience. Five items captured self- rated computer skills, computer experiences, typing skills and usage. For example, one item asked participants to rate their skill levels with microcomputers on 5-point scales ranging from very low to very high. Internal consistency reliability for was .97.

7. Pretraining WordPerfect knowledge. A ten-item multiple choice quiz assessed WordPerfect knowledge before the start of the training. Based on an analysis of corrected item-total correlations, the test assessed homogenous content sampling of WordPerfect features. Internal consistency reliability was .86.

Independent Variables

8. Training approach. The work training approach was coded as “0” and the play training approach as “1”.

9. Employee age. Age was treated as a continuous variable in all analyses. For purposes of diagramming the interaction between age and training approach, two age groups (consistent with the ADEA) formed the basis for examining age effects: age less than 40, and age greater than or equal to 40.

Training Outcomes

10. Post-training motivation to learn. We utilized the same motivation to learn measure as in the pretraining questionnaire. Participants

completed the present measure under the knowledge that the training was to continue (as part of a larger study (Martocchio & Webster, 1991)). Internal consistency reliability was .87. Test performance. A ten-item multiple choice quiz assessed learning at the end of the training. Based on an analysis of corrected item- total correlations, the test assessed homogeneous content sampling of WordPerfect merging features. Internal consistency reliability was .85.

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turning work into play: implications for microcomputer software training

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