Compukm in Human Behavior; Vol. 9, pp. 411-426,1993 Plinted in the U.S.A. All rights reserved.

0747-563203 $6.00 + .OO Copyright 0 1993 Pergamon Press Ltd.

The Dimensionality and Correlates of Flow in Human-Computer Interactions

Jane Webster, Linda Klebe Trevino, and Lisa Ryan

Smeal College of Business Administration The Pennsylvania State University

Abstract - Past research on playfulness in human-computer interactions has demonstrated that computers can encourage playfulness and that playfulness can have positive and negative work-related consequences. Thus, playfulness in human-computer interactions represents a potentially important topic for information systems research. This article first defines playfulness in human-computer interactions in terms of Csikszentmihalyis (1975) flow theory and explores the dimensional@ of the flow construct. Second, it reports the results of two studies conducted to investigate the factor structure and correlates offlow in human-computer interactions. Finally, implications are discussed.

Researchers have proposed that playfulness can influence the success of human-computer interactions in organizations (e.g., Katz, 1987; Starbuck & Webster, 1991; Webster, Heian, & Michelman, 1990; Webster & Martocchio, 1992). However, difficulties in defining and measuring playfulness (Carroll & Thomas, 1988) have contributed to a lack of research in the area. Further, most of the existing research on playfulness has studied children rather than adults (e.g., Kay, 1985; Malone, 198 1; Papert, 1980; Turkle, 1984), has focused narrowly on games rather than on playfulness in a wider sense (e.g., Mehrabian & Wixen, 1986), or has occurred in classroom settings rather than in organizations (e.g., Webster et al., 1990). Thus, researchers have called for further studies of playful- ness in information systems (e.g., Carroll & Thomas, 1988; Davis, 1989; Kamouri, Kamouri, dz Smith, 1986; Katz, 1987; Malone, 1980; Ord, 1989).

We study playfulness in human-computer interactions from the perspective of flow theory (Csikszentmihalyi, 1975), a theory that has generated much interdisci- plinary interest and research in recent years (Bowman, 1982; Csikszentmihalyi & LeFevre, 1989; Day, 1981; Ellis, 1973; Miller, 1973; Kusyszyn, 1977). Flow has

Requests for reprints should be addressed to Jane Webster, Department of Management Science and Information Systems, Smeal College of Business Administration, The Pennsylvania State University, University Park, PA 16802.

411

been suggested as a useful construct for u~ders~nding and studying playfulness in general (Csikszentmihalyi, 1975) and, in particular, human interactions with comput- ers (Csikszentmihalyi, 1990; Ghani, Supnick, & Rooney, 1991). Flow characterizes the subjective ~~~n~u~~u~er interaction experience as playful and exploratory.

The studies reported here focus on the dimensio~ality and correlates of flow in human-computer interactions. Although flow has recently been incorporated into models of human-computer interactions (e.g., Ghani et al., 1991; Trevino & Webster, 1992), previous research has not explored the full dimensionali~ of this important construct. Thus, Study 1 extends previous empirical research on playful- ness and computer interactions by empirically examining an a priori multifactor model of flow, by studying adults rather than children, and by focusing on widely used spreadsheet software (Lotus l-2-3) rather than on games. Study 2 provides generalizability for the first study’s findings by examining the correlates of flow in a sample of employees using a different type of software - electronic mail.

PLAYFULNESS AND COMPUTER INTERACTIONS IN ORGANIZATIONS

The terms play, playful, and ~~uyf~~~es~ have been used in multiple ways in the lit- erature (Csikszentmihalyi, 1975; Day, 1981). Playfulness has been represented as an opposition to work (Kabanoff, 1980}, as an individual trait (Lieberman, 1977), and as a state (Ellis, 1973). In this article, the term ~~~y~lnes~ is used to represent the state of playfulness - more specifically, an aspect of users’ subjective experi- ences during computer interactions that is characterized by perceptions of pleasure and involvement (Sandelands & Buckner, 1989; Starbuck & Webster, 1991).

Research suggests that playfulness may be a useful construct for understanding human~omputer interactions. For example, Hiemstra (1983) reported that employ- ees frequently used the word play in describing their computer interactions, Similarly, Webster (1989) found that white-collar computer users often discussed their computer interactions in terms of play.

Playfulness has potentially important practical implications for information sys- tems design and use. For example, previous research suggests that higher playful- ness results in immediate subjective experiences such as positive mood and satis- faction {Csikszentmihalyi~ 1975; Levy, 1983; McGrath & Kelly, 1986; Sandelands, Ashford, & Dutton, 1983). Therefore, employees who interact more playfully with computers should view computer interactions more positively than those who inter- act less playfully. Consequently, they may be more motivated to engage in comput- er interactions in the future. Research also indicates that playfulness can result in longer term positive outcomes such as learning (Miller, 1973).

However, playfulness may also have negative effects, such as longer time to task completion (Sandelands, 1988) and over-involvement (Csikszentmihalyi, 1975). For example, playfulness may lead to nonproductive computer interactions, such as playing computer games at work, or making trivial revisions to the format of a doc- ument (Nash, 1990). In sum, playfulness in human-computer interactions can have signi~cant positive and negative practical consequences for organizations, suggest- ing the need for further research in the area.

Now and Playfulness

Research on playfulness in computer interactions requires a strong theoretical foundation and reliable measures. We draw upon Csikszentmihalyi’s (1975) moti-

Flow in human-computer interactions 413

vational theory of flow to provide the theoretical basis for understanding and mea- suring the playfulness state in human-computer interactions. According to flow theory, flow experiences are those optimal and enjoyable experiences in which we feel “in control of our actions, masters of our own fate . . . we feel a sense of exhil- aration, a deep sense of enjoyment” (Csikszentmihalyi, 1990, p. 3). When in the flow state,

players shift into a common mode of experience when they become absorbed in their activ- ity. This mode is characterized by a narrowing of the focus of awareness, so that irrelevant perceptions and thoughts are filtered out; by loss of self-consciousness, by a responsive- ness to clear goals and unambiguous feedback; and by a sense of control over the environ- ment . . . it is this common flow experience that people adduce as the main reason for per- forming the activity. (Csikszentmihalyi, 1975, p. 72)

According to flow theory, flow can occur when an activity challenges an indi- vidual enough to encourage playful, exploratory behaviors, without the activity being beyond the individual’s reach. For example, if the activity is too demanding it may produce anxiety rather than flow. Or, if it is not challenging enough, bore- dom, not flow, may be the result (Csikszentmihalyi, 1975, 1990).

Flow theory suggests that the flow state is characterized by four dimensions (Csikszentmihalyi, 1975; Csikszentmihalyi 8z LeFevre, 1989; Malone, 1980). According to Trevino and Webster (1992), within the human-computer interaction experience, flow incorporates the extent to which (a) the user perceives a sense of control over the computer interaction, (b) the user perceives that his or her atten- tion is focused on the interaction, (c) the user’s curiosity is aroused during the interaction, and (d) the user finds the interaction intrinsically interesting.

Control. Control has been cited as a particularly important element of flow (Csikszentmihalyi, 1975). For an activity to encourage playful, exploratory behav- iors, individuals should experience feelings of control over the computer interac- tion. One way computer technologies provide this feeling of control is by adapting to feedback from the individual in a way that is not possible with more static tech- nologies. For example, a typing mistake on an electronic typewriter can be correct- ed by pressing the Erase key to delete an incorrect character. However, computer- ized word processing provides for more control in that it allows text to be moved, modified, deleted, copied, and stored. Another way the computer can provide the individual with control is by providing explicit choices among alternatives (Malone & Lepper, 1987).

Attention focus. Attention focus is another important element of flow. When in the flow state, the individual’s focus of attention is narrowed to a limited stimulus field, filtering out irrelevant thoughts and perceptions. The person in flow loses self-consciousness, becomes absorbed in the activity, and becomes more intensely aware of his or her own mental processes (Csikszentmihalyi, 1975). With computer technologies, the computer screen can serve as the limited stimulus field and the focus of the individual’s attention. Computer users have reported being “mesmer- ized” during their computer interactions (Webster, 1989).

Curiosity. According to Malone (1981), an individual’s sensory or cognitive curiosity is aroused when in the flow state. Sensory curiosity may be aroused through varied, novel, and surprising stimuli (Berlyne, 1960). For example, com- puter technologies can encourage sensory curiosity through such technology char-

414 Webster; Trevino, and Ryan

acteristics as color and sound. Cognitive curiosity and the desire to attain compe- tence with the technology or the software may be stimulated by providing options such as menus that encourage exploration (Malone 8z Lepper, 1987). Users then become excited and curious about the possibilities available (Webster, 1989).

Int~~sic interest. When in the flow state, individuals find the activity in~nsically interesting (Csikszentmihalyi, 1975). Thus, they are involved in the activity for its own pleasure and enjoyment rather than for some utilitarian purpose. For example, employees may interact with the computer not just to get a job done but because they enjoy the interaction with the computer. They find themselves “playing” with text, spreadsheets, or graphics, getting involved - even lost in what they are doing (Collins, 1989).

Previous investigations have considered the role of flow in human~omputer interactions (Ghani, 1991; Trevino & Webster, 1992). However, these studies either measured flow as a two-dimensional construct (enjoyment and concentration) (Ghani, 1991; Ghani et al., 1991), similar to two of the four proposed dimensions, intrinsic interest and attention focus. Or, they used single items to measure each flow dimension (Trevino & Webster, 1992), making investigations of its dimen- sionality impossible. This study investigates the dimensionality of flow. We pro-

pose that flow is a ~ult~di~e~sional construct characterized by the four dimen- sions discussed above: control, attention focus, ca~~os~~, and intrinsic interest.

Csikszentmihalyi (1975) argued that the four dimensions of flow are linked and interdependent. A computer interaction that is perceived to be high in flow is simultaneously perceived as providing feelings of control, focusing the user’s attention, arousing the user’s curiosity, and being intrinsically interesting. On the other hand, individual flow dimensions can occur without the flow experience. For example, attention focus may occur during highly stressful activities as well as dur- ing flow experiences (Sandelands & Buckner, 1989). Thus, we explore the interre- lationships of the flow dimensions in this research. IVe expect the four dioecious offlow to be correlated.

Previous theory and research suggest that flow is associated with specific char- acteristics of computer software, and with technology use behaviors such as experi- mentation and voluntary use. Thus, we also explore the correlates of flow in this investigation. Specific hypotheses are discussed below.

Correlates of Flow

Perceived characteristics of the software. Perceived characteristics of computer software may engender the flow experience. For example, the user’s perceptions of the program's flexibility and modifiability may contribute to flow. Users may enjoy the idea that they can tailor software to their individual needs, varying commands, response speeds, difficulty levels, sounds, or colors (Malone & Lepper, 1987). In Ghani’s (1991) survey, computer users who developed their own applications and explored the capabilities of the software spent more time in computer interactions, were more involved, and reported greater enjoyment.

Hypothesis 1: Flow will be positively correlated with perceptions of flexibility and modifiability of the soBware.

Experimentation. The flow experience is thought to encourage exploratory behaviors (Csikszentmihalyi, 1975). Computer users who experience their interac- tion as higher in flow are more involved in what they are doing, expanding the

Flow in human-computer interactions 41.5

time and effort devoted to exploring new options and experimenting with new possibilities. For example, microcomputer systems encourage playfulness more than mainframe systems and result in more experimentation (Katz, 1987). Respondents to Ghani’s (1991) survey who reported higher experiences of flow also reported higher experimentation.

Hypothesis 2: Flow will be positively correlated with experimentation.

Expected voluntary use. When in the flow state during a computer interaction, involvement with the system is enjoyable and becomes an end in itself. The positive subjective experience becomes an important reason for performing the activity. In motivational terms, the pleasurable flow experience should be intrinsically motivat- ing. It is involving, enjoyable, and encourages repetition. Thus, the activity is per- formed for its own sake and the individual will voluntarily choose to engage in it (Davis, Bagozzi, & Warshaw, in press; Deci, 1975, 1981; Pinder, 1984). Therefore, employees who are involved in computer interactions that facilitate flow should be more motivated to voluntarily engage in future computer interactions.

Hypothesis 3: Flow will be positively correlated with expectations of future vol- untary computer interactions.

STUDY 1

This study was designed to investigate the two stated research expectations, that flow is multidimensional and that these dimensions are intercorrelated, and to test Hypotheses 1 through 3, that flow is positively correlated with perceptions of flexi- bility and modifiability, with experimentation, and with expectations of future vol- untary use.

Sample

Subjects were 133 first-year MBA students at a large northeastern university who were attending a one-day Lotus l-2-3 course prior to the start of the MBA pro- gram. The average participant was 27 years old, had 4.6 years of full-time work experience, and 1.8 years of management experience. Fifty-nine percent of the par- ticipants were males.

Measures

Surveys were distributed to participants at the end of the Lotus l-2-3 training class. Measures included flow, flexibility, modifiability, experimentation, and expected future use of the program.

Flow. A self-report scale measured the flow state. Although researchers have called for less emphasis on questionnaires as measurement tools (Webb, Campbell, Schwartz, Sechrest, & Grove, 1981), self-reports are appropriate for studying sub- jective states such as playfulness or flow (Davis, 1986; Sandelands & Buckner, 1989). Thus, we adapted and expanded a flow scale from Trevino and Webster (1992) that was based on the research of Csikszentmihalyi (1975), Malone (1980), and Sandelands and colleagues (1983). The Trevino and Webster (1992) four-item

416 Webster Trevino, and Ryun

scale included one item per flow dimension and was designed to measure flow in interactions with computer-mediated communication technologies. We expanded the scale to include three items per dimension (see Appendix A) and adapted it for use with individuals who were interacting with Lotus l-2-3 software. Responses were scored on 7-point scales ranging from strongly disagree (1) to ,~~~o~gZy agree (7), and were added together to create the measure of flow. The scale demonstrated good reliability (Cronbach’s alpha = .82),

Other measures. The measures proposed to relate to flow were developed based on Davis (1989) and Ghani (1991). All measures were found to be reliable: flexibility (alpha = .78), modifiability (alpha = .83), experimentation (alpha = .92), and expected voluntary use (alpha = .78). Responses were scored on 7-point scales ranging from ~~~~~g~y &agree to strongly agree. Appendix B presents the specific items used for each measure.

Confirmatory factor analysis (CFA) (LISREL 7; Joreskog & Sorbom, 1989) tested Hypotheses 1 and 2. CFA allows a model to be specified based on theory and allows for a rigorous test of an hypo~esized a priori factor structure. To investigate the pro- posed dimensionali~ of flow, a pattern matrix of zeros and ones was entered for the items, such that ones indicated items which should load on a factor based on theory (e.g., Item 1 should load on the factor of “control”), and zeros indicated those items that should not load on a factor (e.g., Item 4 should not load on “control”). Tratios determined the significance of loadings of the items on the factors. Examination of standardized residuals and modification indices provided an indication of whether the model needed to be reestimated (Joreskog & Sorbom, 1989).

To investigate the proposed interco~elations among factors, a pattern matrix of zeros and ones was entered for the factors, such that ones indicated the pairs of fac- tors that were free to relate. In this case, we proposed that all factors would relate. Again, t ratios dete~ined the signi~cance of the relationships between the factors.

LISREL also provides several indicators for assessing the overall fit of the model. Chi-square provides a test of the model. However, chi-square tests are less useful for large-sample studies. For large samples, chi-square should be less than three times the degrees of freedom (Burke, Brief, George, Roberson, & Webster, 1989). Thus, chi-square to degrees of freedom ratios (Wheaton, hImhen, Alwin, & Summers, 1977), adjusted goodness of fit tests (Bentler & Bonnet6 198O), and root mean square residuals (Hertig & Costner, 1985), were used to assess the fit of the overall model, Small chi-square to degrees of freedom ratios, adjusted goodness of fit tests above .90, and small root mean square residuals indicate a good fit.

Before testing whether flow related to the other constructs as proposed, we conducted a factor analysis, using varimax rotation, on the items making up the ~exibility, modi~ability, and ex~rimentatiou measures (because of their potential overlap). Pearson correlations were then calculated between flow and the result- ing measures.

Table 1 presents the factor loadings (lambda X) for the CFA. Each item loads sig- nificantly on its proposed factor, providing empirical support for the proposed four dimensions. Table 2 presents the estimated co~elations between the factors (phi). It should be noted that Factors 3 and 4 (Curiosity and Intrinsic Interest) are highly

Flow in human-computer interactions 417

Table 1. Factor Loading Matrix (Lambda X)

Factor Loadings

Flow Item Control Attention Intrinsic

Focus Curiosity interest

1 ,874’ 2 .633’ 3 .481* 4 ,460’ 5 ,280’ 6 .925’ 7 ,817’ 8 ,822’ 9 ,611’

10 .571 l 11 ,713’ 12 ,804’

‘p < .05.

correlated (r = .97), suggesting that they represent a common dimension. Thus, our expectation that the factors would be interrelated is generally supported. That is, the correlations between factors are significant except for the correlation between control and attention focus.

The LISREL analysis resulted in a chi-square to degrees of freedom ratio of 2.27, a goodness of fit test of .88, an adjusted goodness of fit test of JO, and a root mean square residual of 0.09. A chi-square of 108.97 is 2.3 times the degrees of freedom. Thus, the original model fits the data adequately, but could be improved.

First, an examination of the standardized residuals and modification indices indi- cates that Item 3 presents a problem (standardized residuals are large, and two modification indices for Item 3 are larger than 10). Second, the high correlation between Factors 3 and 4 (.97) suggests that these factors represent the same dimen- sion. Thus, the LISREL analyses were rerun hypothesizing three factors (Items 7 through 12 on the third factor and dropping Item 3). The overall fit of the model improved to a chi-square to degrees of freedom ratio of 1.71, a goodness of fit test of .90, an adjusted goodness of fit test of .84, and a root mean square residual of 0.07. A chi-square of 70.20 is 1.7 times the degrees of freedom, indicating an improved fit. In the revised model, all items loaded significantly on their proposed factors, and all modification indices were low (less than 6).

The factor analysis of the items making up the flexibility, modifiability, and experimentation measures resulted in two factors with eigenvalues greater than

Table 2. Relationships Between Flow Factors

Factor

Attention Intrinsic Control Focus Curiosity Interest

Control 1 Attention Focus ,134 1 Curiosity ,469’ ,436’ 1 Intrinsic Interest .559* ,400’ ,973’ 1

l p < .05.

418 Webstec Trevino, and Ryan

one. All of the items from the flexibility and modifiability measures loaded on the first factor (eigenvalue = 3.2, 53.4% of variance explained). Thus, we have com- bined these variables into a single variable labeled flexibility/modifiability. Cronbach’s alpha for flexibility/modifiability was computed to be .83. The experi- mentation measure items loaded on the second factor (eigenvalue = 1.3, 22.3% of variance explained).

Table 3 presents the means, standard deviations, and Pearson correlation coeffr- cients above the diagonal, using 11 items to measure flow (dropping item 3). The correlations between flow and flexibility/ modifiability, experimentation, and expected voluntary use were significant and in the expected direction, supporting Hypotheses 1,2, and 3.

STUDY 2

Study 2 was undertaken to provide generalizability for the first study’s findings by testing the correlational hypotheses (1 through 3) in a sample of workers in an actual organizational setting who were using a different type of computer software - electronic mail. Additional hypotheses (4 and 5) regarding actual and perceived work outcomes that are expected to correlate with flow are discussed below and tested in study two.

Flow and Work Outcomes

Flow has been related to work outcomes such as actual technology use and effec- tiveness. According to flow theory, the positive subjective experience associated with flow becomes an important reason for performing an activity (Csikszentmihalyi, 1975). People are more likely to do what feels good. Therefore, employees involved in computer interactions that facilitate flow are expected to actually use the technology more. Trevino and Webster (1992) found that electron- ic mail and voice mail users who reported higher flow also perceived increases in quantity of communication.

Table 3. Correlations of Study Variables

Study 1 Study 2

1 2 3 4 5 6 M SD M SD

1. Flow (11 items) - 57 38 64 53.92 9.30 47.82 7.70 2. Flexibility/Modifiability 64 - 39 63 19.83 4.40 19.26 5.30 3. Experimentation 47 49 - 39 9.64 2.87 8.91 2.98 4. Voluntary Use 70 76 31 - 10.27 3.08 11.55 3.17 5. Communication

Quantity 38 64 34 64 - 36.67 9.69 6. Communication

Effectiveness 56 71 39 61 92 - 48.12 15.50 7. Actual Use -82 -61 -21 -37 18 -79 4.37 3.16

Nofe. Study 1 correlations are above the diagonal (all correlations are significant at p < ,001). Study 2 correlations are below the diagonal (correlations above .30 are significant at p < .05, correlations above .46 are significant at p < .Ol, correlations above .54 are significant at p -Z ,001). For “actual use,” the sample size was different. Therefore, correlations above .77 are significant at p c .05. Decimals are omitted. Blank spaces indicate unmeasured variables.

Flow in human-computer interactions 419

Hypothesis 4: Flow will be positively correlated with actual technology use.

Further, people exercise and develop skills through the exploratory behaviors that characterize flow interactions. Thus, learning should result from the flow experience (Miller, 1973), leading to higher quality and/or quantity of outputs or products from the interactions. Ghani (1991) found that users reporting higher flow in computer interactions also reported higher learning. Trevino and Webster (1992) found that flow in computer-mediated technology interactions was positively asso- ciated with perceived communication quantity and effectiveness.

Hypothesis 5: Flow will be positively correlated with perceived communication quantity and eflectiveness.

Thus, Study 2 tests the correlation of flow with the following variables: flexibili- ty, modifiability, experimentation, expected voluntary use, actual electronic mail use, communication effectiveness, and perceived quantity of communication.

Sample

Study 2 data were collected as part of a larger study of electronic mail use in an accounting department (Ryan, 1991). Forty-three employees in the accounting department of a large organization in the computer industry who were electronic mail users were surveyed. The average participant was 38 years old, had 2 years of postsecondary education, 14.4 years of full-time work experience, and 6.7 years of management experience. Forty-four percent of the participants were males.

Measures

Flow. The 1 l-item measure of flow described in Study 1 was adapted for use here, with the questions referring to the electronic mail system rather than the Lotus soft- ware (Cronbach’s alpha = .72).

Other measures. As in Study 1, flexibility/modifiability (alpha = 83) experimenta- tion (alpha = .98), and expected voluntary use (alpha = 86) measured constructs that were hypothesized to relate to flow. Further, in this study, participants also responded to items measuring perceived communication effectiveness (alpha = .98) and per- ceived quantity of communication (alpha = .88) (both from Trevino 8z Webster, 1992; see Appendix B). Finally, in this study, we collected data on an objective quan- tity measure - the actual number of unread electronic mail messages in the respon- dent’s electronic mailbox (see Appendix B). Because employees reporting higher flow would be expected to read and respond to their electronic mail messages, we expect a negative relationship between flow and number of unread messages.

Analyses

Pearson correlations were computed for the relationship of flow with flexibility/modifiability, experimentation, expected voluntary use, the actual num- ber of unread electronic mail messages, perceived communication effectiveness, and perceived quantity of communication.

Results

Table 3 presents the means, standard deviations, and Pearson correlations below the diagonal. All correlations with flow were significant and in the proposed direc- tion, providing additional support for all of the hypotheses.

420 Webstel; Trevino, and Ryan

DISCUSSION

Researchers have called for further studies on playfulness in information systems (e.g., Carroll & Thomas, 1988; Davis, 1989; Kamouri et al., 1986; Katz, 1987; Malone, 1980; Ord, 1989). The studies reported here draw upon flow theory (Csikszentmihalyi, 1975) to provide a theoretical approach to measuring and studying playfulness in human-computer interactions. Flow represents a subjec- tive psychological experience that characterizes the human-computer experience as playful and exploratory. Based upon flow theory, we proposed that flow was a multidimensional construct comprised of four dimensions - control, attention focus, curiosity, and intrinsic interest - and that these dimensions would be inter- related. Further, we hypothesized that flow would be significantly correlated with modifiability, experimentation, expected software use, perceived communication effectiveness, quantity of communication, and actual electronic mail system use. Consequently, Study 1 was designed to develop a reliable measure of flow, and to explore the factor structure and correlates of flow in human-computer interac- tions. Study 2 provided an opportunity to consider additional correlates of flow in an actual work setting with a different type of computer software, providing exter- nal validity to the findings.

The measure of flow developed for this study was reliable (alpha = .82 in Study 1 and .72 in Study 2) and should be useful for future studies that wish to incorpo- rate the flow construct. Reliability was substantially higher in Study 1, perhaps because the measure of flow was taken immediately after the computer interaction. Flow represents a state, a temporary subjective experience of the human-computer interaction, that can be captured more reliably when measured immediately during or after the interaction, In contrast, in Study 2, employees recalled past interactions with electronic mail. This finding suggests that future studies should attempt to measure flow during or soon after the computer interaction.

The first study’s results suggest that, within computer interactions, flow consists of three dimensions rather than the four dimensions originally proposed based upon flow theory. The third dimension is comprised of the curiosity and intrinsic interest dimensions and could be labeled cognitive enjoyment. The curiosity and intrinsic interest aspects of the flow experience appear to be highly interdependent in interactions with computers. In computer interactions, flow involves a certain kind of intrinsic interest. This is not the intrinsic interest that arises from a game of chance, for example, but rather the intrinsic interest that accompanies cognitive arousal and use of the imagination.

All correlational hypotheses were supported in both studies, suggesting that the flow experience is associated with perceived characteristics of the computer soft- ware as well as with relevant work-related outcomes. This finding has implications for information system development. For example, systems that are designed to provide more user control, focus the user’s attention, and incite their cognitive enjoyment may result in more positive attitudes, more system use, and more posi- tive work outcomes such as perceived communication effectiveness.

The strength of these studies involves the investigation of the state of playful- ness in several settings: employees in an organization and graduate students in a training lab. Further, these studies used actual software, rather than contrived soft- ware designed for experimental purposes, furthering the external validity of the findings. In Study 2 we were also able to collect objective data on actual electronic mail use and found a strong correlation between flow and usage of the electronic

Flow in humun-computer interactions 421

mail system. Finally, we focused on the dimensionality and validity of flow in order to provide a stronger base for future studies of human-computer interaction that wish to incorporate this relatively new construct into their investigations. The significance of this work will increase to the extent that researchers find it useful to adopt the measure developed and tested here.

These studies are also subject to a number of limitations. The data are cross-sec- tional, perceptual (with the exception of the number of unread electronic mail mes- sages), and subject to common method variance. Future research would benefit from longitudinal research designs and the inclusion of more objective outcome data. Further, because of a limited sample size in Study 2, we were unable to utilize analytic techniques that could account for the correlations among the dependent variables in that study. Finally, this study of the dimensionality and correlates of flow represents only an initial assessment of construct and concurrent validity. Future research will be needed to extend and build upon this study to further evalu- ate the validity of the flow construct.

Implications for Future Research

These studies suggest that playfulness is clearly a relevant topic for continuing info~ation systems research. Future studies may wish to pursue additional mea- sures of playfulness as well as additional positive and negative consequences of the flow experience.

~e~~u~~g p~yf~Z~es~. Because the state of playfulness is a subjective experience, behavioral measures (such as those captured through videotapes and keystrokes) may be less useful than self-reports in measuring playfulness (Sandelands & Buckner, 1989). However, future research should continue to examine alternative measures of the state of playfulness. For example, Sansone, Sachau, and Weir (1989) explored two behavioral measures of playfulness. These related to the increased focus of attention during playfulness: an individual’s reaction time in response to a buzzer, and the individua~‘s estimate of how much time had passed before the buzzer sounded. Similarly, Sandelands and Buckner (1989) proposed that the number of interruptions reported, or the amount of incidental learning dur- ing an activity, could indicate playfulness. Finally, exploratory behaviors may be measured indirectly by examining outcome measures such as learning resulting from the interaction (e.g., Webster & Martocchio, 1992), quality of the outputs from the interaction (e.g., Glynn, 1988), or creativity of the final product.

~~~~~e aptd negative outcomes of ~~yf~l~e~~. This research focused on several positive outcomes of playfulness. However, it may be possible to consider addi- tional positive outcomes, as well as potentially negative outcomes of playfulness. For instance, creativity represents a potentially positive outcome of playfulness requiring further research attention (Eiam & Mead, 1990). Ellis (1973) argued that a playful response is a creative one that develops an individual’s flexibility. Several studies have demonstrated that playfulness contributes to creativity (e.g., Csiksze~tmihalyi, 1975; Lieberman, 1977). Extrapolating to work situations, Glynn (1988) argued that increased flexibility due to playfulness can aid in organi- zational problem-solving. Lieberman (1977) suggested that encouraging playful- ness at work would lead to greater creativity on the job, making “the difference between humdrum performance and real productivity” (p. 143). Consequently, future research should examine creativity resulting from playfulness in human-computer interactions.

422 Webster; Trevino, and Ryan

Future research also needs to examine the negative consequences of playfulness, such as time to task completion and overinvolvement. During more playful interac- tions, exploratory behaviors lead to a longer time to task completion (Bateson, 1955; Miller, 1973; Sandelands, 1988). For example, Sandelands (1988) found that a task labeled as “play” took longer to complete than the same task labeled as “work.” In support of this finding in the information systems area, Jarvenpaa and Dickson (1988) argued that a great deal of flexibility in business graphics programs has a negative effect on productivity. They proposed that employees will use unnecessary trial-and-error approaches while interacting with these programs. Therefore, more playful interactions with computers at work may take longer to complete than less playful interactions.

Overinvolvement represents another possible negative outcome of playful- ness. The holding power of higher playfulness may produce mental and physical strain (Csikszentmihalyi, 1975). For example, when outside of the world of com- puter games, children have reported feeling cut off and depressed; they may neglect other activities (Turkle, 1984). Extending this phenomenon to work, more playful computer systems may be so enjoyable that employees neglect other tasks. This research suggests many avenues for future research to explore the potential negative consequences of playfulness in the human-computer inter- action context.

Implications for Work Outcomes

The positive and negative outcomes discussed above suggest that playfulness can have both advantages and disadvantages for organizations. First, playfulness may have productivity implications in terms of output quality. More effective learning should lead to higher quality outputs or products. Further, this learning should result in users who are better able to react to new situations or tasks. Consequently, higher playfulness may result in higher individual and organizational creativity and flexibility, and organizations that encourage playfulness may be more adaptable to changing environments (Glynn, 1988; Levy, 1983; Miller, 1973; Starbuck & Webster, 1991).

However, because increased playfulness may also have negative consequences, playfulness should not be encouraged in all situations. Rather, it may be appropri- ate to encourage playfulness in some situations and to discourage it in others. Because playfulness usually results in longer time to task completion (but more interest in the task), it would be inappropriate to encourage playfulness for tasks where speed is essential, or where there is a known best way to accomplish the task. In contrast, it may be appropriate to encourage playfulness for learning new systems (e.g., in computer training), constructing creative solutions to problems (e.g., in new system design), interacting with the same system frequently (e.g., for the dominant job task), and solving problems with no known solutions (e.g., in brainstorming about strategic information systems).

In conclusion, this article supports the idea that playfulness in computer inter- actions represents a key topic for researchers. It provides a useful measure of flow, support for its dimensionality, and support for its correlates in two settings. Future research should continue to explore the antecedents and outcomes of playfulness, as well as its relative advantages and disadvantages in human-com- puter interactions.

Acknowledgments - The authors thank Janis Gogan for assistance in data collection and Gloria Hsieh for consultation on statistical analysis.

Flow in human-computer interactions 423

REFERENCES

Bateson, G. (1955). A theory of play and fantasy. Psychiatric Reports, 2, 39-51. Bentler, P. M., & Bonnett, D. G. (1980). Significance tests and goodness of fit in the analysis of

covariance structures. Psychological Bulletin, 88, 588-606. Berlyne, D. E. (1960). Con.ict, arousal and curiosity. New York: McGraw Hill. Bowman, R. F., Jr. (1982). A pat-man theory of motivation: Tactical implications for classroom

instruction. Educational Technology, 22(9), 14-16. Burke, M. J., Brief, A. P., George, J. M., Roberson, L., & Webster, J. (1989). Measuring affect at

work: Confirmatory analyses of competing mood structures with conceptual linkage to cortical regulatory systems. Journal of Personality and Social Psychology, 57, 1091-1102.

Carroll, J. M., & Thomas, J. C. (1988). Fun. SZGCHZ Bulletin, 19(3), 21-24. Collins, J. (1989, Winter/Spring). Creativity and the personal computer. The Wall Street Journal

National Business Employment Weekly, pp. 10-l 1. Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco: Jossey-Bass. Csikszentmihalyi, M. (1990). The psychology of optimal experience. New York: Harper & Row. Csikszentmihalyi, M., & LeFevre, J. A. (1989). Optimal experience in work and leisure. Journal of

Personality and Social Psychology, 56, 815-822. Davis, F. D., Bagozzi, R. I?, & Warshaw, P. R. (in press). Extrinsic and intrinsic motivation to use

computers in the workplace. Journal of Applied Social Psychology. Davis, F. D., Jr. (1986). A technology acceptance model for empirically testing new end-user informa-

tion systems: Theory and results. Unpublished doctoral dissertation, MIT Sloan School of Management, Cambridge, MA.

Davis, F. D., Jr. (1989). Perceived usefulness, perceived ease of use and user acceptance of informa- tion technology. MIS Quarterly, 13, 319-342.

Day, H. I. (1981). Play. In H. I. Day (Ed.), Advances in intrinsic motivation and aesthetics (pp. 225-250). New York: Plenum Press.

Deci, E. L. (1975). Intrinsic motivation. New York: Plenum Press. Deci, E. L. (1981). The psychology of self-&termination. Lexington, MA: Heath. Elam, J. J., & Mead, M. (1990). Can software influence creativity?. Information Systems Research, 1,

l-22. Ellis, M. J. (1973). Why people play. Englewood Cliffs, NJ: Prentice-Hall. Ghani, J. A. (1991). Flow in human-computer interactions: Test of a model. In J. Carey (Ed.), Human

factors in management information systems: An organizational perspective. Norwood, NJ: Ablex. Ghani, J. A., Supnick, R., & Rooney, P (1991). The experience of flow in computer-mediated and in

face-to-face groups. In J. I. DeGross, I. Benbasat, G. DeSanctis, & C. M. Beath (Eds.), Proceedings of the Twelfh International Conference on Information Systems (pp. 229-237). New York: ICIS.

Glynn, M. A. (1988). The perceptual structuring of tasks: A cognitive approach to understanding task attitudes and behavior Unpublished doctoral dissertation, Columbia University, New York.

Hertig, J. R., & Costner, H. L. (1985). Respecification in multiple indicator models. In H. M. Blalock (Ed.), Causal models in the social sciences (pp. 321-393). New York: Aldine.

Hiemstra, G. (1983). You say you want a revolution? “Information technology” in organizations. Communication Yearbook, 7,802-827.

Jarvenpaa, S. L., & Dickson, G. W. (1988). Graphics and managerial decision making: Research based guidelines. Communications of the ACM, 31,764-774.

Joreskog, K. G., & Sorbom, D. (1989). Lisrel 7. A guide to the program and applications (2nd ed.). Chicago: SPSS.

Kabanoff, B. (1980). Work and nonwork: A review of models, methods, and findings. Psychological Bulletin, 88,60-77.

Kamouri, A. L., Kamouri, J., & Smith, K. H. (1986). Training by exploration: Facilitating the transfer of procedural knowledge through analogical reasoning. International Journal of Man-Machine Studies, 24, 171-192.

Katz, J. A. (1987). Playing at innovation in the computer revolution. In M. Frese, E. Ulich, & W. Dzida (Eds.), Psychological issues of human computer interaction in the work place (pp. 97-l 12). Amsterdam: North-Holland.

Kay, A. (1985). Software’s second act. Science Magazine, 6, 122-126. Kusyszyn, I. (1977). How gambling saved me from a misspent sabbatical. Journal of Humanistic

Psychology, 17, 19-34. Levy, J. (1983). Play behavior. Malabar, FL: Robert E. Krieger.

424 Webster Trevino, and Ryan

Lieberman, J. N. (1977). Playfilness. New York: Academic Press. Malone, T. W. (1980). What makes thingsfin to learn? A study of intrinsically motivating computer

games (Cognitive and Instructional Science Series, Vol. CIS-7, SSL-80-11). Palo Alto, CA: Xerox.

Malone, T. W. (1981). Toward a theory of intrinsically motivated instruction. Cognitive Science, 4, 333-369.

Malone, T. W., & Lepper, M. R. (1987). Making learning fun: A taxonomy of intrinsic motivations for learning. In R. E. Snow & M. J. Farr (Eds.), Aptitude, learning, and instruction: III. Conative and afsective process analyses (pp. 223-251). Hillsdale, NJ: Erlbaum.

McGrath, J. E., & Kelly, J. R. (1986). Time and human interaction. New York: Guilford. Mehrabian, A., & Wixen, W. J. (1986). Preferences for individual video games as a function of their

emotional effects on players. Journal of Applied Social Psychology, 16, 3-l 5. Miller, S. (1973). Ends, means, and galumphing: Some leitmotifs of play. American Anthropologist,

75,87-98. Nash, J. E. (1990). Working at working. Computer fritters. Journal of Contemporary Ethnography,

19.207-225. Ord, J. G. (1989). Who’s joking? The information system at play. Interacting With Computers: The

Interdisciplinary Journal of Human-Computer Interaction, 1, 118-128. Papert, S. (1980). Mindstorms. New York: Basic Books. Pinder, C. C. (1984). Multiple regression in behavioral research. Glenview, IL: Scott, Foresman. Ryan, L. A. (1991). Perceived impacts of electronic mail on accounting personnel. Unpublished hon-

ors thesis, The Pennsylvania State University, University Park. Sandelands, L. E. (1988). Effects of work and play signals on task evaluation. Journal of Applied

Social Psychology, 18, 1032-1048. Sandelands, L. E., Ashford, S. J., & Dutton, J. E. (1983). Reconceptualizing the overjustification

effect: A template-matching approach. Motivation and Emotion, 7, 229-255. Sandelands, L. E., & Buckner, G. C. (1989). Of art and work: Aesthetic experience and the psycholo-

gy of work feelings. In L. L. Cummings & B. M. Staw (Eds.), Research in organizational behav- ior (pp. 105-l 3 1). Greenwich, CT: JAI Press.

Sansone, C., Sachau, D. A., & Weir, C. (1989). Effects of instruction on intrinsic interest: The impor- tance of context. Journal of Personality and Social Psychology, 57,819-829.

Starbuck, W. H., & Webster, J. (1991). When is play productive?. Accounting, Management, and Information Technology, 1, 71-90.

Trevino, L. K., & Webster, J. (1992). Flow in computer-mediated communication: Electronic mail and voice mail evaluation and impacts. Communication Research, 19,539-573.

Turkle, S. (1984). The second self. New York: Simon & Schuster. Webster, J. (1989). Playfulness and computers at work. Unpublished doctoral dissertation, New York

University. Webster, J., Heian, J. B., & Michelman, J. E. (1990). Computer training and computer anxiety in the

educational process: An experimental analysis. In J. I. DeGross, M. Alavi, & H. Oppeland (Eds.), Proceedings of the Eleventh International Conference on Information Systems (pp. 171-182). Copenhagen: ICIS.

Webster, J., & Mattocchio, J. J. (1992). Microcomputer playfulness: Development of a measure with workplace implications. MIS Quarterly, 16, 201-226.

Wheaton, B., Muthen, B., Alwin, D., & Summers, G. (1977). Assessing reliability and stability in panel models. In D. Heise (Ed.), Sociological methodology (pp. 84-136). San Francisco: Jossey-Bass.

APPENDIX A

ITEMS USED TO MEASURE FLOW DIMENSIONS

Control

1. When using Lotus l-2-3, I felt in control. 2. I felt that I had no control over my interaction with Lotus l-2-3. (Reverse-

scored) 3. Lotus l-2-3 allowed me to control the computer interaction.

Flow in human-computer interactions 425

Attention Focus

4. When using Lotus l-2-3, I thought about other things. (Reverse-scored) 5. When using Lotus l-2-3, I was aware of distractions. (Reverse-scored) 6. When using Lotus l-2-3, I was totally absorbed in what I was doing.

Curiosity

7. Using Lotus l-2-3 excited my curiosity. 8. Interacting with Lotus made me curious. 9. Using Lotus l-2-3 aroused my imagination.

Intrinsic Interest

10. Using Lotus l-2-3 bored me. (Reverse-scored) 11. Using Lotus l-2-3 was intrinsically interesting. 12. Lotus l-2-3 was fun for me to use.

APPENDIX B

ITEMS USED TO MEASURE CORRELATES OF FLOW

Flexibility

l Lotus l-2-3 was flexible to interact with. l Lotus provided many alternative ways to interact with it.

Modifiability

l I can modify Lotus l-2-3 to meet my own personal needs. l Lotus l-2-3 is responsive to my needs.

Experimentation

l When using Lotus l-2-3, I experimented with new commands. l When using Lotus l-2-3, I explored new commands.

Expected Voluntary Use

l I would still use Lotus l-2-3 even if it were not required. l I would only use Lotus l-2-3 if it were required. (Reverse-scored)

Communication Effectiveness

l Keeping others up-to-date l Size of my communication network l Speed of information sharing l Effectiveness of my work l Overall quality of my work l Quantity of work output l Speed of decision-making l My control over my communications l Ability to reach people

426 Webster Trevino, and Ryan

Quantify of Communication

l Communication initiated by me l Communication received by me l Information overload l Communication in work group l Total amount of communication l Frequency of communication l Variety of communication partners

Actual Number of Unread Messages

We would now like you to access your Electronic Mail system, and record: “Number of new messages not read

7,

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