Using information technology: engagement modes, flow experience, and personality orientations

Computers in Human Behavior Computers in Human Behavior 22 (2006) 899–916 www.elsevier.com/locate/comphumbeh

Using information technology: engagement modes, flow experience, and personality orientations Parvaneh Sharafi a

a,*

, Leif Hedman b, Henry Montgomery

a

Department of Psychology, Stockholm University, Stockholm 106-91, Sweden b Department of Psychology, Umea˚ University, 901-87 Umea˚, Sweden Available online 9 April 2004

Abstract The engagement mode (EM) model describes how an IT user (subject) engages in an activity with an object in a certain mode. The model specifies five engagement modes (Enjoying/Acceptance, Ambition/Curiosity, Avoidance/Hesitation, Frustration/Anxiety, and Efficiency/Productivity), which are characterized on three dimensions (evaluation of object, locus of control between subject and object, and intrinsic or extrinsic focus of motivation). Using questionnaire data from 290 participants, we extended previous empirical support for the model as well as described the model’s relationship to flow experience. In addition, it was found that autonomy, controlled and impersonal orientation in conjunction with socio-demographic variables differentiated among specific engagement modes and flow experience. We conclude that the EM-model, flow experience, and causality orientation theories provide a uniform framework for understanding how people adapt to information technology. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Engagement modes; Flow experience; Motivational and personality orientation; Information technology

*

Corresponding author. Tel.: +46 8 16 38 23. E-mail address: [email protected] (P. Sharafi).

0747-5632/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.chb.2004.03.022

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1. Introduction The most obvious reasons for using IT are to have an effective and enjoyable interaction with technology that does not frustrate the user or waste the user’s time (Norman, 1998). The challenge for researchers is to identify and describe essential conditions of this interaction in terms of positive and negative aspects associated with technology. Recent findings in usability research have emphasized the need for enhanced descriptive models that can capture the psychological characteristics of users and how users interact with computer technology (Carroll, 1991, 1997, 2002). Descriptive models that address the human side of interaction with IT would provide valuable and practical information regarding the design of useful IT-systems (Bannon, 1991; Carroll, 1997; Grudin, 1990; Nardi, 1996). To fulfill these needs, we have presented a descriptive model, the engagement mode (EM) model; to address the types of interaction users engage in using IT. In addition, this model assumes that the user needs some skill to use IT and that IT helps the user acquire skills (Montgomery, Sharafi, & Hedman, in press). We define IT as the use of computers to accomplish a task such as searching and receiving information and using computers as a communication tool at work and during leisure time. Previously, the concept of engagement mode has been used to describe general properties of people’s activities in relation to the external world (Heidegger, 1927/1996). The EM-model describes an individual’s different modes of engagement with IT, the underlying psychological aspects of these modes, and how they are related to the flow experience. The validity of the EM-model is supported by multivariate analysis (multidimensional scaling and factor analysis) of self-reported data from more than 300 participants (Montgomery et al., in press). The main purpose of this paper is to examine the reliability of the model by testing it on new data and to examine the validity of the model in a broader sense than in our previous study by exploring how it is related to certain aspects of the user’s personality. 1.1. The engagement mode model The EM-model generally involves three interrelated topics concerning how a subject (e.g., an IT user) interacts with an object (e.g., an IT-application), all of which were examined empirically in our previous study. These topics are: (i) dimensions in engagement modes, (ii) how levels on these dimensions are combined to form engagement modes, and (iii) how engagement modes are related to the flow experience. 1.1.1. Dimensions in engagement modes The EM-model assumes that when a subject (e.g., an IT user) is involved in an activity with an object (e.g., with an IT-application), he or she perceives this activity on three fundamental bipolar dimensions: (a) the extent to which the object is positive or negative (evaluation dimension); (b) the extent to which the subject controls the object (Locus S) or the object controls the subject (Locus O) (locus of control dimension); and (c) the extent to which the activity is focused on goals inherent in the activity itself (Focus I) or on external goals (Focus E) (focus of motivation dimension). These dimensions are perhaps the most frequently addressed constructs in cognitive, personality, and developmental psychology.

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1.1.2. Engagement modes It is assumed that a subject’s evaluation of an activity involving a certain object depends on how the activity is perceived on the other two engagement mode dimensions (Fig. 1). Depending on whether the locus of control is congruous or incongruous with the focus of motivation, the resulting activity will be positively or negatively evaluated. Congruity or incongruity means that the possibilities afforded by the locus of control (Locus S or I) match or mismatch the rewards that might be provided by the activity (Focus I or E). Let us go through all four possible combinations of Locus and Focus to see what this means. When the subject perceives himself or herself as controlling an object (Locus S), this skill may be used to attain various external ends (Focus E). A subject’s skill will be congruous with a focus on external rewards. To the extent that subjects perceive that this congruity is at hand, they will perceive themselves as being efficient and/or productive (engagement mode Efficiency/Productivity), which is experienced as something positive. However, if the subjects lack the skill needed to attain an external goal (Focus E) and if they need to learn this skill (Locus O), Locus and Focus will be incongruous. As a result, the subject will be frustrated (engagement mode Frustration/Anxiety). On the other hand, if subjects think that they can master the object, they will experience ambition and maybe curiosity (engagement mode Ambition/Curiosity). Thus the hope or ambition of changing incongruity to congruity will result in a positive evaluation. Consider now engagement modes where the subject’s motivation is focused on the activity itself in different ways (Focus I). If the subjects’ activities are controlled by the advantages that the object affords them (Locus O) and if they are interested in accepting Focus E (Extrinsic motivation)

Frustration/ Anxiety

Efficiency/ Productivity

Ambition/ Curiosity Locus O (O controls S)

Locus S (S controls O) Pleasure/ Acceptance

Avoidance/ Hesitation

(Intrinsic motivation) Focus I Fig. 1. Overview of the engagement modes model: Dimensions and each engagement mode. The evaluation dimension is represented in terms of positive and negative engagement modes. Bold phrases: Negative engagement modes. Not-bold phrases: Positive engagement modes. Focus E represents the Extrinsic Focus of motivation and Focus I the Intrinsic Focus of motivation on the motivation dimension. Locus O represents the Object’s Locus of control and Locus S represents the Subjects Locus of control on the control dimension.

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the advantages (Focus I), locus and focus will be congruous and the mode of Enjoying/ Acceptance emerges, which obviously is associated with positive evaluation. However, when the subject experiences a high degree of control of the object (Locus S), the activity involving the object will provide little advantage to the subject. That is, there is little to be learned from the activity itself. Nevertheless, if the subject is focused on rewards inherent in the activity (Focus I), locus and focus will be incongruous and the result will be the Avoidance/Hesitation mode, which obviously is associated with negative evaluation. When using computers, people hope for the Efficiency/Productivity and even Enjoying/ Acceptance modes. However, the various kinds of problems either related to the design of different applications or the person’s lack of skill and knowledge may lead the user to experience Avoidance/Hesitation and Frustration/Anxiety. According to the EM-model, a possible way out of this compelling challenge is the Ambition/Curiosity mode that produces more skilled and competent users, which may lead the user toward a better interaction and hopefully even the experience of flow. 1.2. Engagement modes and flow experience Flow has been described as an extremely rewarding experience that occurs when a person is fully involved in an activity (Csikszentmihalyi, 1993). To experience flow while being engaged in any activity, individuals must perceive a balance between their skills and the challenges posed by the object with which they interact, and both their skills and challenges must be above a critical threshold (Csikszentmihalyi, 1975, 1990). As noted, skill corresponds to locus of control in the subject (Locus S). In contrast, challenge corresponds to locus of control in the object. Thus, the higher the challenge, the more the subject’s activities are controlled by the object. Ghani and Deshpande (1994) explored the flow experience in individuals using computers by including skill as well as challenge. They found that flow occurs when challenge and skill are both high. They proposed five components of flow: pleasure, control, concentration, experimentation, and challenge. In their model, the flow experience consisted of both pleasure and concentration components. However, they noted their model lacked the motivational components. In presenting the EM-model, we aimed at bringing the motivational aspect of interaction into more focus and to find the compatibility of engagement modes with flow components. The EM-model describes the conditions for the emergence of flow in terms of an optimal combination of the three positive engagement modes. More specifically, flow occurs when the subject is encountered with a challenge that is experienced as pleasurable (engagement mode Enjoying/Acceptance) but is also possible to master (engagement mode Efficiency/Productivity). We assume that the flow experience implies that the subject shifts between these modes because they correspond to different positions of the locus of control dimension, which cannot exist simultaneously. The shifting is driven by ambition or curiosity (engagement mode Ambition/Curiosity) that encourages subjects to find new challenges to master. 1.3. Engagement modes and the user’s personality Recent research on user interaction with IT points out the need to assess the user’s personality, adaptabilities, and goal-orientation (Salas & Cannon-Bowers, 2001). In our

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previous study, we found that the involvement with certain engagement modes in people’s interaction with IT may be related to the user’s level of competence (skill) in using IT (Montgomery et al., in press). In this paper, we look for possible relationships among the different engagement modes, flow experience, and personality/motivational characteristics of the users. We may expect that users with different behavioral and personality orientations show different degrees of positive and negative engagement modes and different levels of flow experience. Specifically, we want to find out how different types of engagement modes with IT are related to motivational and personality factors. Furthermore, we want to examine the types of orientation that individuals have learned during their life through different experiences. The Causality Orientation Theory (Deci & Ryan, 1985a, 1985b, 1987) explains how individuals interpret the causality of events, other people’s behavior, and their own influence in different situations. This theory distinguishes among three types of behavioral and motivational orientation as relatively enduring aspects of personality and a person’s disposition to events and objects. Koestner and Zuckerman (1994) found close similarities between Deci and Ryan’s model and the goal-oriented behavior described by Dweck (1986). The causality orientation theory proposes three fundamental orientations: the autonomous, controlled and impersonal orientation, which can predict and explain a significant amount of variations in people’s cognition, affect and behavior (Ryan & Deci, 2000b). These orientations define how conditions of events influence people’s behavior. They also correspond to contextual factors that may advocate and reinforce the occurrence of these three types of personality and motivational orientations (Deci & Ryan, 1985a; Ryan & Deci, 2000b). The autonomous orientation is initiated and regulated by the person’s choice of actions and thoughts in order to reach goals and satisfy needs. The autonomous orientation correlates with a high level of self-esteem, self-awareness, confidence, internal locus of control, attribute of success to ability and effort, absence of boredom, and an effective approach for achievement (Deci & Ryan, 1985b; Ryan, Kuhl, & Deci, 1997). Autonomous people show more task involvement than ego involvement (Knee & Zuckerman, 1996). They tend to view unsolved problems as challenges to be mastered and not as reflecting their failures (Koestner & Zuckerman, 1994). We may expect that autonomous-oriented people show high scores in positive engagement modes and higher levels of flow experience since they are task-oriented (Acceptance/Enjoyment) and see problems as challenges (Ambition/Curiosity), which in turn may lead to mastering relevant objects. In particular, we expect that autonomous-oriented persons show high levels of Ambition/Curiosity since this engagement mode encourages the subject to master the challenges that are involved in different interactions. However, autonomous-oriented people may also experience the negative modes (Frustration/Anxiety and Avoidance/Hesitation), which may be experienced when the search for and mastering of challenges are met with problems. The controlled orientation is explained as being determined by imperative rewards and environmental factors. Therefore, the initiation and regulation of a person’s behavior depends on the demand and control of other people, the environment, and other extrinsic factors. The controlled orientation is motivated by the person’s need for achievement and doing well in a task assigned by someone else. It is determined by others’ influences, expectations, threats, and/or rewards. Deci and Ryan (1985b) found that controlled orientation is associated with the type A behavior pattern and a general awareness to the viewpoint of others. People with this type of motivational orientation want to look good to the controllers or

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evaluators. Controlled-oriented people sometimes show an explosive reaction to the controlling environment when it does not provide pleasure and when it is too threatening. It can be expected that participants with high scores on controlled orientation may show more efficiency and productivity among the positive modes since they aim to achieve higher control for looking good, receiving external rewards, or escaping pain. They may display avoidance and hesitation when the expected pleasure is not provided or if the situation seems to threaten desired results. However, their interaction may involve flow experience as well. Since flow is the function of necessary level of skill and challenge and since controlledoriented people generally feel pressured to be more skilled in order to master the situation, they may also experience flow. The third type of orientation is the impersonal orientation in which the initiation of behaviors is perceived to be beyond the person’s control or even independent of that person (for example, the person is not capable of mastering the situation). People with impersonal orientation do not see the causation of events as being related or regulated to them. They believe that they neither can choose nor regulate their own behaviors. In addition, they also believe events cannot be transformed or manipulated in a way that they desire. The impersonal orientation has shown association with social anxiety and personal helplessness, self-blaming, and depression, especially when faced with obstacles and difficulties (Deci & Ryan, 1985a). In relation to engagement modes, we may expect that participants with high impersonal orientation would score low in positive engagement modes and high in negative modes and would generally not be able to experience flow. This is because the causation and control of events are not experienced as being related to them. Therefore, they may simply give up or only feel the pressure and anxiety of their involvement. As Deci and Ryan (1985a) and Ryan and Deci (2000b) proposed, the three causality orientations correspond to three classes of contextual conditions. People experience events and situations that help them to behave in an autonomous, controlled or in an impersonal way. For example, classrooms, workplaces, and people’s work assignments can facilitate or inhibit people’s personality/motivation orientations (Ryan & Deci, 2000b). This definition particularly shows the effect of the contextual conditions that either prevent or promote autonomy, controlled and impersonal orientation and consequently influences different ways of interactions. Zuboff (1988) noted that encouraging employees to explore and innovate in different tasks help them to adjust better to new technology and also adapt it more effectively to their day-to-day work. In relation to different types of engagement modes, contextual factors that promote and prevent the controllability of tasks and events and encourage the user’s motivation and positive evaluation play an important role for efficiency and enjoyment of work and people’s well-being. Therefore, we may expect that the contextual aspects of interaction with IT and its different applications, which promote or prevent different causal orientation, may influence different engagement modes. 1.4. Present study In this study, we examined whether the results from our previous study can be replicated regarding the relationship between the proposed dimensions, engagement modes, and flow experience when using computer technology. We also aimed at constructing a shorter version of the engagement mode questionnaire developed in the previous study. More specifically, we aim at exploring how engagement modes, number of years of experience,

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IT-competence, and flow experience are related to the users’ socio-demographic characteristics and motivational and personality orientations. 2. Methods 2.1. Participants Representing different degrees of experience with IT-technology, 290 participants (mean age=29.20 years, 168 women and 122 men) took part in the study either voluntarily or for course credit. Thirty-five percent of participants were unemployed and were searching for a new job either on the Internet or through the state job agency. Sixty-five percent of the participants were students at the University of Stockholm and Royal Institute of Technology in Stockholm. Fifty-four percent of the students had a second job beside their permanent work. Fifty-two percent of the participants had a university degree and the rest had graduated from high school. The average number of years of experience with computers for the whole group was nine years. Fifty-six percent of the participants used computers everyday. 2.2. Questionnaire The questionnaire consisted of five different sections. The first section included eight socio-demographic items. The second section consisted of questions about the participant’s IT-competence. Thirty-nine ‘‘yes-no’’ items were used to assess the participant’s competence in using IT. The items covered a wide variety of IT-related activities performed when using a computer and its applications at work (either at a distance or in a centralized location), in education, at home, and during leisure time (e.g., testing new functions or services, using word processing, drawing figures, making tables, writing computer programs, testing different search engines, creating a home page, reading news and newspapers, shopping, and playing on-line computer games). IT-experience was estimated by asking how long the participants had used computers and how often. In the third section, the EM-scale was used (Montgomery et al., in press). It used 38 items to measure the modes of Enjoying/Acceptance, Efficiency/Productivity, Ambition/Curiosity, Avoidance/Hesitation, and Frustration/Anxiety. The fourth section consisted of a Swedish translation of the flow-scale (Ghani & Deshpande, 1994) with 16 questions that measured the five components of flow experience while using computers: pleasure, concentration, control, exploration, and challenge. Finally, a Swedish translation of the General Causality Orientation Scale (GCOS) (Deci & Ryan, 1985b) with a 12-vignette version (51 items) was used to assess the level of autonomous, controlled, and impersonal orientations. The items in this scale measure how the respondents perceive the causality of events, other people’s behavior, and their ability to influence different situations. Responses in the EMscale, flow-scale, and GCOS were given on 5-point scales ranging from strongly disagree (1) to strongly agree (5). The instructions in the questionnaire informed the participants that they were taking part in a study to measure their IT-competence and different types of interaction with IT (engagement modes in relation to IT). The use of IT was defined as any typical use of computer and Internet to perform a task, searching for information, communicating in any possible form, and working location (at work or in leisure).

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2.3. Procedure The participants were contacted by announcing the need for participants who would like to take part in a questionnaire study about people’s interaction with IT. Their participation was voluntary, and they were allowed to fill out the questionnaire where they found it suitable. They returned the questionnaire to the investigator (in person or by mail) within a few days. It took about 60 minutes to fill out the questionnaire. 3. Results 3.1. Replication of the proposed structure In order to test whether the co-variations among the items in the EM questionnaire and the flow-scale were in line with the EM-model, a non-metric multidimensional scaling was performed using the SPSS 10.0 statistical package with three dimensions. Generally, the multi-dimensional scaling method is used to give an overview of the structure of the given variables, which theoretically are assumed to have certain relationships with each other (cf. Borg, 1985). The co-variations between variables are reflected in the distances among variables in the multidimensional space. A shorter distance corresponds to larger co-variation among the variables. In the present case, we hypothesized that the three engagement mode dimensions would be reflected in how items are located along the dimensions in the multidimensional scaling. More specifically, we hypothesized that items would cluster as predicted by the EM-model (Fig. 1). Finally, we predicted that flow components would be distributed across the three positive engagement modes in the three-dimensional space. The Euclidean distance procedure with z-transformed values between variables was used. The fit of the three-dimensional solution was acceptable, the stress value being 0.21. Fig. 2 shows the location of related items to the space constructed by the specific combination of dimensions explained by the EM-model. The first dimension in the multi-dimensional scaling reflects the evaluative dimension in the EM-model. This dimension is illustrated by the shading of the circles. The darker circles represent the positive and lighter circles represent the negative aspects of IT-interaction. For example, items such as ‘‘I can organize things better with the help of IT’’ or ‘‘IT is a fun toy’’ represent positive engagement modes and items such as ‘‘I am afraid that IT use will change my identity’’ or ‘‘IT use is demanding’’ represent negative engagement modes. The size of the circles in the plots shows the size of the positive or negative value of the first dimension. Hence larger circles correspond to more extreme scale values on the evaluative dimension. When we inspect items at the extremes of the vertical and horizontal coordinates, the Locus and Focus dimensions are visible in the data; they correspond to opposite poles of each of the two dimensions. The vertical coordinate reflects the Locus dimension, with Locus S at the upper side (IT controls the subject or interaction, e.g., ‘‘I am pushed to learn about IT’’) and Locus O at the bottom (subject controls IT or interaction, e.g., ‘‘I have a better control over my life when I use IT’’). The Focus dimension is reflected by the horizontal coordinate with Focus E (Extrinsic focus of motivation, e.g., ‘‘using IT gives me time for other things’’) on the left side and Focus I (Intrinsic focus of motivation, e.g., ‘‘I think IT is a collaborator’’) on the right side. Typically, the five engagement modes were located in the three-dimensional space as could be expected from the EM-model (Fig. 1). In Fig. 2 the items related to the

Control I think IT restricts my life A/H

Exploring

I wonder about how much I use IT A/H

I have better control E/P I want to change my use of IT A/H I want keep totally away I can organize everything better E/P IT will change my social identity A/H I manage to do many things E/P IT is a positive challenge Using IT gives me time E/P

I think IT is a collaborator I become more independent

I want to learn more A/C

Pleasure

Everything becomes more fun

I can be more effective E/P

IT enriches my social life E/A

I want to take more of IT A/C IT enriches my social life E/A

IT is interesting A/C I am not satisfied F/A I feel stupid F/A I want to do better A/C

IT is a partner E/A

It is difficult to stop

IT gives new knowledge about life E/A

Challenge

IT is a great demand F/A

IT is a fun toy E/A I think IT is an entertainer E/A IT serves as a model

Concentration

I am not good enough F/A I am pushed to learn F/A

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I wonder about the role IT plays in my life A/H

Fig. 2. Multidimensional scaling of items indicating positive and negative evaluations. A/C: Items with high loading on the Ambition/Curiosity factor. E/A: items with high loading on the Enjoying/Acceptance factor. E/P: items with high loading on the Efficiency/Productivity factor. F/A: items with high loadings on the Frustration/Anxiety factor. A/H: items with high loadings on the Avoidance/Hesitation factor. The size of the circles shows the size of deviation from zeroevaluation. Some items are abbreviated. Flow components are given in boldface.

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Efficiency/Productivity mode are marked as E/P (in bold), and they are located in upper left side of the Fig. 2, where Locus S (subject’s locus of control) and Focus E (extrinsic motivation) dimensions are combined. Examples of relevant items include the following: ‘‘I can have a better control over my life when I use IT and I can be more effective using IT.’’ The items related to Enjoyment/Acceptance (E/A) such as ‘‘IT is a fun toy and IT enriches my social life’’ have found their place in the region corresponding to the object’s locus of control (Locus O) and intrinsic focus of motivation (Focus I) in the low right side of Fig. 2. The Acceptance/Curiosity (A/C) items such as ‘‘I want to learn more about IT’’ are located in the space where the combination of Locus O and Focus E meet in the lower left side of Fig. 2. In addition, items related to engagement mode Frustration/Anxiety (F/A) in the lower left side of figure correspond to the same combination (Locus O and Focus E) with items such as ‘‘when I have problem with IT I feel stupid.’’ Finally in the upper right side of the figure, the items such as ‘‘I wonder about how much I use IT’’ corresponds to the combination of Locus S and Focus I and the Avoidance/Hesitation (A/H) engagement mode. The flow components (in bold) found their location among the engagement modes’ items and in relationship to the given dimensions. The flow component control and exploring appeared in the dimensional space among Efficiency/Productivity items as in the previous study. The component concentration appeared among Enjoyment/Acceptance mode items and was close to the component pleasure and challenge in the lower region of Fig. 2. 3.2. Five engagement modes In order to find out whether the engagement modes reported by Montgomery et al. (in press) can be extracted, a Principal Components Analysis (PCA) with Oblimin rotation on the items of engagement modes was performed. To select the most relevant items for each factor and to obtain a shorter version of the EM-scale, the items with high loading on more than one factor were removed. As was expected, the Oblimin rotation showed that the five distinctive factors with loadings higher than 0.40 were identical with our previous findings. Thus the following five factors were found: (a) Enjoying/Acceptance; (b) Ambition/Curiosity; (c) Efficiency/Productivity; (d) Frustration/Anxiety; and (e) Avoidance/ Hesitation. In order to assess the homogeneity of items reflecting each engagement mode, Cronbach alpha was computed for each index of variables (engagement modes) and items with lower correlation than the overall Cronbach alpha of corresponding index were removed as long as such items could be found. A new Principal Components Analysis (PCA) with Oblimin rotation on the remaining 23 items was performed and five distinctive factors were extracted. The internal consistency reliability (Cronbach alpha) of these items and their factor loading (> 0.40) are presented in Fig. 2. The results of factor analysis and reliability test show that the replication of PCA was satisfactory and the construct of a shorter version (see Table 1). 3.3. Users’ socio-demographic and personality characteristics The second main purpose of this study was to find the relationship between users’ different engagement modes and their socio-demographic and personality characteristics. Therefore, indexes were computed (mean values across relevant items) for each engagement mode

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Table 1 Items of the engagement modes scale, factor loading of each item, and alpha coefficients for each engagement mode Factors and items

Factor loading and alpha coefficient

Enjoying/Acceptance I think IT is an entertainer IT is a fun toy I think many things become more fun when I use IT as help IT can give me knowledge about life IT enriches my social life Alpha

0.835 0.794 0.544 0.536 0.522 0.764

Avoidance/Hesitation I wonder about the role IT plays in my life I wonder about how much I use IT I think that IT restricts my life I am afraid that use of IT will change my identity I want to change how IT becomes useful for me Alpha

0.803 0.793 0.721 0.567 0.410 0.748

Frustration/Anxiety When I have problem using IT I feel stupid I am not satisfied about my capability to manage IT When there is a problem in my use of IT I become frightened I experience that others think I am bad in using IT I am pushed to learn about IT Alpha

0.844 0.806 0.756 0.729 0.669 0.824

Efficiency/Productivity I can organize everything better with the help of IT I have more control over my life when I use IT I can be more effective using IT I mange to do more things done with the help of IT Alpha

0.773 0.751 0.702 0.618 0.835

Ambition/Curiosity I want to learn more about IT I want to do better when I am using IT It is interesting to learn how IT functions I want to understand more about IT’s possibilities Alpha

0.857 0.833 0.665 0.655 0.834

and flow component. IT-competence scores were assessed from the 39 IT-competence items, with answers coded as 1 or 0 depending on whether they indicated IT-competence or not. Before generating indexes for the autonomy, controlled, and impersonal orientations, Cronbach alpha was computed for three groups of items, one for each orientation (autonomy = 0.76, controlled = 0.71, and impersonal = 0.80). The Pearson correlation coefficient between autonomy and controlled was low and not significant (r = 0.09), whereas the correlation between autonomy and impersonal was negative and significant (r = 0.17, p < 0.001). The correlation between controlled and impersonal orientation was positive and significant (r = 0.30, p < 0.001). To find out how much the socio-demographic variables (i.e., age, gender, amount of experience with IT, and IT-competence) and causality orientation accounted for the

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Independent variables

Ambition/ Curiosity Beta

Age Gendera IT-experience IT-competence Autonomy orientation Controlled orientation Impersonal orientation F R2

0.07 0.14 0.10 0.38 0.20 0.09 0.08 13.05** 0.22

Enjoying/ Acceptance t-Value 1.33 2.44* 1.77 6.72** 3.50** 1.60 1.40

Notes. n = 290. a Men were coded as 1 and women as 2. * p < 0.05. ** p < 0.01. *** p < 0.001.

Beta 0.06 0.03 0.04 0.21 0.14 0.13 0.04 5.06** 0.10

t-Value 1.13 0.46 0.66 3.54** 2.53* 2.04* 0.69

Efficiency/ Productivity

Avoidance/ Hesitation

Frustration/ Anxiety

Flow experience

Beta

Beta

Beta

Beta

0.016 0.00 0.09 0.49 0.18 0.15 0.05 20.78** 0.31

t-Value 0.32 0.05 1.80 9.40** 3.44** 2.82*** 0.79

0.04 0.30 0.02 0.07 0.12 0.27 0.16 12.90** 0.21

t-Value 0.74 5.23** 0.30 1.32 2.13* 4.80** 2.70***

0.03 0.06 0.08 0.34 0.19 0.16 0.25 22.22** 0.32

t-Value 0.50 1.20 1.60 6.50** 3.60** 2.60*** 4.50**

0.12 0.22 0.02 0.23 0.17 0.15 0.14 6.25** 0.12

t-Value 2.05* 3.60** 0.36 3.80** 2.80* 2.43* 2.30*

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Table 2 Results of regression analyses of engagement mode, flow experience, socio-demographic characteristics, and personality characteristics

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variance in engagement modes and flow scores, multiple regression analysis were performed for each engagement mode and flow experience as the dependent variables and the socio-demographic variables and the causality orientations as the independent variables. The results are shown in Table 2. First, we will consider the results for the sociodemographic variables. Age was associated positively with flow experience. Females scored higher than males on Ambition/Curiosity and flow experience, and they showed less Avoidance/Hesitation than males. The number of years of experience that the participants had spent with computers showed no significant association for different dependent variables. IT-competence was positively associated with the three positive engagement modes (particularly Efficiency/Productivity and Ambition/Curiosity) and with flow experience. The relationship between IT-competence and Frustration/Anxiety was high and negative, and there was no significant association between Avoidance/Hesitation and IT-competence (see Table 2). The following observations can be made with respect to which causality orientations could predict engagement modes and flow. Autonomy orientation showed a positive and significant relationship with all positive engagement modes (particularly with Ambition/Curiosity) and flow experience as compared to other orientations. Autonomy was also positively related to Frustration/Anxiety and Avoidance/Hesitation modes. Controlled orientation showed a stronger association with the Avoidance/Hesitation mode than with Frustration/Anxiety among the negative engagement modes and with Efficiency/Productivity among the positive engagement modes. In contrast to the autonomous orientation, there was no significant association with Ambition/Curiosity mode for the controlled orientation. The controlled orientation also showed a positive and significant relationship with flow experience. Impersonal orientation showed a positive relationship with negative engagement modes, but there were no statistically significant associations with any positive engagement mode. On the other hand, this orientation has a negative and significant relationship to flow experience. Among the dependent variables, the largest proportion of variance was explained for Frustration/Anxiety and Efficiency/Productivity (0.32 and 0.31). The proportion of explained variances for flow experience and Enjoying/Acceptance was relatively low (0.12 and 0.10) and was moderately high for Avoidance/Hesitation and Ambition/ Curiosity (0.21 and 0.22). The explained mean variance was around 0.23. 4. Discussion The multidimensional analysis and the factor analysis demonstrate that the results from our previous study can be replicated with respect to the basic structure of engagement modes in people’s interaction with IT. The reproduction of the same results suggests that the EM-model is a robust descriptive model that empirically can capture psychological characteristics of people’s interaction with IT. Moreover, the derived five factors, which were identical with the initial presentation of the EM-scale, suggest that the shorter version of our questionnaire can be is a practical tool for examining a user’s engagement modes. The relationship between engagement modes and flow components was also largely replicated. This relationship shows the reliable compatibility of EM-model and flow theory, although the location of the Concentration component is not identical with our previous study (Montgomery et al., in press). Ghani and Deshpande (1994) showed that flow

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experience is the function of pleasure and concentration components, which is in line with the present results. Thus the results suggest a better fit of the EM-model and flow theory than was the case in our previous study. To conclude, the overall results of the replication suggest that the common characteristics of IT-users’ behavior can be conceptualized and demonstrated empirically in terms of three dimensions and five engagement modes and flow components. The positive co-variation of IT-competence and the ‘‘positive’’ engagement modes suggests that the acquisition of necessary skills facilitates efficient, enjoyable, and ambitious engagement with IT, although a causal relation in the opposite direction cannot be excluded. In addition, there was a strong negative association between IT-competence and Frustration/Anxiety mode among the negative engagement modes, which supports the proposed suggestion. This relationship has been identified in several studies. For example, Cohen and Waugh (1989) and Weil and Rosen (1995) reported that the total amount of competence in using computers was correlated with low computer anxiety and negative attitudes toward technology. However, in our study, Avoidance/Hesitation did not show a negative association with IT-competence. In general, the positive engagement modes seem to facilitate the process of interaction during the complex processes of skill acquisition and development. The negative engagement modes can be seen as resulting from incompatibility between the level of competence and the given challenges or problems. It is noteworthy that the amount of experience (number of years of using IT) showed no negative or positive significant relationship with any of the dependent variables. This is in line with Ericsson and Lehmann (1996) findings that the number of years of work and experience in a domain is not an accurate predictor of attained performance. According to these researchers the knowledge and experience relevancy in a specific area is the most determining factor for the quality of the decision-making and consequently the performance. Norman (1998) proposed that when people use technology, they generally expect productivity, efficiency, and benefit from the technology without pain, agony, stress, and anxiety. In line with this proposal, the present results indicate that the enjoyment of using a computer and its related tasks and the desire for efficiency and productivity are factors that contribute to good functional interaction, which in its turn enhances the process of learning and acquisition of skill and competence (cf. Salas & Cannon-Bowers, 2001). Users also experience frustration and anxiety, which lead them to avoidance even though it is temporary (Carroll, 1991; Norman, 1998). It can be suggested that the ambition and curiosity engagement mode works as a force and resource factor for coping with the problems related to the use of IT. To challenge continuing problems or changes, a user’s level of competence and skill as well as ambition and curiosity seem to play important roles for their interaction with IT and flow experience. Several meaningful relationships were found among the users with regard to causality orientations and different engagement modes, although any generalizations or causal effects should be taken cautiously. For example, the results indicate that a high level of autonomous orientation is more related to the person’s curiosity, interest, enjoyment, efficiency, productivity, and flow experience (the positive experience of IT) and less to anxiety and avoidance (the negative experience) than is the case for controlled and impersonal orientations. The autonomous orientation, which is closely related to Bandura’s self-efficacy belief (1997), Dweck’s mastery-oriented behavior (1986), and Higgins and Silbermannotion of promotion-orientation (1998) seem to be determining factors for the amount of

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effort that a person needs to master different challenges. It can be suggested that in a productive and enjoyable interaction an individual’s self-determined behavior is needed to manage the given problems and difficulties. Such a relationship could be stimulated by contextual factors such as the usable design of tools and stimulating work assignments. These factors may encourage the autonomous efforts that are necessary for better performance. As Bandura (1977, 1997) suggested, since an individual’s behavior, cognition, and environment are all highly interrelated to expand the level of self-efficacy belief, the individual must be able to rely on his or her own abilities as well as the environment’ support. Staple, Hulland, and Higgins (1998) also reported that the employee’s self-efficacy and autonomy with respect to IT played a critical role for their work effectiveness, perceived productivity, job satisfaction, and ability to cope with problems. As the definition of controlled orientation implies, the external conditions sometimes control people’s behavior or alternatively the person may choose the controlled orientation either as a coping strategy or because it is rewarding and decreases the threatening effects. However, no matter what has caused the controlled orientation the outcome behaviors may be high enjoyment and efficiency (Enjoying/Acceptance and Efficiency/Productivity modes). The external rewards or threats may also cause the person to experience the negative modes like the Frustration/Anxiety mode and especially the Avoidance/Hesitation mode. Perhaps the low level of relationship between controlled-oriented behavior and the Ambition/Curiosity mode compared to autonomous-oriented users provides more evidence that controlled-oriented individuals use IT mostly for its external possibilities and credits or to avoid possible threats. In comparison to the autonomous and controlled orientation, the impersonal orientation was unrelated to positive engagement modes. Participants scoring high on impersonal orientation experienced the negative aspects of interaction and negative association with flow experience. This is perhaps due to the experience of personal helplessness, self-blaming, and social anxiety or because the contextual conditions did not enhance the experience of required control and prevented their independent and effective involvement. The impersonal orientation is closely related to Seligman (1975) helplessness and Bandura (1997) a motivated behavior. Further studies are needed to search for more detailed reasons behind people’s impersonal orientation in IT interaction. Studies show that the voluntary use of computer technology with curiosity and playfulness as compared to the forced or constrained types of interacting are recognized as playing important roles in people’s volitional interactions and communications when using IT (Ghani & Deshpande, 1994; Hoffman & Novak, 1996; Zuboff, 1988). In addition, the usable and useful design and its applications should give the users more control and more freedom of choice (Bannon, 1991; Norman, 1998). The present study suggests that the level of freedom and the amount of control for making free choices improve people’s competence and skill in having a more efficient (Efficiency/Productivity mode), enjoyable (Enjoying/Acceptance mode), and interesting (Ambition/Curiosity mode) interaction when using IT. It may seem at first that autonomous people who are more intrinsically motivated and task oriented should experience only the enjoyment of the activities and thus not experience avoidance and frustration. However, as Ryan and Deci (2000b) have explained, the autonomous- and controlled-oriented people can be both intrinsically and extrinsically motivated. It can be suggested that people with autonomous and controlled orientation may both experience the positive and negative aspects of interaction although due to different

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reasons as compared to impersonal oriented users. Nonetheless, the higher score of frustration and anxiety for impersonal oriented people indicates that the low perceived control and interpretation of situation as demanding are the pervasive contributors to low enjoyment, efficiency, and curiosity. The results suggest that frustration and anxiety produced during the interaction are related to low perceived control and low IT-competence. However, the experience of low control and low IT-competence should not be interpreted only as reflecting the individual’s choice or lack of skill but also related to work assignments, contextual aspects, and IT-applications. Moreover, the high significant positive association between IT-competence and flow experience shows that in order to experience flow the individuals should be more competent and know how to use different IT-application (fluency in usage). The fluency of usage, as defined by Papert and Resnick (1993), involves not only knowing how to use IT but also knowing how to construct things of significance with IT. Because the autonomous orientation also showed the highest association with flow experience compared to other personality orientations, flow experience can be considered as the sign and indicator of the effective functional and innovative interaction. Generally, the present results suggest that the user’s skill, competence, and fluency in usage are his or her individual contributions to the process of efficient, enjoyable, and flow involved interaction, which is most probably achieved through self-regulated use of the needed ambition and curiosity. From the user’s side of interaction, the autonomous and even controlled personality orientation will advance the quality of interaction, efficiency, and pleasure of the activity (although the autonomous orientation is more favorable). However, the context of interaction in the usable design (Carroll, 1997, 2002) and a stimulating work climate and assignment (Staple et al., 1998) should also facilitate the user’s self-regulated behavior that contributes to the quality of the user’s IT-interaction. Taken together, the reliable relationship between the EM-model, flow theory, and causality orientation theory indicates that they can jointly provide a theoretical framework to describe the characteristics of the user’s cognitive capabilities in adapting to new technology. Future research should examine in more detail the coherent and dynamic system of the user (subject), the IT application (object), and the situation (context) in light of the EM-model, flow, and causality orientation theories. Studies should be designed to understand how the effective and enjoyable interactions are influenced by personality factors, and situational and contextual circumstances. Moreover, research should examine whether enjoyment, efficiency, and curiosity lead to higher IT-competence or whether IT-competence leads to enjoyment, efficiency, and curiosity. Acknowledgement This research was supported by Grant No. 1998-0239 from the Swedish Transport and Communication Research Board and Grant No. 220-155600 from EU Goal 1 North of Sweden. References Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman and Company.

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