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An overview of and factor analytic approach to flow theory in online contexts
Technology in Society 61 (2020) 101228
Contents lists available at ScienceDirect
Technology in Society journal homepage: http://www.elsevier.com/locate/techsoc
An overview of and factor analytic approach to flow theory in online contexts Ahmed Y. Mahfouz *, Kishwar Joonas, Emmanuel U. Opara Prairie View A&M University, P.O. Box 519, MS 2300, Prairie View, TX, 77446-0519, USA
A R T I C L E I N F O
A B S T R A C T
Keywords: Flow Flow theory User experience Ecommerce Cognitive enjoyment Factor analysis
An overview of flow theory is presented from the literature across multiple disciplines, including information systems, ecommerce, marketing, digital gaming, user interface, management, and cultural contexts. Flow can play a pivotal role in the user experience and impact the user interaction with a site, computing device, or app. It is worthwhile to examine the effects of flow experience on users and incorporate these findings in designing engaging user experiences and interfaces in both web sites and mobile applications. To further understand these implications, the present study gave a questionnaire to 310 participants in a computer laboratory setting following an online shopping episode. The factor analysis revealed three dimensions of flow experience: control, attention focus, and cognitive enjoyment. All three dimensions had very low correlations. No gender effect on flow was found.
1. Introduction and literature review Flow is a state in which persons are so involved in an experience that they might be oblivious to the world around them and potentially lose track of time, space, and possibly self [1–3]. This flow experience, state of flow, or optimal experience makes individuals feel that they are in control of their own decisions and actions, while sensing enjoyment and exhilaration during the activity. During this engaged experience, the degree of task challenges and the individuals’ skills are both equally high. For example, athletes observe they have entered the zone at certain vigorous moments of their game or exercise routine, or digital gamers are lost in the experience in 3D virtual worlds in augmented and mixed reality games [4–6]. In order to induce a sense of flow, web sites and mobile apps need to stimulate and respond to users. Otherwise, boredom, anxiety, and apathy occur [1,3,7]. Boredom results when the user interface (UI) or site is not challenging enough, while anxiety occurs if the system is too cumbersome or difficult to use. When skills of users and challenges of sites, apps, and games are too low, apathy materializes, while a flow experience takes place when individuals’ skills match the level of the challenges of the interaction or situation [1,3]. In essence, flow is created when individuals achieve concentration effortlessly and expe rience enjoyment while completing a certain task or objectives that require responses and feedback, at the work, home, in leisure, or in
social situations [4]. A very relevant component of flow theory or experience is that it is an end in itself or a reward for its own sake, not a means to an end: hence, flow is autotelic, from the Greek word auto or self, and telos or goal [3]. An autotelic experience is intrinsically motivated and encompasses establishing objectives with goals, with the outcome being engaged in an activity, paying attention and concentrating intensely during a given experience, while enjoying this interaction immensely. Mentoring stu dents to teach them is not autotelic, but educating them because a person enjoys interacting with students is autotelic [2]. Numerous activities, disciplines, and studies have observed flow experiences. Some examples include rock climbing, surgical procedures, sports, arts, dancing, song compositions, and management [2,3]; online gaming [5,8–10]; online interactions, interactive interfaces, and web site design [6,11–15]; shopping online [7,16–20]; database and systems development [21]; instant messaging [22]; social media engagements [23,24]; travel and tourism [25,26]; and, academic learning [27]. Flow theory has actually been studied in different cultural contexts and countries, such as in Finland [28]; Taiwan [29,30]; China [22,25]: India [26]; Turkey [27]; South Africa [31]; and, Iran [21]. Table 1 lists studies that have examined flow theory in a multitude of fields, including technology, marketing, management, and human behavior. Besides quantitative and experimental studies, several quali tative studies investigated flow [11,36–39], including semi-structured
* Corresponding author. E-mail addresses: [email protected] (A.Y. Mahfouz), [email protected] (K. Joonas), [email protected] (E.U. Opara). https://doi.org/10.1016/j.techsoc.2020.101228 Received 16 April 2018; Received in revised form 11 October 2019; Accepted 26 January 2020 Available online 1 February 2020 0160-791X/© 2020 Elsevier Ltd. All rights reserved.
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interviews with online visitors who experienced flow [37,40]. Kaye [5] explored group flow or shared flow within the context of cooperative digital gamers. Several researchers, furthermore, integrated flow theory with other theoretical streams. As an example, Lu et al. [22] combined the tech nology acceptance model (TAM) and flow theory, and evidenced the importance of extrinsic, as well as intrinsic motivations, in instant messaging (IM) adoption. These researchers found that IM users desire fun and an enjoyable flow experience, in addition to a utilitarian and user-friendly system. In addition, Chang and Wang [29] integrated flow and TAM, to establish that flow not only impacts behavioral intentions but also mediates the relationship between attitudinal factors and behavioral intentions. Li [30] studied constructs from flow theory, along with TAM and the Elaboration Likelihood Model (ELM). Results show a stronger impact of source quality, as well as argument quality on atti tude, and through it, on behavioral intent, compared to playfulness. Further, Wang et al. [25] integrated flow theory with TAM to study user experiences in the tourism industry in China and concluded that com ponents of motivation, as well as flow, are responsible for the creation of behavioral intentions. Moreover, at least two researchers focused on the role of procrasti nation in flow. Flow experience was evidenced to have a lower impact on problematic Internet use, in comparison with procrastination and time spent on the Internet, which is a variety of problematic Internet use [31]. Revisiting the procrastination construct, based on two studies, researchers found that online procrastination and the propensity to delay purchase decisions affect the likelihood of a purchase. In addition, online age, the degree of tendency to delay decisions, as well as the type of online activity, determine the probability of purchase [32]. Takatalo et al. [28] also propounded the Presence-Flow Framework (PFF) in a study of 68 participants observed in a virtual environment (VE), followed by a self-report survey. These authors provided a holistic measurement of VE experiences through incorporating perceptual, attentional, cognitive-affective, and motivational constructs. These studies not only reveal that people experienced flow in computer-mediated environments but also described characteristics of such an experience. As a whole, prior research of flow shows validity and applicability of flow theory in an IS and many other contexts. Next, we will discuss a general mechanism of how online contexts facilitate the flow experience and its consequences. Online interactions facilitate flow [12,13,34]. Activities conducted online that induce flow can be categorized as the VE itself, Internet browsing, online shopping, chats, and digital games [5,10,11]. These activities may exhibit many characteristics of a flow experience. When individuals access the Internet, they usually have a specified goal or objective, such as searching for information, products, and services, or playing digital games. They, in turn, receive an instant response or feedback from the system, site, app, or game. Such activities pose challenges that necessitate certain online skills and user awareness, con trol, and attention on the task-at-hand. Users interact with a site or an app via the UI and control various objects and customize personal profiles or various options presented in a game or a site. Particularly, in interactive, digital 3D gaming or simulations, individuals may feel immersed in the experience to the point they are oblivious to their surroundings, losing track of time, and even losing self-consciousness [10,41]. Coupled with feelings of enjoyment and telepresence (as in being transported to the VE), all these sensory, affective, and cognitive interactions induce a flow experience [12,14,33,34]. Finally, the entire online episode becomes autotelic or intrinsically motivated when the activity is simply carried out for its own sake, and not as a means to any specific end. Csikszentmihalyi [2] notes that following a flow episode, an indi vidual may experience two dichotomous but concurrent feelings: dif ferentiation and integration. Differentiation is a sense of separation and uniqueness of self as opposed to others. Simultaneously, the same in dividual would feel connected to others in union, hence integration. When a user customizes an online product purchase or personalizes a
Table 1 Sample of studies dealing with flow theory. Source
Contribution
Wani et al. [26]
A replication research conducted in India confirmed the role of both utilitarian and hedonic measures in evaluating a travel web site. Two studies evidenced that purchase is determined by online procrastination and the propensity to delay the purchase decision. Differences in the degree of tendency to delay decisions, online users’ age, and type of online activity affect the likelihood of purchase. Online regret experience is determined by user demographics and flow experience components. A flow search measurement instrument in social networking was developed and validated. Empirical research investigated the relationships between hedonic characteristics, utilitarian attributes, flow experience, trust, brand equity, and loyalty among Gen Y customers. The study evidenced the moderating role of utilitarian motivations on the relationships between flow and replay intention, and how uncertainty impacts online gaming. The study was conducted in relation to the tourism industry in China, based on flow theory in conjunction with TAM. Observed that behavioral intent is a function of motivation, as well as flow components. The impact of experience and guidance on flow and retention in 3-D learning environments was investigated. Flow theory was integrated with TAM and ELM. Compared to playfulness, a greater influence of source credibility and argument quality on perceived ease of use and perceived usefulness was found in a workplace online system. User self-efficacy affects database interface interactivity, flow, and scientific behavior change. Flow experience mediates the relationship between interactivity and scientific behavior adaptation. Flow experience also mediates the relationship between user efficacy and scientific behavior adaptation. Group flow is studied in the context of social processes of cooperative digital gaming. Flow explains how site interactivity impacts consumer cognition, affect, and behaviors. Empirical research examines flow and brand equity in 3D virtual worlds. Perceived ease of use, immediate feedback, skill and challenge are antecedents of flow in e-games. The most important aspect of flow is enjoyment. Extrinsic, as well as intrinsic, motivations are important factors in instant IM adoption. IM users not only desire a useful and user-friendly platform but also seek fun and an enjoyable flow experience. Online games involving a human-controlled opponent generated higher levels of presence, flow, and enjoyment, compared to those involving a computer. The effect of presence on enjoyment is mediated by flow. Determinants of behavioral intentions include perceived usefulness, perceived ease of use, interactivity, flow, and attitude toward use. Flow plays a role as a determinant, as well as a moderator in the creation of behavioral intentions. The study proposed an integrated 3D PFF for measuring human experiences in VEs, comprising physical presence, situational interaction, and competence. The relationship between procrastination and the amount of time spent online and problematic Internet use (PIU) was found to be stronger than the relationship between flow experience and PIU. Site revisit frequency is determined by shopping enjoyment and site perceived usefulness. Temporal dissociation, heightened enjoyment, focused immersion, curiosity, and control are characteristics of cognitive enjoyment. Flow increased based on an updated model with control and skill, arousal and challenge, focused attention, telepresence, and interactivity. Reduction of Trevino and Webster’s [35] 4 dimensions to 3 in a 12-item flow instrument: cognitive enjoyment, which merges intrinsic interest and curiosity. Control, attention focus, curiosity, and intrinsic interest are 4 flow characteristics.
Zanjani et al. [32]
Kaur et al. [23] Kaur et al. [24] Bilgihan [17]
Liu [33] Wang et al. [25]
Baydas et al. [27] Li [30]
Hosseini and Fattahi [21]
Kaye [5] Van Noort el al [34]. Nah el al [6]. Hsu [8] Lu et al. [22]
Weibel et al. [10]
Chang and Wang [29]
Takatalo et al. [28] Thatcher et al. [31]
Koufaris [20] Agrawal and Karahanna [16] Novak et al. [12] Webster et al. [15] Trevino and Webster [35]
2
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profile listing, the user experiences differentiation. When individuals of similar interest join online groups and collaborate in real-time regarding a common cause, they experience integration. Table 2 [2,42] lists the aforementioned flow characteristics. Engaged users may experience flow during an immersive, online activity [5,12,13]. This engagement is accomplished via a device’s or a site’s interface, particularly its interactivity level and features. This interactivity can be classified across four models, along two continuums, as shown in Fig. 1 [43]. One of these four models relates to flow. The first continuum is source of control: human versus machine. The second continuum is UI: apparent versus transparent, consequently creating four quadrants. First, machine-based interaction includes data-entry tasks, such as completing personal profiles or forms; this is equivalent to push technology by the system to the user. Second, human-based communication involves more control by users, for example, when they manipulate tools, such as apps, databases, and word processors; this is more equivalent to pull technology from the user to the system. Third, when the system adjusts itself to accommodate the level of skills of users, such as in learning systems or gaming platforms, then the result is the third quadrant, or adaptive communication [8,9,27]; here, both push and pull technologies happen between senders and receivers of a given communication. Finally, the fourth quadrant is flow, whereby in dividuals are engaged actively and communicating with a system or device. Its interface is virtually transparent since users are immersed in the activity and oblivious to their surroundings, such as in the case with gaming apps with mixed and augmented reality features [5,6,8–10,13, 17,21,23,33]; here, both sender and receiver of a given communication are in a participatory mode, serving equivalent and sometimes inter changeable roles of both transmitter and recipient of the interaction. Hence, in a flow state, users feel immersed and one with the experience or task-at-hand. In terms of consequences of an interaction or communication with a system, site, or app, flow experienced during an activity tends to repeat with similar, behavioral outcomes, such as increased site revisits in online shopping, intent-to-purchase, trust, and brand loyalty [17,20,34]. Flow serves both as a factor and a moderator affecting behavioral in tentions [29]. Furthermore, components of flow in conjunction with TAM impact behavioral intent [25]. Other examples of behavioral out comes of flow include regret experience in social media [23], as well as enhanced scientific behavior adaptation and change in user interactivity and interface environments [21].
We applied Webster et al.‘s [15] instrument in an ecommerce context to a sample of 310 subjects, following an online shopping episode. We used SAS 9.40 for data analysis.
2. Material and methods
2.3. Validity and factor analysis
The current study examined the dimensionality of the flow experi ence. In IS literature, flow experience is depicted with 3 or 4 dimensions.
Drawing from the literature and using previously designed scales increases validity of a given study, and researchers are advised to use already existing and validated scales [46]. Webster et al. [15]. flow experience scales were used in the present investigation. Furthermore, construct validity was determined using exploratory factor analysis, in order to determine and group the variables of interest under their cor responding factors [47]. Cook and Campbell [48] define construct val idity as the measure to ascertain if the variables are true constructs of the observed phenomenon under investigation. Construct validity answers the question: is the study measuring what it is supposed to be measuring [47]? To increase the level of validity of the present study, the subjects participated by navigating and completing the online shopping search task in a computer laboratory. This ensured a greater degree of tight control, as well as facilitated a closed environment whereby any external distractions were eliminated or reduced. All screen interactions of the subjects were recorded using Camstasia Recorder (http://www.tech smith.com) and subsequently reviewed to ensure all instructions and protocols were followed. Exploratory factor analysis with maximum likelihood extraction method with equamax rotation was used to assess construct validity. After using a scree test, the highest contribution to the proportion for
2.1. Sample and instrument In order to examine relevant dimensions of flow theory, an online questionnaire was given to subjects in a computer laboratory, after they completed an online shopping task by navigating an apparel commercial web site and browsing the site for clothing items. Subjects were assigned a computer lab. Once they walked in, a ses sion packet of information was given to each participant. The first step was to read and sign a consent form. This took 15 min. Then, they were given instructions to visit a commercial apparel web site to navigate and search for a suitable product of interest for 30 min, while browsing the various features of the site. After the task was completed, the subjects filled out the Webster et al. [15] flow instrument, and this final stage was allocated 15 min. The subjects of the study were undergraduate college students in a Southwestern university in the United States. Each individual had basic Internet skills, such as using a web browser. All students were full-time, traditional students. Comprising the sample was 310 subjects with ages ranging from 18 to 28 years old, with 96% from ages 19–24. The labo ratory setting facilitated a far tighter degree of control, resulting in minimal distractions. The scales used in the study were taken from and based on Webster et al. [15] flow instrument. Each scale item had a 7-point Likert scale, with 7 anchors, ranging from 1 ¼ strongly disagree, 4 ¼ neutral, to 7 ¼ strongly agree. 2.2. Reliability Reliability is the degree to which an item, scale, or an entire assessment mechanism will yield similar values when given in varying times, places, or samples [44,45]. Internal consistency reliability is the extent to which individual scale items (i.e. the variables) correlate with each other or with the entire scale [45]. A scale is internally consistent if each item or variable in a scale measures the same concept or construct. The most commonly used index of internal consistency reliability is Cronbach’s alpha, which was used to measure the reliability of the present study. Cronbach’s alpha should be�0.70 [45]; for the present study it was 0.87, although Webster et al. [15]. reported this as 0.82.
Table 2 Flow theory and experience characteristics. Flow Characteristic
Description
Specified goals and objectives Challenges ¼ skills
Clear tasks with instant response and feedback
Union of awareness and action Control Attention on the task-athand Perceived time distortion Dissolution of selfconsciousness Autotelic state Post Flow Experience Differentiation Integration
Opportunities to take action, along with an individual’s ability to take action Merging of oneself with the experience Perceived sense of control during the interaction or activity Deep concentration given to tasks-at-hand, ignoring irrelevant input Perception of time as fleeting Feelings of transcendence to a greater purpose Intrinsically-motivated action done for its own sake Feelings of uniqueness of self Feelings of self being in union with others
3
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Fig. 1. Four models of human-to-machine interactivity (adapted from Ref. [43].
variance accounted for by a given factor was used to determine the number of interpretable factors to retain. Variables, which are related, load on and group under the same factor. Variables, which are the items in a given scale, are kept if their |factor loadings| are�.50. Otherwise, they are dropped. Items or variables loading on a factor other than the original factor from the given scale in the existing literature, or loading on multiple factors concurrently, were also dropped [46]. The variables that were kept after factor analysis are shown in Table 3. FlwAttn3 was not retained since it loaded on a factor other than the original factor in the literature. The three interpretable factors are consistent with the literature [15]. As shown in Table 4, the factors are congruent with the original scales. For instance, control was interpreted as the first factor, based on the following variables or scale items: FlwCtrl1 (user control over the use of the site), FlwCtrl2 (no control over the interaction, reverse-coded), and FlwCtrl3 (control over the interaction). Attention focus and cognitive enjoyment were the other two interpretable factors, 2 and 3, respectively.
Table 4 Scales and items retained after factor analysis. Variable Control FlwCtrl1 FlwCtrl2
When using the web site, I felt in control. I felt that I had no control over my interaction with the site.* FlwCtrl3 The site allowed me to control the computer interaction. Attention Focus FlwAttn1 When using the site, I thought about other things.* FlwAttn2 When using the site, I was aware of distractions.* FlwAttn3 The site allowed me to control the computer interaction. (Dropped) Cognitive Enjoyment FlwCEnj1 Using the site excited my curiosity. FlwCEnj2 Interacting with the site made me curious. FlwCEnj3 Using the site aroused my imagination. FlwCEnj4 Using the site bored me.* FlwCEnj5 Using the site was intrinsically interesting. FlwCEnj6 The site was fun for me to use.
3. Discussion
Factors & Factor Loadings 1
2
3
.80* .67* .70* .11 .10 .25 .25 .14 .12 .25 .18 .26
.08 .15 .13 .74* .73* .39 .15 .21 .17 .24 .08 .23
.13 .06 .26 .16 .06 .51* .76* .77* .70* .66* .75* .79*
S.D.
5.23 5.39
1.05 1.08
5.35
1.03
3.55 3.66 4.39
1.49 1.37 1.42
4.46 4.31 4.10 4.61 4.35 4.51
1.37 1.35 1.41 1.45 1.23 1.40
analysis, the present study confirmed the same three dimensions but in an online context, in which users navigated a web site and searched for products online. Control, attention focus, and cognitive enjoyment are these three components [1–3,15]. Control is the perceived feelings of control by the user over the interaction with a device, site, or app, based on commands and alternative choices, while such a system responds and gives feed back to the user. The state in which users are so engaged in an experi ence or interaction with a site or app that they become oblivious to their surroundings is attention focus. Curiosity and intrinsic interest combine to form cognitive enjoyment [15]. Curiosity is achieved via new and enjoyable means to interact with a given system. Intrinsic interest occurs when users undertake a task or an activity just for its own sake, not expecting a specific outcome or gain. Agrawal and Karahanna [16] expand this definition of flow and cognitive enjoyment to a similar construct, cognitive absorption, which includes the following flow di mensions: focused immersion, temporal dissociation, control, height ened enjoyment, and curiosity [16]. This enjoyment is an important aspect of any flow experience [8,10,33]. As shown in Table 5, the highest reported mean is for control (M ¼
Table 3 Factor loadings of retained items after factor analysis.
FlwCtrl1 FlwCtrl2 FlwCtrl3 FlwAttn1 FlwAttn2 FlwAttn3 FlwCEnj1 FlwCEnj2 FlwCEnj3 FlwCEnj4 FlwCEnj5 FlwCEnj6 FlwAttn3 was dropped.
Mean
An asterisk (*) indicates an item is reverse-coded, as stated in the original scale. Each item had a 7-point Likert scale, with following anchors: 1 ¼ strongly disagree, and 7 ¼ strongly agree.
Trevino and Webster’s [35] study investigated flow as a multidi mensional construct in work settings with the following dimensions: control, attention focus, and cognitive enjoyment. Originally, curiosity (both cognitive and sensory) and intrinsic interest were separate di mensions but were later combined as cognitive enjoyment. Based on factor
Variables
Scale Item
4
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5.32), which also has the smallest standard deviation (SD ¼ 1.05), i.e. users differed less in their perceived responses to the level of control over the interaction. Subjects felt they were probably in more control of the interaction and cognitively enjoying it than they did focusing on it with their attention. This is understandable since the task-at-hand was a structured task, searching for a clothing item, and users probably felt in control over the web site’s interface and its features. Moreover, all pairs of the three dimensions had low bivariate correlations: control and attention focus, r ¼ 0.30; control and cognitive enjoyment, r ¼ 0.46; and, attention focus and cognitive enjoyment, r ¼ 0.42. These low bivariate correlations indicate that the dimensions are not reflecting the same construct.
Table 6 Results of the t-Test between gender and flow. Variable: Flow
Given the sample was predominately female, it was interesting to examine a gender effect on flow. There were 195 females and 115 males. Females comprised 63% of the sample. The independent samples pooled t-test was used to test for a gender effect, given the variances of both groups were statistically equivalent. H1 There is a difference between females and males in experiencing flow, while completing an online shopping episode. As seen from Table 6, there was no significant difference between females and males in terms of their flow experience, t308 ¼ 1.84, p ¼ 0.067, at the 0.05 significant level. The means for both groups are relatively similar, on a scale of 1 ¼ strongly disagree to 7 ¼ strongly agree, M ¼ 4.49 for females, SD ¼ 0.89; M ¼ 4.31, SD ¼ 0.76, males. 3.2. Contributions The present study provides several contributions to scholarly research. An overview of flow dimensions and characteristics was dis cussed from multiple disciplines and contexts, as well as post-flow ex periences, differentiation and integration. Additionally, the present study attempted to add to the base of existing studies that investigate flow theory in online environments in an ecommerce setting, while replicating the results of Webster et al. [15]’s work, which examined flow theory in a work and not a commercial web site setting. Also, the sample of the present study was 310, cf. With Webster et al. [15]’s 2 sample studies, with N ¼ 133 and 43, respectively. Furthermore, the assessment instrument comprised valid, reliable scales grounded in literature. This fact, combined with an appropriate sample size, yielded statistically robust results. This contributes to the online consumer behavior and human-computer interaction literature. Information sys tems and marketing scholars can look at these flow dimensions and benefit from linking the research findings to create new avenues for future research in web site design, digital gaming, online consumer behavior, and mixed and augmented reality devices and interfaces. Google, Apple, and Microsoft are betting that the next wave of innova tion in interface design will be mixed reality devices. In terms of pragmatic significance of the present study, the findings should help inform developers of interaction and interface design, in dustrial design, ecommerce apps, and digital gaming, facilitating the creation of sites and games which are more responsive to users. They would provide more control over the user interaction with a site, game, or app, and create interactive features that facilitate user attention focus
Control Attention Focus Cognitive Enjoyment Note: N ¼ 310.
1 2 3
SD
5.32 3.61 4.39
1.05 1.43 1.38
2
3
1.00 .30 .46
1.00 .42
1.00
S.D.
Std. Err.
4.488 4.305 0.183 DF 308.00 270.02
0.894 0.761 0.847 t-value 1.84 1.91
0.064 0.071 0.099 Pr > |t| 0.067 0.057
Variances Equal Unequal
Our exploratory study had few inherent limitations. As an example, our research was constrained to utilizing a laboratory setting among students, which may not necessarily reflect the real-world. However, the sample reflected young online users, and the setting represented a very commonly searched and purchased category of products, apparel. Col lege students still represent and serve as a useful group for samples in studies [53]. Moreover, we used a self-report instrument, which is subjective in nature, resulting in perceived variables instead of measuring such variables through direct observation. Further, our study was conducted in a single university in the U.S. With these limitations, future research opportunities to expand on the present study’s findings do exit. For example, a major area of future research could involve further integration of several theories in combination with flow theory. One example is TAM. The attitude-intention-behavior relationship is a rele vant one that could be a foundation for this research. Heijden et al. [54] integrate both theory of reasoned action (TRA) and TAM in online purchase intentions and surmise that perceived risk and perceived ease of use directly predict attitude towards purchase. Furthermore, contrary to TAM, theories such as theory of cognitive dissonance, theory of pas sive learning, self-perception theory, and social judgment theory show a reverse relationship to TAM, resulting in a behavior-attitude relation ship. Assael [55] states that this reverse relationship shows that behavior can impact subsequent postpurchase attitude, based on cognitive dissonance theory, theory of passive learning, and social judgment theory. Such theories can be further examined in conjunction with flow. Future research, moreover, can focus on group flow within the
Correlation 1
Mean
195 115
3.3. Limitations and future research
Table 5 Means, SDs, and bivariate correlations of flow. M
N
and cognitive enjoyment of the experience. In turn, developers will have potential guidelines to follow as they design and create such applica tions that further engage users. Web site designers and managers need to consider integrating feelings of control, attentions focus, and cognitive enjoyment to make online shopping experiences more enjoyable. Consequently, this will potentially increase purchase intentions, traffic, and repeat visits on a commercial site [20], resulting in positive site attitudes and more frequent or longer visits. Flow may increase duration or frequency of revisits to sites or usage of mobile apps or platforms, known as site stickiness [49]; eyeballs, or the number of site views or impressions; or, mindshare, the public’s knowledge of a product, site, vendor, or mobile app, i.e. brand equity [50,51]. Consequences of user experiences affect online retailers’ profits and hence are important to ecommerce companies, site designers, and game developers. Such interactive user experiences help to bridge the gap between virtual and traditional brick-and-mortar venues [52], especially given the recent decline in the traditional retail channels in malls and physical store fronts. Developers also need to underscore rich content with more user control over the interface, as well create sites and digital games which may induce attention focus and cognitive enjoyment for users. In addi tion, the interactivity defined by McMillan [43] and Van Noort et al. [34], can be used as a framework for designing and testing interface alternatives in facilitating flow experiences.
3.1. Gender effect
Dimension
Gender F M Diff (1–2) Method Pooled Satterthwaite
5
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context of digital gaming and social media interactions. These collabo rative experiences can extend to include investigations in the workplace and team-oriented academic learning platforms, combining a sense of belonging and shared experiences [5]. Investigations of users’ relational experiences, coupled with intended or actual behavioral outcomes and cognitive effects, can also be observed within flow, especially its cognitive enjoyment dimension. Furthermore, in today’s global business environment, it is crucial to investigate cultural influences on flow. In addition, with greater Internet access being achieved with smart phones rather than computers, the impact of mobile media on flow needs research attention. Subjectivity and social issues associated with self-reporting might be mitigated with objective measures. Longitudinal studies would reveal the impact of changing environmental conditions on flow.
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Wu, Empirical study on flow experience in China tourism ecommerce market, J. Ind. Eng. Manag. 8 (2) (2015) 349–364. [26] M. Wani, V. Raghavan, D. Abraham, V. Kleist, Beyond utilitarian factors: user experience and travel company website successes, Inf. Syst. Front 19 (4) (2017) 769–785. [27] O. Baydas, T. Karakus, F.B. Topu, R. Yilmaz, M.E. Ozturk, Y. Goktas, Retention and flow under guided and unguided learning experience in 3D virtual worlds, Comput. Hum. Behav. 44 (2015) 96–102. [28] J. Takatalo, G. Nyman, L. Laaksonen, Components of human experience in virtual environments, Comput. Hum. Behav. 24 (1) (2008) 1–15. [29] H.H. Chang, I.C. Wang, An investigation of user communication behavior in computer mediated environments, Comput. Hum. Behav. 24 (5) (2008) 2336–2356. [30] C. Li, The effects of source credibility and argument quality on employees’ responses toward information system usage, Asia Pac. Manag. Rev. 20 (2) (2015) 56–64. [31] A. Thatcher, G. Wretschko, P. Fridjhon, Online flow experiences, problematic Internet use and Internet procrastination, Comput. Hum. Behav. 24 (5) (2008) 2236–2254. [32] S.H. Zanjani, , A., G.R. Milne, E.G. Miller, Procrastinators’ online experience and purchase behavior, Acad. Market. Sci. J. 44 (5) (2016) 568–585. [33] C. Liu, A model for exploring players flow experience in online games, Inf. Technol. People 30 (1) (2017) 139–162. [34] G. Van Noort, H. Voorveld, E.A. Van Reijmersdal, Interactivity in brand web sites: cognitive, affective, and behavioral responses explained by consumer’s online flow experience, J. Interact. Market. 26 (4) (2012) 223–234. [35] L.K. Trevino, J. Webster, Flow in computer-mediated communication, Commun. Res. 19 (5) (1992) 539–573. [36] K. Kiili, Content creation challenges and flow experience in educational games: the IT-Emperor case, Internet High Educ. 8 (3) (2005) 183–198. [37] S. Pace, A grounded theory of the flow experiences of Web users, Int. J. Hum. Comput. Stud. 60 (3) (2004) 327–363. [38] E.M. Pilke, Flow experiences in information technology use, Int. J. Hum. Comput. Stud. 61 (3) (2004) 347–357. [39] A. Shoham, Flow experiences and image making: an online chat-room ethnography, Psychol. Market. 21 (10) (2004) 855–882. [40] R. Rettie, An exploration of flow during Internet use, Internet Res.: Electron. Network. Appl. Pol. 11 (2) (2001) 103–113. [41] W. Swartout, M. Van Lent, Making a game of system design, Commun. ACM 46 (7) (2003) 32–39. [42] M. Csikszentmihalyi, K. Rathunde, The measurement of flow in everyday life: toward a theory of emergent motivation, in: J.E. Jacobs (Ed.), Nebraska Symposium on Motivation 1992, University of Nebraska Press, Lincoln, NE, USA, 1993, pp. 57–97. [43] S.J. McMillan, Exploring models of interactivity from multiple research traditions: users, documents, and systems, in: L. Lievrouw, S. Livingstone (Eds.), The Handbook of New Media, Sage Publications, Thousand Oaks, CA, USA, 2002, pp. 163–182. [44] L.J. Cronbach, Coefficient alpha and the internal structure of tests, Psychometrika 16 (1951) 297–333. [45] J.C. Nunnally, I.H. Bernstein, Psychometric Theory, third ed., McGraw-Hill, Inc., New York, 1994. [46] D.W. Straub, M. Boudreau, D. Gefen, Validation guidelines for IS positivist research, Commun. Assoc. Inf. Syst. 13 (2004) 380–427. [47] F.N. Kerlinger, H.B. Lee, Foundations of Behavioral Research, fourth ed., Wadsworth Thomas Learning, Belmont, CA, 2000. [48] T.D. Cook, D.T. Campbell, Quasi-experimentation Design and Analysis Issues for Field Settings, Houghton Mifflin Company, Boston, 1979.
4. Conclusion The present study examined flow theory, drawing from multiple disciplines. An exploratory factor analysis was conducted using Webster et al. [15]’s instrument. The questionnaire was given to 310 subjects after they completed an online search and shopping episode. Based on the results of the factor analysis, three dimensions of flow were inter preted: control, attention focus, and cognitive enjoyment. Control is the experience of user influence over and ability to manipulate the computer interaction, resulting from the system’s response to user commands or choices among alternatives [1–3,15]. Attention focus is a state in which individuals are so absorbed or engaged in an activity that they might be oblivious to the world around them, filtering out impertinent stimulus [1–3,15]. Cognitive enjoyment is the combination of curiosity and intrinsic interest [15]. Curiosity is stimulated through new and fun ways to interact with a site or app, or computer playfulness. Intrinsic interest is carrying out a task just for its own sake or enjoyment. No gender effect on flow was found. Implications of future research integrating flow theory in combination with other theories were discussed. Acknowledgement The authors would like to thank Ms. Ada Till, a retired accounting lecturer, for proof-reading and giving feedback regarding the final draft of this manuscript. References [1] M. Csikszentmihalyi, Beyond Boredom and Anxiety: Experiencing Flow in Work and Play, Jossey-Bass, San Francisco, 1975. [2] M. Csikszentmihalyi, Flow: the Psychology of Optimal Experience, Harper and Row, New York, 1990. [3] M. Csikszentmihalyi, Beyond Boredom and Anxiety: Experiencing Flow in Work and Play, Jossey-Bass, San Francisco, 2000. [4] M. Csikszentmihalyi, Finding Flow: the Psychology of Engagement with Everyday Life, Basic Book, New York, 1997. [5] L.K. Kaye, Exploring flow experiences in cooperative digital gaming contexts, Comput. Hum. Behav. 55 (2016) 286–291. [6] F. Nah, B. Eschenbrenner, D. DeWester, S. Park, Impact of flow and brand equity in 3D virtual worlds, J. Database Manag. 21 (3) (2010) 69–89. [7] Y. Guo, B. Klein, Beyond the test of the four channel model of flow in the context of online shopping, Commun. Assoc. Inf. Syst. 24 (2009) 837–856. [8] C. Hsu, Exploring the player flow experience in e-game playing, Int. J. Technol. Hum. Interact. 6 (2) (2010) 47–64. [9] A.S. Jennings, Creating an interactive science murder mystery game: the optimal experience of flow, IEEE Trans. Prof. Commun. 45 (4) (2002) 297–301. [10] D. Weibel, B. Wissmath, S. Habegger, Y. Steiner, R. Groner, Playing online games against computer- vs. human-controlled opponents: effects on presence, flow, and enjoyment, Comput. Hum. Behav. 24 (5) (2008) 2274–2291. [11] H. Chen, R.T. Wigand, M.S. Nilan, Optimal experience of web activities, Comput. Hum. Behav. 15 (5) (1999) 585–608. [12] T.P. Novak, D.L. Hoffman, Y. Yung, Measuring the customer experience in online environments: a structural modeling approach, Market. Sci. 19 (1) (2000) 22–42. [13] T.P. Novak, D.L. Hoffman, A. Duhachek, The influence of global-directed and experiential activities on online flow experiences, J. Consum. Psychol. 13 (1/2) (2003) 3–16.
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[49] C.H. Park, Online purchase paths and conversion dynamics across multiple websites, J. Retailing 93 (3) (2017) 253–265. [50] J. Creswell, Analysts Wary of LinkedIn’s Stock Surge. The New York Times, 2011, May 22. Retrieved from, http://www.nytimes.com/2011/05/23/technology/23lin kedin.html?hpw. [51] S. Kim, T.H. Baek, Y. Kim, K. Yoo, Factors affecting stickiness and word of mouth in mobile applications, J. Res. Indian Med. 10 (3) (2016) 177–192. [52] A.Y. Mahfouz, An apparel etailer’s success story: bridging the gap between physical and virtual shopping, J. Internet Commer. 4 (2) (2005) 49–64.
[53] Y. Seock, L.R. Bailey, The influence of college students’ shopping orientations and gender differences on online information searches and purchase behaviors, Int. J. Consum. Stud. 32 (2) (2008) 113–121. [54] H. Heijden, T. Verhagen, M. Creemers, Understanding online purchase intentions: contributions from technology and trust perspectives, Eur. J. Inf. Syst. 12 (1) (2003) 41–48. [55] H. Assael, Consumer Behavior and Marketing Action, South Western College Publishing, Cincinnati, OH, 1998.
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Contents lists available at ScienceDirect
Technology in Society journal homepage: http://www.elsevier.com/locate/techsoc
An overview of and factor analytic approach to flow theory in online contexts Ahmed Y. Mahfouz *, Kishwar Joonas, Emmanuel U. Opara Prairie View A&M University, P.O. Box 519, MS 2300, Prairie View, TX, 77446-0519, USA
A R T I C L E I N F O
A B S T R A C T
Keywords: Flow Flow theory User experience Ecommerce Cognitive enjoyment Factor analysis
An overview of flow theory is presented from the literature across multiple disciplines, including information systems, ecommerce, marketing, digital gaming, user interface, management, and cultural contexts. Flow can play a pivotal role in the user experience and impact the user interaction with a site, computing device, or app. It is worthwhile to examine the effects of flow experience on users and incorporate these findings in designing engaging user experiences and interfaces in both web sites and mobile applications. To further understand these implications, the present study gave a questionnaire to 310 participants in a computer laboratory setting following an online shopping episode. The factor analysis revealed three dimensions of flow experience: control, attention focus, and cognitive enjoyment. All three dimensions had very low correlations. No gender effect on flow was found.
1. Introduction and literature review Flow is a state in which persons are so involved in an experience that they might be oblivious to the world around them and potentially lose track of time, space, and possibly self [1–3]. This flow experience, state of flow, or optimal experience makes individuals feel that they are in control of their own decisions and actions, while sensing enjoyment and exhilaration during the activity. During this engaged experience, the degree of task challenges and the individuals’ skills are both equally high. For example, athletes observe they have entered the zone at certain vigorous moments of their game or exercise routine, or digital gamers are lost in the experience in 3D virtual worlds in augmented and mixed reality games [4–6]. In order to induce a sense of flow, web sites and mobile apps need to stimulate and respond to users. Otherwise, boredom, anxiety, and apathy occur [1,3,7]. Boredom results when the user interface (UI) or site is not challenging enough, while anxiety occurs if the system is too cumbersome or difficult to use. When skills of users and challenges of sites, apps, and games are too low, apathy materializes, while a flow experience takes place when individuals’ skills match the level of the challenges of the interaction or situation [1,3]. In essence, flow is created when individuals achieve concentration effortlessly and expe rience enjoyment while completing a certain task or objectives that require responses and feedback, at the work, home, in leisure, or in
social situations [4]. A very relevant component of flow theory or experience is that it is an end in itself or a reward for its own sake, not a means to an end: hence, flow is autotelic, from the Greek word auto or self, and telos or goal [3]. An autotelic experience is intrinsically motivated and encompasses establishing objectives with goals, with the outcome being engaged in an activity, paying attention and concentrating intensely during a given experience, while enjoying this interaction immensely. Mentoring stu dents to teach them is not autotelic, but educating them because a person enjoys interacting with students is autotelic [2]. Numerous activities, disciplines, and studies have observed flow experiences. Some examples include rock climbing, surgical procedures, sports, arts, dancing, song compositions, and management [2,3]; online gaming [5,8–10]; online interactions, interactive interfaces, and web site design [6,11–15]; shopping online [7,16–20]; database and systems development [21]; instant messaging [22]; social media engagements [23,24]; travel and tourism [25,26]; and, academic learning [27]. Flow theory has actually been studied in different cultural contexts and countries, such as in Finland [28]; Taiwan [29,30]; China [22,25]: India [26]; Turkey [27]; South Africa [31]; and, Iran [21]. Table 1 lists studies that have examined flow theory in a multitude of fields, including technology, marketing, management, and human behavior. Besides quantitative and experimental studies, several quali tative studies investigated flow [11,36–39], including semi-structured
* Corresponding author. E-mail addresses: [email protected] (A.Y. Mahfouz), [email protected] (K. Joonas), [email protected] (E.U. Opara). https://doi.org/10.1016/j.techsoc.2020.101228 Received 16 April 2018; Received in revised form 11 October 2019; Accepted 26 January 2020 Available online 1 February 2020 0160-791X/© 2020 Elsevier Ltd. All rights reserved.
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interviews with online visitors who experienced flow [37,40]. Kaye [5] explored group flow or shared flow within the context of cooperative digital gamers. Several researchers, furthermore, integrated flow theory with other theoretical streams. As an example, Lu et al. [22] combined the tech nology acceptance model (TAM) and flow theory, and evidenced the importance of extrinsic, as well as intrinsic motivations, in instant messaging (IM) adoption. These researchers found that IM users desire fun and an enjoyable flow experience, in addition to a utilitarian and user-friendly system. In addition, Chang and Wang [29] integrated flow and TAM, to establish that flow not only impacts behavioral intentions but also mediates the relationship between attitudinal factors and behavioral intentions. Li [30] studied constructs from flow theory, along with TAM and the Elaboration Likelihood Model (ELM). Results show a stronger impact of source quality, as well as argument quality on atti tude, and through it, on behavioral intent, compared to playfulness. Further, Wang et al. [25] integrated flow theory with TAM to study user experiences in the tourism industry in China and concluded that com ponents of motivation, as well as flow, are responsible for the creation of behavioral intentions. Moreover, at least two researchers focused on the role of procrasti nation in flow. Flow experience was evidenced to have a lower impact on problematic Internet use, in comparison with procrastination and time spent on the Internet, which is a variety of problematic Internet use [31]. Revisiting the procrastination construct, based on two studies, researchers found that online procrastination and the propensity to delay purchase decisions affect the likelihood of a purchase. In addition, online age, the degree of tendency to delay decisions, as well as the type of online activity, determine the probability of purchase [32]. Takatalo et al. [28] also propounded the Presence-Flow Framework (PFF) in a study of 68 participants observed in a virtual environment (VE), followed by a self-report survey. These authors provided a holistic measurement of VE experiences through incorporating perceptual, attentional, cognitive-affective, and motivational constructs. These studies not only reveal that people experienced flow in computer-mediated environments but also described characteristics of such an experience. As a whole, prior research of flow shows validity and applicability of flow theory in an IS and many other contexts. Next, we will discuss a general mechanism of how online contexts facilitate the flow experience and its consequences. Online interactions facilitate flow [12,13,34]. Activities conducted online that induce flow can be categorized as the VE itself, Internet browsing, online shopping, chats, and digital games [5,10,11]. These activities may exhibit many characteristics of a flow experience. When individuals access the Internet, they usually have a specified goal or objective, such as searching for information, products, and services, or playing digital games. They, in turn, receive an instant response or feedback from the system, site, app, or game. Such activities pose challenges that necessitate certain online skills and user awareness, con trol, and attention on the task-at-hand. Users interact with a site or an app via the UI and control various objects and customize personal profiles or various options presented in a game or a site. Particularly, in interactive, digital 3D gaming or simulations, individuals may feel immersed in the experience to the point they are oblivious to their surroundings, losing track of time, and even losing self-consciousness [10,41]. Coupled with feelings of enjoyment and telepresence (as in being transported to the VE), all these sensory, affective, and cognitive interactions induce a flow experience [12,14,33,34]. Finally, the entire online episode becomes autotelic or intrinsically motivated when the activity is simply carried out for its own sake, and not as a means to any specific end. Csikszentmihalyi [2] notes that following a flow episode, an indi vidual may experience two dichotomous but concurrent feelings: dif ferentiation and integration. Differentiation is a sense of separation and uniqueness of self as opposed to others. Simultaneously, the same in dividual would feel connected to others in union, hence integration. When a user customizes an online product purchase or personalizes a
Table 1 Sample of studies dealing with flow theory. Source
Contribution
Wani et al. [26]
A replication research conducted in India confirmed the role of both utilitarian and hedonic measures in evaluating a travel web site. Two studies evidenced that purchase is determined by online procrastination and the propensity to delay the purchase decision. Differences in the degree of tendency to delay decisions, online users’ age, and type of online activity affect the likelihood of purchase. Online regret experience is determined by user demographics and flow experience components. A flow search measurement instrument in social networking was developed and validated. Empirical research investigated the relationships between hedonic characteristics, utilitarian attributes, flow experience, trust, brand equity, and loyalty among Gen Y customers. The study evidenced the moderating role of utilitarian motivations on the relationships between flow and replay intention, and how uncertainty impacts online gaming. The study was conducted in relation to the tourism industry in China, based on flow theory in conjunction with TAM. Observed that behavioral intent is a function of motivation, as well as flow components. The impact of experience and guidance on flow and retention in 3-D learning environments was investigated. Flow theory was integrated with TAM and ELM. Compared to playfulness, a greater influence of source credibility and argument quality on perceived ease of use and perceived usefulness was found in a workplace online system. User self-efficacy affects database interface interactivity, flow, and scientific behavior change. Flow experience mediates the relationship between interactivity and scientific behavior adaptation. Flow experience also mediates the relationship between user efficacy and scientific behavior adaptation. Group flow is studied in the context of social processes of cooperative digital gaming. Flow explains how site interactivity impacts consumer cognition, affect, and behaviors. Empirical research examines flow and brand equity in 3D virtual worlds. Perceived ease of use, immediate feedback, skill and challenge are antecedents of flow in e-games. The most important aspect of flow is enjoyment. Extrinsic, as well as intrinsic, motivations are important factors in instant IM adoption. IM users not only desire a useful and user-friendly platform but also seek fun and an enjoyable flow experience. Online games involving a human-controlled opponent generated higher levels of presence, flow, and enjoyment, compared to those involving a computer. The effect of presence on enjoyment is mediated by flow. Determinants of behavioral intentions include perceived usefulness, perceived ease of use, interactivity, flow, and attitude toward use. Flow plays a role as a determinant, as well as a moderator in the creation of behavioral intentions. The study proposed an integrated 3D PFF for measuring human experiences in VEs, comprising physical presence, situational interaction, and competence. The relationship between procrastination and the amount of time spent online and problematic Internet use (PIU) was found to be stronger than the relationship between flow experience and PIU. Site revisit frequency is determined by shopping enjoyment and site perceived usefulness. Temporal dissociation, heightened enjoyment, focused immersion, curiosity, and control are characteristics of cognitive enjoyment. Flow increased based on an updated model with control and skill, arousal and challenge, focused attention, telepresence, and interactivity. Reduction of Trevino and Webster’s [35] 4 dimensions to 3 in a 12-item flow instrument: cognitive enjoyment, which merges intrinsic interest and curiosity. Control, attention focus, curiosity, and intrinsic interest are 4 flow characteristics.
Zanjani et al. [32]
Kaur et al. [23] Kaur et al. [24] Bilgihan [17]
Liu [33] Wang et al. [25]
Baydas et al. [27] Li [30]
Hosseini and Fattahi [21]
Kaye [5] Van Noort el al [34]. Nah el al [6]. Hsu [8] Lu et al. [22]
Weibel et al. [10]
Chang and Wang [29]
Takatalo et al. [28] Thatcher et al. [31]
Koufaris [20] Agrawal and Karahanna [16] Novak et al. [12] Webster et al. [15] Trevino and Webster [35]
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profile listing, the user experiences differentiation. When individuals of similar interest join online groups and collaborate in real-time regarding a common cause, they experience integration. Table 2 [2,42] lists the aforementioned flow characteristics. Engaged users may experience flow during an immersive, online activity [5,12,13]. This engagement is accomplished via a device’s or a site’s interface, particularly its interactivity level and features. This interactivity can be classified across four models, along two continuums, as shown in Fig. 1 [43]. One of these four models relates to flow. The first continuum is source of control: human versus machine. The second continuum is UI: apparent versus transparent, consequently creating four quadrants. First, machine-based interaction includes data-entry tasks, such as completing personal profiles or forms; this is equivalent to push technology by the system to the user. Second, human-based communication involves more control by users, for example, when they manipulate tools, such as apps, databases, and word processors; this is more equivalent to pull technology from the user to the system. Third, when the system adjusts itself to accommodate the level of skills of users, such as in learning systems or gaming platforms, then the result is the third quadrant, or adaptive communication [8,9,27]; here, both push and pull technologies happen between senders and receivers of a given communication. Finally, the fourth quadrant is flow, whereby in dividuals are engaged actively and communicating with a system or device. Its interface is virtually transparent since users are immersed in the activity and oblivious to their surroundings, such as in the case with gaming apps with mixed and augmented reality features [5,6,8–10,13, 17,21,23,33]; here, both sender and receiver of a given communication are in a participatory mode, serving equivalent and sometimes inter changeable roles of both transmitter and recipient of the interaction. Hence, in a flow state, users feel immersed and one with the experience or task-at-hand. In terms of consequences of an interaction or communication with a system, site, or app, flow experienced during an activity tends to repeat with similar, behavioral outcomes, such as increased site revisits in online shopping, intent-to-purchase, trust, and brand loyalty [17,20,34]. Flow serves both as a factor and a moderator affecting behavioral in tentions [29]. Furthermore, components of flow in conjunction with TAM impact behavioral intent [25]. Other examples of behavioral out comes of flow include regret experience in social media [23], as well as enhanced scientific behavior adaptation and change in user interactivity and interface environments [21].
We applied Webster et al.‘s [15] instrument in an ecommerce context to a sample of 310 subjects, following an online shopping episode. We used SAS 9.40 for data analysis.
2. Material and methods
2.3. Validity and factor analysis
The current study examined the dimensionality of the flow experi ence. In IS literature, flow experience is depicted with 3 or 4 dimensions.
Drawing from the literature and using previously designed scales increases validity of a given study, and researchers are advised to use already existing and validated scales [46]. Webster et al. [15]. flow experience scales were used in the present investigation. Furthermore, construct validity was determined using exploratory factor analysis, in order to determine and group the variables of interest under their cor responding factors [47]. Cook and Campbell [48] define construct val idity as the measure to ascertain if the variables are true constructs of the observed phenomenon under investigation. Construct validity answers the question: is the study measuring what it is supposed to be measuring [47]? To increase the level of validity of the present study, the subjects participated by navigating and completing the online shopping search task in a computer laboratory. This ensured a greater degree of tight control, as well as facilitated a closed environment whereby any external distractions were eliminated or reduced. All screen interactions of the subjects were recorded using Camstasia Recorder (http://www.tech smith.com) and subsequently reviewed to ensure all instructions and protocols were followed. Exploratory factor analysis with maximum likelihood extraction method with equamax rotation was used to assess construct validity. After using a scree test, the highest contribution to the proportion for
2.1. Sample and instrument In order to examine relevant dimensions of flow theory, an online questionnaire was given to subjects in a computer laboratory, after they completed an online shopping task by navigating an apparel commercial web site and browsing the site for clothing items. Subjects were assigned a computer lab. Once they walked in, a ses sion packet of information was given to each participant. The first step was to read and sign a consent form. This took 15 min. Then, they were given instructions to visit a commercial apparel web site to navigate and search for a suitable product of interest for 30 min, while browsing the various features of the site. After the task was completed, the subjects filled out the Webster et al. [15] flow instrument, and this final stage was allocated 15 min. The subjects of the study were undergraduate college students in a Southwestern university in the United States. Each individual had basic Internet skills, such as using a web browser. All students were full-time, traditional students. Comprising the sample was 310 subjects with ages ranging from 18 to 28 years old, with 96% from ages 19–24. The labo ratory setting facilitated a far tighter degree of control, resulting in minimal distractions. The scales used in the study were taken from and based on Webster et al. [15] flow instrument. Each scale item had a 7-point Likert scale, with 7 anchors, ranging from 1 ¼ strongly disagree, 4 ¼ neutral, to 7 ¼ strongly agree. 2.2. Reliability Reliability is the degree to which an item, scale, or an entire assessment mechanism will yield similar values when given in varying times, places, or samples [44,45]. Internal consistency reliability is the extent to which individual scale items (i.e. the variables) correlate with each other or with the entire scale [45]. A scale is internally consistent if each item or variable in a scale measures the same concept or construct. The most commonly used index of internal consistency reliability is Cronbach’s alpha, which was used to measure the reliability of the present study. Cronbach’s alpha should be�0.70 [45]; for the present study it was 0.87, although Webster et al. [15]. reported this as 0.82.
Table 2 Flow theory and experience characteristics. Flow Characteristic
Description
Specified goals and objectives Challenges ¼ skills
Clear tasks with instant response and feedback
Union of awareness and action Control Attention on the task-athand Perceived time distortion Dissolution of selfconsciousness Autotelic state Post Flow Experience Differentiation Integration
Opportunities to take action, along with an individual’s ability to take action Merging of oneself with the experience Perceived sense of control during the interaction or activity Deep concentration given to tasks-at-hand, ignoring irrelevant input Perception of time as fleeting Feelings of transcendence to a greater purpose Intrinsically-motivated action done for its own sake Feelings of uniqueness of self Feelings of self being in union with others
3
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Fig. 1. Four models of human-to-machine interactivity (adapted from Ref. [43].
variance accounted for by a given factor was used to determine the number of interpretable factors to retain. Variables, which are related, load on and group under the same factor. Variables, which are the items in a given scale, are kept if their |factor loadings| are�.50. Otherwise, they are dropped. Items or variables loading on a factor other than the original factor from the given scale in the existing literature, or loading on multiple factors concurrently, were also dropped [46]. The variables that were kept after factor analysis are shown in Table 3. FlwAttn3 was not retained since it loaded on a factor other than the original factor in the literature. The three interpretable factors are consistent with the literature [15]. As shown in Table 4, the factors are congruent with the original scales. For instance, control was interpreted as the first factor, based on the following variables or scale items: FlwCtrl1 (user control over the use of the site), FlwCtrl2 (no control over the interaction, reverse-coded), and FlwCtrl3 (control over the interaction). Attention focus and cognitive enjoyment were the other two interpretable factors, 2 and 3, respectively.
Table 4 Scales and items retained after factor analysis. Variable Control FlwCtrl1 FlwCtrl2
When using the web site, I felt in control. I felt that I had no control over my interaction with the site.* FlwCtrl3 The site allowed me to control the computer interaction. Attention Focus FlwAttn1 When using the site, I thought about other things.* FlwAttn2 When using the site, I was aware of distractions.* FlwAttn3 The site allowed me to control the computer interaction. (Dropped) Cognitive Enjoyment FlwCEnj1 Using the site excited my curiosity. FlwCEnj2 Interacting with the site made me curious. FlwCEnj3 Using the site aroused my imagination. FlwCEnj4 Using the site bored me.* FlwCEnj5 Using the site was intrinsically interesting. FlwCEnj6 The site was fun for me to use.
3. Discussion
Factors & Factor Loadings 1
2
3
.80* .67* .70* .11 .10 .25 .25 .14 .12 .25 .18 .26
.08 .15 .13 .74* .73* .39 .15 .21 .17 .24 .08 .23
.13 .06 .26 .16 .06 .51* .76* .77* .70* .66* .75* .79*
S.D.
5.23 5.39
1.05 1.08
5.35
1.03
3.55 3.66 4.39
1.49 1.37 1.42
4.46 4.31 4.10 4.61 4.35 4.51
1.37 1.35 1.41 1.45 1.23 1.40
analysis, the present study confirmed the same three dimensions but in an online context, in which users navigated a web site and searched for products online. Control, attention focus, and cognitive enjoyment are these three components [1–3,15]. Control is the perceived feelings of control by the user over the interaction with a device, site, or app, based on commands and alternative choices, while such a system responds and gives feed back to the user. The state in which users are so engaged in an experi ence or interaction with a site or app that they become oblivious to their surroundings is attention focus. Curiosity and intrinsic interest combine to form cognitive enjoyment [15]. Curiosity is achieved via new and enjoyable means to interact with a given system. Intrinsic interest occurs when users undertake a task or an activity just for its own sake, not expecting a specific outcome or gain. Agrawal and Karahanna [16] expand this definition of flow and cognitive enjoyment to a similar construct, cognitive absorption, which includes the following flow di mensions: focused immersion, temporal dissociation, control, height ened enjoyment, and curiosity [16]. This enjoyment is an important aspect of any flow experience [8,10,33]. As shown in Table 5, the highest reported mean is for control (M ¼
Table 3 Factor loadings of retained items after factor analysis.
FlwCtrl1 FlwCtrl2 FlwCtrl3 FlwAttn1 FlwAttn2 FlwAttn3 FlwCEnj1 FlwCEnj2 FlwCEnj3 FlwCEnj4 FlwCEnj5 FlwCEnj6 FlwAttn3 was dropped.
Mean
An asterisk (*) indicates an item is reverse-coded, as stated in the original scale. Each item had a 7-point Likert scale, with following anchors: 1 ¼ strongly disagree, and 7 ¼ strongly agree.
Trevino and Webster’s [35] study investigated flow as a multidi mensional construct in work settings with the following dimensions: control, attention focus, and cognitive enjoyment. Originally, curiosity (both cognitive and sensory) and intrinsic interest were separate di mensions but were later combined as cognitive enjoyment. Based on factor
Variables
Scale Item
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5.32), which also has the smallest standard deviation (SD ¼ 1.05), i.e. users differed less in their perceived responses to the level of control over the interaction. Subjects felt they were probably in more control of the interaction and cognitively enjoying it than they did focusing on it with their attention. This is understandable since the task-at-hand was a structured task, searching for a clothing item, and users probably felt in control over the web site’s interface and its features. Moreover, all pairs of the three dimensions had low bivariate correlations: control and attention focus, r ¼ 0.30; control and cognitive enjoyment, r ¼ 0.46; and, attention focus and cognitive enjoyment, r ¼ 0.42. These low bivariate correlations indicate that the dimensions are not reflecting the same construct.
Table 6 Results of the t-Test between gender and flow. Variable: Flow
Given the sample was predominately female, it was interesting to examine a gender effect on flow. There were 195 females and 115 males. Females comprised 63% of the sample. The independent samples pooled t-test was used to test for a gender effect, given the variances of both groups were statistically equivalent. H1 There is a difference between females and males in experiencing flow, while completing an online shopping episode. As seen from Table 6, there was no significant difference between females and males in terms of their flow experience, t308 ¼ 1.84, p ¼ 0.067, at the 0.05 significant level. The means for both groups are relatively similar, on a scale of 1 ¼ strongly disagree to 7 ¼ strongly agree, M ¼ 4.49 for females, SD ¼ 0.89; M ¼ 4.31, SD ¼ 0.76, males. 3.2. Contributions The present study provides several contributions to scholarly research. An overview of flow dimensions and characteristics was dis cussed from multiple disciplines and contexts, as well as post-flow ex periences, differentiation and integration. Additionally, the present study attempted to add to the base of existing studies that investigate flow theory in online environments in an ecommerce setting, while replicating the results of Webster et al. [15]’s work, which examined flow theory in a work and not a commercial web site setting. Also, the sample of the present study was 310, cf. With Webster et al. [15]’s 2 sample studies, with N ¼ 133 and 43, respectively. Furthermore, the assessment instrument comprised valid, reliable scales grounded in literature. This fact, combined with an appropriate sample size, yielded statistically robust results. This contributes to the online consumer behavior and human-computer interaction literature. Information sys tems and marketing scholars can look at these flow dimensions and benefit from linking the research findings to create new avenues for future research in web site design, digital gaming, online consumer behavior, and mixed and augmented reality devices and interfaces. Google, Apple, and Microsoft are betting that the next wave of innova tion in interface design will be mixed reality devices. In terms of pragmatic significance of the present study, the findings should help inform developers of interaction and interface design, in dustrial design, ecommerce apps, and digital gaming, facilitating the creation of sites and games which are more responsive to users. They would provide more control over the user interaction with a site, game, or app, and create interactive features that facilitate user attention focus
Control Attention Focus Cognitive Enjoyment Note: N ¼ 310.
1 2 3
SD
5.32 3.61 4.39
1.05 1.43 1.38
2
3
1.00 .30 .46
1.00 .42
1.00
S.D.
Std. Err.
4.488 4.305 0.183 DF 308.00 270.02
0.894 0.761 0.847 t-value 1.84 1.91
0.064 0.071 0.099 Pr > |t| 0.067 0.057
Variances Equal Unequal
Our exploratory study had few inherent limitations. As an example, our research was constrained to utilizing a laboratory setting among students, which may not necessarily reflect the real-world. However, the sample reflected young online users, and the setting represented a very commonly searched and purchased category of products, apparel. Col lege students still represent and serve as a useful group for samples in studies [53]. Moreover, we used a self-report instrument, which is subjective in nature, resulting in perceived variables instead of measuring such variables through direct observation. Further, our study was conducted in a single university in the U.S. With these limitations, future research opportunities to expand on the present study’s findings do exit. For example, a major area of future research could involve further integration of several theories in combination with flow theory. One example is TAM. The attitude-intention-behavior relationship is a rele vant one that could be a foundation for this research. Heijden et al. [54] integrate both theory of reasoned action (TRA) and TAM in online purchase intentions and surmise that perceived risk and perceived ease of use directly predict attitude towards purchase. Furthermore, contrary to TAM, theories such as theory of cognitive dissonance, theory of pas sive learning, self-perception theory, and social judgment theory show a reverse relationship to TAM, resulting in a behavior-attitude relation ship. Assael [55] states that this reverse relationship shows that behavior can impact subsequent postpurchase attitude, based on cognitive dissonance theory, theory of passive learning, and social judgment theory. Such theories can be further examined in conjunction with flow. Future research, moreover, can focus on group flow within the
Correlation 1
Mean
195 115
3.3. Limitations and future research
Table 5 Means, SDs, and bivariate correlations of flow. M
N
and cognitive enjoyment of the experience. In turn, developers will have potential guidelines to follow as they design and create such applica tions that further engage users. Web site designers and managers need to consider integrating feelings of control, attentions focus, and cognitive enjoyment to make online shopping experiences more enjoyable. Consequently, this will potentially increase purchase intentions, traffic, and repeat visits on a commercial site [20], resulting in positive site attitudes and more frequent or longer visits. Flow may increase duration or frequency of revisits to sites or usage of mobile apps or platforms, known as site stickiness [49]; eyeballs, or the number of site views or impressions; or, mindshare, the public’s knowledge of a product, site, vendor, or mobile app, i.e. brand equity [50,51]. Consequences of user experiences affect online retailers’ profits and hence are important to ecommerce companies, site designers, and game developers. Such interactive user experiences help to bridge the gap between virtual and traditional brick-and-mortar venues [52], especially given the recent decline in the traditional retail channels in malls and physical store fronts. Developers also need to underscore rich content with more user control over the interface, as well create sites and digital games which may induce attention focus and cognitive enjoyment for users. In addi tion, the interactivity defined by McMillan [43] and Van Noort et al. [34], can be used as a framework for designing and testing interface alternatives in facilitating flow experiences.
3.1. Gender effect
Dimension
Gender F M Diff (1–2) Method Pooled Satterthwaite
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context of digital gaming and social media interactions. These collabo rative experiences can extend to include investigations in the workplace and team-oriented academic learning platforms, combining a sense of belonging and shared experiences [5]. Investigations of users’ relational experiences, coupled with intended or actual behavioral outcomes and cognitive effects, can also be observed within flow, especially its cognitive enjoyment dimension. Furthermore, in today’s global business environment, it is crucial to investigate cultural influences on flow. In addition, with greater Internet access being achieved with smart phones rather than computers, the impact of mobile media on flow needs research attention. Subjectivity and social issues associated with self-reporting might be mitigated with objective measures. Longitudinal studies would reveal the impact of changing environmental conditions on flow.
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4. Conclusion The present study examined flow theory, drawing from multiple disciplines. An exploratory factor analysis was conducted using Webster et al. [15]’s instrument. The questionnaire was given to 310 subjects after they completed an online search and shopping episode. Based on the results of the factor analysis, three dimensions of flow were inter preted: control, attention focus, and cognitive enjoyment. Control is the experience of user influence over and ability to manipulate the computer interaction, resulting from the system’s response to user commands or choices among alternatives [1–3,15]. Attention focus is a state in which individuals are so absorbed or engaged in an activity that they might be oblivious to the world around them, filtering out impertinent stimulus [1–3,15]. Cognitive enjoyment is the combination of curiosity and intrinsic interest [15]. Curiosity is stimulated through new and fun ways to interact with a site or app, or computer playfulness. Intrinsic interest is carrying out a task just for its own sake or enjoyment. No gender effect on flow was found. Implications of future research integrating flow theory in combination with other theories were discussed. Acknowledgement The authors would like to thank Ms. Ada Till, a retired accounting lecturer, for proof-reading and giving feedback regarding the final draft of this manuscript. References [1] M. Csikszentmihalyi, Beyond Boredom and Anxiety: Experiencing Flow in Work and Play, Jossey-Bass, San Francisco, 1975. [2] M. Csikszentmihalyi, Flow: the Psychology of Optimal Experience, Harper and Row, New York, 1990. [3] M. Csikszentmihalyi, Beyond Boredom and Anxiety: Experiencing Flow in Work and Play, Jossey-Bass, San Francisco, 2000. [4] M. Csikszentmihalyi, Finding Flow: the Psychology of Engagement with Everyday Life, Basic Book, New York, 1997. [5] L.K. Kaye, Exploring flow experiences in cooperative digital gaming contexts, Comput. Hum. Behav. 55 (2016) 286–291. [6] F. Nah, B. Eschenbrenner, D. DeWester, S. Park, Impact of flow and brand equity in 3D virtual worlds, J. Database Manag. 21 (3) (2010) 69–89. [7] Y. Guo, B. Klein, Beyond the test of the four channel model of flow in the context of online shopping, Commun. Assoc. Inf. Syst. 24 (2009) 837–856. [8] C. Hsu, Exploring the player flow experience in e-game playing, Int. J. Technol. Hum. Interact. 6 (2) (2010) 47–64. [9] A.S. Jennings, Creating an interactive science murder mystery game: the optimal experience of flow, IEEE Trans. Prof. Commun. 45 (4) (2002) 297–301. [10] D. Weibel, B. Wissmath, S. Habegger, Y. Steiner, R. Groner, Playing online games against computer- vs. human-controlled opponents: effects on presence, flow, and enjoyment, Comput. Hum. Behav. 24 (5) (2008) 2274–2291. [11] H. Chen, R.T. Wigand, M.S. Nilan, Optimal experience of web activities, Comput. Hum. Behav. 15 (5) (1999) 585–608. [12] T.P. Novak, D.L. Hoffman, Y. Yung, Measuring the customer experience in online environments: a structural modeling approach, Market. Sci. 19 (1) (2000) 22–42. [13] T.P. Novak, D.L. Hoffman, A. Duhachek, The influence of global-directed and experiential activities on online flow experiences, J. Consum. Psychol. 13 (1/2) (2003) 3–16.
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