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Exploring flow in the mobile interface context
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Exploring flow in the mobile interface context ⁎
Clark D. Johnson , Brittney C. Bauer, Nitish Singh Richard A. Chaifetz School of Business, Saint Louis University, 3674 Lindell Blvd, St. Louis, MO 63108, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Mobile interface Flow Optimal experience Compulsive usage Technostress
While flow has been researched extensively in computer-mediated environments, it has been scarcely researched in the mobile interface context. Specifically, we have virtually no knowledge about what concrete traits of mobile interfaces (as opposed to user perceptions) encourage the flow state. Flow has been associated with many positive outcomes in human-computer interactions, making it vital for practitioners and scholars to understand the traits of mobile interfaces that encourage flow. Therefore, we synthesize the results of a literature search and a modified Delphi study to develop an inventory of traits and perceptions that can promote the flow experience. We provide initial evidence for the predictive validity of our inventory through a survey study which demonstrates that composite ratings of the inventory traits are positively associated with the flow state, which in turn, leads to compulsive usage and technostress. In doing so, this paper also extends flow research by exploring the potential negative outcomes of flow in the mobile interface context.
1. Introduction Flow is the state of becoming “lost” in an activity that occurs when an individual's skills are matched with the level of challenge of a task (Csikszentmihalyi, 1990). This important mental state has been extensively researched in the context of desktop-mediated internet usage and has been associated with several important outcomes regarding consumer attitudes, intentions, and behaviors (Hoffman and Novak, 2009). However, flow has scarcely been examined in the mobile interface (MI) context, and—compared with desktop websites—frameworks for evaluating MIs are sparse in the literature (Al-Khalifa, 2014). In fact, while we have limited knowledge about certain user perceptions of MIs that are associated with the flow state, such as information and system quality (Gao and Bai, 2014), no research has attempted to identify the specific traits of MIs that lead to the flow state. The focus on user perceptions, as opposed to MI traits, severely limits the diagnostic value that this stream of research can provide to practitioners. In other words, the broadness of perceptions like perceived ease of use and information quality allows us to examine the impact of user perceptions of MIs. However, marketers and developers lack an in-depth understanding of specific MI features, which, in conjunction, influence the broader outcomes, such as flow. This is surprising and concerning given that two-thirds of the world's 7.6 billion inhabitants have a mobile phone, and over half of those are “smart” devices (McDonald, 2018). Thus, this study seeks to examine a key guiding research question: What aspects of mobile interfaces promote the
⁎
flow state? This paper builds an inventory of MI characteristics that can lead to the flow state. To ground our inventory in prior work on flow theory, we first study what has been shown to lead to flow in the traditional desktop-mediated environment. Then, we determine which of these aspects apply to the mobile environment. To establish content and face validity, we utilize a Delphi approach in a survey study which allows us to adjust the inventory according to expert knowledge. Next, to provide evidence of predictive validity, our final study tests the positive association between the inventory and flow. We believe this study is essential to demonstrate that concrete characteristics of MIs are predictive of user cognitive states and responses, and should be studied as opposed to the user perceptions which have dominated much of the research in this area. To further extend flow research, this study also examines the relationships between flow and the ‘dark side’ outcomes of compulsive usage and technostress. Finally, we discuss the implications of our work and lay out an agenda for future research on flow in the MI context. 2. The need for a mobile interface flow inventory One can easily think of many occurrences of flow while using MIs. Many readers will remember observing people walking around while captivated by their smartphone, perhaps on a college campus or in a shopping mall. Anyone who scans the scene of a restaurant is sure to be able to identify tables where all of the diners have their cell phones out,
Corresponding author. E-mail addresses: [email protected] (C.D. Johnson), [email protected] (B.C. Bauer), [email protected] (N. Singh).
https://doi.org/10.1016/j.jretconser.2019.01.013 Received 4 September 2018; Received in revised form 25 January 2019; Accepted 25 January 2019 0969-6989/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Clark D. Johnson, Brittney C. Bauer and Nitish Singh, Journal of Retailing and Consumer Services, https://doi.org/10.1016/j.jretconser.2019.01.013
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and conversation is absent. While everyone knows about the perils of texting and driving, it seems that people can become so ‘lost’ while using their mobile devices that many cannot help but to do so. We cannot assume that all of these individuals are in the flow state; however, the vast number of individuals absorbed with their mobile phones indeed suggests that experiencing flow while using a MI is pervasive. Therefore, it is surprising that little attention has been paid in the academic literature regarding the role of the flow experience in the mobile context. The limited research conducted in this area has either 1) only examined limited or vague antecedents of flow, or 2) focused on positive outcomes of flow. First, information quality, system quality, trust, perceived complementarity (as in additional complementary services) and perceived ease of use have all been shown to lead to flow while using a mobile device (Gao and Bai, 2014; Zhou, 2012, 2013; Zhou et al., 2010). Second, flow has been shown to lead to loyalty towards mobile social networking sites (Gao and Bai, 2014; Zhou et al., 2010), continuance intention toward mobile payment services (Zhou, 2013), and even usage intention for mobile banking services (Zhou, 2012). However, the broadness of constructs like the perceived ease of use and information quality begs the question, what are the specific traits or aspects of a mobile interface that can encourage flow? The paucity of research examining the particular traits of mobile interfaces that lead to flow suggests a need for more extensive study. An inventory of traits that encourage the flow state would be a powerful tool for marketers and interface designers. With the important outcomes that have been associated with flow in human-computer interactions, the use of such an inventory would be sure to enhance the effectiveness of any MI. Therefore, we set out to develop a mobile interface flow inventory (MIFI) that is grounded in prior research on the flow state in the human-computer interaction context.
excluded. Next, we examined each article to determine if it studied a characteristic of an online interface as an antecedent to flow. Then, two of the authors independently coded each studied antecedent based on whether it was a user's perception of the interface, or a trait of the interface. Finally, literature searches were performed to find studies that examined traits of mobile interfaces that lead to the perceptions in our list. Any newly identified traits were then added to the table under the appropriate perception.
3. Methodology
3.2. Delphi study
To initiate our study on the antecedents of flow in the MI context, we utilize a series of three methodological approaches: a systematic literature review to identify potential traits, a modified Delphi approach in a survey study to provide face validity, and a survey study to demonstrate predictive validity. This allows us to triangulate our findings and to demonstrate how the MIFI may be applied in future research.
To ensure the face validity of the inventory, we conducted a modified Delphi study (i.e., only one round of participation). A Delphi method is an appropriate approach for our study because it can simultaneously bring together the critical thoughts of a geographicallydispersed expert panel, while also addressing potential issues and concerns for the future. A Delphi study involves forming a panel of subject experts and surveying these experts who provide anonymous qualitative responses to questions or informational prompts (Skinner et al., 2015). Our panelists were selected from the authors’ professional network based on their knowledge and experience with MI design, rather than being randomly surveyed. The panel included individuals in academia and in the digital marketing industry. Therefore, in this context, the panel was deemed sufficiently knowledgeable and representative to provide a solid base for the results. The goal of this approach was to generate and explore ideas, as opposed to forming a strict consensus. Therefore, panelists participated in the Delphi study online and were randomly assigned to receive one of the five dimensions in the MIFI. They were also given descriptions of any traits that needed additional explanation (see descriptions in Results). Panelists were asked to review the traits in the given dimension carefully, and to advise if 1) any of the traits do not apply to a MI, 2) any of the traits would need to be adapted for a MI and how they should be modified, and 3) there are any missing MI traits that would lead to the perceptions listed in the given dimension. This feedback was utilized by the authors in discussion to make appropriate changes to the inventory. The resulting version of the MIFI dimensions, perceptions, and traits are described below in more detail.
3.1.1. Results of the systematic literature review The initial search resulted in a total of 88 articles which examined flow in the context of internet usage. Upon examining each article to determine if it studied an antecedent to flow in an online interface, we were left with 24 applicable articles. We then compared the results of the independent coding, which achieved a percent agreement of 94%. Discrepancies between the two were resolved by discussion. An indepth examination of the resulting list suggested that the perceptions could be grouped into five categories based on dimensions of user perceptions of websites developed in prior research (e.g., Ha and Stoel, 2009; Sicilia et al., 2005): Informativeness, Interactiveness, Hedonic, Functionality, and Trust/Security. After the perceptions were grouped, we then categorized each trait according to which perception the trait generates. This resulted in the initial version of the MIFI. 3.1.2. Discussion of the systematic literature review While the initial version of the MIFI provided us with a list of traits and perceptions that may apply in the MI context, we need to understand whether they apply directly to this context or if they should be modified to better fit the context. Therefore, to establish face validity and increase the comprehensiveness and parsimony of the MIFI, we conducted a modified Delphi study which is described next.
3.1. Systematic literature review First, we conduct a systematic literature review (Tranfield et al., 2003) to identify antecedents of flow that have been studied in the desktop computer context. This allows us to theoretically ground our inventory in prior literature and identify key contributions to the study of the flow state in human-computer interactions. MIs are similar to traditional desktop interfaces, but are designed for the small screens of mobile devices (Al-Khalifa, 2014) and typically have more touch features. Therefore, a logical progression for developing an inventory for MIs is to determine which previously studied antecedents in the desktop context would apply to MIs. We utilized two databases (ABI/Inform and Business Source Premier) to search for several keywords. These included “flow” in the title and “website” in the abstract (resulting in 40 and 39 articles for the ABI/Inform and Business Source Premier databases, respectively), “flow” in the title and “internet” in the abstract (resulting in 61 and 84 articles, respectively), and “flow” in the title and “online” in the abstract (resulting in 116 and 134 articles, respectively). The searches were limited to scholarly, peer-reviewed journals, and we only collected articles in the English language. We examined the titles and abstracts of each paper to determine if the article was examining the mental state of flow. For example, several of the articles were examining other topics, such as information flow, and these papers were
3.2.1. Results of the Delphi study To structure the presentation of the results, we now discuss each dimension and the related MI characteristics that may be less intuitive and more technical in nature. 2
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Table 1 (continued)
Table 1 Mobile Interface Flow Inventory. Dimension 1—Informativeness Perceptions: Accurate/complete/correct/content reliability
Dimension 1—Informativeness Perceptions:
Up-to-datea,b,c
Easy to complete transactions
Variety of productsa,b Content easy to understand Knowledgeable Meets information needs for informed decisions
Provides relevant information
Resourceful
Dimension 2—Interactiveness Perceptions: Adaptable to a variety of needs
The environment made communicating quick and easy
Prompt in responding to inquiries Unique interactive functions
Dimension 3—Hedonic Perceptions: Aesthetically pleasing/vividness
Agreeable/delightful/enjoyment/ pleasure Artistic sophistication/creativity/ imaginative Entertaining/exciting/flashy/thrilling Experience something new Fun to browse/engaging
Humor Increases the quality of life Professional
The sound is lively/natural/proper for the contents Dimension 4—Functionality Perceptions: Challenge Convenient/useful
Traits:
Traits:
Feedback featuresa,b,c
Easy to navigate
Provides useful, valuable, and various information on products and servicesa,b,c Recommender system (needs to be accurate)a Value-added search mechanisms (e.g., Bestsellers, New and Notable, etc.)a Factual quality of user-generated content through control mechanismsa,c Possible to register to receive updatesa,b,c Links to similar websitesa,b,c Offers external information sources a, b,c Provides comprehensive research toolsa,b,c
Enables effective use of my time Innovative technical features Little waiting time Perceived ease of use Function reliability
Traits: Allows interaction to receive information according to preferencesa,b,c Personalized communication featuresa,b,c Being with people visiting this sitea,b,c
Well organized information
Can speak with a representativea,b,c Communicating with people visiting this sitea,b,c Hearing what others saya,b,c Interacting with people visiting this sitea,b,c Online contact forma,b,c Person-interactivitya,b,c Machine-interactivitya,b,c Attribute-based user interface designa Location-based information (using GPS)a,b,c Functional control (sampling different functions of products through the computer) a Option to recommend the site to a frienda,b,c Text-to-speech voice (when interacting with a CSR)a,b,c
Dimension 5—Trust/Security Perceptions: Believable/legitimacy Delivers service as promised Feel comfortable/secure/confident
Trust administrators Trust with financial information
Interactive store locatora,b Offers order confirmationa Option to order products onlinea Payment optionsa,b Present of shopping cart a Purchase tracking servicesa A clear process for browsinga,b,c Global search featurea,b,c Non-linear product catalog with internal hyperlinksa,b,c Proximity through hierarchya,b,c Search and filter functions (needs to be accurate)a,b,c Site mapa,b,c All needs can be completed via sitea,b,c Un-do buttona,b,c 3D product demonstrationa Able to choose clothes by using an avatara Fast loadinga,b,c The site always availablea,b,c Operating system interoperabilitya,b,c Critical functions perform reliably (e.g., add to cart, checkout, etc.)a,b,c Clear display of page contentsa,b,c Presence of clear menu items on each pagea,b,c Traits: Return policies and measures of accountability are cleara Assurance of privacya,b,c Access to account information (e.g., terms of service, etc.)a,b,c Declaration of intended usea,b,c An indication of security/secure sitea,b,c Information regarding the security of paymentsa, b Secure payment methodsa, b Visual security features, such as trustmarksa,b,c Physical security features, such as finger print authorizationa,b,c
Notes. a Traits rated for e-commerce apps. b Traits rated for finance apps. c Traits rated for ‘other’ apps.
Traits: Consistency (arrangement of elements, colors, fonts, etc.)a,b,c Consistent web page designa,b,c Visual localization (localizing visual aspects based on cultural norms and expectations)a,b,c Use of an avatar on online shopping sitea,b,c
3.2.1.1. Informativeness. The Informativeness dimension had 6 perceptions with a total of 11 traits. Table 1 presents the MIFI in its entirety. For clarification, we describe two of the less recognizable traits in this dimension. First, value-added search mechanisms are those that provide entire lists of search results based on a search category, such as Bestsellers, New and Notable, etc. (Koufaris, 2002). Second, factual quality of user-generated content through control mechanism means the information that is generated by users and displayed on the MI is monitored through control mechanisms, such as ratings on whether the information is useful or factual (Mahnke et al., 2015).
Product images as thumbnailsa,b,c Provides images, audio, or video previews of the products a Visual control (allows consumers to manipulate product images with mice and keyboards)a
3.2.1.2. Interactiveness. The Interactiveness dimension had 4 perceptions with a total of 15 traits. This dimension had several traits which deserve further explanation. For instance, communicating with people visiting this site, being with people visiting this site, hearing what others say, and interacting with people visiting this site all suggest that the MI should provide a way for users to communicate with one another. Some examples of this might include comment and public feedback features. Person-interactivity is “interactivity between people that occurs through a medium or unmediated, as in the case of face-to-face
Company logoa,b,c Luciditya,b,c Retailer brand congruitya,b
Traits: Language optionsa,b,c Gift servicesa
3
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flow.
communication” (Wu and Chang, 2005, p. 939). Alternatively, machineinteractivity is “the extent to which users can participate in modifying the form and content of a mediated environment in real time” (Wu and Chang, 2005, p. 939). Next, according to Kamis et al., (2010, p. 158), an attribute-based user interface design is one where:
3.3.1. Predictive validity hypotheses As the traits in the MIFI have been selected based on either their established positive impact on flow in prior research or their logical connection to this state of absorption as suggested by our panelists, we expect that the composite rating score of these traits for a given MI will be positively associated with the flow experience while utilizing that MI. As such, we posit:
“the customer is presented with a list of attributes and the different values for each attribute. The customer then chooses a combination of the different values and is presented with a view of the customized product. Customers can iteratively choose different values and experiment until they feel they have developed an acceptable product.”
H1. The composite ratings of MIFI traits will be positively associated with the flow state while utilizing the MI. Additionally, to further extend flow research, we examine two potential ‘dark side’ outcomes of flow in the MI context—compulsive behavior and technostress (Lee et al., 2014). Prior research has shown that mobile phone usage for entertainment or stress alleviation can provide immediate gratification, but it can also lead to a diminished sense of volitional control and induce persistent activity (Thomée et al., 2011). As such, flow researchers have begun to develop a stream regarding the dark side of flow (Schüler, 2012). Various individual characteristics, such as locus of control and need for touch, have been associated with compulsive mobile phone usage and technostress (i.e., end users experiencing stress due to information and communication overload; Lee et al., 2014). Hence, we initiate the study of the dark side of flow in the mobile interface context by examining the relationship between flow, compulsive usage, and technostress. We suggest that the flow state may lead to the diminished sense of volitional control and persistent activity associated with compulsive usage of MIs. First, however, we must differentiate between control of in-task behavior and control of usage. While flow certainly increases the sense of control over in-task behavior (Hoffman and Novak, 2009), it has also been shown to lead to a decreased ability to control engagement, or rather disengagement, with the task. For example, Bridges and Florsheim (2008) find that the hedonic elements of flow are positively associated with pathological internet use, and Chou and Ting (2003) find that the flow state encourages addictive behavior to cyber-games. Therefore, we propose that flow will be associated with compulsive usage of the MI. Additionally, prior research has demonstrated that compulsive usage is highly associated with technostress (Lee et al., 2014), and we, therefore, posit that flow and compulsive usage will mediate the positive relationship between ratings on the MIFI traits and technostress.
Finally, the functional control means that the MI enables consumers to sample different functions of products through their mobile devices (Jiang and Benbasat, 2004). 3.2.1.3. Hedonic. The Hedonic dimension had 10 perceptions with a total of 10 traits. We examine some of the more abstract traits in greater detail. First, by consistency, we are referring to the arrangement of elements, colors, fonts, etc. (Mahnke et al., 2015). Similarly, visual control allows consumers to manipulate product images with touch, similar to how a desktop-mediate environment would allow manipulation with a mouse and keyboard (Jiang and Benbasat, 2004). Next, lucidity means that the MI has a clear delimitation of displayed elements, a low font variety, and a low number of modest colors (Mahnke et al., 2015). Lastly, retailer brand congruity means that, compared with the physical store, the MI matches the user's impression of the physical store (Landers et al., 2015). 3.2.1.4. Functionality. The dimension of Functionality had 10 perceptions with a total of 24 traits. This dimension had several less recognizable traits which require further description. For instance, we placed language options under challenge perception because allowing users to access information on the interface in their preferred language will allow them to experience the optimal match of challenge and skill, which is a primary condition for the flow experience (Csikszentmihalyi and LeFevre, 1989). Non-linear product catalog with internal hyperlinks can best be explained by an example: If the user is browsing in the shoe section and wishes to start looking at coats, they are able to click a link to jump straight to the coat section without having to click ‘back’ to access the main menu. Finally, proximity through hierarchy “seeks to reduce eye movement distances, cursor and scrolling movement distances, and the number of clicks toward receiving the relevant elements” (Mahnke et al., 2015, p. 68). Mahnke et al. (2015) note that these approaches may include vertical hierarchy (the most important elements are displayed at the top of the page), level hierarchies (important elements are provided at high levels of the site structure), and size hierarchy (important elements have increased relative size).
H2. Flow and compulsive usage will mediate the positive relationship between composite ratings of MIFI traits and technostress. 3.3.2. Results of the survey study 3.3.2.1. Sample and procedures. The sample consisted of 124 students at a large private university in the Midwestern United States. The sample was comprised of exactly half males, and the average age was about 20 years old. Correlations were employed to determine whether gender might be related to the outcome variables of flow, compulsive usage, and technostress. Gender was not significantly related to any of these constructs (rflow = 0.08, p = .38; rcompulsive = 0.17, p = .07; rtechnostress = 0.14, p = .13). Variables that are not significantly related to the criteria should not be included in the main analyses as doing so unnecessarily reduces statistical power (Becker, 2005). Thus, we did not include gender in our analyses. Participants were 73.4% White, 14.5% Asian, 5.6% Black or African American, 4.8% Latino or Hispanic, and 1.6% Other. Participants were first asked if they had a smartphone and were thanked for their time if they answered ‘no.’ Then, the remaining participants were randomly assigned to answer if they had used either a financial or e-commerce MI in the past week. If the participant answered ‘no,’ they were then asked about the alternative type of MI. If they answered ‘no’ to both questions, they were asked whether they had
3.2.1.5. Trust/Security. The dimension of Trust/Security had 5 perceptions with a total of 9 traits. The trust and security traits are the most discernible of any of the dimensions because they have to be extremely deliberate for the user to experience a sense of trust in the MI. 3.2.2. Discussion of the Delphi study The synthesis of our systematic literature search and modified Delphi study provided us with the initial MIFI. However, for the MIFI to be useful for future research, we sought to demonstrate the predictive validity of the traits and how the inventory may be applied in future studies. 3.3. Survey study To provide evidence of the predictive validity of the MIFI, we next conducted a survey study to examine the impact of MIFI trait scores on 4
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used any other type of MI in the past week. This group formed our ‘other’ condition. Next, participants were asked to enter the name of one MI in this category that they had used in the past week. Then, participants completed a 3-item scale measuring their flow experience while using this MI (Hsu et al., 2012). Thus, we addressed common method bias by presenting the dependent variable first. At this point, participants were asked to access their smartphones and the MI that they had indicated. Lab assistants ensured that participants physically accessed their phone and monitored the completion of the remaining tasks. Participants were presented with a set of 24–25 randomly selected traits from the MIFI that pertained to the type of MI they had selected. A reduced number of traits, as opposed to the entire inventory, was utilized to avoid fatigue. Table 1 indicates which traits were included for each type of MI. Participants were asked to rate the MI on each of the traits that they were presented with on a scale from 1 = Very weak to 7 = Very strong. Finally, participants completed a short battery of questions about their subjective experience using the MI, their objective behaviors while using the MI, social desirability, and demographic information.
Table 3 Impact of MIFI Scores on Flow, Compulsive Usage, and Technostress. Direct Effects:
b
SE
Indirect Effects:
95% CI
MIFI→Flow
0.434*
0.176
MIFI→Flow→Tech
SocDes→Flow MIFI→Comp
− 0.143 − .041
0.221 0.132
MIFI→Flow→Comp→Tech MIFI→Comp→ Tech
[− 0.040, .052] [.032, .313] [− 0.273, .204]
Flow→ Comp SocDes→ Comp MIFI→Tech Flow→ Tech Comp→ Tech SocDes→ Tech
0.328*** − .204† − 0.082 0.007 0.931*** 0.095
0.084 0.104 0.074 0.050 0.075 0.073
Notes: † p < .10; * p < .05; * * p < .01; * ** p < .001. MIFI: Rating on MIFI traits, Comp: Compulsive Usage, SocDes: Social Desirability, Tech: Technostress.
using a finance MI (Me-commerce = 3.83, Mfinance = 3.11, p = .03). There were no significant differences between the finance and other or the ecommerce and other types. Next, we employed PROCESS (Model 6) in SPSS 25 using bootstrapping with 5000 samples (Preacher and Hayes, 2004) to examine the impact of the MIFI scores on flow, and in turn, the impact of flow on the compulsive usage of the MI and technostress. PROCESS results (see Table 3) support H1 that the MIFI composite scores were positively associated with the flow state (p = .015; 95% CI: 0.085–0.782). This provides initial support for the predictive validity of the MIFI. As would be expected, higher scores on the social desirability scale were associated with marginally less self-reported compulsive usage (p = .055; 95% CI: −0.388 to 0.004). Additionally, the flow state was positively associated with compulsive usage of the MI (p < .001; 95% CI: 0.161–0.495). Finally, consistent with prior research, compulsive usage of the MI lead to increased levels of technostress (p < .001; 95% CI: 0.783–1.08). Examination of the indirect effects revealed that, in support of H2, scores on the MIFI were positively associated with technostress, and this relationship is fully mediated by flow and compulsive usage (95% CI: 0.032–0.313) as there is no direct effect of MIFI scores or flow on technostress.
3.3.2.2. Measures. As aforementioned, the dependent variable of interest is a 3-item flow scale (Hsu et al., 2012), for which participants indicated how often they experienced the flow state while using the MI in the past week on a scale from 1 = Never to 7 = Very often. This scale demonstrated strong reliability (α = 0.75). An example item is “When using this app/mobile site, I felt totally captivated.” Additionally, we used a 13-item compulsive usage scale and a 6-item technostress scale (Lee et al., 2014), which also exhibited strong reliabilities (α = 0.93 and 0.96, respectively). For these scales, participants indicated the extent to which they agree with various statements about their compulsive usage of the MI (e.g., “I find it hard to control my use of this app or mobile interface”) and how the MI negatively impacts their life (e.g., “I feel my personal life is being invaded by this app or mobile interface”) on a scale from 1 = Strongly disagree to 7 = Strongly agree. To compute the independent variable, we averaged each participant's ratings across the 24–25 MIFI traits to which they were randomly assigned. This allows us to provide initial evidence of the predictive validity of the MIFI. Finally, participants completed a 13-item social desirability scale (Reynolds, 1982), which we used as a control variable, as compulsive usage and technostress could be viewed as socially undesirable states. Table 2 presents the means, standard deviations, and correlations among the substantive variables.
4. General discussion This paper develops the initial version of the MIFI, which contains traits of MIs that are posited to lead to the flow state. We establish face validity of the MIFI by surveying expert panelists in a modified Delphi approach. We also provide preliminary evidence for the predictive validity of the MIFI, by showing that users rating a given MI higher on the MIFI traits also reported more flow experiences while using the MI. This is an important contribution and provides insights for both researchers and practitioners.
3.3.3. Results of the survey study 3.3.3.1. Hypotheses testing. As we had a continuous outcome variable (i.e., flow) and three self-selected conditions (i.e., finance, e-commerce, and other MI types), we first conducted a univariate ANOVA to check for differences in the flow experience between the three MI types. Our data met the ANOVA assumption of the homogeneity of variance as demonstrated by the insignificant result of a Levene's test (F(2, 121) = 0.53, p = .59). The data indicate that there were differences in the flow experience between the three MI types (F(2121) = 3.53, p = .03). Bonferroni-adjusted simple effects specify that those using an ecommerce MI experienced significantly more flow states than those
4.1. Theoretical implications First, most research thus far has focused on perceptions of MIs (e.g., perceived ease of use), which does not provide actionable
Table 2 Means, Standard Deviations, and Correlations.
Flow MIFI Score Compulsive Technostress Social Desirability
Mean
S.D.
Flow
MIFI Score
Compulsive
Technostress
3.38 5.25 1.80 1.58 3.00
1.38 0.78 1.06 1.17 0.57
1 0.249** 0.430** 0.354** − 0.079
1 0.086 0.016 − 0.083
1 0.837** − 0.141
1 − 0.069
** Correlation significant at the 0.01 level. 5
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developing a strong desire to experience flow through continued use. The individual may then experience the negative characteristic of overestimating his or her abilities and determine that they are able to use the MI while driving. Further, the flow experience will narrow their attention to the MI, which makes the situation even more dangerous than distracted driving in a non-flow state. Therefore, future research may examine the relationship between flow and distracted driving via mobile usage. Finally, while our categorization of the MIFI traits into the five dimensions was grounded in prior literature, we acknowledge that these dimensions may not emerge in the minds of users in future empirical studies, as prior research has generated numerous ways of categorizing user perceptions (see Ha and Stoel, 2009, pp. 565–567). Thus, future empirical work should examine whether these traits load into the five categories suggested here, as well as examine any potential causal relationships between the dimensions.
recommendations for marketers or MI developers. A theoretical implication of this study is that specific traits of MIs can, and should, be identified which encourage the flow state while using the MI. Theoretically, the MIFI unites traits from the various software screen design theories that link design to user cognition (for a review of software screen design theory, see Chalmers, 2003, pp. 599–604). The second major contribution of this paper is the placement of the MIFI in the nomological network of flow in the MI context. A theoretical implication of this positioning is that we add to the human-computer interaction literature and the research stream on the dark side of flow by showing that flow can lead to compulsive usage of the MI and, in turn, technostress. The impact of mobile technology on health and well-being has become a topic of great importance (Valkenburg et al., 2006). By showing that flow can lead to negative behaviors for the consumer, we balance the significant stream of research showing positive outcomes of flow for digital marketers (Hoffman and Novak, 2009). As such, scholars, marketers, and MI developers may need to develop ways to realize the positive, brand- or product-related outcomes of flow while ensuring that their consumers do not fall prey to the dark side of flow.
5. Conclusion We hope the MIFI will be an evolving tool for use by academics and practitioners. We believe it will allow scholars at the intersection of human-computer interaction and consumer behavior to provide practitioners with more actionable recommendations than can be achieved by merely studying the impact of user perceptions. We hope this paper prompts further research in this important area for digital marketers.
4.2. Practical implications This paper also has important practical implications for developers and marketers who can directly utilize the MIFI as a rubric when analyzing their own MI to ensure that it includes as many traits as are applicable to their context. If the negative psychological consequences of compulsive usage and technostress eventually cause consumers to discontinue using the MI altogether, then marketing managers may find it equally important to actually monitor MI overuse to help consumers circumvent the dark side of flow. In other words, developers may seek to find a way to ‘interrupt’ the path between flow and compulsive usage. In fact, some organizations may already be doing this without fully understanding the rationale behind their actions. For example, Apple has new tools built into iOS 12 that are designed to help alert consumers to the time they spend interacting with their devices and allow them to better manage their usage (Cipriani, 2018). Clearly, this is an important area for future research and for marketers and developers to consider when designing MIs.
References Al-Khalifa, H.S., 2014. A framework for evaluating university mobile websites. Online Inf. Rev. 38 (2), 166–185. Becker, T.E., 2005. Potential problems in the statistical control of variables in organizational research: a qualitative analysis with recommendations. Organ. Res. Methods 8 (3), 274–289. Bridges, E., Florsheim, R., 2008. Hedonic and utilitarian shopping goals: the online experience. J. Bus. Res. 61 (4), 309–314. Chalmers, P.A., 2003. The role of cognitive theory in human–computer interface. Comput. Human. Behav. 19 (5), 593–607. Chou, T.-J., Ting, C.-C., 2003. The role of flow experience in cyber-game addiction. Cyber. Behav. 6 (6), 663–675. Cipriani, J., 2018. (2018, September 17. iOS 12: Getting to know Screen Time and stronger parental controls Retrieved November 15, 2018, from 〈https://www.cnet. com/how-to/ios-12-screen-time-and-stronger-parental-controls/〉. Csikszentmihalyi, M., 1990. Flow: the psychology of optimal experience. Harper & Collins, New York. Csikszentmihalyi, M., LeFevre, J., 1989. Optimal experience in work and leisure. J. Personal. Social. Psychol. 56 (5), 815–822. Gao, L., Bai, X., 2014. An empirical study on continuance intention of mobile social networking services: integrating the IS success model, network externalities and flow theory. Asia Pac. J. Mark. Logist. 26 (2), 168–189. Ha, S., Stoel, L., 2009. Consumer e-shopping acceptance: antecedents in a technology acceptance model. J. Bus. Res. 62 (5), 565–571. Hoffman, D.L., Novak, T.P., 2009. Flow online: lessons learned and future prospects. J. Interact. Mark. 23 (1), 23–34. Hsu, C.-L., Chang, K.-C., Chen, M.-C., 2012. The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators. Inf. Syst. e-Bus. Manag. 10 (4), 549–570. Jiang, Z., Benbasat, I., 2004. Virtual product experience: effects of visual and functional control of products on perceived diagnosticity and flow in electronic shopping. J. Manag. Inf. Syst. 21 (3), 111–147. Kamis, A., Stern, T., Ladik, D.M., 2010. A flow-based model of web site intentions when users customize products in business-to-consumer electronic commerce. Inf. Syst. Front. 12 (2), 157–168. Koufaris, M., 2002. Applying the technology acceptance model and flow theory to online consumer behavior. Inf. Syst. Res. 13 (2), 205–223. Landers, V.M., Beatty, S.E., Wang, S., Mothersbaugh, D.L., 2015. The effect of online versus offline retailer-brand image incongruity on the flow experience. J. Mark. Theory Pract. 23 (4), 370–387. Lee, Y.-K., Chang, C.-T., Lin, Y., Cheng, Z.-H., 2014. The dark side of smartphone usage: psychological traits, compulsive behavior and technostress. Comput. Human. Behav. 31, 373–383. Mahnke, R., Benlian, A., Hess, T., 2015. A grounded theory of online shopping flow. Int. J. Electron. Commer. 19 (3), 54–89. McDonald, N., 2018. Digital in 2018: World’s internet users pass the 4 billion mark. Retrieved from 〈https://wearesocial.com/us/blog/2018/01/global-digital-report2018〉. Preacher, K.J., Hayes, A.F., 2004. SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav. Res. Methods, Instrum., Comput. 36 (4),
4.3. Limitations and directions for future research Given the dearth of research on flow in the MI context, there is much work to be done. Most importantly, research should continue to examine the traits specified in the MIFI with regard to their impact on flow in the MI context. We acknowledge that the MIFI may not be completely comprehensive or parsimonious given the rapid advancements in technology. As such, future research should continue to expand and revise the MIFI as technology evolves. Traits can be added, amended, or removed from future revised versions of the MIFI to ensure that both scholars and practitioners have state-of-the-art knowledge. Another limitation is that we do not examine the relative importance of each trait. A valuable direction for future research would be to identify the most important traits for the flow experience, perhaps through the utilization of conjoint analysis. This would give MI designers information on which traits to start with when building an interface. As discussed above, compulsive usage of MIs can cause serious problems in our society. Csikszentmihalyi and Rathunde submit that the main characteristic of flow is becoming “completely involved in something to the point of forgetting time, fatigue, and everything else but the activity itself” (as cited in Schüler, 2012, p. 124). The dark side of flow characteristics include neglecting further goals and values, a narrowed focus of attention excluding additional information, overestimating one's abilities, and neglecting temporal information although it is relevant (Schüler, 2012). Especially in light of our results, it is not a stretch to imagine an individual becoming addicted to a MI and 6
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evidence‐informed management knowledge by means of systematic review. Br. J. Manag. 14 (3), 207–222. Valkenburg, P.M., Peter, J., Schouten, A.P., 2006. Friend networking sites and their relationship to adolescents' well-being and social self-esteem. Cyber. Behav. 9 (5), 584–590. Wu, J.-J., Chang, Y.-S., 2005. Towards understanding members' interactivity, trust, and flow in online travel community. Ind. Manag. Data Syst. 105 (7), 937–954. Zhou, T., 2012. Examining mobile banking user adoption from the perspectives of trust and flow experience. Inf. Technol. Manag. 13 (1), 27–37. Zhou, T., 2013. An empirical examination of continuance intention of mobile payment services. Decis. Support Syst. 54 (2), 1085–1091. Zhou, T., Li, H., Liu, Y., 2010. The effect of flow experience on mobile SNS users' loyalty. Ind. Manag. Data Syst. 110 (6), 930–946.
717–731. Reynolds, W.M., 1982. Development of reliable and valid short forms of the MarloweCrowne Social Desirabililty Scale. [Article]. J. Clin. Psychol. 38 (1), 119–125. Schüler, J., 2012. The dark side of the moon. In: Engeser, S. (Ed.), Advances in flow research. Springer, New York, pp. 123–137. Sicilia, M., Ruiz, S., Munuera, J.L., 2005. Effects of interactivity in a web site: the moderating effect of need for cognition. J. Advert. 34 (3), 31–44. Skinner, R., Nelson, R.R., Chin, W.W., Land, L., 2015. The Delphi Method Research Strategy in Studies of Information Systems. CAIS 37 (2), 31–63. Thomée, S., Härenstam, A., Hagberg, M., 2011. Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study. BMC Public Health 11 (1), 66–76. Tranfield, D., Denyer, D., Smart, P., 2003. Towards a methodology for developing
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Contents lists available at ScienceDirect
Journal of Retailing and Consumer Services journal homepage: www.elsevier.com/locate/jretconser
Exploring flow in the mobile interface context ⁎
Clark D. Johnson , Brittney C. Bauer, Nitish Singh Richard A. Chaifetz School of Business, Saint Louis University, 3674 Lindell Blvd, St. Louis, MO 63108, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Mobile interface Flow Optimal experience Compulsive usage Technostress
While flow has been researched extensively in computer-mediated environments, it has been scarcely researched in the mobile interface context. Specifically, we have virtually no knowledge about what concrete traits of mobile interfaces (as opposed to user perceptions) encourage the flow state. Flow has been associated with many positive outcomes in human-computer interactions, making it vital for practitioners and scholars to understand the traits of mobile interfaces that encourage flow. Therefore, we synthesize the results of a literature search and a modified Delphi study to develop an inventory of traits and perceptions that can promote the flow experience. We provide initial evidence for the predictive validity of our inventory through a survey study which demonstrates that composite ratings of the inventory traits are positively associated with the flow state, which in turn, leads to compulsive usage and technostress. In doing so, this paper also extends flow research by exploring the potential negative outcomes of flow in the mobile interface context.
1. Introduction Flow is the state of becoming “lost” in an activity that occurs when an individual's skills are matched with the level of challenge of a task (Csikszentmihalyi, 1990). This important mental state has been extensively researched in the context of desktop-mediated internet usage and has been associated with several important outcomes regarding consumer attitudes, intentions, and behaviors (Hoffman and Novak, 2009). However, flow has scarcely been examined in the mobile interface (MI) context, and—compared with desktop websites—frameworks for evaluating MIs are sparse in the literature (Al-Khalifa, 2014). In fact, while we have limited knowledge about certain user perceptions of MIs that are associated with the flow state, such as information and system quality (Gao and Bai, 2014), no research has attempted to identify the specific traits of MIs that lead to the flow state. The focus on user perceptions, as opposed to MI traits, severely limits the diagnostic value that this stream of research can provide to practitioners. In other words, the broadness of perceptions like perceived ease of use and information quality allows us to examine the impact of user perceptions of MIs. However, marketers and developers lack an in-depth understanding of specific MI features, which, in conjunction, influence the broader outcomes, such as flow. This is surprising and concerning given that two-thirds of the world's 7.6 billion inhabitants have a mobile phone, and over half of those are “smart” devices (McDonald, 2018). Thus, this study seeks to examine a key guiding research question: What aspects of mobile interfaces promote the
⁎
flow state? This paper builds an inventory of MI characteristics that can lead to the flow state. To ground our inventory in prior work on flow theory, we first study what has been shown to lead to flow in the traditional desktop-mediated environment. Then, we determine which of these aspects apply to the mobile environment. To establish content and face validity, we utilize a Delphi approach in a survey study which allows us to adjust the inventory according to expert knowledge. Next, to provide evidence of predictive validity, our final study tests the positive association between the inventory and flow. We believe this study is essential to demonstrate that concrete characteristics of MIs are predictive of user cognitive states and responses, and should be studied as opposed to the user perceptions which have dominated much of the research in this area. To further extend flow research, this study also examines the relationships between flow and the ‘dark side’ outcomes of compulsive usage and technostress. Finally, we discuss the implications of our work and lay out an agenda for future research on flow in the MI context. 2. The need for a mobile interface flow inventory One can easily think of many occurrences of flow while using MIs. Many readers will remember observing people walking around while captivated by their smartphone, perhaps on a college campus or in a shopping mall. Anyone who scans the scene of a restaurant is sure to be able to identify tables where all of the diners have their cell phones out,
Corresponding author. E-mail addresses: [email protected] (C.D. Johnson), [email protected] (B.C. Bauer), [email protected] (N. Singh).
https://doi.org/10.1016/j.jretconser.2019.01.013 Received 4 September 2018; Received in revised form 25 January 2019; Accepted 25 January 2019 0969-6989/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Clark D. Johnson, Brittney C. Bauer and Nitish Singh, Journal of Retailing and Consumer Services, https://doi.org/10.1016/j.jretconser.2019.01.013
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and conversation is absent. While everyone knows about the perils of texting and driving, it seems that people can become so ‘lost’ while using their mobile devices that many cannot help but to do so. We cannot assume that all of these individuals are in the flow state; however, the vast number of individuals absorbed with their mobile phones indeed suggests that experiencing flow while using a MI is pervasive. Therefore, it is surprising that little attention has been paid in the academic literature regarding the role of the flow experience in the mobile context. The limited research conducted in this area has either 1) only examined limited or vague antecedents of flow, or 2) focused on positive outcomes of flow. First, information quality, system quality, trust, perceived complementarity (as in additional complementary services) and perceived ease of use have all been shown to lead to flow while using a mobile device (Gao and Bai, 2014; Zhou, 2012, 2013; Zhou et al., 2010). Second, flow has been shown to lead to loyalty towards mobile social networking sites (Gao and Bai, 2014; Zhou et al., 2010), continuance intention toward mobile payment services (Zhou, 2013), and even usage intention for mobile banking services (Zhou, 2012). However, the broadness of constructs like the perceived ease of use and information quality begs the question, what are the specific traits or aspects of a mobile interface that can encourage flow? The paucity of research examining the particular traits of mobile interfaces that lead to flow suggests a need for more extensive study. An inventory of traits that encourage the flow state would be a powerful tool for marketers and interface designers. With the important outcomes that have been associated with flow in human-computer interactions, the use of such an inventory would be sure to enhance the effectiveness of any MI. Therefore, we set out to develop a mobile interface flow inventory (MIFI) that is grounded in prior research on the flow state in the human-computer interaction context.
excluded. Next, we examined each article to determine if it studied a characteristic of an online interface as an antecedent to flow. Then, two of the authors independently coded each studied antecedent based on whether it was a user's perception of the interface, or a trait of the interface. Finally, literature searches were performed to find studies that examined traits of mobile interfaces that lead to the perceptions in our list. Any newly identified traits were then added to the table under the appropriate perception.
3. Methodology
3.2. Delphi study
To initiate our study on the antecedents of flow in the MI context, we utilize a series of three methodological approaches: a systematic literature review to identify potential traits, a modified Delphi approach in a survey study to provide face validity, and a survey study to demonstrate predictive validity. This allows us to triangulate our findings and to demonstrate how the MIFI may be applied in future research.
To ensure the face validity of the inventory, we conducted a modified Delphi study (i.e., only one round of participation). A Delphi method is an appropriate approach for our study because it can simultaneously bring together the critical thoughts of a geographicallydispersed expert panel, while also addressing potential issues and concerns for the future. A Delphi study involves forming a panel of subject experts and surveying these experts who provide anonymous qualitative responses to questions or informational prompts (Skinner et al., 2015). Our panelists were selected from the authors’ professional network based on their knowledge and experience with MI design, rather than being randomly surveyed. The panel included individuals in academia and in the digital marketing industry. Therefore, in this context, the panel was deemed sufficiently knowledgeable and representative to provide a solid base for the results. The goal of this approach was to generate and explore ideas, as opposed to forming a strict consensus. Therefore, panelists participated in the Delphi study online and were randomly assigned to receive one of the five dimensions in the MIFI. They were also given descriptions of any traits that needed additional explanation (see descriptions in Results). Panelists were asked to review the traits in the given dimension carefully, and to advise if 1) any of the traits do not apply to a MI, 2) any of the traits would need to be adapted for a MI and how they should be modified, and 3) there are any missing MI traits that would lead to the perceptions listed in the given dimension. This feedback was utilized by the authors in discussion to make appropriate changes to the inventory. The resulting version of the MIFI dimensions, perceptions, and traits are described below in more detail.
3.1.1. Results of the systematic literature review The initial search resulted in a total of 88 articles which examined flow in the context of internet usage. Upon examining each article to determine if it studied an antecedent to flow in an online interface, we were left with 24 applicable articles. We then compared the results of the independent coding, which achieved a percent agreement of 94%. Discrepancies between the two were resolved by discussion. An indepth examination of the resulting list suggested that the perceptions could be grouped into five categories based on dimensions of user perceptions of websites developed in prior research (e.g., Ha and Stoel, 2009; Sicilia et al., 2005): Informativeness, Interactiveness, Hedonic, Functionality, and Trust/Security. After the perceptions were grouped, we then categorized each trait according to which perception the trait generates. This resulted in the initial version of the MIFI. 3.1.2. Discussion of the systematic literature review While the initial version of the MIFI provided us with a list of traits and perceptions that may apply in the MI context, we need to understand whether they apply directly to this context or if they should be modified to better fit the context. Therefore, to establish face validity and increase the comprehensiveness and parsimony of the MIFI, we conducted a modified Delphi study which is described next.
3.1. Systematic literature review First, we conduct a systematic literature review (Tranfield et al., 2003) to identify antecedents of flow that have been studied in the desktop computer context. This allows us to theoretically ground our inventory in prior literature and identify key contributions to the study of the flow state in human-computer interactions. MIs are similar to traditional desktop interfaces, but are designed for the small screens of mobile devices (Al-Khalifa, 2014) and typically have more touch features. Therefore, a logical progression for developing an inventory for MIs is to determine which previously studied antecedents in the desktop context would apply to MIs. We utilized two databases (ABI/Inform and Business Source Premier) to search for several keywords. These included “flow” in the title and “website” in the abstract (resulting in 40 and 39 articles for the ABI/Inform and Business Source Premier databases, respectively), “flow” in the title and “internet” in the abstract (resulting in 61 and 84 articles, respectively), and “flow” in the title and “online” in the abstract (resulting in 116 and 134 articles, respectively). The searches were limited to scholarly, peer-reviewed journals, and we only collected articles in the English language. We examined the titles and abstracts of each paper to determine if the article was examining the mental state of flow. For example, several of the articles were examining other topics, such as information flow, and these papers were
3.2.1. Results of the Delphi study To structure the presentation of the results, we now discuss each dimension and the related MI characteristics that may be less intuitive and more technical in nature. 2
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Table 1 (continued)
Table 1 Mobile Interface Flow Inventory. Dimension 1—Informativeness Perceptions: Accurate/complete/correct/content reliability
Dimension 1—Informativeness Perceptions:
Up-to-datea,b,c
Easy to complete transactions
Variety of productsa,b Content easy to understand Knowledgeable Meets information needs for informed decisions
Provides relevant information
Resourceful
Dimension 2—Interactiveness Perceptions: Adaptable to a variety of needs
The environment made communicating quick and easy
Prompt in responding to inquiries Unique interactive functions
Dimension 3—Hedonic Perceptions: Aesthetically pleasing/vividness
Agreeable/delightful/enjoyment/ pleasure Artistic sophistication/creativity/ imaginative Entertaining/exciting/flashy/thrilling Experience something new Fun to browse/engaging
Humor Increases the quality of life Professional
The sound is lively/natural/proper for the contents Dimension 4—Functionality Perceptions: Challenge Convenient/useful
Traits:
Traits:
Feedback featuresa,b,c
Easy to navigate
Provides useful, valuable, and various information on products and servicesa,b,c Recommender system (needs to be accurate)a Value-added search mechanisms (e.g., Bestsellers, New and Notable, etc.)a Factual quality of user-generated content through control mechanismsa,c Possible to register to receive updatesa,b,c Links to similar websitesa,b,c Offers external information sources a, b,c Provides comprehensive research toolsa,b,c
Enables effective use of my time Innovative technical features Little waiting time Perceived ease of use Function reliability
Traits: Allows interaction to receive information according to preferencesa,b,c Personalized communication featuresa,b,c Being with people visiting this sitea,b,c
Well organized information
Can speak with a representativea,b,c Communicating with people visiting this sitea,b,c Hearing what others saya,b,c Interacting with people visiting this sitea,b,c Online contact forma,b,c Person-interactivitya,b,c Machine-interactivitya,b,c Attribute-based user interface designa Location-based information (using GPS)a,b,c Functional control (sampling different functions of products through the computer) a Option to recommend the site to a frienda,b,c Text-to-speech voice (when interacting with a CSR)a,b,c
Dimension 5—Trust/Security Perceptions: Believable/legitimacy Delivers service as promised Feel comfortable/secure/confident
Trust administrators Trust with financial information
Interactive store locatora,b Offers order confirmationa Option to order products onlinea Payment optionsa,b Present of shopping cart a Purchase tracking servicesa A clear process for browsinga,b,c Global search featurea,b,c Non-linear product catalog with internal hyperlinksa,b,c Proximity through hierarchya,b,c Search and filter functions (needs to be accurate)a,b,c Site mapa,b,c All needs can be completed via sitea,b,c Un-do buttona,b,c 3D product demonstrationa Able to choose clothes by using an avatara Fast loadinga,b,c The site always availablea,b,c Operating system interoperabilitya,b,c Critical functions perform reliably (e.g., add to cart, checkout, etc.)a,b,c Clear display of page contentsa,b,c Presence of clear menu items on each pagea,b,c Traits: Return policies and measures of accountability are cleara Assurance of privacya,b,c Access to account information (e.g., terms of service, etc.)a,b,c Declaration of intended usea,b,c An indication of security/secure sitea,b,c Information regarding the security of paymentsa, b Secure payment methodsa, b Visual security features, such as trustmarksa,b,c Physical security features, such as finger print authorizationa,b,c
Notes. a Traits rated for e-commerce apps. b Traits rated for finance apps. c Traits rated for ‘other’ apps.
Traits: Consistency (arrangement of elements, colors, fonts, etc.)a,b,c Consistent web page designa,b,c Visual localization (localizing visual aspects based on cultural norms and expectations)a,b,c Use of an avatar on online shopping sitea,b,c
3.2.1.1. Informativeness. The Informativeness dimension had 6 perceptions with a total of 11 traits. Table 1 presents the MIFI in its entirety. For clarification, we describe two of the less recognizable traits in this dimension. First, value-added search mechanisms are those that provide entire lists of search results based on a search category, such as Bestsellers, New and Notable, etc. (Koufaris, 2002). Second, factual quality of user-generated content through control mechanism means the information that is generated by users and displayed on the MI is monitored through control mechanisms, such as ratings on whether the information is useful or factual (Mahnke et al., 2015).
Product images as thumbnailsa,b,c Provides images, audio, or video previews of the products a Visual control (allows consumers to manipulate product images with mice and keyboards)a
3.2.1.2. Interactiveness. The Interactiveness dimension had 4 perceptions with a total of 15 traits. This dimension had several traits which deserve further explanation. For instance, communicating with people visiting this site, being with people visiting this site, hearing what others say, and interacting with people visiting this site all suggest that the MI should provide a way for users to communicate with one another. Some examples of this might include comment and public feedback features. Person-interactivity is “interactivity between people that occurs through a medium or unmediated, as in the case of face-to-face
Company logoa,b,c Luciditya,b,c Retailer brand congruitya,b
Traits: Language optionsa,b,c Gift servicesa
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flow.
communication” (Wu and Chang, 2005, p. 939). Alternatively, machineinteractivity is “the extent to which users can participate in modifying the form and content of a mediated environment in real time” (Wu and Chang, 2005, p. 939). Next, according to Kamis et al., (2010, p. 158), an attribute-based user interface design is one where:
3.3.1. Predictive validity hypotheses As the traits in the MIFI have been selected based on either their established positive impact on flow in prior research or their logical connection to this state of absorption as suggested by our panelists, we expect that the composite rating score of these traits for a given MI will be positively associated with the flow experience while utilizing that MI. As such, we posit:
“the customer is presented with a list of attributes and the different values for each attribute. The customer then chooses a combination of the different values and is presented with a view of the customized product. Customers can iteratively choose different values and experiment until they feel they have developed an acceptable product.”
H1. The composite ratings of MIFI traits will be positively associated with the flow state while utilizing the MI. Additionally, to further extend flow research, we examine two potential ‘dark side’ outcomes of flow in the MI context—compulsive behavior and technostress (Lee et al., 2014). Prior research has shown that mobile phone usage for entertainment or stress alleviation can provide immediate gratification, but it can also lead to a diminished sense of volitional control and induce persistent activity (Thomée et al., 2011). As such, flow researchers have begun to develop a stream regarding the dark side of flow (Schüler, 2012). Various individual characteristics, such as locus of control and need for touch, have been associated with compulsive mobile phone usage and technostress (i.e., end users experiencing stress due to information and communication overload; Lee et al., 2014). Hence, we initiate the study of the dark side of flow in the mobile interface context by examining the relationship between flow, compulsive usage, and technostress. We suggest that the flow state may lead to the diminished sense of volitional control and persistent activity associated with compulsive usage of MIs. First, however, we must differentiate between control of in-task behavior and control of usage. While flow certainly increases the sense of control over in-task behavior (Hoffman and Novak, 2009), it has also been shown to lead to a decreased ability to control engagement, or rather disengagement, with the task. For example, Bridges and Florsheim (2008) find that the hedonic elements of flow are positively associated with pathological internet use, and Chou and Ting (2003) find that the flow state encourages addictive behavior to cyber-games. Therefore, we propose that flow will be associated with compulsive usage of the MI. Additionally, prior research has demonstrated that compulsive usage is highly associated with technostress (Lee et al., 2014), and we, therefore, posit that flow and compulsive usage will mediate the positive relationship between ratings on the MIFI traits and technostress.
Finally, the functional control means that the MI enables consumers to sample different functions of products through their mobile devices (Jiang and Benbasat, 2004). 3.2.1.3. Hedonic. The Hedonic dimension had 10 perceptions with a total of 10 traits. We examine some of the more abstract traits in greater detail. First, by consistency, we are referring to the arrangement of elements, colors, fonts, etc. (Mahnke et al., 2015). Similarly, visual control allows consumers to manipulate product images with touch, similar to how a desktop-mediate environment would allow manipulation with a mouse and keyboard (Jiang and Benbasat, 2004). Next, lucidity means that the MI has a clear delimitation of displayed elements, a low font variety, and a low number of modest colors (Mahnke et al., 2015). Lastly, retailer brand congruity means that, compared with the physical store, the MI matches the user's impression of the physical store (Landers et al., 2015). 3.2.1.4. Functionality. The dimension of Functionality had 10 perceptions with a total of 24 traits. This dimension had several less recognizable traits which require further description. For instance, we placed language options under challenge perception because allowing users to access information on the interface in their preferred language will allow them to experience the optimal match of challenge and skill, which is a primary condition for the flow experience (Csikszentmihalyi and LeFevre, 1989). Non-linear product catalog with internal hyperlinks can best be explained by an example: If the user is browsing in the shoe section and wishes to start looking at coats, they are able to click a link to jump straight to the coat section without having to click ‘back’ to access the main menu. Finally, proximity through hierarchy “seeks to reduce eye movement distances, cursor and scrolling movement distances, and the number of clicks toward receiving the relevant elements” (Mahnke et al., 2015, p. 68). Mahnke et al. (2015) note that these approaches may include vertical hierarchy (the most important elements are displayed at the top of the page), level hierarchies (important elements are provided at high levels of the site structure), and size hierarchy (important elements have increased relative size).
H2. Flow and compulsive usage will mediate the positive relationship between composite ratings of MIFI traits and technostress. 3.3.2. Results of the survey study 3.3.2.1. Sample and procedures. The sample consisted of 124 students at a large private university in the Midwestern United States. The sample was comprised of exactly half males, and the average age was about 20 years old. Correlations were employed to determine whether gender might be related to the outcome variables of flow, compulsive usage, and technostress. Gender was not significantly related to any of these constructs (rflow = 0.08, p = .38; rcompulsive = 0.17, p = .07; rtechnostress = 0.14, p = .13). Variables that are not significantly related to the criteria should not be included in the main analyses as doing so unnecessarily reduces statistical power (Becker, 2005). Thus, we did not include gender in our analyses. Participants were 73.4% White, 14.5% Asian, 5.6% Black or African American, 4.8% Latino or Hispanic, and 1.6% Other. Participants were first asked if they had a smartphone and were thanked for their time if they answered ‘no.’ Then, the remaining participants were randomly assigned to answer if they had used either a financial or e-commerce MI in the past week. If the participant answered ‘no,’ they were then asked about the alternative type of MI. If they answered ‘no’ to both questions, they were asked whether they had
3.2.1.5. Trust/Security. The dimension of Trust/Security had 5 perceptions with a total of 9 traits. The trust and security traits are the most discernible of any of the dimensions because they have to be extremely deliberate for the user to experience a sense of trust in the MI. 3.2.2. Discussion of the Delphi study The synthesis of our systematic literature search and modified Delphi study provided us with the initial MIFI. However, for the MIFI to be useful for future research, we sought to demonstrate the predictive validity of the traits and how the inventory may be applied in future studies. 3.3. Survey study To provide evidence of the predictive validity of the MIFI, we next conducted a survey study to examine the impact of MIFI trait scores on 4
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used any other type of MI in the past week. This group formed our ‘other’ condition. Next, participants were asked to enter the name of one MI in this category that they had used in the past week. Then, participants completed a 3-item scale measuring their flow experience while using this MI (Hsu et al., 2012). Thus, we addressed common method bias by presenting the dependent variable first. At this point, participants were asked to access their smartphones and the MI that they had indicated. Lab assistants ensured that participants physically accessed their phone and monitored the completion of the remaining tasks. Participants were presented with a set of 24–25 randomly selected traits from the MIFI that pertained to the type of MI they had selected. A reduced number of traits, as opposed to the entire inventory, was utilized to avoid fatigue. Table 1 indicates which traits were included for each type of MI. Participants were asked to rate the MI on each of the traits that they were presented with on a scale from 1 = Very weak to 7 = Very strong. Finally, participants completed a short battery of questions about their subjective experience using the MI, their objective behaviors while using the MI, social desirability, and demographic information.
Table 3 Impact of MIFI Scores on Flow, Compulsive Usage, and Technostress. Direct Effects:
b
SE
Indirect Effects:
95% CI
MIFI→Flow
0.434*
0.176
MIFI→Flow→Tech
SocDes→Flow MIFI→Comp
− 0.143 − .041
0.221 0.132
MIFI→Flow→Comp→Tech MIFI→Comp→ Tech
[− 0.040, .052] [.032, .313] [− 0.273, .204]
Flow→ Comp SocDes→ Comp MIFI→Tech Flow→ Tech Comp→ Tech SocDes→ Tech
0.328*** − .204† − 0.082 0.007 0.931*** 0.095
0.084 0.104 0.074 0.050 0.075 0.073
Notes: † p < .10; * p < .05; * * p < .01; * ** p < .001. MIFI: Rating on MIFI traits, Comp: Compulsive Usage, SocDes: Social Desirability, Tech: Technostress.
using a finance MI (Me-commerce = 3.83, Mfinance = 3.11, p = .03). There were no significant differences between the finance and other or the ecommerce and other types. Next, we employed PROCESS (Model 6) in SPSS 25 using bootstrapping with 5000 samples (Preacher and Hayes, 2004) to examine the impact of the MIFI scores on flow, and in turn, the impact of flow on the compulsive usage of the MI and technostress. PROCESS results (see Table 3) support H1 that the MIFI composite scores were positively associated with the flow state (p = .015; 95% CI: 0.085–0.782). This provides initial support for the predictive validity of the MIFI. As would be expected, higher scores on the social desirability scale were associated with marginally less self-reported compulsive usage (p = .055; 95% CI: −0.388 to 0.004). Additionally, the flow state was positively associated with compulsive usage of the MI (p < .001; 95% CI: 0.161–0.495). Finally, consistent with prior research, compulsive usage of the MI lead to increased levels of technostress (p < .001; 95% CI: 0.783–1.08). Examination of the indirect effects revealed that, in support of H2, scores on the MIFI were positively associated with technostress, and this relationship is fully mediated by flow and compulsive usage (95% CI: 0.032–0.313) as there is no direct effect of MIFI scores or flow on technostress.
3.3.2.2. Measures. As aforementioned, the dependent variable of interest is a 3-item flow scale (Hsu et al., 2012), for which participants indicated how often they experienced the flow state while using the MI in the past week on a scale from 1 = Never to 7 = Very often. This scale demonstrated strong reliability (α = 0.75). An example item is “When using this app/mobile site, I felt totally captivated.” Additionally, we used a 13-item compulsive usage scale and a 6-item technostress scale (Lee et al., 2014), which also exhibited strong reliabilities (α = 0.93 and 0.96, respectively). For these scales, participants indicated the extent to which they agree with various statements about their compulsive usage of the MI (e.g., “I find it hard to control my use of this app or mobile interface”) and how the MI negatively impacts their life (e.g., “I feel my personal life is being invaded by this app or mobile interface”) on a scale from 1 = Strongly disagree to 7 = Strongly agree. To compute the independent variable, we averaged each participant's ratings across the 24–25 MIFI traits to which they were randomly assigned. This allows us to provide initial evidence of the predictive validity of the MIFI. Finally, participants completed a 13-item social desirability scale (Reynolds, 1982), which we used as a control variable, as compulsive usage and technostress could be viewed as socially undesirable states. Table 2 presents the means, standard deviations, and correlations among the substantive variables.
4. General discussion This paper develops the initial version of the MIFI, which contains traits of MIs that are posited to lead to the flow state. We establish face validity of the MIFI by surveying expert panelists in a modified Delphi approach. We also provide preliminary evidence for the predictive validity of the MIFI, by showing that users rating a given MI higher on the MIFI traits also reported more flow experiences while using the MI. This is an important contribution and provides insights for both researchers and practitioners.
3.3.3. Results of the survey study 3.3.3.1. Hypotheses testing. As we had a continuous outcome variable (i.e., flow) and three self-selected conditions (i.e., finance, e-commerce, and other MI types), we first conducted a univariate ANOVA to check for differences in the flow experience between the three MI types. Our data met the ANOVA assumption of the homogeneity of variance as demonstrated by the insignificant result of a Levene's test (F(2, 121) = 0.53, p = .59). The data indicate that there were differences in the flow experience between the three MI types (F(2121) = 3.53, p = .03). Bonferroni-adjusted simple effects specify that those using an ecommerce MI experienced significantly more flow states than those
4.1. Theoretical implications First, most research thus far has focused on perceptions of MIs (e.g., perceived ease of use), which does not provide actionable
Table 2 Means, Standard Deviations, and Correlations.
Flow MIFI Score Compulsive Technostress Social Desirability
Mean
S.D.
Flow
MIFI Score
Compulsive
Technostress
3.38 5.25 1.80 1.58 3.00
1.38 0.78 1.06 1.17 0.57
1 0.249** 0.430** 0.354** − 0.079
1 0.086 0.016 − 0.083
1 0.837** − 0.141
1 − 0.069
** Correlation significant at the 0.01 level. 5
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developing a strong desire to experience flow through continued use. The individual may then experience the negative characteristic of overestimating his or her abilities and determine that they are able to use the MI while driving. Further, the flow experience will narrow their attention to the MI, which makes the situation even more dangerous than distracted driving in a non-flow state. Therefore, future research may examine the relationship between flow and distracted driving via mobile usage. Finally, while our categorization of the MIFI traits into the five dimensions was grounded in prior literature, we acknowledge that these dimensions may not emerge in the minds of users in future empirical studies, as prior research has generated numerous ways of categorizing user perceptions (see Ha and Stoel, 2009, pp. 565–567). Thus, future empirical work should examine whether these traits load into the five categories suggested here, as well as examine any potential causal relationships between the dimensions.
recommendations for marketers or MI developers. A theoretical implication of this study is that specific traits of MIs can, and should, be identified which encourage the flow state while using the MI. Theoretically, the MIFI unites traits from the various software screen design theories that link design to user cognition (for a review of software screen design theory, see Chalmers, 2003, pp. 599–604). The second major contribution of this paper is the placement of the MIFI in the nomological network of flow in the MI context. A theoretical implication of this positioning is that we add to the human-computer interaction literature and the research stream on the dark side of flow by showing that flow can lead to compulsive usage of the MI and, in turn, technostress. The impact of mobile technology on health and well-being has become a topic of great importance (Valkenburg et al., 2006). By showing that flow can lead to negative behaviors for the consumer, we balance the significant stream of research showing positive outcomes of flow for digital marketers (Hoffman and Novak, 2009). As such, scholars, marketers, and MI developers may need to develop ways to realize the positive, brand- or product-related outcomes of flow while ensuring that their consumers do not fall prey to the dark side of flow.
5. Conclusion We hope the MIFI will be an evolving tool for use by academics and practitioners. We believe it will allow scholars at the intersection of human-computer interaction and consumer behavior to provide practitioners with more actionable recommendations than can be achieved by merely studying the impact of user perceptions. We hope this paper prompts further research in this important area for digital marketers.
4.2. Practical implications This paper also has important practical implications for developers and marketers who can directly utilize the MIFI as a rubric when analyzing their own MI to ensure that it includes as many traits as are applicable to their context. If the negative psychological consequences of compulsive usage and technostress eventually cause consumers to discontinue using the MI altogether, then marketing managers may find it equally important to actually monitor MI overuse to help consumers circumvent the dark side of flow. In other words, developers may seek to find a way to ‘interrupt’ the path between flow and compulsive usage. In fact, some organizations may already be doing this without fully understanding the rationale behind their actions. For example, Apple has new tools built into iOS 12 that are designed to help alert consumers to the time they spend interacting with their devices and allow them to better manage their usage (Cipriani, 2018). Clearly, this is an important area for future research and for marketers and developers to consider when designing MIs.
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4.3. Limitations and directions for future research Given the dearth of research on flow in the MI context, there is much work to be done. Most importantly, research should continue to examine the traits specified in the MIFI with regard to their impact on flow in the MI context. We acknowledge that the MIFI may not be completely comprehensive or parsimonious given the rapid advancements in technology. As such, future research should continue to expand and revise the MIFI as technology evolves. Traits can be added, amended, or removed from future revised versions of the MIFI to ensure that both scholars and practitioners have state-of-the-art knowledge. Another limitation is that we do not examine the relative importance of each trait. A valuable direction for future research would be to identify the most important traits for the flow experience, perhaps through the utilization of conjoint analysis. This would give MI designers information on which traits to start with when building an interface. As discussed above, compulsive usage of MIs can cause serious problems in our society. Csikszentmihalyi and Rathunde submit that the main characteristic of flow is becoming “completely involved in something to the point of forgetting time, fatigue, and everything else but the activity itself” (as cited in Schüler, 2012, p. 124). The dark side of flow characteristics include neglecting further goals and values, a narrowed focus of attention excluding additional information, overestimating one's abilities, and neglecting temporal information although it is relevant (Schüler, 2012). Especially in light of our results, it is not a stretch to imagine an individual becoming addicted to a MI and 6
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