Facets of simplicity for the smartphone interface: A structural model

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Int. J. Human-Computer Studies 70 (2012) 129–142 www.elsevier.com/locate/ijhcs

Facets of simplicity for the smartphone interface: A structural model Junho H. Choin, Hye-Jin Lee Graduate School of Information, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea Received 31 December 2010; received in revised form 27 July 2011; accepted 22 September 2011 Communicated by C. Bartneck Available online 8 October 2011

Abstract Motivated by the need to develop an integrated measure of simplicity perception for a smartphone user interface, our research incorporated visual aesthetics, information design, and task complexity into an extended construct of simplicity. Drawn from three distinct domains of human–computer interaction design and related areas, the new development of a simplicity construct and measurement scales were then validated. The final measurement model consisted of six components: reduction, organization, component complexity, coordinative complexity, dynamic complexity, and visual aesthetics. The following phase aimed at verifying the relationship between simplicity perception of the interface and evaluations of user satisfaction. The hypothesis was accepted that user satisfaction was positively affected by simplicity perception and that the relationship between the two constructs was very strong. The findings imply that a simplified interface design of the task performance, information hierarchy, and visual display attributes contributes to positive satisfaction evaluations when users interact with their smartphone as they engage in communication, information search, and entertainment activities. & 2011 Elsevier Ltd. All rights reserved. Keywords: Smartphone; Simplicity; Satisfaction; Visual aesthetics; Information design; Task complexity

1. Introduction Since the introduction of the iPhone in 2007, the smartphone has become a dominant mobile device for communication, information, and entertainment. The rapid transition to the smartphone in the mobile market has also brought significant changes to the user interface design and the usability of mobile devices. A smartphone is a converged device for mobile computing and communication, and thus requires revised definitions of the principal concepts of Human–Computer Interaction, which may be distinguished from those of the PC and the feature phone. Still limited by a small screen size, a smartphone has the functions of a broadband enabled computer in a mobile use context. Usability and HCI concepts, developed mainly for the use context and interface conditions of a personal computer, should thus be reconfigured for the current and future mainstream mobile devices.

Along with other concepts in usability and user interface design, simplicity has emerged as a key issue in the smartphone. For instance, Apple’s iPhones (iOS3 and iOS4) were acclaimed for their minimalist design and simple user interface. Competitors also adopted the zeitgeist and ‘simple and easy’ has become the catchphrase for mobile UI design. However, in the realm of human–computer interaction and user experience design, simplicity has been only defined as one of factors of visual aesthetics mostly in a PC use context. Thus, emerging inquiries are what it means by simplicity in the context of smartphone user interface, how we can develop a comprehensive measurement scale of simplicity, and to what extent the simplicity perception affects positive outcomes such as user satisfaction. To grasp with the new user experience of the smartphone, an extensive and revamped conceptualization of simplicity is needed first.

1.1. Simplicity and visual aesthetics in web usability n

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1071-5819/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhcs.2011.09.002

The history of simplicity concept is rather recent in human– computer interaction research. While human–computer

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interaction research has traditionally focused on utility and usability issues, the field has expanded its focus and perspective into the whole user experience including aesthetics and emotions (Kim et al., 2003; Hassenzahl and Tractinsky, 2006; Schmidt et al., 2009; International Organization for Standardization, 2010). As a new realm in the HCI field, visual aesthetics has shown positive associations with perception of usability (Tractinsky et al., 2000), evaluations of content quality (Aladwani and Palvia, 2002), and emotional satisfaction (Cyr et al., 2008; Zhang and von Dran, 2000). In sum, beautiful designs are perceived as usable and an interface with good visual aesthetics can improve task performance. Simplicity has been identified as one of the major UI factors influencing perceived visual aesthetics (Ngo et al., 2003), along with diversity and complexity (Pandir and Knight, 2006; Tuch et al., 2009). Also, HCI research has repeatedly found simplicity as an important component in the aesthetic evaluations of websites (Lavie and Tractinsky, 2004; Ngo et al., 2003; Thielsch and Hirscheld, 2010). The mechanism between simplicity and aesthetic perception can be found in processing fluency theory. Processing fluency theory posits that aesthetic pleasure is a function of the user’s stimulus processing dynamics (Reber et al., 2004). That is, the more fluently users can process interface stimuli, the more positive their aesthetic evaluation. Referred to as the ease of processing visual object, fluency is affected by figural goodness, the amount of information, symmetry, clarity, contrast, and the familiarity of visual objects (Lavie and Tractinsky, 2004; Moshagen and Thielsch, 2010). High fluency leads to a positive judgment because users can recognise and process stimuli more successfully with fewer errors and less uncertainty. If user processing demands determine aesthetic appraisal, simple layouts would be processed more fluently and thus valued more positively. In a study of scale construction for the visual aesthetics of websites (Moshagen and Thielsch, 2010), simplicity is defined as the aspects that facilitate the perception and processing of a layout. Mainly focused on the static webpage design, the construct of simplicity consists of five facets: clarity, orderliness, homogeneity, balance, and the proper grouping of visual elements on the screen. Those five facets clearly echo the Gestalt psychologists’ figural goodness concepts (Arnheim, 1974). In an experimental study on the websites, the simplicity facet is found to be strongly related to evaluations of perceived usability and utility/ usefulness (Moshagen and Thielsch, 2010). The strong correlations may reflect the conceptual overlap between simplicity and usability, but may also suggest that simplicity is a property of both aesthetics and usability. 1.2. Simplicity for the smartphone use context: beyond visual aesthetics and web usability Simplicity is an important component of the aesthetic impression of webpages on PC screens. However, in mobile devices, it should be reconfigured as more than a

component of visual aesthetics. The caveat of an aesthetic approach to simplicity in HCI research is that simplicity in mobile computing may be not a unified concept but a multidimensional one. That is, processing fluency demands for performing diverse tasks on a small screen exist in multiple and dynamic interaction modes as well as in the single and static visual objects. In the interactionist view on the beauty, aesthetic impression has three defining features: value positive, intrinsic, and objectified (Moshagen and Thielsch, 2010; Santayana, 1955). Thus, as a main component of beauty, simplicity is value positive because it provides pleasure. It is intrinsic because an object can be perceived as simple immediately without any cognitive work or reasoning about expected utility. Lastly, simplicity is directed toward an object rather than toward a neurological sensation. Simplicity of mobile devices may share those basic features of aesthetic impression. However, mobile phone UI designers and users should always consider the utility of the object. Thus, the domain of simplicity needs to be extended into other dimensions such as information design and task performance usability. The rationale for the conceptual extension of simplicity in a mobile use context can be found in the different interaction modes and usability requirements of a smartphone compared to those of the Web and a PC. Since the birth of the Xerox Star, WIMP and GUI have been a main framework of user interface and the usability in the PC OS and the Web. Icons and GUI remain a main components of the smartphone interface, but other interface components such as pointing and windowing interfaces have evolved quite differently in the smartphone OS. Firstly, for portability, smartphones have a limited screen size, which generates the condition for thinking how to maximise the utility of the limited space. The overlapping or stretching of multiple windows in the same screen mode should be limited, and displaying a full set of menu items should be depressed. Secondly, the clearest transition from a PC and Web usability is a haptic interface. Using fingertips instead of a mouse pointing device diminishes the precision of control; thus, error prevention for correct navigation and input becomes a priority in the smartphone interaction design. For instance, icons and navigation buttons should be large enough for correct touch input by a fingertip. Furthermore, iPhone has a single hard key for multiple navigation and control functions creating a different condition from the PC’s rather precise control with a mouse. Different use context from PC is another rationale for the extension of the simplicity concept in a mobile interface. Because people carry and use a smartphone ubiquitously, the utility of the object can be transformed by different contexts of use, such as space, time, privacy condition, and personal needs. In contrast to the use context of the Web in a PC, mobile phone users feel the pressure of time and monetary costs due to the limited battery life and usage charges. Also, when using their smartphones outdoors, users face numerous disruptions such as an unclear display while in sunlight. The

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unavailability of full concentration on the screen for a relatively long time will lead users to expectations of high efficiency and low cognitive loads in information processing and task performance. Reasoning about expected utility is an innate use context for both mobile phone users and UI designers. Thus, aesthetic impression may depend on the use context and on reasoning about utility. The conceptual overlapping of simplicity with other domains is demonstrated well in the practical user interface design realm. SAP’s (2004) software design guideline clearly argues for an initiative to expand the simplicity concept by illustrating multifaceted or multidimensional meanings of a simple user interface. For instance, they define simplification of the application design as a combination of effective information design, cognitive ease of learning & use, and task performance efficiency. Clearly, these have been the main concepts of usability domain. If HCI researchers feel the obligation of providing to practitioners a reliable and valid methodology with solid concepts and theoretical models, reconceptualization and modelling of simplicity are required for an emerging smartphone interface design. 2. Literature review: dimensions of simplicity Following reviews of the relevant literature, this study posits that the concept of simplicity is multidimensional and can be classified into three dimensions: (1) visual aesthetics, (2) information design, and (3) task complexity. The last two dimensions are the new addition to the traditional concept of simplicity. Main components in each dimension of simplicity are reviewed in the following sections. 2.1. Simplicity in visual aesthetics Simplicity has been regarded as a partial component of visual aesthetics. Moshagen and Thielsch (2010) generated a structural model of the visual aesthetics of websites and enlisted four lower order factors: simplicity, diversity, colourfulness, and craftsmanship. Among them, simplicity reflects web screen design aspects that facilitate the perception of a layout. Clarity, orderliness, homogeneity, grouping, balance, and symmetry are internal items of simplicity (Bauerly and Liu, 2008; Moshagen and Thielsch, 2010; Tractinsky et al., 2006). These attributes are also classified as classical aesthetics whereas other attributes such as originality, creativity, use of special effects as expressive aesthetics in evaluations of attractiveness (Lavie and Tractinsky, 2004; Shaik and Ling, 2009). Definitely, these classical aesthetics attributes, or simplicity, are clearly related to Gestalt psychologists’ figural goodness (Arnheim, 1974). Consistent with previous studies on the relationship between simplicity and usability perception (Lavie and Tractinsky, 2004), visual simplicity facet is positively related to the usability perception and pragmatic quality.

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In contrast, diversity plays a counteracting role in visual aesthetics. A merely simple stimulus can lead users to boredom because a reduction of visual cues can result in low arousal and eventually in a negative response. Visual richness or novelty boosts the arousal potential of a stimulus by provoking interest and tension (Hekkert et al., 2003). ‘‘Unity in diversity’’ (Fechner, 1876) is still the golden rule of interface design, but visual richness or diversity may not be a preference for mobile users. Compared to a webpage on a PC monitor, a mobile screen is more limited in terms of screen size and user attention. Given that a smartphone screen does not allow enough space for complex visual cues or multiple windows for various tasks, mobile phone’s functions are separated into small packet of pages and user’s attention to a fixed page is also limited.

2.2. Simplicity in information design Simplicity not only applies to the aesthetic perception of a visual layout, but also to the information design, that is, the organization, structure, flow, and frame of interface items (Maeda, 2006). Often referred to as information architecture (Wurman, 1997), information design is a key domain of HCI, which investigates the way in which information is represented or the processes of data classification, formation, organization, and presentation for meaningful and effective interaction (Tufte, 1990, 1997, 2006; Marsico and Levialdi, 2004). Simplicity can be obtained through the optimal structuring of interface items, and consequently lowering the complexity of the visual information presented in textual and/or graphic forms. For a smartphone with dozens of applications, balancing functional complexity and an easy interface is critical for usability. Simplicity of the information structure is classified into four sub-constructs: reduction, organization, integration, and prioritising (Maeda, 2006; SAP, 2004). Reduction refers to task performance with fewer steps. Designers need to sacrifice functionality to offset the reduced steps in applications. A smartphone user demands a low depth of menu to run an application. The setting menu often requires several steps to access the final stage of control options, but low depth of a menu structure can improve simplicity. For example, most iPhone applications can be accessed via the one-step touch of an icon, but the setting menu of the iPhone 3 has 3–4 depths in average with 6 depths in maximum. Organization is chosen as a core component of simplicity by both Maeda (2006) and SAP (2004). It refers to minimising the cognitive load of a user for the efficient processing of information chunks. Similar items are organized into categories in the setting menu. Most smartphones have multi-page main screens with icons arranged in a grid, and newer versions have an option to create folders to organize icons into the categories.

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Integration refers to the coherence of interface items across different applications. Isolated tasks can leave users lost in a maze of functionality. Simplicity can be obtained through a consistent framework for easier accessibility based on common user mental models. iOS phones maintain a consistent interface for navigation and action keys. Prioritising means classifying and presenting functions by the degree of importance. By highlighting some information cues and hiding others, users can recognise the important information cues more easily and correctly. The importance is given by interface designers usually in setting menu and different ordering can be found in different OSs: the airplane mode setting is on the top of setting list and Wi-Fi is next in the iOS, but the Wi-Fi setting is on the top and Call is next in the Android OS. Prioritising can also be achieved by user customization. In most smartphones, users can re-position icons into a preferred location on the screen. Android phones have separate home screens in which users can customise the screen menu. 2.3. Simplicity in task complexity We adopt Wood’s (1986) and Nadkarni and Gupta’s (2007) framework of task complexity to define perceived smartphone UI simplicity. Mainly studied in the information systems (IS) and HCI domains (Geissler et al., 2001), this task-based framework is important for the smartphone use context because, beyond the basic calling and messaging functions, users utilise phones to fulfil a variety of task goals such as search, browsing, social networking, personal management, gaming, and entertainment. There have long been contrasting arguments regarding the relationship between complexity and simplicity. In studies of visual aesthetics, one perspective is that complexity is a deterrent to the perception of beauty, whereas simplicity is a contributing factor (Birkhoff, 1933). An opposing perspective is that simplicity and complexity are dialectic relationship and that the harmonious ‘combination of diversity in unity’ is the central principle of aesthetics (Eysenck, 1941; Moshagen and Thielsch, 2010). Visual richness or diversity provokes interest and tension, while merely a simple stimulus results in low aesthetic arousal. In studies of information systems, there are similar views regarding a contrasting relationship between complexity and simplicity in terms of usability. Perceived complexity can increase the richness of information cues and provide a satisfying user experience (Hall and Hanna, 2004; Palmer, 2002). At the same time, complexity can create frustration in users and hinder user satisfaction. Nadkarni and Gupta (2007) propose a median stance that the relationship is not linear but inverted-U shape (Berlyne, 1974; Pandir and Knight, 2006). Low levels of perceived complexity create boredom whereas high levels of it generate confusion (Tuch et al., 2009). The idea that median levels of complexity maximise user satisfaction is a valid proposition for the PC experience but may not be proper for the

mobile experience. Because use duration of a smartphone per task is relatively short, confusion is a more serious usability issue than boredom in the mobile use context. Thus, a lack of complexity or obstruction can be defined as simplicity (Nielsen, 2000). Therefore, setting complexity as a reversed measure of simplicity in mobile task performance presents a reasonable relationship (Schaik and Ling, 2005). For smartphone users, information cues on the small screen are central to performing each task goal. Users need to make a series of judgments during the performance of a task, and these judgments are based on information cues, which are clues about the attributes of stimulus objects (Wood, 1986). These information cues are perceived from various task stimulus objects including text, icons, background images, the layout, navigation tools, and soft and hard keys. The visual interface of a smartphone is the primary medium through which users interact with the information cues. The less complex the information cues for fulfilling a task are, the simpler users will perceive the user interface of the smartphone. Wood (1986) posits that task complexity describes the relationships between task inputs and resources for successful task performance. Task inputs set upper limits on knowledge and skills. He propose that perceived task complexity is a combination of three sub-dimensions: component, coordinative, and dynamic. Nadkarni and Gupta (2007) adopt the definition and concepts into the judgment of website complexity as follows: 2.3.1. Component complexity Component complexity is a function of the number of distinct information cues that must be processed in the performance of a task (Berlyne, 1974; Campbell, 1988; Nadkarni and Gupta, 2007). As the number of information cues increases, the knowledge and skill requirements for the task also increase. Thus, fewer information cues and formats on the screen decrease the cognitive loads of the users. For a smartphone, component complexity refers to the visual density of information cues. In a screen layout, long labels, too many images, and a mix of multiple colours are dense cues. A task stimulus with dense information cues is perceived as more complex than one with sparse and similar cues. 2.3.2. Coordinative complexity This refers to the range of and interdependencies among the different information clusters in the task stimulus (Campbell, 1988; Steinmann, 1976). The timing, frequency, intensity, and the sequencing between task inputs and outputs determine the degrees of the relationships among groups of related topics (Wood, 1986). The wider the range of an information cluster and the less uniform the interrelationships among the clusters, the greater the perception of coordinative complexity. That is, high coordinative complexity is caused by a wide range of topics covered, a high number of sub pages, and too many paths or links.

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For a smartphone, this issue usually emerges when users try to access to a certain menu or execute a certain function in an application that has illogically categorised menu or setting items or vaguely connected task functions. 2.3.3. Dynamic complexity This refers to the ambiguity and uncertainty that users face while carrying out task performance activities (Campbell, 1988; March and Simon, 1958). Ambiguity refers to the level of different possible interpretations of the same information cue and uncertainty is the level of predictability of an action–outcome relationship. Thus, dynamic complexity is a function of clarity and predictability in the cause–effect chain or means-ends hierarchy (Wood, 1986). It may also be translated into the intuitiveness of user interaction cues. For a smartphone, unpredictable navigation or outcome expectation creates dynamic complexity and undermine the perceived simplicity of the task performance.

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layout, well-organized information, clarity, visual attractiveness, and ease of use have been regarded as factors of perceived system quality and satisfaction (Abels et al., 1997; Bailey and Pearson, 1983; Doll and Torkzadeh, 1988; McKinney et al., 2002). These factors (i.e., access, navigation, and interactivity) have been widely investigated in the use context of PC, and interface design factors of simplicity can be directly applied to mobile use. In a mobile use context simplicity can be more important than other usability factors of efficiency and effectiveness. In the smartphone user experience, user satisfaction is a critical outcome of user experience, and simplicity may be a strong predictor of user satisfaction. Thus, in order to test the relationship between simplicity perception and satisfaction evaluation, we proposed a hypothesis for the testing of structural model. Hypothesis. The perception of simplicity in the smartphone interface influences positive user satisfaction.

2.4. Simplicity and outcomes: user satisfaction

2.5. The present research and a conceptual model

Main components of three dimensions of simplicity for smartphone are reviewed above. Once simplicity is conceptualised for the scale development, it is important to verify that the simplicity perception of a smartphone user interface leads to positive outcomes which are critical to the success of a smartphone design. Among several usability outcomes, user satisfaction occupies a central position in usability and user experience design (Bevan, 2001; Abran et al., 2003; Hassenzahl, 2004; Hornbaek, 2006; Bevan, 2008; Cyr et al., 2008). When users are satisfied with a system, they are more likely to use the system frequently (DeLone and McLean, 1992), return to a website (Hoffman et al., 1996), purchase products at e-commerce sites and recommend the sites to others (McKinney et al., 2002). End-user or customer satisfaction is defined as a positive attitude towards the use experience. It is an affective state of freedom from discomfort (ISO 9241-11, 1998) and of representing a favourable emotional reaction to the system use experience (McKinney et al., 2002). User satisfaction is the consequence of the various stages of use experience. For example, the e-commerce website use experience is a sequence of purchasing stages: need arousal, information search, evaluation, decision, and post-purchase behaviour (Kotler, 1997). Though not identical to the product purchasing experience, the smartphone use experience can be understood as a sequence of need, search, and execution stages for performing a specific task. The satisfaction with the usability is an evaluation of the efficiency and effectiveness of the process. Simplicity may be a determinant of user satisfaction, but the relationship has yet to be proven. Smartphone users will have higher satisfaction when they have a positive evaluation of the user interface and simplified interface design critically influences on the satisfying user experience (Wolfinbarger and Gilly, 2001). A simple

Simplicity is the key issue for interface design and usability engineering, especially in mobile devices. However, there has been scarce empirical validation of simplicity in terms of conceptualization and measurement thus far. This is not surprising considering the recent introduction of the smartphone and the relatively new user experience of mobile computing. The conceptualization of simplicity in a smartphone and its usability outcome will provide a new insight into the field of mobile HCI and a competitive advantage for design practitioners. Easy to use and visually pleasing mobile interface will be improved by considering simplicity factors. This study aims for quantitative scale development of simplicity concept and modelling of its structural relationship with sub-components and attitudinal outcome. Given that the conceptualization of simplicity for a smartphone is a new attempt and the purpose of study is explorative in nature, qualitative methods may also be productive for developing the meaning structure of simplicity embedded with the user experiences in everyday lives. Many qualitative methods such as ethnography, naturalistic inquiry, focus group interview, diary studies have been widely used in HIC research and usability testing (Choe et al., 2006; Jones and Marsden, 2006; Sohn et al., 2008). Providing a rich and ‘thick description’ (Geertz, 1973) on the user’s point of view would be helpful for understanding the meanings and interpretations of simplicity in people’s everyday experiences. For instance, through qualitative brainstorming processes, SAP (2004) have identified underlying facets of simplicity. In addition, Maeda (2004, 2006) extracted 10 principles of simplicity design based on the interpretive paradigm. In spite of these benefits of qualitative approach, there is an apparent need for a validated assessment instrument and theoretical modelling for emerging constructs in HCI (Lavie

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Simplicity in Information Design Organization

Simplicity in Task Complexity

Reduction

Reduction

Organization

Component Complexcity

Coordinative Complexcity

Simplicity

Satisfaction

Dynamic Complexcity

Visual Aesthetics

Simplicity in Visual Aesthetics Fig. 1. Conceptual model diagram.

and Tractinsky, 2004; Moshagen and Thielsch, 2010). Adopting important facets of simplicity concept identified in earlier qualitative research, this study utilises a quantitative approach to measurement validation and model testing because developing valid measures and modelling relationships between related concepts are a prerequisite for followup studies of simplicity in the UI design and usability testing. Thus, this study comprises of two phases. The initial phase of this research aims to develop a conceptual foundation of simplicity in the smartphone user experience and to validate the proposed measurement model. In our measurement model, simplicity is expected to be composed of three dimensions: aesthetics, information architecture, and task (un)complexity. The second phase seeks to verify the relationship between simplicity perception and user satisfaction because the satisfaction occupies a central position as an outcome in usability and user experience design. Smartphone user’ satisfaction is expected to be influenced by the simplicity perception of the user interface. Fig. 1 depicts the overall research model of this study which combines a measurement model for simplicity perception and a structural model for the relationship between simplicity and satisfaction. 3. Scale construction for smartphone user interface simplicity Incorporating visual aesthetics, information design, and task complexity into an extended concept of smartphone user interface simplicity perception, a measurement model is proposed and validated in this section. 3.1. Data collection An online survey was conducted in July of 2010, and only smartphone users with at least one month of use

experience were qualified to participate in the survey. To collect survey participants, encouraging text messages were sent to approximately 400 people who were attending a large university in the capital of South Korea. A total of 205 smartphone users responded to the questionnaire (51% response rate). In an effort to study perceptions of the simplicity and relevant constructs in the natural environment, the participants were asked to answer survey questions based on their own smartphone use experiences. Thus, no experimental design material or manipulated task scenario was presented to the participants. Fifty-two percent of the sample used the iPhone and forty-eight percent used other OSs (Android 36%, Symbian 10%, Blackberry OS 2%). Almost half of the users had 500 MB data usage monthly pricing plan and twenty-one percent had 1 GB or more. More female users (70%) participated in the survey. The oversampling of female users was due to the technical unavailability of automatic screening by the online survey site. The results of t-test to check for a possible gender effect showed no significant differences between female and male users in usage pattern (e.g., data usage, number of applications) and in any of the constructs used in this study (e.g., reduction, organization, component complexity, coordinative complexity, dynamic complexity, visual aesthetics, and satisfaction). 3.2. Measurement items From diverse domains of the literature, we adapted measures of eight constructs in three dimensions for simplicity perception. Because most of the original measurement statements were drawn from Web usability studies, we rephrased each items to match the properties of mobile interfaces. Simplicity was set as a second order formative factor. Each sub-construct of simplicity represents specific interface and usability attribute and, at the

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same time, forms the higher order construct, simplicity, as a whole. That is, any change in a first-order sub-construct would affect the perception of simplicity. Each subconstruct was measured with multiple items, mostly three or four, by 7 point Likert-type scale. Survey questions of each sub-construct are presented in the Appendix. The four constructs of reduction, organization, integration, and prioritising in the information architecture depend on Maeda’s (2006) simplicity design principles and SAP’s (2004) practical guidelines. These four constructs were assessed adapting the measurement scale for simplicity perception of webblogs by Lee et al.’s. (2007). For example, reduction was measured by questions of how much users perceive their phones as having unnecessary, difficult, or complicated steps to use the functions (reversed coding). Organization was rated on the perceived degree of a systematic or wellstructured arrangement of menu categories, content, functions, and information. Respondents rated integration as the perceived degree of coherently combined interfaces, such as the grouping of menu items, one-step functions to menus or to settings. Prioritising was rated by the degree of ease of recognition of recent or frequent use functions and the active/ passive mode of functions Three constructs in task (un)complexity were measured by adapting perceived website complexity (PWC) scales (Wood, 1986; Nadkarni and Gupta, 2007). Component complexity was assessed by the perceived degree of visual density in text, images, icons, and layouts. Coordinated complexity was measured according to the perceived degree of logical connectedness in the relationship between interface items and functions such as paths to functions, screen transitions, and information clusters. Dynamic complexity was rated on the perceived degree of certainty or predictability in the control input–action output and information presentation on the succeeding screens. Aesthetic simplicity was assessed by three items drawn from Moshagen and Thielsch’s (2010) Visual aesthetics for Website Inventory (VisAWI) scale. Respondents were asked to rate the extent to which they perceive their phone’s screen user interface as neat, modern, and balanced. Satisfaction as an outcome of simplicity perception was measured by three items drawn from McKinney et al’s. (2002) Web-customer satisfaction scale. The items used were ‘‘I am satisfied with the smartphone I use,’’ ‘‘I like the smartphone I use,’’ and ‘‘I am disappointed with the smartphone I use (reversed coding).’’ 3.3. Analysis and validation procedures With the measurement items and the relationships between the constructs explained above, this study involves a three-step analysis procedure using Structural Equation Model (SEM) in AMOS 15 because SEM can assess both measurement properties and test the theoretical relationships simultaneously. The first step is to refine the proposed measurement model for the parsimony and

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predictability. The assessment of model validity is conducted by checking goodness-of-fit indices. If the fit indices are not acceptable, the proposed model should be refined for the improvement of the measurement model by dropping problematic variables and measured items. Then, with the refined model, a first-order factor analysis is analysed to check the reliability and validity of the constructs measured by the questionnaire. Using maximum likelihood, confirmatory factor analysis (CFA) is needed to verify the reliability, convergent validity, and discriminant validity of each variable or construct. The results are described in the following section. Secondly, after verifying the validity and reliability of each construct in the refined model with CFA, a secondorder CFA is carried out to measure the relative importance of each component of simplicity (Section 3.5). Our measurement model proposed that the simplicity perception is a formative indicator and composed of multiple components of information design, task (un)complexity, and visual aesthetics. The components or latent variables in the refined measurement model serves as measurement variables of simplicity scale. After the first- and second-order CFA, the proposed structure model is tested in order to examine the relationship between simplicity perception and user satisfaction (Section 4). Assuming that the simplicity construct composed of multiple components affects satisfaction eventually, the last process seeks to verify the theoretical relationship between the verified simplicity construct and the evaluation of user satisfaction. To develop a new scale or construct, a validity check is critical to ensure the accuracy of the measurements. Construct validity is defined as the extent to which measured items actually reflect the theoretical construct as intended and one of the benefits of structural equation modelling is that it assesses the construct validity (Hair et al., 2010). Usually, the construct is validated by two components of construct validity: convergent and discriminant validity. Convergent validity refers to the extent of the agreement of proposed measures of related constructs, whereas discriminant validity (a.k.a. divergent validity) is the degree of disagreement of theoretically unrelated constructs. In the first-order and second-order confirmatory factor analyses in the following sections, we tested whether the three-factor scale of simplicity measure was closely related yet had separate dimensions of user’s perception of smartphone simplicity. Face validity, also known as content validity, is the assessment of the correspondence of the measured items in the scale and their conceptual definition. That is, it refers to the match between the observed reality and the theoretical definition. The degree of correspondence between measured item and the concept is subjectively assessed mostly by expert judges. Though borrowing scales from previous studies does not always guarantee face validity, we carefully selected established scales and adapted the measurement items to the smartphone use context.

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3.4. Measurement model refinement: confirmatory factor analysis As shown in the conceptual model diagram in Fig. 1, we initially set a total of 10 constructs in our model. Among them, except for simplicity, nine constructs were directly measured with multiple items. Simplicity was set as a second-order formative construct, which acts as an index of eight components derived of three domains of information design, task (un)complexity, and visual aesthetics. CFA was conducted to provide a confirmatory test of the proposed measurement model, which specifies how measured variables logically and systematically represent constructs in the model. In order to achieve more accurate and parsimonious model, the measurement scales were modified. The initial measurement model had 40 observed variables for 9 latent variables. To confirm the fitness of the proposed model, Chi-square, Chi-square/d.f., GFI, AGFI, RMR, RMSEA, NFI, and CFI were assessed. These multiple goodness-of-fit (GOF) measures indicate how well the estimated covariance matrix of the specified measurement model represents the observed covariance matrix of the data. Typically, using three to four fit indices including Chi-square and Chi-square/d.f. as key values provides adequate evidence of model fit (Hair et al., 2010). Out of the fit measures for the initial measurement model, only x2 and x2/d.f. were acceptable by recommended criteria. Because the initial model did not satisfy acceptable validation, model refinement was required for the improvement of the measurement model. The refined measurement model was finalised with 21 observed variables for 7 constructs. Two latent variables and their six observed variables were deleted from the initial measurement model. The two latent variables omitted in the refined model were integration and prioritising in information design domain. Additionally, 13 observed variables, which belonged to reduction, organization, component complexity, coordinative complexity, or dynamic complexity were deleted from the initial measurement model for parsimony and better goodness-of-fit of the final measurement model. For example, omitted were two observed variables belonged to coordinative complexity (‘‘The paths to certain functions are logical’’) and reduction (‘‘My phone has unnecessary steps to use certain functions’’). After some of the observed variables and latent variables were trimmed through model refinement process, all the fit measures for the final measurement model were acceptable only except for AGFI (.869) and RMR (.094), which were slightly short of recommended criteria. The fit measures for the initial and refined measurement models are compared in Table 1. With the refined measurement model, we conducted CFA to confirm whether the constructs in the model were valid and reliable. Firstly, convergent validity was tested. Convergent validity is confirmed when the measured items of a specific construct share a high proportion of variance in common. Factor loadings and Cronbach’s alpha

Table 1 Fit measures for the measurement models. Fit index

Recommended criteria

Initial measurement Refined measurement model model

x2

Z .05

x2/d.f. GFI AGFI RMR RMSEA NFI CFI

r 3.0 Z .90 Z .90 r .05 r .05 Z .90 Z .90

1478.348nnn (d.f.¼ 704) 2.100 .735 .692 .171 .073 .752 .850

228.451nn (d.f.¼168) 1.360 .905 .869 .094 .042 .935 .982

Note: Items and variables omitted from the refined model are marked in the Appendix. nn po.01. nnn po .001.

reliability score are the estimates of convergent validity in the structural equation modelling. Table 2 shows confirmatory factor loadings of each measured item and reliability scores of each construct. Factor loadings above .5 are considered acceptable in general and most of loadings exceeded .6. Cronbach’s alpha reliability scores above .7 are considered acceptable and all scores exceeded .8. Thus, convergent validity was confirmed successfully. Secondly, discriminant validity was checked. Discriminant validity is the extent to which a construct is unique and distinct from other constructs. The correlation coefficients and the squared root of average variance extracted (AVE) are the estimates for the confirmation of discriminant validity and the result is shown in Table 3. Among 7 variables, all correlations were significant and there was the highest correlation between dynamic complexity and visual aesthetics (r¼ .582). In the table the diagonal elements represent the squared root of average variance extracted (AVE), providing a measure of the variance shared between each construct and its measures. Because the squared root of AVE scores were higher than the correlations between the constructs, the assessment of discriminant validity did not reveal any problems. Lastly, to verify the internal consistency or unidimensionality of each latent variable, we also measured the construct reliability and Table 4 shows its result. Generally, it is acceptable when composite reliability is higher than .7 and AVE is higher than .5. Most of the scores were above the criteria indicating sufficient construct reliability. 3.5. Simplicity scale measurement model: second-order factor analysis Based on previous literatures, eight components or subconstructs for the simplicity scale were initially proposed. After verifying the validity and reliability of each variable in the refined measurement model with CFA as above, we chose six components for simplicity scale for smartphone

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Table 2 Convergent validity: factor loadings and reliability scores. Latent constructs

Measured items

Mean (SD)

Confirmatory factor loadings

Reliability (Cronbach a)

(1) Reduction

Reduction 1 Reduction 2 Reduction 3

4.293 (1.454)

.644a .741nnn .764nnn

0.882

(2) Organization

Organization 2 Organization 3 Organization 4

4.925 (1.339)

.677a .748nnn .706nnn

0.879

(3) Component complexity

Component Component Component Component

4.661 (1.357)

.503a .812nnn .843nnn .779nnn

0.914

(4) Coordinative complexity

Coordinative 2 Coordinative 3

5.012 (1.246)

.898a .600nnn

0.845

(5) Dynamic complexity

Dynamic 1 Dynamic 2 Dynamic 3

4.889 (1.212)

.637a .711nnn .800nnn

0.881

(6) Visual aesthetics

Aesthetic 1 Aesthetic 2 Aesthetic 3

5.613 (1.162)

.897nnn .803nnn .735a

0.927

(7) User satisfaction

Satisfaction 1 Satisfaction 2 Satisfaction 3

5.291 (1.385)

.906a .907nnn .651nnn

0.92

1 2 3 4

Note: Full statements of measured items are listed with numbers in the Appendix. nnn po.001. a Loading was set to 1.0 to fix construct variance.

Table 3 Discriminant validity: correlations of the latentables and the square root of the AVE. Latent variables

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(1) (2) (3) (4) (5) (6) (7)

(.793) .313nnn .279nnn .294nnn .309nnn .384nnn .525nnn

(.724) .293nnn .453nnn .480nnn .449nnn .548nnn

(.750) .302nnn .303nnn .330nnn .311nnn

(.784) .493nnn .439nnn .433nnn

(.762) .582nnn .498nnn

(.859) .567nnn

(.803)

Reduction Organization Component complexity Coordinative complexity Dynamic complexity Visual aesthetics User satisfaction

nnn

po0.001.

Table 4 Construct reliability: composite reliability and AVE of latent variables. Latent variables

Composite reliability

AVE

(1) (2) (3) (4) (5) (6) (7)

0.745 0.767 0.836 0.760 0.805 0.894 0.844

0.494 0.524 0.563 0.615 0.580 0.737 0.644

Reduction Organization Component complexity Coordinative complexity Dynamic complexity Visual aesthetics User Satisfaction

user interface. Two initial components dropped were integration and prioritising in the information design dimension. Because smartphones offer default user

customization of icon movement, users seem not to perceive integration and prioritising as components of simplicity. The six components of simplicity for smartphone user interface were reduction and organization in information design domain, component complexity, coordinative complexity, and dynamic complexity in task (un)complexity domain, and visual aesthetics. Comprising these six components, the second-order CFA was carried out to measure the relative importance of each component of simplicity. In order to estimate formative indicator, six latent variables served as measurement variables of simplicity. Fit measures for the second-order CFA model showed that only AGFI and RMR slightly did not reach the recommended criteria,

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simplicity construct, composed of six components, affects user satisfaction eventually as stated in the Hypothesis (Section 2.4). Given that user satisfaction is critical to the success of a product or service, we examined whether the simple user interface design of a smartphone actually leads to user satisfaction as a token of positive outcome. For the confirmation of relationship between two concepts, both fit measures and path weights should be satisfied. Table 6 shows fit measures of the structural model. According to the given fit indices, most of the fit statistics were acceptable but GFI and AGFI, which were slightly lower than the recommended criteria. RMR was marginally higher than the recommended criteria.

but all other fit measures were acceptable (Table 5). Thus, the simplicity scale was successfully validated. Fig. 2 displays a result of second-order CFA with unstandardised path weight. Among 6 components, dynamic complexity showed the highest path weight (1.03) and component complexity presents the lowest (0.48). It denotes that dynamic complexity, which refers to low uncertainty and high predictability in performing task with a smartphone is a stronger cause of simplicity perception than component complexity, which refers to the density of information cues. 4. Structural model for simplicity and user satisfaction After the first and second-order CFA, the proposed structural model was tested. We assumed that the Table 5 Fit measures for the second-order CFA: simplicity scale model.

Table 6 Fit measures for the structural model.

Fit index

Fit index criteria

Recommended criteria

x2 x2/d.f. GFI AGFI RMR RMSEA NFI CFI

Structural model

Z.05 r3.0 Z.90 Z.90 r.05 r.05 Z.90 Z.90

257.529nnn (d.f.¼ 182) 1.415 .895 .866 .117 .045 .926 .977

x x2/d.f. GFI AGFI RMR RMSEA NFI CFI

156.635n (d.f.¼ 129) 1.214 .920 .895 .097 .032 .943 .989

Z .05 r 3.0 Z .90 Z .90 r .05 r .05 Z .90 Z .90

Recommended

2

nnn

po 0.05.

p o.001. Simplicity in Information Design

e1

1

Reduction 1 1.00 0.87

1

Component 1

0.461

Component 2

e19

1

e20

0.56

1

e21

d3

1.00

1

e26

1.51

Component 3

1.55

Component 4

1.50

Coordinative 2 Coordinative 3

e27

Reduction

Component Complexcity

1.00

0.50

1

e33

1

Dynamic 2 Dynamic 3

1.19

0.74 1.00 0.75

Simplicity

Coordinative Complexcity

0.95 1.03

0.48

1 d6

Visual Aesthetics

1.00

1.08

d1

0.99

0.43

1.11

Dynamic Complexcity

Aesthetic 1

1.02

Aesthetic 2 1

1 0.17 e35

1.00

Aesthetic 3 1

0.28 e36

0.42 e37

Simplicity in Visual Aesthetics

Fig. 2. Simplicity scale measurement model.

0.90

1 Organization

.75

1 1.80

0.96

0.96

d2

d4

d5

1 Dynamic 1

1

1

Organization 3 Organization 4

1.00

0.87

0.70 e32

Organization 2

1.13

0.48

1

0.781

0.35

Reduction 3

1.11

e8

1

1

1

0.15

e34

Reduction 2

e7

e6

1

0.70

e18

0.39

e3

e2

1

Simplicity inTask Complexity

n

Simplicity scale model

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Fig. 3. Structural model for simplicity and satisfaction in the smartphone. (Note: All path coefficients were significant at the level of po .001).

Fig. 3 shows a diagram of the final structural model and results with standardized regression weights. All of the paths were significant at the level of po .001. As a secondorder factor, simplicity affected user satisfaction with a high path weight (path coefficient¼ .78). Thus, the hypothesis was accepted that simplicity perception of a smartphone user interface, composed of visual aesthetics, organization, reduction, dynamic complexity, coordinative complexity, and component complexity, affects user satisfaction. The standardized path weights between simplicity and components were slightly changed from those in simplicity measurement model (Fig. 2) because the structural model includes an additional construct of satisfaction. Visual aesthetics and dynamic complexity appeared as the most influential factors of simplicity perception when the relationship with satisfaction was considered simultaneously. Reduction and component complexity showed rather low path weights.

5. Discussion and implications Motivated by the need to develop an integrated measurement scale of the simplicity perception of smartphone user interfaces, our research incorporated visual aesthetics, information design, and task complexity into an extended concept of smartphone simplicity perception. Drawn from three distinct domains of human–computer interaction research and related areas, the new development of the simplicity construct and the measures were refined and then validated (Sections 3.4 and 3.5). Given the exploratory orientation of construct development, the initial measurement model was revised and two components in the information design domain were dropped. Thus, the final measurement model consisted of six components: reduction, organization, component complexity, coordinative complexity, dynamic complexity, and visual aesthetics. These six components formed a scale for the measurement

139

of simplicity perception of a smartphone user interface. Implications of each component are summarised below. Visual aesthetics appears to be a very strong component of simplicity perception. Past studies of simplicity and visual aesthetics have assumed a partial and multidimensional relationship: simplicity is only a part of the visual aesthetic factors. Diversity, colourfulness, and craftsmanship (Moshagen and Thielsch, 2010) or expressive aesthetics (Lavie and Tractinsky, 2004) were considered as other, sometimes contrasting, components of visual aesthetics. Given that these multiple dimensions of visual aesthetics were generated for the rather complex Web screen usability design, an assessment of visual aesthetics for a small screen smartphone needs to be adapted into a single dimension of visual simplicity or classical aesthetics. A clean, modern, and balanced arrangement of graphic and textual items is actually the main design motto of smartphone UI. These variables were successfully validated as a factor of visual aesthetics for simplicity. All three of the task complexity components were successfully integrated into the measurement model for simplicity. Interestingly, dynamic complexity was the one of the most influential factor as a simplicity measure in the structural model, along with visual simplicity. This fact denotes that the core area of simplicity perception is related to the intuitive predictability and action–outcome certainty. Coordinative complexity was shown to be another strong factor of simplicity. Consistency through a logical relationship among various interface items appears to lessen the cognitive load and task complexity, and eventually to heighten overall simplicity perception when users interact with a wide range of task stimuli when using their smartphones. Component complexity was incorporated into a factor of simplicity perception, but the degree of influence was the lowest compared to the other factors. Fewer information formats and cues were thought to decrease the cognitive load for task performance and contribute to the perception of simplicity. However, the impact of visual density on task performance was not so strong that a low density of visual cues on the screen had a weaker relationship with simplicity perception. It appears that non-active smartphone users perceive density as detrimental to simplicity, whereas active users overcome the cognitive load and feel density less than non-active users. A post-hoc analysis showed that the degree of smartphone usage, measured by the number of applications installed, had a statistically significant negative correlation with visual density perception (r¼  .25, po.01). The number of applications installed does not necessarily mean that a user uses those applications very often, but it indicates how actively the user utilises the diverse functions of a smartphone besides the basic calling or messaging functions. Thus, we can infer that a learning effect comes into play as users experience more applications and actively interact with their smartphones. In the information design domain four components have been claimed both by researchers and practitioners as the core factors of simplicity: reduction, organization,

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integration, and prioritising (Maeda, 2006; Lee et al., 2007; SAP, 2004). However, integration and prioritising were omitted from the measurement model while two others factors, reduction and organization, were retained. It is likely that integration and prioritising were not supported by our data due to the different interface frameworks between the iPhone and other smartphones because the Android OS provides separate home screens while the iPhone OS has unified main screens. The post-hoc analysis demonstrated that iPhone users had a higher perception of integration than Android users (t=2.77, p o .01). In contrast, Android users perceived more customizability than iPhone users (t=2.05, p o .05). Meanwhile, reduction and organization showed a moderate level of influence on the perception of simplicity. A reduced number of steps to access a specific function and a well-categorised menu structure seem to create a positive, though not strong, mental mode of easy and quick understanding of the information hierarchy. The later phase of this research sought to verify the relationship between simplicity perception of the interface and the evaluation of user satisfaction (Section 4). Through a series of model validation, the hypothesis was accepted that user satisfaction is positively affected by simplicity perception and the relationship between the constructs was found very strong. User satisfaction is critical in the design of a mobile interface because smartphones are very personal and, at the same time, very observable objects. People carry them nearly always for everyday activities, and dissatisfaction with such a necessity creates numerous difficulties in their everyday lives. Moreover, a mobile phone is easily exposed to other people and an unhappy user could disseminate negative comments on the phone to latent customers. Because smartphone interface belongs to the nature of experience goods (Shapiro and Varian, 1998), it is difficult for users to evaluate the value of a smartphone before actually purchasing and then using the device. Also, because most users enter into a long-term contract with telephone service operators, simplicity perception may have a weak direct relationship with purchase intention or continuous use. However, simplicity appears to have a strong indirect relationship with those behavioural outcomes through satisfaction as satisfied users would establish a very positive brand image and recommend the device to others who may be future customers. 6. Conclusion The findings of this study imply that a simplified interface design of the task performance, information hierarchy, and visual display attributes contributes to positive satisfaction evaluations when users interact with their smartphones as they engage in diverse tasks ranging from communication and information search to entertainment. The main contribution of this study lies in the fact that it is an attempt to integrate three distinct conceptual approaches into a unified measure of simplicity perception. We adapted measurement items, traditionally intended for

the Web usability on PC, to revised ones for the smartphone. There has been a lack of integrated conceptualization or measurement scale for the simplicity despite the fact that it has been a core catchphrase for the interaction design practitioners. In each domain simplicity has been conceptualised as a different attribute: information hierarchy in HCI, task performance attribute in IS, and visual aesthetics in the newly emerged emotion approach in UX design. The measurement scale developed and validated in this study can be termed as an ‘Integrated Scale of Simplicity for Smartphone Interface’. As broadband mobile devices have suddenly become mainstream consumer products and changed the way people communicate, work, and entertain, the initiative to develop a validated measurement scale and a theoretical modelling is needed. These scale and model can explain which interface attributes in the smartphone contribute to the users’ perception of simplicity and, then, how the interface design for simplicity can generate positive outcomes. There are some limitations of this study. This study is based on data gathered at a rather early period of smartphone diffusion. Thus, more early adopters were sampled who may have higher levels of knowledge and skills for digital devices than average users. Some caveats should be stated for a clearer interpretation of this study’s findings. Because our concern was to develop a generalisable measurement and relational model regardless of the variations in the diverse smartphone OSs or manufacturers, we did not focus on analysing the differences between smartphone OSs. Moreover, partially due to insufficient sample size for split-group analysis, validating the model with separate user groups, such as iPhone vs. Android users, was not attempted. However, the two dominant smartphone OSs may create a different model of the user perception of simplicity. Cultural variation may be another possible caveat. This study targeted smartphone users in South Korea because the iPhone and Android phones were introduced in the market with only a six-month gap, due to a delayed contract with Apple. Thus, South Korean smartphone users were valuable in that the sample may be devoid of temporal bias due to the iPhone’s pre-eminent market exposure. However, simplicity perception even for the same smartphone, especially in aesthetic evaluation, varies across countries because users have distinct cultural values and prior experiences. Thus, multicultural model validation is suggested for future research. In addition, for parsimony of model construction, this study excluded other usability concepts such as ease of use and usefulness and behavioural outcomes such as brand loyalty and (re)purchase intention. To fully represent HCI issues in the smartphone interface design, these concepts should be included for the construction of extended model. Acknowledgements This work was supported in part by Yonsei University research fund of 2011.

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Table A1 Domains

Constructs (latent variables)

Measure items (observed variables)

Information design

Reduction

(1) (2) (3) (4)

My My My My

Organization

(1) (2) (3) (4)

My phone shows menu categories systematically.b My phone provides content systematically. My phone is designed to provide functions consistently. Information in my phone is well structured and systematic.

Integrationb

(1) My phone groups similar menu items in the same category. (2) My phone offers one-step function to run certain menus. (3) My phone offers one-step function to change settings.

Prioritizingb

(1) I can identify the most recent function run. (2) I can set the frequently used functions. (3) I can set the active/inactive mode of functions I want to use.

Component complexity

(1) (2) (3) (4)

Coordinative complexity

(1) The paths to certain functions are logical.a,b (2) The backgrounds across screen transitions are consistent. (3) The information clusters in screens are interrelated.

Dynamic complexity

(1) Information on the succeeding page is predictable. (2) Each control input takes me to the desired action. (3) Information presented on the next screen is certain.

Aesthetic simplicity

(1) Screen design is neat. (2) Screen design is modern. (3) Screen design is well balanced.

Task complexity

Aesthetic simplicity

Satisfaction

a

The The The The

phone phone phone phone

has has has has

unnecessary steps to use certain functions.a difficult steps to use certain functions.a complicated steps to use certain functions.a unnecessary functions I don’t want.a,b

text directions are too long.a background images are visually dense.a icon images in the screen are visually dense.a layout of screen is visually dense.a

(1) I am satisfied with the smartphone I use. (2) I like the smartphone I use. (3) I am disappointed with the smartphone I use.a

Reversed items. Dropped from the final measurement scale of simplicity.

b

Appendix A. Survey items (initial measurement) See Table A1. References Abels, E.G., White, M.D., Hahn, K., 1997. Identifyinguser-based criteria for Web pages. Internet Res.: Electron. Network. Appl. Policy 7, 252–262. Abran, A., Khelifi, A., Suryn, W., Seffah, A., 2003. Usability meanings and interpretations in ISO standards. Software Qual. J. 11, 325–338. Aladwani, A.M., Palvia, P.C., 2002. Developing and validating an instrument for measuring user-perceived web quality. Inf. Manage. 39, 467–476.

Arnheim, R., 1974. Art and Visual Perception: A Psychology of the Creative Eye. University of California Press, Berkeley. Bailey, J., Pearson, S.W., 1983. Development of a tool formeasuring and analyzing computer user satisfaction. Manage. Sci. 29, 530–545. Bauerly, M., Liu, Y.L., 2008. Effects of symmetry and number of compositional elements on interface and design aesthetics. Int. J. Human–Comput. Interact. 24, 275–287. Berlyne, D.E., 1974. Studies in the New Experimental Aesthetics: Steps Toward an Objective Psychology of Aesthetic Appreciation. Hemisphere Pub.Corp,, Washington. Bevan, N., 2008. UX, usability and ISO standards. In: Proceeding of the CHI Conference. Florence, Italy. Bevan, N., 2001. International standards for HCl and usability. Int. J. Human–Comput. Interact. 55, 533–552.

142

J.H. Choi, H.-J. Lee / Int. J. Human-Computer Studies 70 (2012) 129–142

Birkhoff, G.D., 1933. Aesthetic Measure. Harvard University Press, Cambridge. Campbell, D.J., 1988. Task complexity: a review and analysis. Acad. Manage. Rev. 13, 40–52. Choe, P., Kim, C., Lehto, M., Lehto, X., Allebach, J., 2006. Evaluating and improving a self-help technical support website: use of focus group interviews. Int. J. Human–Comput. Interact. 21, 333–354. Cyr, D., Kindra, G.S., Dash, S., 2008. Web site design, trust, satisfaction and e-loyalty: the Indian experience. Online Inf. Rev. 32, 773–790. DeLone, W.H., McLean, E.R., 1992. Information systems success: the quest for the dependent variable. Inf. Syst. Res. 3, 60–95. Doll, W.J., Torkzadeh, G., 1988. The measurement of end-user computing satisfaction. MIS Q. 12, 259–274. Eysenck, H., 1941. The empirical determination of an aesthetic formula. Psychol. Rev. 48, 83–92. Geertz, C., 1973. The Interpretation of Cultures: Selected Essays. Basic Books, New York. Geissler, G., Zinkhan, G., Watson, R.T., 2001. Web home page complexity and communication effectiveness. J. Assoc. Inf. Syst. 2, 1–46. Hair, J.F., Black, W., Babin, B., Anderson, R., 2010. Multivariate Data Analysis 7th ed Pearson, Upper Saddle River, NJ. Hall, R.H., Hanna, P., 2004. The impact of web page text-background colour combinations on readability, retention, aesthetics and behavioural intention. Behav. Inf. Technol. 23, 183–195. Hassenzahl, M., 2004. The interplay of beauty, goodness, and usability in interactive products. Human–Comput. Interact. 19, 319–349. Hassenzahl, M., Tractinsky, N., 2006. User experience—a research agenda. Behav. Inf. Technol. 25, 91–97. Hekkert, P., Snelders, D., van Wieringen, P.C., 2003. Most advanced, yet acceptable: typicality and novelty as joint predictors of aesthetic preference in industrial design. Br. J. Psychol. 94, 111–124. Hoffman, D.L., Kalsbeek, W.D., Novak, T.P., 1996. Internet and Web use in the US. Commun. ACM 39, 36–46. Hornbaek, K., 2006. Current practice in measuring usability: challenges to usability studies and research. Int. J. Human–Comput. Interact. 64, 79–102. International Organization for Standardisation (ISO), 2010. Human Centered Design Process for Interactive Systems. ISO 13407. International Organization for Standardization (ISO), 1998. Ergonomic Requirements for Office Work With Visual Display Terminals (VDTs) Part11: Guidance on Usability. ISO9241-11. Jones, M., Marsden, G., 2006. Mobile Interaction Design. Wiley, Chichester, U.K. Kim, J., Lee, J., Choi, D., 2003. Designing emotionally evocative homepages: an empirical study of the quantitative relations between design factors and emotional dimensions. Int. J. Human–Comput. Interact. 59, 899–940. Kotler, P., 1997. Marketing Management: Analysis, Planning, Implementation, and Control. Prentice Hall, Englewood Cliff. Lavie, T., Tractinsky, N., 2004. Assessing dimensions of perceived visual aesthetics of web sites. Int. J. Human–Comput. Interact. 60, 269–298. Lee, D., Moon, J., Kim, Y.-J., 2007.The effects of simplicity and perceived control on perceived ease of use. In: Proceeding of the Americas Conference on Information Systems, Association for Information Systems, pp. 1–16. Maeda, J., 2006. The Laws of Simplicity. MIT Press, Cambridge. March, J., Simon, H., 1958. Organizations. Wiley, New York. De Marsico, M., Levialdi, S., 2004. Evaluating web sites: exploiting user’s expectations. Int. J. Human–Comput. Interact. 60, 381–416.

McKinney, V., Yoon, K., Zahedi, F., 2002. The measurement of webcustomer satisfaction: an expectation and disconfirmation approach. Inf. Syst. Res. 13, 296–315. Moshagen, M., Thielsch, M.T., 2010. Facets of visual aesthetics. Int. J. Human–Comput. Interact. 68, 689–709. Nadkarni, S., Gupta, R., 2007. A task-based model of perceived website complexity. MIS Q. 31, 501–524. Ngo, D.C.L., Teo, L.S., Byrne, J.G., 2003. Modeling interface aesthetics. Inf. Sci. 152, 25–46. Nielsen, J., 2000. Designing Web Usability. New Riders Publishing, Indianapolis. Palmer, J.W., 2002. Web site usability, design, and performance metrics. Inf. Syst. Res. 13, 151–167. Pandir, M., Knight, J., 2006. Homepage aesthetics: the search for preference factors and the challenges of subjectivity. Interact. Comput. 18, 1351–1370. Reber, R., Schwarz, N., Winkielman, P., 2004. Processing fluency and aesthetic pleasure: is beauty in the perceiver’s processing experience? Pers. Soc. Psychol. Rev. 8, 364–382. Santayana, G., 1955. The Sense of Beauty. Dover, New York. SAP, 2004. Simplifying for Usability. SAP Design Guide. Schaik, P., Ling, J., 2005. Five psychometric scales for online measurement of the quality of human-computer interaction in web sites. Int. J. Human–Comput. Interact. 18, 309–322. Schaik, P., Ling, J., 2009. The role of context in perceptions of the aesthetics of web pages over time. Int. J. Hum.–Comput. Stud. 67, 79–89. Schmidt, K.E., Liu, Y.L., Sridharan, S., 2009. Webpage aesthetics, performance and usability: design variables and their effects. Ergonomics 52, 631–643. Shapiro, C., Varian, H.R., 1998. Information Rules: A Strategic Guide to the Network Economy. Harvard Business School Press, Boston. Sohn, T., Li, K., Griswold, W., Hollan, J. 2008. A Diary study of mobile information needs. In: Proceeding of the CHI Conference 2008, Florence, Italy. Steinmann, D., 1976. The effects of cognitive feedback and task complexity in multiple-cue probability learning. Organ. Behav. Human Perform. 15, 168–179. Thielsch, M.T., Hirschfeld, G., 2010. High and low spatial frequencies in website evaluations. Ergonomics 53, 972–978. Tractinsky, N., Cokhavi, A., Kirschenbaum, M., Sharfi, T., 2006. Evaluating the consistency of immediate aesthetic perceptions of web pages. Int. J. Human–Comput. Interact. 64, 1071–1083. Tractinsky, N., Katz, A.S., Ikar, D., 2000. What is beautiful is usable. Interact. Comput. 13, 127–145. Tuch, A.N., Bargas-Avila, J.A., Opwis, K., Wilhelm, F.H., 2009. Visual complexity of websites: effects on users’ experience, physiology, performance, and memory. Int. J. Human–Comput. Interact. 67, 703–715. Tufte, E.R., 1990. Envisioning Information. Graphics Press, Cheshire. Tufte, E.R., 1997. Visual Explanations: Images and Quantities, Evidence and Narrative. Graphics Press, Cheshire. Tufte, E.R., 2006. Beautiful Evidence. Graphics Press, Cheshire. Wood, R.E., 1986. Task complexity: definition of the construct. Organ. Behav. Human Decision Processes 37, 60–82. Wolfinbarger, M., Gilly, M.C., 2001. Shopping online for freedom, control, and fun. Calif. Manage. Rev. 43, 34–55. Wurman, R.S., 1997. Information Architects. Graphics Inc, New York. Zhang, P., von Dran, G., 2000. Satisfiers and dissatisfiers: a two-factor model for website design and evaluation. Journal of the American Society for Information Sciences 51, 1253–1268.