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A First Look at the Effectiveness of Personality Dimensions in Promoting Users’ Satisfaction With the System:

A First Look at the Effectiveness of Personality Dimensions in Promoting Users’ Satisfaction With... Personalization of the user interface (UI) to certain individuals’ characteristics is crucial for ensuring satisfaction with the service. Unfortunately, the attention in most UI personalization methods have been shifted from being behavioral- personalization to self-personalization. Practically, we explored the potential of linking users’ personality dimensions with their design preferences to shape the design of an interface. It is assumed that such design may effectively promote users’ satisfaction with the service. A total of 87 participants were used to design the UI for certain personality types, and 50 students were used to evaluate their satisfaction with the UI. The results that UI designed based on the users’ personality characteristics helped to stimulate their satisfaction in a mobile learning context. This study offers a new way for customizing the design of the interface based on the correlational link between individuals’ preferences and the structure of personality characteristics. Keywords personality, satisfaction, mobile UI, HCI, UX the designers of an interface are leveraging users’ personality Introduction in the design of interactive environments for the aim of User experience (UX) encompasses the concepts of usability improving the interaction factors between users and environ- and affective engineering. It broadly explains major interac- ment. Thus, we explored the association between personality tion aspects between a user and a product such as interface. profile and mobile user interface design elements (MUIDEs) Thus, to have better interface experience, several methods to provide an effective experience for learners. have been proposed, and individuals’ personality characteris- Previous studies also showed how classical concept of tics is one of those proposed methods. Users’ personality fea- usability (Rudy, 1997) has been extended to involve user tures can be a strategic advantage for the design of adaptive satisfaction in certain context. This is because satisfaction and personalized user interfaces (UIs; Al-Samarraie, with a service or technology in general can be obtained Eldenfria, & Dawoud, 2017; de Oliveira, Karatzoglou, through tailoring the objects that an individual prefers to Concejero Cerezo, Armenta Lopez de Vicuña, & Oliver, use. A study by Oliveira, Cherubini, and Oliver (2013) 2011). This can be formed clearly in interface design ele- addressed the importance of studying users’ different per- ments such as the color element, and previous studies such as sonalities for promoting user satisfaction with mobile phone Marcus and Gould (2000); Brazier, Deakin, Cooke, Russell, services. This is because individual differences have a con- and Jones (2001); and Reinecke and Bernstein (2013) high- siderable impact on user’s overall feelings (Ziemkiewicz lighted the significant role of the color in the interface. In et al., 2011). This led us to say that understanding how to contrast, many studies have been conducted to clarify gen- provide a better UI in a mobile context can help to increase eral theories that characterizes its psychological impact our satisfaction in a way that objects of presentation are con- (Al-Samarraie, Sarsam, Alzahrani, Alalwan, & Masood, figured to reflect certain usage behavior (mental model; Sun 2016; Karsvall, 2002), in which personality has been linked with technology in several various manners (Svendsen, Johnsen, Almås-Sørensen, & Vittersø, 2013). Precisely, from Universiti Sains Malaysia, Penang, Malaysia the personality perspectives, users’ differences in personality Corresponding Author: dimensions may results in certain preferences and tendencies Hosam Al-Samarraie, Centre for Instructional Technology & Multimedia, to adopt particular habits or pattern when learning (Butt & Universiti Sains Malaysia, 11800 Penang, Malaysia. Phillips, 2008). Nunes, Cerri, and Blanc (2008) noted that Email: hosam@usm.my Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open & May, 2013). Lan, Jianjun, and Qizhi (2013) have pointed Yannou, 2015). According to Kujala, Roto, Väänänen- out that personalized interface design is commonly associ- Vainio-Mattila, Karapanos, and Sinnelä (2011), the purpose ated with user-cantered design to which it provides user a of UX is to produce a general positive utility experience to distinctive visual satisfaction and interaction. Later, studies the user, usage simplicity and pleasure that can be obtained like Viveros, Rubio, and Ceballos’ (2014) have asserted that through active interaction with the display, which produces users’ personality and cognitive abilities could influence the the satisfaction level of utility. Hence, creating a positive way user perceive the design of activity in mobile applica- experience becomes necessary demand in retaining a com- tions. From this, we assumed that the personality of a person petitive edge (Djamasbi et al., 2014), especially in the design can play a significant role in his or her learning experience of mobile UI. with mobile applications. Moreover, satisfaction is the On the contrary, UX with the device or service may vary aggregate of individual’s feelings or behavior to the issues from one application to another. UI design preferences can that inspire a certain circumstance (Liaw & Huang, 2013). exist in the design of mobile phone UI and websites. From When browsing information, it is important to understand the literature designing UI of mobile devices, it can be the process involved in designing the interface to accom- observed that, having a particular design format may influ- modate the cognitive demands while performing such task. ence one’s experience based on their familiarity with the dis- This would help to attract users’ attention and get them played objects. For instance, Welch and Kim (2013) found involved in the task (Bose, Singhai, Patankar, & Kumar, that increasing the size of menu elements results in a signifi- 2016). cant increase in user’s performance. However, in terms of Prior study in human–computer interaction (HCI) consid- designing web page, the behavior of the users can be changed ered the use of the psychology in the design of UI; hence, specially that some users practiced to see web objects such as personalization research of UI design was established under search, home button, and navigation at particular location in various frameworks like adaptive UI, user modeling, and the web page (Roth, Schmutz, Pauwels, Bargas-Avila, & intelligent UIs. Maybury and Wahlster (1998) defined such Opwis, 2010; Roth, Tuch, Mekler, Bargas-Avila, & Opwis, adaptive UIs as “human-machine interfaces that aim to 2013) after exploring object placement on different types of improve the efficiency, effectiveness and naturalness of websites (online shops, online newspapers, and company human-machine interaction by representing, reasoning and web pages). Researchers found that placing web objects at acting on models of the user, domain, task, discourse and expected locations and designing their display according to media (e.g., graphics, natural language, gesture)” (p. 3). user expectations facilitates orientation that is useful experi- However, in spite of this significant role of psychology in ence for first impressions and the overall UX as well. building the design of the technologies, an evidence from the Meanwhile, de Barros, Leitão, and Ribeiro (2014) asserted literature (like Zhou & Lu, 2011) pointed out that the effects the potential of different types of navigations (Panorama or of personality traits have seldom been examined. Moreover, Panorama along with Pivot controls, and home screen menu) according to Agarwal and Prasad’s (1999) personality differ- in regulating UX. They recommend the idea of displaying all ences, which were previously ignored. This forms clear the application’s main functionalities on the start screen to understanding of personality differences is necessary as vari- offer more control of the screen contents. ous personalities are expected to interact differently with design of UI and this can be due to different personal factors Method such as motivation. Arazy, Nov, and Kumar (2015) stated that UI personalization methods have been divorced from psycho- The process of incorporating the personality features of indi- logical theories of personality, and the user profiles derived viduals into the design of an interface was fully explained in from the exited personalization approaches may not be related the work of Sarsam and Al-Samarraie (2018). to the personality traits tested in the prior work of psychology. Nevertheless, current design of information visualization sys- Participants tems are still applying one-standard-design format to accom- modate perceptual needs of all users without considering their A total of 87 undergraduate students (37 male, and 50 female) different demands (Steichen, Carenini, & Conati, 2013). This were used to shape the design of the UI for certain personal- would negatively affect how learners interact with the dis- ity characteristics. They were screened in the initial phase to play. Therefore, we designed in this study a UI based on users’ ensure that they have an acceptable level of experience and personality types in a mobile learning context. familiarity with mobile applications. Their ages ranged between 18 and 23 years old. Assessment of Personality and Design Preferences Design Features Addressing user preferences is a fundamental issue in devel- The design phases of UI are represented in Figure 1, where we oping successful learning applications (Chen, Conner, & firstly assessed learners’ personality characteristics to identify Sarsam and Al-Samarraie 3 Figure 1. UI design phases. Note. UI = user interface; MUIDEs = mobile user interface design elements. the design preferences for each personality type. The Big Five 5. Font size: It refers to the size of the text. Four font model of personality developed by Goldberg (1981) and sizes were provided (40, 51, 53, and 75 points). Norman (1963) was used to build the main dimensions for 6. Buttons: It refers to the action script for performing articulating one’s personality (McCrae & Costa, 1985, 1987); an action. In this study, three types of buttons were these were Neuroticism, Extraversion, Openness, used, such as buttons with name, button with image, Agreeableness, and Conscientiousness. The IPIP-NEO and button with name and image. (International Personality Item Pool Representation of the 7. Color: Different types of color schemes were used. NEO PI-R™) designed by Goldberg (1999) was used in this The selection of colors was in accordance to hue, study to examine the association between different personality saturation, and brightness. related traits of a person. It is commonly termed as the “Big 8. List: It refers to the way of listing items on a page. It Five” which consists of extraversion, agreeableness, conscien- helps to divide complex information into chunks. tiousness, neuroticism, and openness to experience. The IPIP- Three types of lists were used, such as expanding list, NEO scale includes 120-items, and its items can be found at infinite list, and thumbnail list. http://www.personal.psu.edu/~j5j/IPIP/ipipneo120.htm. 9. Information density: It denotes to the volume of Learners were asked to provide their name, sex, age, and graphical and textual elements in the display. In the country before start answering the personality questions. Items present study, three types of information density were of this instrument were designed to ensure covering different used (low, medium, and high information density). personal aspects where a 5-point Likert-type scale was used 10. Support: It indicates the hints that are usually embed- (very inaccurate, moderately inaccurate, neither accurate nor ded within the design. Two types of support items in inaccurate, moderately accurate, and very accurate). terms of iconic button and short help tips were used Then, we administrated the second instrument to help us in this study. gain further insights about learners’ preferences of certain 11. Alignment: It refers to the arrangement of informa- MUIDEs. The MUIDE instrument consists of multiscale tion (i.e., justify, left and center). questions with graphs (see Supplementary). It was based on a 10-point Likert-type scale (low preference to high prefer- Participants’ viewpoints about various design principles ence). The main MUIDEs were as follows: were also determined. This was essential to indicate any pos- sible differences in users’ familiarity with design principles 1. Information structure: It refers to the organization of (quantity, clarity, simplicity, and affordance of the general the data. It consists of linear structure, hierarchical design) with regard to the MUIDEs. These principles were structure, network structure, and matrix structure. formed based on recommendations of Hewitt and Scardamalia 2. Navigation: It refers to the process of controlling the (1998) and Al-Samarraie, Selim, and Zaqout (2016). movement from one page to another. In this study, six However, to prepare the content for each design cluster, the types of navigations were considered, such as drill book of “Fundamentals of Multimedia” written by Li, Drew, down navigation, list navigation, segmented control, and Liu (2004) was used. Furthermore, materials of the book stepping, scroll thumb, and slidable top navigation. address various learning aspects related to the design of 3. Layout: It refers to the arrangement of the interface effective multimedia content. components. Linear layout, relative layout, and web view layout were used in this study. Clustering of Personality Characteristics 4. Font style: It refers to the properties applied to change the appearance of the text. We used the commonly Clustering is a technique that can be used when there is no used font styles of Arial, Times New Roman, Georgia, class attribute to be predicted. In cluster method, instances and Verdana. are divided into natural groups “clusters,” where they reflect 4 SAGE Open Figure 2. Personality facets for each group. certain pattern or profile in accordance to the source of the the formation of the hierarchical decomposition. instances (Ian, Frank, & Hall, 2011). These instances are Several studies applied the hierarchical clustering shaped according to their similarities or distances (Das, Sau, method because of its role in producing classifica- & Panigrahi, 2015). Based on Das et al. (2015), there are two tion tree and generating similarity scores from dis- types of clustering: (a) hierarchical clustering method and (b) tances of ratio-level variables (Swobodzinski & nonhierarchical clustering method. However, since the num- Jankowski, 2015). Hence, in this study, hieratical ber of clusters is unknown yet in our study, we used hierar- clustering was applied using Ward’s cluster method chical clustering algorithm to identify the number of clusters to identify the patterns associated with learners’ per- to be used in K-means algorithm. sonality in accordance to their MUIDEs prefer- ences. The clustering result yield two-cluster 1. Hierarchical clustering: It is a technique that cre- solutions at the coefficient value of r = .45. The ates a hierarchical decomposition of the data set personality facets for each group are presented in (Han, Kamber, & Pei, 2011). According to Han Figure 2. After obtaining the number of clusters, et al. (2011), the hierarchical method can be catego- K-means algorithm was invoked to identify the rized into either agglomerative or divisive based on instances of the two clusters. Sarsam and Al-Samarraie 5 2. K-means clustering algorithm: It is one of the most personality traits in both clusters is identical, an ANOVA was popular unsupervised algorithms used for classifying used in which a significant difference (p < .05) in all personal- instances among clusters (Wang, Wang, Ke, Zeng, & ity traits among personality traits in the two clusters. This in Li, 2015). It regulates the objects of a specific set into turn confirms that the instances of personality traits in the one numerous clusters. It also arranges the objects into K cluster differs from other instances in other clusters. After partitions which used to shape the clusters based on identifying the personality groups, an association rules method the similarity and dissimilarity features-function- was used to identify the design preferences for each personal- based distance (Han et al., 2011) by applying the ity type. This was described in the following section. centroid-based partitioning approach. The difference between instance and cluster’s representative was Association Rules Technique measured using Euclidean distance. In addition, the Association rules method was used to predict the associa- quality of cluster was measured based on the within- tions between MUIDEs in each personality group. cluster variation. Here, we applied K-means algo- Association rules is a well-known method in data mining that rithm to the two-cluster solutions from the previous is fundamentally used to figure out meaningful and valuable phase to allocate the MUIDEs in accordance to the information from large data sets (Kamsu-Foguem, Rigal, & personality profiles (or clusters). Based on this, we Mauget, 2013; Singh, Ram, & Sodhi, 2013). For this pur- used K-means algorithm to the two clusters from the pose, Waikato Environment for Knowledge Analysis (Weka; previous phase, to match group the personality pro- Chauhan & Chauhan, 2014; Lekha, Srikrishna, & Vinod, files in each cluster with their associated preferences 2013) was used in this study. The Apriori algorithm is the of MUIDEs. most common algorithm to help researchers generate and define patterns within set of items or selections (Ian et al., For the first cluster, we noted that learners in this cluster 2011). It generates association rules that fulfill minimum scored high in neuroticism (M = 62.75, SD = 20.47) followed support and confidence thresholds. We configured the by agreeableness (M = 34.06, SD = 25.60), extraversion (M = Apriori parameters by setting the delta value to 0.05, and 0.1 31.00, SD = 15.22), conscientiousness (M = 29.72, SD = as the value of lower bound for minimum support. Table 2 17.768), and openness to experience (M = 20.31, SD = 13.44), illustrates the constructed rules of MUIDEs in both clusters. respectively (see Table 1). Thus, for simplicity, we labeled this cluster as “Neuroticism” it reserves the highest mean. Participants of the second cluster were found to score high in both extraversion (M = 67.66, SD = 16.49) and conscientious- Table 1. Results of K-Means Algorithm. ness (M = 66.04, SD = 20.87). It is also notable that the dimension of agreeableness (M = 50.57, SD = 22.35) scored Cluster 1 Cluster 2 higher than openness to experience (M = 44.23, SD = 19.15) Personality traits M SD M SD and neuroticism (M = 39.61, SD = 22.83), respectively. As Extraversion 31.00 15.22 67.66 16.49 extraversion is having the highest mean followed by consci- entiousness as compared with other traits, we labeled this Agreeableness 34.06 25.60 50.57 22.35 cluster as the “Extra-conscientiousness” cluster. Conscientiousness 29.72 17.76 66.04 20.87 Figure 3 shows a three-dimensional (3D) graphical repre- Neuroticism 62.75 20.47 39.61 22.83 sentation of the personality nuances mean for the first and the Openness to experience 20.31 13.44 44.23 19.15 second cluster along with Table 1. To validate participants’ Figure 3. 3D graphical representation for the two personality groups. Note. 3D = three-dimensional. 6 SAGE Open Table 2. Association Rules Results. No. Rules for the neuroticism group Confidence (%) 1 Alignment center → Network structure 100 2 Network structure → Layout relative layout 98 3 Low information density → Buttons photo 100 4 Verdana header 53-point → Segmented control 100 5 Scroll thumb → Layout relative layout 99 6 Font text size 40-point → Layout relative layout 100 7 Buttons photo → Expanding list 100 8 Font header 53-point → Verdana font type 96 9 Font text size 40-point = Verdana font type → Color hue 99 10 Stepping → Expanding list 100 Rules of the extra-conscientiousness group 1 Slidable top navigation → Network structure 100 2 Font size 14 point → layout relative layout 100 3 High information density → Buttons photo 100 4 Slidable top navigation → Scroll thumb 97 5 Buttons name and photo → Scroll thumb 100 6 Font header 75-point → Arial font type 100 7 Font text size 51-point → Arial font type 99 8 Scroll thumb → Color hue 100 9 Align text left → Font text size 51-point 100 10 Scroll thumb → Segmented control 99 11 Relative layout = Font text size 51-point → Align text left 100 Figure 4. Storyboard design for the two personality types. Sarsam and Al-Samarraie 7 Figure 5. Mobile UI for the two personality types. Note. UI = user interface. Storyboards is the initial design of major design elements a design course. Their age ranged between 18 and 22 years such as navigation, interface standards guide, and so on. In (M = 21.66, SD = 0.47). They were familiar with using this study, there were two mobile UIs based on the identified mobile device in learning. personality groups. Figure 4 shows the storyboard design for the two personality types. Then, all the design elements for each personality type Assessing Satisfaction were assembled using Java programming tool (see Figure 5). According to Briggs and Sindhav (2015), “satisfaction is a key indicator of the system’s success, and so it has been the Evaluation subject of much Information System (IS) research” (p. 5). It Fifty undergraduate students (15 male, and 35 female) were is the aggregate of individual’s feelings or behavior to the recruited in this study. All the participants were enrolled in issues that inspire a certain circumstance (Liaw & Huang, 8 SAGE Open Table 3. UIS Questionnaire Result for the Neuroticism Group. Neuroticism group Extra-conscientiousness group M SD M SD 6.39 2.60 6.10 2.24 Element M SD Median Mode M SD Median Mode Overall reaction to the software Q1 6.23 1.73 6 7 5.92 2.53 7 8 Q2 7.53 1.95 8 9 6.37 2.3 7 9 Q3 6.52 1.51 7 8 6.45 1.32 7 7 Q4 5.81 1.97 5 5 6.69 1.73 7 8 Q5 5.15 2.58 6 6 6.53 1.57 7 8 Q6 5.84 1.9 6 8 6.38 1.65 7 8 Screen Q1 7.70 1.33 8 9 3.68 1.90 8 9 Q2 7.23 2.32 8 8 5.77 1.45 8 8 O3 7.62 1.31 8 9 4.78 1.51 7 9 Q4 7.05 2.32 7 9 5.97 1.14 7 8 Terminology and system information Q1 7.30 1.25 8 9 7.92 1.16 8 9 Q2 6.36 2.07 7 7 5.50 2.16 8 9 Q3 6.40 4.95 7 7 7.07 1.80 7 9 Q4 4.40 3.53 6 1 4.84 3.15 7 8 Q5 3.21 2.67 1 1 4.76 3.26 6 1 Q6 4.13 3.29 5 1 3.15 2.93 1 1 Learning Q1 7.30 1.80 7 7 6.76 2.12 7 8 Q2 7.31 3.70 8 9 9.83 2.59 8 8 Q3 6.80 1.44 7 7 6.80 1.27 7 5 Q4 7.57 1.10 8 8 9.59 1.36 8 8 Q5 5.10 3.44 6 1 5.50 3.10 7 1 Q6 5.52 3.40 7 1 5.92 3.42 8 9 System capabilities Q1 8.07 5.17 9 9 7.30 2.13 8 7 Q2 8.23 4.14 9 9 7.60 3.51 8 8 Q3 7 2.53 9 9 7.53 3 9 9 Q4 1.92 2.39 1 1 1.71 2.21 1 1 Q5 7.13 4.77 8 9 6.46 4.80 6 6 Note. UIS = User Interface Satisfaction. 2013). Liaw and Huang (2013) stated that enhancing indi- Results viduals’ satisfaction of environmental conditions would sig- Participants’ respond to the UIS questionnaire was analyzed nificantly increase the positive learning behavior. Satisfaction using SPSS software. Every participant in both groups can be considered as a measure of a learner’s reaction toward responded to all the questions after using the two designs. This a particular learning context. From the literature, we can see was assumed to provide a better understanding of participants’ that considering users’ satisfaction has become a crucial behavior when using the design that was shaped according to aspect of the design. This led later studies to consider the their personality characteristics and the one shaped according satisfaction when using an application as an important to other personality types. Table 4 shows the UIS results for usability dimension (Long, Karpinsky, Döner, & Still, 2016). the neuroticism and the extra-conscientiousness group. The Thus, we considered examining learners’ level of satisfaction results showed that the satisfaction of the participants in the when using the proposed interface using the “User Interface neuroticism group was higher when using their preferred Satisfaction” or “UIS” questionnaire developed by Chin, interface (M = 6.39, SD = 2.60) than when using the design of Diehl, and Norman (1988). Sarsam and Al-Samarraie 9 Table 4. UIS Questionnaire Result for the Extra-Conscientiousness Group. Neuroticism group Extra-conscientiousness group M SD M SD 5.92 2.57 6.45 2.61 Element M SD Median Mode M SD Median Mode Overall reaction to the software Q1 3.63 5.43 7 7 6.30 1.78 7 9 Q2 2.43 1.80 7 7 6.72 2.63 7 9 Q3 2.80 3.71 6 7 6.63 2.15 8 8 Q4 6.34 2.35 5 5 5.43 2.94 5 5 Q5 5.62 1.51 6 7 6.27 1.67 5 5 Q6 6.18 1.94 6 6 6.45 1.91 6 9 Screen Q1 7.10 1.41 7 8 7.51 1.12 7 7 Q2 6.71 1.13 7 7 7 1.60 7 5 Q3 7.27 2.10 7 7 7.29 2.10 8 8 Q4 7.72 1.12 8 8 7.20 2.02 8 8 Terminology and system information Q1 7.36 2.28 7 7 7.27 1.27 7 7 Q2 7.15 2.60 7 7 7.18 2.21 7 7 Q3 7.36 2.50 7 7 7.34 3.26 7 7 Q4 5.54 3.75 6 7 3.60 3.12 5 1 Q5 4.72 2.10 6 6 5.09 3.30 6 1 Q6 3.63 3.27 1 1 3.72 3.22 1 1 Learning Q1 7.45 3.48 8 8 7.52 1.64 8 8 Q2 3.70 2.36 8 8 6.27 3.72 8 8 Q3 4.70 2.34 7 7 6.81 1.83 7 7 Q4 6.70 1.13 8 8 7.63 2.70 8 8 Q5 5.80 3.30 7 7 5.9 3.26 7 7 Q6 4.17 3.11 5 1 4.09 3.11 5 1 System capabilities Q1 8.17 2.47 9 9 8.25 3.15 9 9 Q2 7.90 3.13 8 8 8.18 4.24 9 9 Q3 9 6 9 9 9 3 9 9 Q4 1.72 2.21 1 1 1.72 4.71 1 1 Q5 5.81 2.44 7 7 6 3.5 8 8 Note. UIS = User Interface Satisfaction. the extra-conscientiousness group (M = 5.92, SD = 2.57; see with their preferences toward design elements. Participants’ Table 3). In addition, the same was found for the extra-consci- level of satisfaction was reduced when they used a UI design entiousness group who scored higher satisfaction with the that did not fit their personality profile. This means that UI design that was shaped based on their personality profile design based on personality has the potential to offer the user (M = 6.45, SD = 2.61) than when using the neuroticism design of mobile device with the experience that meets their prefer- (M = 6.10, SD = 2.24; see Table 4). Thus, it can be stated that ences. This finding adds to prior work of Huntsinger (2013), the participants’ level of satisfaction was associated with inter- who stated that low motivational intensity (e.g., satisfaction) face designed based on their personality characteristics in a is related to the goal task completion, which as a result, leads mobile learning context. to attract a broader attention span. We also noticed that other aspects related to the regions that the user visited would properly provide us with the necessary knowledge about user Discussion behavior in behavior aware contexts. This means that both Our results showed that participants had high satisfaction the design structure and the elements’ location on the UI can level when learning with mobile UI design that is associated in some fashion influence (either negatively or positively) 10 SAGE Open learners’ satisfaction. This is considered reasonable because to the area of HCI. For instance, using the proposed it is possible that learners obtain an essential clue about an approach, it is very easy to provide recommendation of the element’s functionality (in a learning system) when learning UI characteristics to suit users’ experience when using the from a UI design tailored to their personality. In addition, in interface in a learning context. our opinion, if learners are in general satisfied with the distri- bution of elements in the design of a UI, this increases their ORCID iD interaction, as the focus of their mental model remains on the Hosam Al-Samarraie https://orcid.org/0000-0002-9861-8989 task itself, whereas, if items are displayed such that their location distracts them when interacting, their satisfaction Declaration of Conflicting Interests and performance is reduced, because they may face difficul- The author(s) declared no potential conflicts of interest with respect ties in processing visual context that are due to the incongru- to the research, authorship, and/or publication of this article. ity of design elements to their mental model. From the cognitive perspective, Segall, Doolen, and Porter Funding (2005) stated that the greater the cognitive burden, the lower The author(s) received no financial support for the research, author- is the users’ satisfaction when learning. Later studies, such as ship, and/or publication of this article. that of Philippe, Koestner, Beaulieu-Pelletier, Lecours, and Lekes (2012), showed that episodic memories are linked with Reference satisfaction, suggesting that satisfaction with a task is con- nected to basic cognitive operations (Greenhoot & McLean, Agarwal, R., & Prasad, J. (1999). Are individual differences ger- 2013). Nevertheless, previous research findings that are rele- mane to the acceptance of new information technologies? vant to forming the relation between individuals’ cognitive Decision Sciences, 30, 361-391. Al-Samarraie, H., Eldenfria, A., & Dawoud, H. (2017). The impact load and their satisfaction are in line with those of our study. of personality traits on users’ information-seeking behavior. Schmutz, Heinz, Métrailler, and Opwis (2009) stated that Information Processing & Management, 53, 237-247. “Cognitive load, that is, working memory demands during Al-Samarraie, H., Sarsam, S. M., Alzahrani, A. I., Alalwan, N., problem solving, reasoning, or thinking, may affect users’ & Masood, M. (2016). The role of personality characteristics general satisfaction and performance when completing com- in informing our preference for visual presentation: An eye plex tasks” (p. 1). Thus, users may face additional cognitive movement study. Journal of Ambient Intelligence and Smart demands and require additional processing effort when work- Environments, 8, 709-719. ing with UIs that do not reflect their cognitive preferences. Al-Samarraie, H., Selim, H., & Zaqout, F. (2016). The effect of Hence, it is recommended that the elements of the design be content representation design principles on users’ intuitive relevant to the users to ensure the minimum level of complex- beliefs and use of e-learning systems. Interactive Learning ity and therefore reducing their cognitive load (Klein, Environments, 24, 1758-1777. Arazy, O., Nov, O., & Kumar, N. (2015). Personalityzation: UI per- Wolkerstorfer, Hochleitner, Fuglerud, & Schulz, 2013). sonalization, theoretical grounding in HCI and design research. AIS Transactions on Human-Computer Interaction, 7(2), 43-69. Conclusion Bose, J., Singhai, A., Patankar, A., & Kumar, A. (2016, October). Attention sensitive web browsing. In Proceedings of the 9th The objective of the present study was to help improve annual ACM India conference (pp. 147-152). ACM. learners’ satisfaction when learning using the UI of a Brazier, K. J., Deakin, A. G., Cooke, R. D., Russell, P. C., & Jones, mobile device. 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(2013). Improving efficiency Ziemkiewicz, C., Crouser, R. J., Yauilla, A. R., Su, S. L., Ribarsky, of Apriori algorithm using transaction reduction. International W., & Chang, R. (2011, October 23–28). How locus of con- Journal of Scientific and Research Publications, 3(1), 1-4. trol influences compatibility with visualization style. Paper Steichen, B., Carenini, G., & Conati, C. (2013, March 19–22). User- presented at the 2011 IEEE Conference on Visual Analytics adaptive information visualization: Using eye gaze data to Science and Technology (VAST), Providence, Rhode Island, infer visualization tasks and user cognitive abilities. Paper pre- USA. sented at the Proceedings of the 2013 International Conference on Intelligent User Interfaces, Santa Monica, California, USA. Author Biographies Sun, X., & May, A. (2013). A comparison of field-based and lab- Samer Muthana Sarsam received his master degrees in based experiments to evaluate user experience of personalised Information Technology (IT) from University Tun Abdul Razak mobile devices. Advances in Human-Computer Interaction, (Malaysia). He received his PhD degree from Universiti Sains 2013, Article 619767. Malaysia. Sarsam’s research is in Human–Computer Interaction Svendsen, G. B., Johnsen, J.-A. K., Almås-Sørensen, L., & (HCI) and Data Mining domains. His work focuses on understand- Vittersø, J. (2013). Personality and technology acceptance: ing users’ behavior when interacting with the interface. The influence of personality factors on the core constructs of the technology acceptance model. Behaviour & Information Hosam Al-Samarraie is a senior lecturer in the Centre for Technology, 32, 323-334. Instructional Technology and Multimedia, Universiti Sains Swobodzinski, M., & Jankowski, P. (2015). Evaluating user inter- Malaysia. His border research area is in Human Computer action with a web-based group decision support system: A Interaction (HCI) with emphasis on visualization, clustering, and comparison between two clustering methods. Decision Support prediction of patterns and/or knowledge. He is also interested in Systems, 77, 148-157. examining various behavioural contexts in multi-disciplinary areas. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png SAGE Open SAGE

A First Look at the Effectiveness of Personality Dimensions in Promoting Users’ Satisfaction With the System:

Sarsam, Samer Muthana; Al-Samarraie, Hosam
SAGE Open , Volume 8 (2): 1 – Apr 11, 2018
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Abstract

Personalization of the user interface (UI) to certain individuals’ characteristics is crucial for ensuring satisfaction with the service. Unfortunately, the attention in most UI personalization methods have been shifted from being behavioral- personalization to self-personalization. Practically, we explored the potential of linking users’ personality dimensions with their design preferences to shape the design of an interface. It is assumed that such design may effectively promote users’ satisfaction with the service. A total of 87 participants were used to design the UI for certain personality types, and 50 students were used to evaluate their satisfaction with the UI. The results that UI designed based on the users’ personality characteristics helped to stimulate their satisfaction in a mobile learning context. This study offers a new way for customizing the design of the interface based on the correlational link between individuals’ preferences and the structure of personality characteristics. Keywords personality, satisfaction, mobile UI, HCI, UX the designers of an interface are leveraging users’ personality Introduction in the design of interactive environments for the aim of User experience (UX) encompasses the concepts of usability improving the interaction factors between users and environ- and affective engineering. It broadly explains major interac- ment. Thus, we explored the association between personality tion aspects between a user and a product such as interface. profile and mobile user interface design elements (MUIDEs) Thus, to have better interface experience, several methods to provide an effective experience for learners. have been proposed, and individuals’ personality characteris- Previous studies also showed how classical concept of tics is one of those proposed methods. Users’ personality fea- usability (Rudy, 1997) has been extended to involve user tures can be a strategic advantage for the design of adaptive satisfaction in certain context. This is because satisfaction and personalized user interfaces (UIs; Al-Samarraie, with a service or technology in general can be obtained Eldenfria, & Dawoud, 2017; de Oliveira, Karatzoglou, through tailoring the objects that an individual prefers to Concejero Cerezo, Armenta Lopez de Vicuña, & Oliver, use. A study by Oliveira, Cherubini, and Oliver (2013) 2011). This can be formed clearly in interface design ele- addressed the importance of studying users’ different per- ments such as the color element, and previous studies such as sonalities for promoting user satisfaction with mobile phone Marcus and Gould (2000); Brazier, Deakin, Cooke, Russell, services. This is because individual differences have a con- and Jones (2001); and Reinecke and Bernstein (2013) high- siderable impact on user’s overall feelings (Ziemkiewicz lighted the significant role of the color in the interface. In et al., 2011). This led us to say that understanding how to contrast, many studies have been conducted to clarify gen- provide a better UI in a mobile context can help to increase eral theories that characterizes its psychological impact our satisfaction in a way that objects of presentation are con- (Al-Samarraie, Sarsam, Alzahrani, Alalwan, & Masood, figured to reflect certain usage behavior (mental model; Sun 2016; Karsvall, 2002), in which personality has been linked with technology in several various manners (Svendsen, Johnsen, Almås-Sørensen, & Vittersø, 2013). Precisely, from Universiti Sains Malaysia, Penang, Malaysia the personality perspectives, users’ differences in personality Corresponding Author: dimensions may results in certain preferences and tendencies Hosam Al-Samarraie, Centre for Instructional Technology & Multimedia, to adopt particular habits or pattern when learning (Butt & Universiti Sains Malaysia, 11800 Penang, Malaysia. Phillips, 2008). Nunes, Cerri, and Blanc (2008) noted that Email: hosam@usm.my Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open & May, 2013). Lan, Jianjun, and Qizhi (2013) have pointed Yannou, 2015). According to Kujala, Roto, Väänänen- out that personalized interface design is commonly associ- Vainio-Mattila, Karapanos, and Sinnelä (2011), the purpose ated with user-cantered design to which it provides user a of UX is to produce a general positive utility experience to distinctive visual satisfaction and interaction. Later, studies the user, usage simplicity and pleasure that can be obtained like Viveros, Rubio, and Ceballos’ (2014) have asserted that through active interaction with the display, which produces users’ personality and cognitive abilities could influence the the satisfaction level of utility. Hence, creating a positive way user perceive the design of activity in mobile applica- experience becomes necessary demand in retaining a com- tions. From this, we assumed that the personality of a person petitive edge (Djamasbi et al., 2014), especially in the design can play a significant role in his or her learning experience of mobile UI. with mobile applications. Moreover, satisfaction is the On the contrary, UX with the device or service may vary aggregate of individual’s feelings or behavior to the issues from one application to another. UI design preferences can that inspire a certain circumstance (Liaw & Huang, 2013). exist in the design of mobile phone UI and websites. From When browsing information, it is important to understand the literature designing UI of mobile devices, it can be the process involved in designing the interface to accom- observed that, having a particular design format may influ- modate the cognitive demands while performing such task. ence one’s experience based on their familiarity with the dis- This would help to attract users’ attention and get them played objects. For instance, Welch and Kim (2013) found involved in the task (Bose, Singhai, Patankar, & Kumar, that increasing the size of menu elements results in a signifi- 2016). cant increase in user’s performance. However, in terms of Prior study in human–computer interaction (HCI) consid- designing web page, the behavior of the users can be changed ered the use of the psychology in the design of UI; hence, specially that some users practiced to see web objects such as personalization research of UI design was established under search, home button, and navigation at particular location in various frameworks like adaptive UI, user modeling, and the web page (Roth, Schmutz, Pauwels, Bargas-Avila, & intelligent UIs. Maybury and Wahlster (1998) defined such Opwis, 2010; Roth, Tuch, Mekler, Bargas-Avila, & Opwis, adaptive UIs as “human-machine interfaces that aim to 2013) after exploring object placement on different types of improve the efficiency, effectiveness and naturalness of websites (online shops, online newspapers, and company human-machine interaction by representing, reasoning and web pages). Researchers found that placing web objects at acting on models of the user, domain, task, discourse and expected locations and designing their display according to media (e.g., graphics, natural language, gesture)” (p. 3). user expectations facilitates orientation that is useful experi- However, in spite of this significant role of psychology in ence for first impressions and the overall UX as well. building the design of the technologies, an evidence from the Meanwhile, de Barros, Leitão, and Ribeiro (2014) asserted literature (like Zhou & Lu, 2011) pointed out that the effects the potential of different types of navigations (Panorama or of personality traits have seldom been examined. Moreover, Panorama along with Pivot controls, and home screen menu) according to Agarwal and Prasad’s (1999) personality differ- in regulating UX. They recommend the idea of displaying all ences, which were previously ignored. This forms clear the application’s main functionalities on the start screen to understanding of personality differences is necessary as vari- offer more control of the screen contents. ous personalities are expected to interact differently with design of UI and this can be due to different personal factors Method such as motivation. Arazy, Nov, and Kumar (2015) stated that UI personalization methods have been divorced from psycho- The process of incorporating the personality features of indi- logical theories of personality, and the user profiles derived viduals into the design of an interface was fully explained in from the exited personalization approaches may not be related the work of Sarsam and Al-Samarraie (2018). to the personality traits tested in the prior work of psychology. Nevertheless, current design of information visualization sys- Participants tems are still applying one-standard-design format to accom- modate perceptual needs of all users without considering their A total of 87 undergraduate students (37 male, and 50 female) different demands (Steichen, Carenini, & Conati, 2013). This were used to shape the design of the UI for certain personal- would negatively affect how learners interact with the dis- ity characteristics. They were screened in the initial phase to play. Therefore, we designed in this study a UI based on users’ ensure that they have an acceptable level of experience and personality types in a mobile learning context. familiarity with mobile applications. Their ages ranged between 18 and 23 years old. Assessment of Personality and Design Preferences Design Features Addressing user preferences is a fundamental issue in devel- The design phases of UI are represented in Figure 1, where we oping successful learning applications (Chen, Conner, & firstly assessed learners’ personality characteristics to identify Sarsam and Al-Samarraie 3 Figure 1. UI design phases. Note. UI = user interface; MUIDEs = mobile user interface design elements. the design preferences for each personality type. The Big Five 5. Font size: It refers to the size of the text. Four font model of personality developed by Goldberg (1981) and sizes were provided (40, 51, 53, and 75 points). Norman (1963) was used to build the main dimensions for 6. Buttons: It refers to the action script for performing articulating one’s personality (McCrae & Costa, 1985, 1987); an action. In this study, three types of buttons were these were Neuroticism, Extraversion, Openness, used, such as buttons with name, button with image, Agreeableness, and Conscientiousness. The IPIP-NEO and button with name and image. (International Personality Item Pool Representation of the 7. Color: Different types of color schemes were used. NEO PI-R™) designed by Goldberg (1999) was used in this The selection of colors was in accordance to hue, study to examine the association between different personality saturation, and brightness. related traits of a person. It is commonly termed as the “Big 8. List: It refers to the way of listing items on a page. It Five” which consists of extraversion, agreeableness, conscien- helps to divide complex information into chunks. tiousness, neuroticism, and openness to experience. The IPIP- Three types of lists were used, such as expanding list, NEO scale includes 120-items, and its items can be found at infinite list, and thumbnail list. http://www.personal.psu.edu/~j5j/IPIP/ipipneo120.htm. 9. Information density: It denotes to the volume of Learners were asked to provide their name, sex, age, and graphical and textual elements in the display. In the country before start answering the personality questions. Items present study, three types of information density were of this instrument were designed to ensure covering different used (low, medium, and high information density). personal aspects where a 5-point Likert-type scale was used 10. Support: It indicates the hints that are usually embed- (very inaccurate, moderately inaccurate, neither accurate nor ded within the design. Two types of support items in inaccurate, moderately accurate, and very accurate). terms of iconic button and short help tips were used Then, we administrated the second instrument to help us in this study. gain further insights about learners’ preferences of certain 11. Alignment: It refers to the arrangement of informa- MUIDEs. The MUIDE instrument consists of multiscale tion (i.e., justify, left and center). questions with graphs (see Supplementary). It was based on a 10-point Likert-type scale (low preference to high prefer- Participants’ viewpoints about various design principles ence). The main MUIDEs were as follows: were also determined. This was essential to indicate any pos- sible differences in users’ familiarity with design principles 1. Information structure: It refers to the organization of (quantity, clarity, simplicity, and affordance of the general the data. It consists of linear structure, hierarchical design) with regard to the MUIDEs. These principles were structure, network structure, and matrix structure. formed based on recommendations of Hewitt and Scardamalia 2. Navigation: It refers to the process of controlling the (1998) and Al-Samarraie, Selim, and Zaqout (2016). movement from one page to another. In this study, six However, to prepare the content for each design cluster, the types of navigations were considered, such as drill book of “Fundamentals of Multimedia” written by Li, Drew, down navigation, list navigation, segmented control, and Liu (2004) was used. Furthermore, materials of the book stepping, scroll thumb, and slidable top navigation. address various learning aspects related to the design of 3. Layout: It refers to the arrangement of the interface effective multimedia content. components. Linear layout, relative layout, and web view layout were used in this study. Clustering of Personality Characteristics 4. Font style: It refers to the properties applied to change the appearance of the text. We used the commonly Clustering is a technique that can be used when there is no used font styles of Arial, Times New Roman, Georgia, class attribute to be predicted. In cluster method, instances and Verdana. are divided into natural groups “clusters,” where they reflect 4 SAGE Open Figure 2. Personality facets for each group. certain pattern or profile in accordance to the source of the the formation of the hierarchical decomposition. instances (Ian, Frank, & Hall, 2011). These instances are Several studies applied the hierarchical clustering shaped according to their similarities or distances (Das, Sau, method because of its role in producing classifica- & Panigrahi, 2015). Based on Das et al. (2015), there are two tion tree and generating similarity scores from dis- types of clustering: (a) hierarchical clustering method and (b) tances of ratio-level variables (Swobodzinski & nonhierarchical clustering method. However, since the num- Jankowski, 2015). Hence, in this study, hieratical ber of clusters is unknown yet in our study, we used hierar- clustering was applied using Ward’s cluster method chical clustering algorithm to identify the number of clusters to identify the patterns associated with learners’ per- to be used in K-means algorithm. sonality in accordance to their MUIDEs prefer- ences. The clustering result yield two-cluster 1. Hierarchical clustering: It is a technique that cre- solutions at the coefficient value of r = .45. The ates a hierarchical decomposition of the data set personality facets for each group are presented in (Han, Kamber, & Pei, 2011). According to Han Figure 2. After obtaining the number of clusters, et al. (2011), the hierarchical method can be catego- K-means algorithm was invoked to identify the rized into either agglomerative or divisive based on instances of the two clusters. Sarsam and Al-Samarraie 5 2. K-means clustering algorithm: It is one of the most personality traits in both clusters is identical, an ANOVA was popular unsupervised algorithms used for classifying used in which a significant difference (p < .05) in all personal- instances among clusters (Wang, Wang, Ke, Zeng, & ity traits among personality traits in the two clusters. This in Li, 2015). It regulates the objects of a specific set into turn confirms that the instances of personality traits in the one numerous clusters. It also arranges the objects into K cluster differs from other instances in other clusters. After partitions which used to shape the clusters based on identifying the personality groups, an association rules method the similarity and dissimilarity features-function- was used to identify the design preferences for each personal- based distance (Han et al., 2011) by applying the ity type. This was described in the following section. centroid-based partitioning approach. The difference between instance and cluster’s representative was Association Rules Technique measured using Euclidean distance. In addition, the Association rules method was used to predict the associa- quality of cluster was measured based on the within- tions between MUIDEs in each personality group. cluster variation. Here, we applied K-means algo- Association rules is a well-known method in data mining that rithm to the two-cluster solutions from the previous is fundamentally used to figure out meaningful and valuable phase to allocate the MUIDEs in accordance to the information from large data sets (Kamsu-Foguem, Rigal, & personality profiles (or clusters). Based on this, we Mauget, 2013; Singh, Ram, & Sodhi, 2013). For this pur- used K-means algorithm to the two clusters from the pose, Waikato Environment for Knowledge Analysis (Weka; previous phase, to match group the personality pro- Chauhan & Chauhan, 2014; Lekha, Srikrishna, & Vinod, files in each cluster with their associated preferences 2013) was used in this study. The Apriori algorithm is the of MUIDEs. most common algorithm to help researchers generate and define patterns within set of items or selections (Ian et al., For the first cluster, we noted that learners in this cluster 2011). It generates association rules that fulfill minimum scored high in neuroticism (M = 62.75, SD = 20.47) followed support and confidence thresholds. We configured the by agreeableness (M = 34.06, SD = 25.60), extraversion (M = Apriori parameters by setting the delta value to 0.05, and 0.1 31.00, SD = 15.22), conscientiousness (M = 29.72, SD = as the value of lower bound for minimum support. Table 2 17.768), and openness to experience (M = 20.31, SD = 13.44), illustrates the constructed rules of MUIDEs in both clusters. respectively (see Table 1). Thus, for simplicity, we labeled this cluster as “Neuroticism” it reserves the highest mean. Participants of the second cluster were found to score high in both extraversion (M = 67.66, SD = 16.49) and conscientious- Table 1. Results of K-Means Algorithm. ness (M = 66.04, SD = 20.87). It is also notable that the dimension of agreeableness (M = 50.57, SD = 22.35) scored Cluster 1 Cluster 2 higher than openness to experience (M = 44.23, SD = 19.15) Personality traits M SD M SD and neuroticism (M = 39.61, SD = 22.83), respectively. As Extraversion 31.00 15.22 67.66 16.49 extraversion is having the highest mean followed by consci- entiousness as compared with other traits, we labeled this Agreeableness 34.06 25.60 50.57 22.35 cluster as the “Extra-conscientiousness” cluster. Conscientiousness 29.72 17.76 66.04 20.87 Figure 3 shows a three-dimensional (3D) graphical repre- Neuroticism 62.75 20.47 39.61 22.83 sentation of the personality nuances mean for the first and the Openness to experience 20.31 13.44 44.23 19.15 second cluster along with Table 1. To validate participants’ Figure 3. 3D graphical representation for the two personality groups. Note. 3D = three-dimensional. 6 SAGE Open Table 2. Association Rules Results. No. Rules for the neuroticism group Confidence (%) 1 Alignment center → Network structure 100 2 Network structure → Layout relative layout 98 3 Low information density → Buttons photo 100 4 Verdana header 53-point → Segmented control 100 5 Scroll thumb → Layout relative layout 99 6 Font text size 40-point → Layout relative layout 100 7 Buttons photo → Expanding list 100 8 Font header 53-point → Verdana font type 96 9 Font text size 40-point = Verdana font type → Color hue 99 10 Stepping → Expanding list 100 Rules of the extra-conscientiousness group 1 Slidable top navigation → Network structure 100 2 Font size 14 point → layout relative layout 100 3 High information density → Buttons photo 100 4 Slidable top navigation → Scroll thumb 97 5 Buttons name and photo → Scroll thumb 100 6 Font header 75-point → Arial font type 100 7 Font text size 51-point → Arial font type 99 8 Scroll thumb → Color hue 100 9 Align text left → Font text size 51-point 100 10 Scroll thumb → Segmented control 99 11 Relative layout = Font text size 51-point → Align text left 100 Figure 4. Storyboard design for the two personality types. Sarsam and Al-Samarraie 7 Figure 5. Mobile UI for the two personality types. Note. UI = user interface. Storyboards is the initial design of major design elements a design course. Their age ranged between 18 and 22 years such as navigation, interface standards guide, and so on. In (M = 21.66, SD = 0.47). They were familiar with using this study, there were two mobile UIs based on the identified mobile device in learning. personality groups. Figure 4 shows the storyboard design for the two personality types. Then, all the design elements for each personality type Assessing Satisfaction were assembled using Java programming tool (see Figure 5). According to Briggs and Sindhav (2015), “satisfaction is a key indicator of the system’s success, and so it has been the Evaluation subject of much Information System (IS) research” (p. 5). It Fifty undergraduate students (15 male, and 35 female) were is the aggregate of individual’s feelings or behavior to the recruited in this study. All the participants were enrolled in issues that inspire a certain circumstance (Liaw & Huang, 8 SAGE Open Table 3. UIS Questionnaire Result for the Neuroticism Group. Neuroticism group Extra-conscientiousness group M SD M SD 6.39 2.60 6.10 2.24 Element M SD Median Mode M SD Median Mode Overall reaction to the software Q1 6.23 1.73 6 7 5.92 2.53 7 8 Q2 7.53 1.95 8 9 6.37 2.3 7 9 Q3 6.52 1.51 7 8 6.45 1.32 7 7 Q4 5.81 1.97 5 5 6.69 1.73 7 8 Q5 5.15 2.58 6 6 6.53 1.57 7 8 Q6 5.84 1.9 6 8 6.38 1.65 7 8 Screen Q1 7.70 1.33 8 9 3.68 1.90 8 9 Q2 7.23 2.32 8 8 5.77 1.45 8 8 O3 7.62 1.31 8 9 4.78 1.51 7 9 Q4 7.05 2.32 7 9 5.97 1.14 7 8 Terminology and system information Q1 7.30 1.25 8 9 7.92 1.16 8 9 Q2 6.36 2.07 7 7 5.50 2.16 8 9 Q3 6.40 4.95 7 7 7.07 1.80 7 9 Q4 4.40 3.53 6 1 4.84 3.15 7 8 Q5 3.21 2.67 1 1 4.76 3.26 6 1 Q6 4.13 3.29 5 1 3.15 2.93 1 1 Learning Q1 7.30 1.80 7 7 6.76 2.12 7 8 Q2 7.31 3.70 8 9 9.83 2.59 8 8 Q3 6.80 1.44 7 7 6.80 1.27 7 5 Q4 7.57 1.10 8 8 9.59 1.36 8 8 Q5 5.10 3.44 6 1 5.50 3.10 7 1 Q6 5.52 3.40 7 1 5.92 3.42 8 9 System capabilities Q1 8.07 5.17 9 9 7.30 2.13 8 7 Q2 8.23 4.14 9 9 7.60 3.51 8 8 Q3 7 2.53 9 9 7.53 3 9 9 Q4 1.92 2.39 1 1 1.71 2.21 1 1 Q5 7.13 4.77 8 9 6.46 4.80 6 6 Note. UIS = User Interface Satisfaction. 2013). Liaw and Huang (2013) stated that enhancing indi- Results viduals’ satisfaction of environmental conditions would sig- Participants’ respond to the UIS questionnaire was analyzed nificantly increase the positive learning behavior. Satisfaction using SPSS software. Every participant in both groups can be considered as a measure of a learner’s reaction toward responded to all the questions after using the two designs. This a particular learning context. From the literature, we can see was assumed to provide a better understanding of participants’ that considering users’ satisfaction has become a crucial behavior when using the design that was shaped according to aspect of the design. This led later studies to consider the their personality characteristics and the one shaped according satisfaction when using an application as an important to other personality types. Table 4 shows the UIS results for usability dimension (Long, Karpinsky, Döner, & Still, 2016). the neuroticism and the extra-conscientiousness group. The Thus, we considered examining learners’ level of satisfaction results showed that the satisfaction of the participants in the when using the proposed interface using the “User Interface neuroticism group was higher when using their preferred Satisfaction” or “UIS” questionnaire developed by Chin, interface (M = 6.39, SD = 2.60) than when using the design of Diehl, and Norman (1988). Sarsam and Al-Samarraie 9 Table 4. UIS Questionnaire Result for the Extra-Conscientiousness Group. Neuroticism group Extra-conscientiousness group M SD M SD 5.92 2.57 6.45 2.61 Element M SD Median Mode M SD Median Mode Overall reaction to the software Q1 3.63 5.43 7 7 6.30 1.78 7 9 Q2 2.43 1.80 7 7 6.72 2.63 7 9 Q3 2.80 3.71 6 7 6.63 2.15 8 8 Q4 6.34 2.35 5 5 5.43 2.94 5 5 Q5 5.62 1.51 6 7 6.27 1.67 5 5 Q6 6.18 1.94 6 6 6.45 1.91 6 9 Screen Q1 7.10 1.41 7 8 7.51 1.12 7 7 Q2 6.71 1.13 7 7 7 1.60 7 5 Q3 7.27 2.10 7 7 7.29 2.10 8 8 Q4 7.72 1.12 8 8 7.20 2.02 8 8 Terminology and system information Q1 7.36 2.28 7 7 7.27 1.27 7 7 Q2 7.15 2.60 7 7 7.18 2.21 7 7 Q3 7.36 2.50 7 7 7.34 3.26 7 7 Q4 5.54 3.75 6 7 3.60 3.12 5 1 Q5 4.72 2.10 6 6 5.09 3.30 6 1 Q6 3.63 3.27 1 1 3.72 3.22 1 1 Learning Q1 7.45 3.48 8 8 7.52 1.64 8 8 Q2 3.70 2.36 8 8 6.27 3.72 8 8 Q3 4.70 2.34 7 7 6.81 1.83 7 7 Q4 6.70 1.13 8 8 7.63 2.70 8 8 Q5 5.80 3.30 7 7 5.9 3.26 7 7 Q6 4.17 3.11 5 1 4.09 3.11 5 1 System capabilities Q1 8.17 2.47 9 9 8.25 3.15 9 9 Q2 7.90 3.13 8 8 8.18 4.24 9 9 Q3 9 6 9 9 9 3 9 9 Q4 1.72 2.21 1 1 1.72 4.71 1 1 Q5 5.81 2.44 7 7 6 3.5 8 8 Note. UIS = User Interface Satisfaction. the extra-conscientiousness group (M = 5.92, SD = 2.57; see with their preferences toward design elements. Participants’ Table 3). In addition, the same was found for the extra-consci- level of satisfaction was reduced when they used a UI design entiousness group who scored higher satisfaction with the that did not fit their personality profile. This means that UI design that was shaped based on their personality profile design based on personality has the potential to offer the user (M = 6.45, SD = 2.61) than when using the neuroticism design of mobile device with the experience that meets their prefer- (M = 6.10, SD = 2.24; see Table 4). Thus, it can be stated that ences. This finding adds to prior work of Huntsinger (2013), the participants’ level of satisfaction was associated with inter- who stated that low motivational intensity (e.g., satisfaction) face designed based on their personality characteristics in a is related to the goal task completion, which as a result, leads mobile learning context. to attract a broader attention span. We also noticed that other aspects related to the regions that the user visited would properly provide us with the necessary knowledge about user Discussion behavior in behavior aware contexts. This means that both Our results showed that participants had high satisfaction the design structure and the elements’ location on the UI can level when learning with mobile UI design that is associated in some fashion influence (either negatively or positively) 10 SAGE Open learners’ satisfaction. This is considered reasonable because to the area of HCI. For instance, using the proposed it is possible that learners obtain an essential clue about an approach, it is very easy to provide recommendation of the element’s functionality (in a learning system) when learning UI characteristics to suit users’ experience when using the from a UI design tailored to their personality. In addition, in interface in a learning context. our opinion, if learners are in general satisfied with the distri- bution of elements in the design of a UI, this increases their ORCID iD interaction, as the focus of their mental model remains on the Hosam Al-Samarraie https://orcid.org/0000-0002-9861-8989 task itself, whereas, if items are displayed such that their location distracts them when interacting, their satisfaction Declaration of Conflicting Interests and performance is reduced, because they may face difficul- The author(s) declared no potential conflicts of interest with respect ties in processing visual context that are due to the incongru- to the research, authorship, and/or publication of this article. ity of design elements to their mental model. From the cognitive perspective, Segall, Doolen, and Porter Funding (2005) stated that the greater the cognitive burden, the lower The author(s) received no financial support for the research, author- is the users’ satisfaction when learning. Later studies, such as ship, and/or publication of this article. that of Philippe, Koestner, Beaulieu-Pelletier, Lecours, and Lekes (2012), showed that episodic memories are linked with Reference satisfaction, suggesting that satisfaction with a task is con- nected to basic cognitive operations (Greenhoot & McLean, Agarwal, R., & Prasad, J. (1999). Are individual differences ger- 2013). Nevertheless, previous research findings that are rele- mane to the acceptance of new information technologies? vant to forming the relation between individuals’ cognitive Decision Sciences, 30, 361-391. Al-Samarraie, H., Eldenfria, A., & Dawoud, H. (2017). The impact load and their satisfaction are in line with those of our study. of personality traits on users’ information-seeking behavior. Schmutz, Heinz, Métrailler, and Opwis (2009) stated that Information Processing & Management, 53, 237-247. “Cognitive load, that is, working memory demands during Al-Samarraie, H., Sarsam, S. M., Alzahrani, A. I., Alalwan, N., problem solving, reasoning, or thinking, may affect users’ & Masood, M. (2016). The role of personality characteristics general satisfaction and performance when completing com- in informing our preference for visual presentation: An eye plex tasks” (p. 1). Thus, users may face additional cognitive movement study. Journal of Ambient Intelligence and Smart demands and require additional processing effort when work- Environments, 8, 709-719. ing with UIs that do not reflect their cognitive preferences. Al-Samarraie, H., Selim, H., & Zaqout, F. (2016). 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(2013). Improving efficiency Ziemkiewicz, C., Crouser, R. J., Yauilla, A. R., Su, S. L., Ribarsky, of Apriori algorithm using transaction reduction. International W., & Chang, R. (2011, October 23–28). How locus of con- Journal of Scientific and Research Publications, 3(1), 1-4. trol influences compatibility with visualization style. Paper Steichen, B., Carenini, G., & Conati, C. (2013, March 19–22). User- presented at the 2011 IEEE Conference on Visual Analytics adaptive information visualization: Using eye gaze data to Science and Technology (VAST), Providence, Rhode Island, infer visualization tasks and user cognitive abilities. Paper pre- USA. sented at the Proceedings of the 2013 International Conference on Intelligent User Interfaces, Santa Monica, California, USA. Author Biographies Sun, X., & May, A. (2013). A comparison of field-based and lab- Samer Muthana Sarsam received his master degrees in based experiments to evaluate user experience of personalised Information Technology (IT) from University Tun Abdul Razak mobile devices. Advances in Human-Computer Interaction, (Malaysia). He received his PhD degree from Universiti Sains 2013, Article 619767. Malaysia. Sarsam’s research is in Human–Computer Interaction Svendsen, G. B., Johnsen, J.-A. K., Almås-Sørensen, L., & (HCI) and Data Mining domains. His work focuses on understand- Vittersø, J. (2013). Personality and technology acceptance: ing users’ behavior when interacting with the interface. The influence of personality factors on the core constructs of the technology acceptance model. Behaviour & Information Hosam Al-Samarraie is a senior lecturer in the Centre for Technology, 32, 323-334. Instructional Technology and Multimedia, Universiti Sains Swobodzinski, M., & Jankowski, P. (2015). Evaluating user inter- Malaysia. His border research area is in Human Computer action with a web-based group decision support system: A Interaction (HCI) with emphasis on visualization, clustering, and comparison between two clustering methods. Decision Support prediction of patterns and/or knowledge. He is also interested in Systems, 77, 148-157. examining various behavioural contexts in multi-disciplinary areas.

Journal

SAGE Open – SAGE

Published: Apr 11, 2018

Keywords: personality; satisfaction; mobile UI; HCI; UX

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