User-experience of tablet operating system: An ...

Computers & Industrial Engineering 73 (2014) 75–84

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User-experience of tablet operating system: An experimental investigation of Windows 8, iOS 6, and Android 4.2 Chen-Fu Chien a,⇑, Kuo-Yi Lin a, Annie Pei-I Yu b a b

Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan, ROC Department of Business Administration, National Chung Cheng University, Chiayi 62102, Taiwan, ROC

a r t i c l e

i n f o

Article history: Received 3 December 2013 Received in revised form 7 April 2014 Accepted 25 April 2014 Available online 9 May 2014 Keywords: User experience Human computer interface Operating system Consumer preference Rough set theory Consumer decision

a b s t r a c t Understanding the user preferences over the operating system (OS) of a computer is a critical for product designers to enhance user satisfaction. For mobile computers such as tablets, the user interface of OS will affect user-experience (UX). However, little research has been done to collect and analyze customer needs of tablet OS to identify the relationship between OS characteristics and assessments of UX. Focusing the needs in real settings, this study aims to develop a user model for OS design based on UX for assisting designers to identify the relationship between user perception and UX. For validation, an empirical study was conducted to compare the prevalent OSs equipped in tablets: Windows 8, iOS 6, and Android 4.2, as the stimuli. In particular, on the basis of the major aspects that constitute satisfactory or unsatisfactory UX, specific rules are derived as references for the OS. The results have shown the practical viability of the proposed framework. In particular, the iOS 6 possesses a satisfactory support architecture and favorable brand image that resulting in satisfactory UX. The Android 4.2 possesses a satisfactory support architecture and functional performance, resulting in satisfactory UX. The Windows 8 possesses a satisfactory functional performance yet the OS is difficult to use, demonstrates inadequate GUI support, is unclear and complicated to learn, and features an unsatisfactory support platform. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Tablet enhances user’s ability and range of interactions and capture user’s imagination of computer. The desire of tablet migrates from useful and usable to fashionable and quickly learnable. Tablet should differ in its ability to engage users and contribute to the user experience (UX) (Hassenzahl & Tractinsky, 2006; Sundar, Bellur, Oh, Xu, & Jia, 2014). UX is the response reflecting the emotions, demands, and desires of users as they interact with products (Kuniavsky, 2007; Park, Han, Kim, Oh, & Moon, 2013). Consumers’ affective responses after adoption provide user perspectives to adapt devices to fit user’s needs (Yang, Han, & Park, 2010). In the design of tablets, operating system (OS) is one of the core components, managing hardware and software within the device. It comprises graphical user interfaces (GUIs) that are linked to the hardware device tough the OS kernel. It integrates a number of applications that are either included in the devices or other additional applications. It determines the ⇑ Corresponding author. Address: Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101 Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan, ROC. Tel.: +886 3 5742648; fax: +886 3 5722685. E-mail address: [email protected] (C.-F. Chien). http://dx.doi.org/10.1016/j.cie.2014.04.015 0360-8352/Ó 2014 Elsevier Ltd. All rights reserved.

features, performance level, and security and distinguishes one from other products offered by competitors (Lee, Lee, & Garrett, 2013; Lin & Ye, 2009). The significance of UX regarding OSs is reflected in numerous aspects of the marketing process. First, UX information enables OS designers to evaluate various concepts in the early stages of development and determine the optimal OS design. Second, OS with ideal features make it to enhance users’ satisfaction for target consumer groups. Third, the satisfactory experience of early adopters form positive word of mouth, encourage more other consumers and prompt additional consumer purchases, thereby increasing sales and the company market share. Focusing on the realistic needs for the UX, Sundar et al. (2014) identified that theoretical explanation should be derived to identify the relationship between OS characteristics and assessments of UX. The explanations can help to develop advanced design principles. Information regarding the UX response is useful for OS firms to provide new versions to continuously strengthen the core capacities to satisfy user needs and maintain competitive advantages. Because of the advent of development of new information technology products and rapid changes of user needs, an ideal OS accompanying with tablet should be launched in the market in a short time. A product design that targets specific consumer

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groups can be produced more objectively and efficiently instead of only relying on the designers’ intuition and experience (Yang & Shieh, 2010). In recent years, numerous researchers have proposed applying the rough set theory (RST) (Pawlak, 1982) to product design (Shi, Sun, & Xu, 2012; Zhai, Khoo, & Zhong, 2009). The ‘‘IF–THEN’’ rules could be valuable for understanding the decisions made by users in uncertain and vague UX circumstances from user preferences of various OSs. However, little research has been conducted regarding OS design. This study aims to propose a user model for OS design based on UX for assisting the designers to identify the relationship between user perception and UX. The proposed framework consists of three steps: problem definition, data preparation, and rule generation. RST was developed to explore UX and the rules of user information and user perception. To evaluate the validity of this study, an empirical study was conducted in Taiwan to demonstrate the practical viability of the proposed model. The rest of this paper is organized as follows: Section 2 introduces user modeling for HCI. Section 3 describes the proposed framework for UX OS Design. Section 4 describes an empirical study for validation. Section 5 concludes with a discussion of contribution and future research directions.

S U A V f X

V ak ID ID(.) DX DX BND(X) aD(x) POSE(D) Reduct(D) Core(D)

an information system S = (U, A, V, f) universe of all objects uj , uj 2 U a non-empty finite set of attributes ak , ak 2 A universe of all the attribute values V ak , V ¼ [ak 2A V ak information function for uj 2 U and ak 2 A, f ðuj ; ak Þ 2 V ak subset of the objects xi , X # U a non-empty subset of the attributes ad 2 D , D # A domain of finite attribute values of the attribute ak , ak 2 A D-indiscernible with respect to D the elementary set of objects with the Dindiscernible relation lower approximation of X in D upper approximation of X in D boundary region between DX and DX accuracy of approximation for set X E-positive region of D set including all the reducts of D the most essential subset of D

2. User Modeling in HCI Fischer (2001) suggested that the purpose of HCI is to make system more usable, more useful and to provide users with experiences fitting their specific background knowledge and objectives. Over the past few decades, user modeling approach has been adopted in different areas relating to interactive interface (Howes & Young, 1997; Kobsa, 2001). Model is defined as a ‘‘models that systems have of users that reside inside a computational environment. In other words, a user model includes a collection of personal data associated with a specific user. It starts with gathering user data, learning user’ preference, analyzing and evaluating gathered information for further system adaptation. In particular, RST is a technique used to extract the simple and useful ‘‘IF–THEN’’ rules hidden among uncertain and vague data. RST is needless to make assumptions about the independence and normality of the data. RST has been proposed for application in several areas, such as business failure prediction (Dimitras, Slowinski, Susmaga, & Zopounidia, 1999), the identification of fault causes (Peng, Chien, & Tseng, 2004), personal selection (Chen & Chien, 2011; Chien & Chen, 2007; Chien & Chen, 2008), predictive maintenance (Magro & Pinceti, 2009), yield enhancement (Hsu, Chien, Lin, & Chien, 2010), knowledge discovery (Pawlak & Andrzej, 2007; Shyng, Wang, Tzeng, & Wu; 2007), and product appearance design (Shi et al., 2012; Zhai et al., 2009). The RST exhibits great potential for application in OS design based on UX and can be used to construct the user modeling in HCI. The approach to generate rules is shown as following sections. The terminology and notations used are defined as follows:

Table 1 Decision table of an example. Object Condition attributes

1 2 3 4 5

1. Gender 2. Job

Decision attribute 3. Usability 4. Efficiency UX

Male Female Female Female Male

Low Low Low High High

Service industry Technology industry Service industry Service industry Service industry

High Low High High High

Good Bad Bad Good Good

2.1. Information system Information systems are associated with the objects and attributes expressed in decision tables (Pawlak, 1997; Pawlak, 2002), which are used to extract the hidden knowledge from OS UX data. In a decision table, rows correspond to the object (e.g., OS UX information) and columns correspond to the attribute (e.g., gender, age, job, usability, efficiency, and stability). Attributes comprise condition and decision attributes. Condition attributes comprise user information and perception that have a potential relationship with decisions. In particular, the information system S is defined as follows:

S ¼ ðU; A; V; f Þ

ð1Þ

where U denotes the universe of all objects uj , uj 2 U, that is a nonempty set of all objects. A is the finite set of all the attributes ak , ak 2 A. V is the set of all the attribute values V ak , such that

V ¼ [ak 2A V ak

ð2Þ

where V ak is a finite set of attribute values of attribute ak . Finally, f denotes an information function such that, for every uj 2 U and ak 2 A,

f ðuj ; ak Þ 2 V ak

ð3Þ

Table 1 illustrates the decision table of five objects and the attributes including the decision attribute (i.e., UX) and four condition attributes (i.e., gender, job, usability, and efficiency). UX is used to identify the reasons based on the information and constructs of four attributes, resulting in a satisfactory or unsatisfactory user experience that is relevant to OSs. As shown in Table 1, Objects 2 and 3 display ‘‘Bad,’’ meaning that the UX of the OS was unsatisfactory. The decision attributes of other objects display ‘‘Good,’’ meaning that the UX of the OS was satisfactory. 2.2. Indiscernibility relation The indiscernibility relation is a fundamental concept of the RST. Considering specific attributes, the objects described by the same values of considered attributes are indiscernible. For

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example, Objects 1 and 5 in Table 1 have the same attribute in ‘‘Gender’’, indicating that Objects 1 and 5 were indiscernible in the attribute ‘‘Gender’’. Let D be a non-empty subset of the set A of all attributes, i.e. D # A. The D-indiscernibility relation (ID) defines the objects x1 and x2 as D-indiscernible in attribute D, as follows:

ðx1 ; x2 Þ 2 ID () f ðx1 ; ad Þ ¼ f ðx2 ; ad Þ 8ad 2 D

X1

{3,4}

{1,5}

ð4Þ

Based on the indiscernibility relation, the universe can be decomposed into several combinations of indiscernible objects. A set of all indiscernible objects with respect to a specific attribute value is called an elementary set. U/ID is the combination of the elementary sets ID(.) with the indiscernible relations of ID. For example, if D = {‘‘Gender’’, ‘‘Efficiency’’}, then three D-elementary sets will be generated and U/ID = {{1, 5}, {2}, {3, 4}}, in which the elementary set ID(.) = {1, 5} means that, given the gender ‘‘Male’’ and the efficiency ‘‘high’’, Objects 1 and 5 are indiscernible.

Fig. 1. The relationship between the sets and the approximations.

Furthermore, the accuracy of the approximation for Xi in D is defined as follows:

aD ðX i Þ ¼

2.3. Approximation of sets RST provides the lower and upper approximations to characterize the relationships among the objects (Pawlak, 2002). The lower approximation of a set is the subset of indiscernible objects that are included in the set. That is, the lower approximation of a set contains all the objects that can be certainly classified into the set given the considered attributes. In particular, the lower approximation of the set X in D of the considered attributes, denoted as DX, is defined as follows:

DX ¼ fxi jID ðxi Þ # Xg

X0

{2}

ð5Þ

cardðDX i Þ

ð14Þ

cardðDX i Þ

where the cardinality, card(.), denotes the number of objects within a set. The range of aD(Xi) is between 0 and 1. If aD(Xi)=1, Xi is an ordinary (exact) set with respect to D. If aD(Xi) < 1, Xi is a rough set with respect to D. For example, considering D = {‘‘Gender’’, ‘‘Efficiency’’},

DX 1 ¼ f1; 5g ) cardðDX 1 Þ ¼ 2;

ð15Þ

DX 1 ¼ f1; 3; 4; 5g ) cardðDX 1 Þ ¼ 4;

ð16Þ

Therefore,

cardðDX 1 Þ

2 ¼ 0:5 4

The upper approximation of a set is the union of indiscernible objects that have a nonempty intersection with the set of the considered attributes. That is, the upper approximation of a set contains all the objects that can be possibly classified into the set given the considered attributes. The upper approximation of X in D, denoted as DX, is defined as follows:

aD ðX 1 Þ ¼

DX ¼ fuj jID ðuj Þ \ X–u; uj 2 Ug

RST reduces the attributes by eliminating redundant criteria. Considering ad 2 D , where D # A,

ð6Þ

The boundary region represents the area that cannot be properly classified using the considered attributes. That is, the boundary region BND(X) is defined as the difference between the upper and lower approximations of X in D as follows:

BND ðXÞ ¼ DX  DX

ð7Þ

For example, the objects in Table 1 can be categorized into two sets, X1 and X0, based on the decision attribute as follows:

X 1 ¼ f1; 4; 5g denotes the objects with satisfactory UX;

ð8Þ

X 0 ¼ f2; 3g denotes the objects with unsatisfactory UX:

ð9Þ

That is, let E={‘‘UX’’}, two elementary sets can be generated and U/IE ={{1,4,5}, {2,3}}.If D = {‘‘Gender’’, ‘‘Efficiency’’}, then U/ID = {{1, 5}, {2}, {3, 4}}.

Since f1; 5g \ X 1 ¼ f1; 5g \ f1; 4; 5g ¼ f1; 5g–/;

ð10Þ

f2g \ X 1 ¼ f2g \ f1; 4; 5g ¼ /;

ð11Þ

f3; 4g \ X 1 ¼ f3; 4g \ f1; 4; 5g ¼ f4g–/

ð12Þ

Thus, DX 1 ¼ fxi jID ðxi Þ # X 1 g ¼ f1; 5g, since f1; 5g # X 1 DX 1 ¼ fuj jID ðuj Þ \ X 1 –u; uj 2 Ug ¼ f1; 3; 4; 5g, according to Eq(12). The boundary region of X1 in D can be derived, as illustrated in Fig. 1, as follows:

BND ðX 1 Þ ¼ DX 1  DX 1 ¼ f1; 3; 4; 5g  f1; 5g ¼ f3; 4g:

ð13Þ

cardðDX 1 Þ

¼

ð17Þ

Since aD(X1)< 1, X1 is a rough set with respect to D. 2.4. Attribute reduction and rule extraction

ad is superfluous in D; if ID ¼ IDfad g ; ad is indispensable in D; otherwise:

ð18Þ

That is, superfluous attributes can be excluded without disintegrating the original classification. Thus, a reduct is the essential knowledge that can preserve all the basic concepts characterized by the indispensable attributes. Let attribute sets D and E have the equivalence relationship over U, where D # A and E # A. The D-positive region of E is defined as

POSD ðEÞ ¼

[ DX ¼

X2U=IE

[ fxi jID ðxi Þ # Xg

X2U=IE

ð19Þ

denoting the set of objects that can be certainly classified into the E-elementary sets employing the knowledge expressed by ID. For example, considering D = {‘‘Gender’’, ‘‘Efficiency’’} and E = {‘‘UX’’}, the elementary sets can be generated: U/ID = {{1, 5}, {2}, {3, 4}} and U/IE = {{1,4,5}, {2,3}}. Then,

POSD ðEÞ ¼ [X2U=IE DX ¼ DX 1 [ DX 0 ¼ f1; 5g [ f2g ¼ f1; 2; 5g: Let ai 2 D; if POSD ðEÞ ¼ POSDfai g ðEÞ; then ai is E  dispensable in D; Otherwise; ai is E  indispensable in D ð20Þ The subset D of F is called the E-reduct of F if and only if D is the E-indispensable subset of F, where POSD(E) = POSF(E). Reducts are the minimal sufficient subset of the indispensable attributes that

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have the same power for discerning the concepts as when all the attributes being used. Considering a1 = ‘‘Gender’’ in F = {‘‘Gender’’, ‘‘Job’’, ‘‘Efficiency’’}, U/IF = {{1,5}, {2}, {3,4}} and POSF(E) = {1,2,5}. Then, since U=IFfa1 g = {{1,3,4,5},{2}} and POSFfa1 g ðEÞ ¼ f2g – POSF ðEÞ, the attribute ‘‘Gender’’ is E-indispensable in F. Similarly, the attribute ‘‘Efficiency’’ is also E-indispensable in F. However, considering a2 = ‘‘Job’’ in F, since U=IFfa2 g ¼ ff1; 5g; f2g; f3; 4gg and POSFfa2 g ðEÞ ¼ f1; 2; 5g ¼ POSF ðEÞ, the attribute ‘‘Job’’ is E-dispensable in F. Therefore, the set D = {‘‘Gender’’, ‘‘Efficiency’’} is a E-reduct of F. Furthermore, core(.) is defined as the interaction of the reducts as follows:

CoreðDÞ ¼ \REDðDÞ

3.1. Problem definition The present problem is defined to extract UX and identify effective designs for accessing high-potential segments, thereby increasing sales and identifying appropriate features of future OS designs. To understand the related issues of OS design, the relationships between users and OSs must be explained according to domain knowledge. Information provided by domain experts can equip analysts with adequate knowledge to ensure the quality of the rules extracted from data. UX enables OS firms to evaluate various OS designs and determine the design features that users require, allowing the designers to identify high-potential users and thus increasing a company’s market share. The process of capturing UX includes recording user information, defining scenario journeys, and surveying user perceptions. Information regarding user activities, interests, and opinions influence user perceptions and decisions related to OSs. Scenario journeys describe the interaction of a subject, OS, and simulation environment using a satisfactory OS design to facilitate evaluations of user acceptance. Scenario journeys refer to a set of tasks that enable customers to achieve their desired outcomes. A scenario journey describes the steps users perform when interacting with OSs, as they relate to realistic OS use, and systematically extracts user attitudes. To identify the conditions that result in satisfactory or unsatisfactory OS UXs, data related to UXs were collected by surveying user attitudes during the UX process.

ð21Þ

where RED(D) is the set of all the reducts of D. That is, core(.) contains the most critical subset of attributes, in which any of the attributes being removed could cause the decrease of the classification power. The derived reducts can be transformed into decision rules based on the conditional information of the indispensable attributes. In particular, Objects 1, 2 and 5 belong to E-positive region of D, with the same condition attributes of reduct = {‘‘Gender’’, ‘‘Efficiency’’}. The derived rules are follows: IF ‘‘Gender = Male’’ and ‘‘Efficiency = High’’, THEN ‘‘UX = Good.’’ IF ‘‘Gender = Female’’ and ‘‘Efficiency = Low’’, THEN ‘‘UX = Bad.’’ 3. User Model for UX OS Design

3.2. Data preparation

The proposed framework consisted of problem definition, data preparation, and rule generation to extract user experience of operating system design as shown in Fig. 2. In particular, RST was applied to derive the user information and perceptions for UX.

Three types of data were recorded and accumulated during the user information recording, scenario journey defining and user feeling surveying: user information data, perception data, and UX decision data:

Problem Definition

User experience Data integration Data cleaning

Data preparation

Data transformation Data Partitioning Training data set

Testing data set

Decision table Reducts Rule generation Rule Generation

Candidate rule

Candidate rule

Domain knowledge Decision rule

Fig. 2. User modeling for UX OS design.

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(i) User information data: User information data are the key factors of users that affect the UX results. User information data included demographic factors, personal character, usage frequency, voluntariness of use, and the personal aesthetic (Bloch, Brunel, & Arnold, 2003) of the users. (ii) Perception data: Perception data are the key factors of the user scenario journey. When users interacted with the OS, several questionnaires related to the acceptance of the OS by the users were conducted. (iii) UX decision data: UX decision data are the decisions of the users based on their satisfaction with the OS (i.e., satisfactory or unsatisfactory UX). Data preparation improves the quality of the data and was used to enhance the efficiency of the analysis of the mining results (Han & Kamber, 2006). First, data was cleaned to identify the outliers and correct the inconsistencies. Second, data was transformed and consolidated into its analytical form. Third, data was partitioned to derive the collected data into a training data set (k%) and testing dataset (1  k%). In particular, the training data set was used to derive the rules, and the testing dataset was used to validate the derived rules. 3.3. Rule Generation The RST was used to generate reducts and derive the candidate rules from the training data according to the following steps.  Step 1: Inspect the decision table. IF there are numerical attributes in the decision table, perform data discretization and start from Step 1 (Liu, Hussain, Tan, & Dash, 2002); otherwise, skip to Step 3.  Step 2: Generate the reducts and remove the inessential attributes (Zhong, Dong, & Ohsuga, 2001).  Step 3: Identify the generated reducts and use the domain knowledge to filter the reducts.  Step 4: Generate the rules using the reducts.  Step 5: Remove the rules having non-focused decision attributes.  Step 6: Calculate the support of all the rules.  Step 7: For each rule, if the support of the rule P the threshold of support, the rule is considered a candidate rule.  Step 8: Arrange all the candidate rules. Firstly, support must be set as the acceptance threshold of the candidate rules derived from the training data that is defined as follows:

SupportU ðRule jÞ ¼ Pðclass U \ subset data selected by Rule jÞ ð22Þ In addition, confidence and lift are used the criteria to validate the appropriateness of the derived rules. In particular, confidence is the prediction accuracy of the subset used to classify the subset into a correct class. Lift is the information gain ratio of the rule.

ConfidenceU ðRule jÞ ¼ Pðtarget class Ujsubset data selected by Rule jÞ LiftU ðRule jÞ ¼

Pðtarget class Ujsubset jÞ Pðtarget class UjpopulationÞ

ð23Þ

liftt ¼ confidencet =the proportion of interested class: Step 4: If confidencet P the thresholds of confidence and liftt P thresholds of lift, select the candidate rule t from the pool of validation rules ; otherwise, delete this rule. Step 5: If t < total number of generated rules, then t = t + 1, and return to Step 1. Step 6: Check and collect all of validation rules. Step 7: Discuss the derived rules with domain experts. 3.4. Discussion The derived rules were discussed with domain experts for further verification and interpretation of their implications on design management. Designers can identify the ideal design and correct function for this information. Useful information concerning user feeling and UX satisfaction can be explored. The knowledge obtained from the RST can help in selecting the product design and developing a new product concept and marketing strategy. Because of the rapid changes in market demand and user needs, the RST results have a life cycle. Thus, the user modeling must be implemented with regularity to capture user need information for continuous improvement and enhancement of the competitiveness of the products. 4. Empirical study 4.1. Problem definition: the launch of Windows 8 and comparison with other OSs in tablets Microsoft’s Windows 8 was launched in the market in 2012 and promoted the Microsoft Surface for OS applications in tablets. The innovative application platform of Windows 8 has not only created application value for notebook computers and tablets, but has also tightened the market competition. In particular, users choose an OS based on their own perceptions, as well as on the recommendations of others, because their opportunities to use various types of OS are limited. Therefore, gaining a deeper understanding of the effects of the OS on the perceptions of users about the advantages and disadvantages of tablets is crucial. We conducted an empirical study to compare the most three prevalent OSs (i.e., Windows 8, iOS 6, and Android 4.2). Focusing on the realistic needs of performance and efficiency of the OS, each OS was installed on the prevailing tablets: Windows 8 was installed on Surface RT, iOS 6 on the New iPad, and Android 4.2 on the Transformer Prime. The empirical study was conducted in Taiwan from 20XX/11/20 to 20XX/1/31 to validate the OS UX data. Among 262 volunteers, 154 users were recruited as subjects for testing the UX of the OS. We chose a demographically mixed group of users to average the effects of particular demographics (e.g., gender and age) which are shown in Table 2. The demographic factors, personal character,

ð24Þ

Then, the testing data was used to estimate the validity of the candidate rules that were selected based on the criteria as follows: Step 1: Set t = 1 Step 2: Set k = 1, p = 0, and q = 0. Summarize the object k,

Calculate the number of objects that matches the condition of rule t as p. Calculate the number of objects that matches the condition and decision of rule t as q. Step 3: Calculate confidencet = q/p and

Table 2 Gender and age of 154 subjects. Subjects

18–22

23–28

29–34

35–40

Total

Male Female Total

18 24 42

19 23 42

25 17 42

13 15 28

75 79 154

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Table 3 Decision table of OS UX. Item

1 2 3 4 5 6 7 8 9

Condition attributes

UX decision

Gender

Age

Job

...

Easy for learning

Easy to use

Stability to use

...

Male Male Male Male Male Male Female Female Female

29–34 29–34 29–34 24–28 24–28 24–28 18–23 18–23 18–23

Technology Technology Technology Financial Financial Financial Service Service Service

... ... ... ... ... ... ... ... ...

3 4 1 1 4 4 3 2 4

5 4 5 5 1 2 4 4 2

4 3 2 2 5 4 3 2 5

... ... ... ... ... ... ... ... ...

Table 4 The level of attributes. Category

Attributes

Level

User-demographic factor

Gender

2

Age Job

4 5

Personal character

Average expenditure on products with OS Educational major Earnings per year

4 5 4

Usage frequency

Usage frequency Years of use

5 5

Voluntariness of use Personal aesthetic

Voluntary to use Personal aesthetic

5 3

usage frequency, voluntariness of use, and personal aesthetic of users were also collected. The OS–UX process was evaluated to determine the perceptions of the users regarding the OS. We conducted a scenario journey for users to interact with the OS and report their attitude concerning Windows 8, iOS 6, and Android 4.2, as well as their overall UX decision. First, each user was assigned the run order of the three OSs. We arranged all the combinations (9 types) of the three OS run orders as a block factor of the experimental trials to minimize the effect on UX. Each run order was conducted on a demographically mixed group of users to average the effects of particular demographics. Second, users followed a process to interact with the OS, which included reading documents, writing e-mail, web surfing, watching videos, playing games, and personal tasks. Two scenarios (carry-on and sit-with) were considered for the users to simulate the real use of the OS. When the interaction process finished, the users completed a questionnaire to report their experiences. The questionnaire results were collected and used as the perception data. After finishing the questionnaire, the users were guided to the next OS to start the interaction process again, and continued this process until completing interactions with all three OSs. Third, as the users experienced all three OS, they provided their UX decision concerning each OS. The item was ‘‘I have a great UX on this OS,’’ for which the user selected a score from five possible scores (1 = strongly disagree, 2 = disagree, 3 = not sure, 4 = agree, 5 = strongly agree). The results were recorded and used as UX data. After finishing the scenario journey of 154 users with Windows 8, iOS 6, and Android 4.2, we collected 462 UX regarding the OSs. To capture the OS UX objectively, the perception data regarding the acceptance of information technology, which was established by applying the unified theory of acceptance and use of technology (UTAUT), which comprises performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention (Venkatesh, Morris, Davis, & Davis, 2003). The perception

5 3 5 5 1 1 3 5 2

data and UX decision questionnaire was based on the domain knowledge related to these aspect of UTAUT and UX. The statements in the OS UX questionnaire are listed in Appendix A. Regarding performance expectancy, the degree to which the OS helped users enhance their job performance comprised Items 1–5. Regarding effort expectancy, the degree of ease associated with the use of the OS comprised Items 6–10. Regarding social influence, the degree to which using the OS was influenced by others comprised Items 11–15. Regarding facilitating conditions, the degree to which the OS was supported by organizational and technical infrastructure comprised Items 16–18 (Huang, Chen, & Ho, 2014). Regarding behavioral intention, the degree of intention to use the OS comprised Items 19–21. Likert scales were used to measure perception data ranging from ‘‘strongly disagree’’ to ‘‘strongly agree’’, to evaluate their user perception (1 = strongly disagree, 2 = disagree, 3 = not sure, 4 = agree, 5 = strongly agree). The questionnaire results demonstrated a high degree of statistical consistency (Cronbach’s Alpha = 0.958). 4.2. Data preparation For data integration, we merged user information data, including perception data and UX decision data, into a coherent data store for arranging the decision system. The RST decision system comprised 31 condition attributes (10 for user information and 21 for user perception) and 1 decision attribute (UX decision data), which are shown in Table 3. In particular, Items 1–3 represented the first perspective of the user regarding different OSs. The user had a satisfactory UX when using OSs (Items 1 and 3). By applying the ‘‘IF–THEN’’ rules, we determined the reasons for satisfactory UX and unsatisfactory UX. For data cleaning, the missing and inconsistent data were completed and corrected by verifying the results with the users on the phone. After the data-cleaning process, we collected 462 data regarding the OS UX. To identify the reasons for satisfactory UX (UX decision = 5) or unsatisfactory UX (UX decision = 1), the average expenditure on products having the OS, earnings per year, usage frequency, years of use, and personal aesthetic were discretizated (Beynon, 2004; Beynon & Peel, 2001) using a K-means method (MacQueen, 1967), which is shown in Table 4. Finally, the data were split into a training data set and a testing data set by performing fivefold cross validation. 4.3. Rule generation The RST was applied to derive the candidate rules of the OS UX. For the first cross validation of training data, candidate rules were selected if there were at least six items supporting the rules associated with a satisfactory OS UX or an unsatisfactory OS UX. The decision system comprised 33 condition attributes and one UX decision; 43 candidate rules were derived, and a sample of them is shown in Table 5. Rule 1 was a reduct with three attributes

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C.-F. Chien et al. / Computers & Industrial Engineering 73 (2014) 75–84 Table 5 Partial first cross-validation of candidate rules about OS UX. Rule

1 2 3 4

IF (Condition attribute) Gender

Voluntary to use

Useful to me

Good functional Performance

Good connection performance

Easy to use

Brand image

Good supporting GUI

Good supporting architecture

x 2 1 x

5 x 5 x

x x x 1

x x 5 x

5 x x x

5 x x x

x 5 x x

x x x 1

x 5 x x

and 38 supporting data, meaning ‘‘IF a user is completely free to use the OS and feels that this OS has a satisfactory connection performance and is easy to use, THEN the user has a satisfactory UX regarding the OS.’’ Rule 4 was another reduct with two attributes and 11 supporting data, meaning ‘‘IF a user feels the OS is not useful and has an unsatisfactory supporting GUI, THEN the user has a unsatisfactory UX regarding the OS.’’ Regarding the validation of the candidate rule, the threshold of confidence level was 70%, and the lift was 1. A sample of the results of the candidate rules is shown in Table 6. For example, Rule 1 is rejected because its confidence level was 63% (1). However, Rule 3 is accepted because its confidence level was 77% (>70%) and the lift was 2.8 (>1). Therefore, 12 rules were identified. Another fourfold cross validation, which was filtered by applying the support threshold (Candidate Rules 44, 42, 36, and 40 associated with the UX decision), was generated. After filtering the test data using the threshold of confidence and lift, the rules were integrated in Table 7. The UX ‘‘IF–THEN’’ rules are able to identify the compositions of the user information and user preference in order to increase the user satisfaction. Knowledge of the relationship between OS characteristics and satisfactory UX can enable OS firms to develop product promotion strategies for increasing sales. In practice, OS firms experience difficulty determining the ideal segment factors for identifying appropriate segments. The iOS 6 possesses a satisfactory support architecture and favorable brand image, resulting in satisfactory UX. The satisfactory support architecture and functional performance of Android 4.2 also results in satisfactory UX. Although the functional performance of Windows 8 is acceptable and generates satisfactory UX, the OS is difficult to use, demonstrates inadequate GUI support, is unclear and complicated to learn, and features an unsatisfactory support platform. Supporting GUIs that are unclear and difficult to learn generate unsatisfactory UX. In summation, several design aspects of Windows 8 should be

THEN (UX decision)

Support

Good Good Good Bad

38 30 29 11

improved, because the functional performance determines whether users experience a positive initial impression. The designers of Windows 8 should consider the advantages and disadvantages of improving the OS design. This study showed that a satisfactory UX is influenced by gender, personal aesthetics, academic major, employment, age, and using experience. Identifying segment factors is the first step in understanding the OS design needs of users. As the candidate rules generated using the proposed approach increase, a superior understanding of the OS market can be obtained to achieve market success. The aforementioned rules were used to identify five factors related to satisfactory UX, which are summarized in Fig. 3. To improve UX, firms should develop product designs strategies that emphasize the following aspects: First, the product function requirements differ between male and female users. Compared with female users, male users are typically satisfied with a product if the OS is equipped with obvious functions. Thus, future product marketing strategies should emphasize the developed OS functions and highlight the crucial differences compared with rival OSs. Second, for certain users, the primary product concern is whether an OS is easy to use. In this case, companies must consider design factors that assist users with operating the product, particularly first-time users. Finally, as expressed in the phrase ‘‘We are what we have’’ (Belk, 1988), people tend to purchase things that express who they are (Aaker, 1999; Malär, Krohmer, Hoyer, & Nyffenegger, 2011). According to the concept of self-congruence, consumers choose products with a brand personality or image that corresponds to their self-image (Aaker, 1999). Self-congruence can enhance consumer responses to a brand, including forming attachments and a high level of loyalty. Thus, for users who typically choose aestheticoriented products over functionality-oriented products, the OS features of product designs can be used to build brand identity and communicate the brand image to generate a strong brand connection. In addition, when establishing a product brand image, marketers emphasize the OS user image to increase the product

Table 6 Partial candidate rules of first cross-validation. Rule

Candidate Rules

Number of items matching the condition part of rule

Number of items matching the condition & decision part of rule

1

IF a user is male and completely voluntary to use the OS, and feels the OS has good functional performance, THEN the user has good UX in the OS. IF a user is female, and feels the OS has a good brand image and good supporting architecture, THEN the user has good UX in the OS. IF a user is male and completely voluntary to use the OS, and feels the OS has good functional performance, THEN the user has good UX in the OS. IF a user is 24–28 and reference to the views of others for using OS, and feels the OS has a good brand image, THEN the user has good UX in the OS. IF a user is 29–34, and feels the OS is NOT clear and understand to learn, THEN the user has bad UX in the OS.

16

10

63

2.3

No

7

4

57

2.1

No

13

10

77

2.8

Yes

10

5

50

1.8

No

8

8

100

11.6

Yes

2

3

4

5

Confidence (%)

Lift

Accept

82

C.-F. Chien et al. / Computers & Industrial Engineering 73 (2014) 75–84

Table 7 Validation of candidate rules for OS UXs. Rule

Rules (fivefold frequency)

Confidence (%)

1

IF a user is male, completely free to use the OS, and believes that the OS has a satisfactory functional performance,

87.67

3.24

Android, Win 8

81.39

3.01

iOS, Android

92.67

3.42

iOS, Android

2

3

4 5

6 7 8 9 10

THEN the user has a satisfactory UX regarding the OS. (5/5) IF a user has a job in the information technology industry, and believes that the OS has a satisfactory supporting architecture, THEN the user has a satisfactory UX regarding the OS. (4/5) IF a user is 29–34 years of age, and believes that the operation of the OS can be learned quickly and that the OS has a satisfactory supporting architecture, THEN the user has a satisfactory UX regarding the OS. (5/5) IF a user feels the OS is NOT easy to use and has an unsatisfactory supporting GUI, THEN the user has an unsatisfactory UX regarding the OS. (4/5) IF a user has more than 8 years of experience using the OS and believes that the OS is useful and has a satisfactory supporting architecture, THEN the user has a satisfactory UX regarding the OS. (4/5) IF a user is majoring in business and management and believes that the OS is NOT clear and understandable to learn, THEN the user has an unsatisfactory UX regarding the OS. (4/5) IF a user is male and believes that the OS has an unsatisfactory supporting platform, THEN the user has an unsatisfactory UX regarding the OS. (5/5) IF a user is majoring in engineering has a high personal aesthetic, and believes that the OS has a favorable brand image, THEN the user has a satisfactory UX regarding the OS. (3/5) IF a user is 29–34 years old and believes that the OS is NOT clear and understandable to learn, THEN the user has an unsatisfactory UX regarding the OS. (3/5) IF a user uses tablets daily and believes that the OS has an unsatisfactory supporting GUI, THEN the user has an unsatisfactory UX regarding the OS. (5/5)

100.00

Lift

11.55

OS

Win 8

93.33

3.45

87.14

10.07

Win 8

100.00

11.55

Win 8

70

2.59

iOS, Android

iOS

100.00

11.55

Win 8

100.00

11.55

Win 8

Fig. 3. Map relating to the satisfactory UX.

appeal. For example, Apple launched a series of campaigns that emphasized the obvious differences between Mac and PC user profiles. This was achieved by developing a distinct OS and different operational user logics; thus, consumers linked the product image to their personality. 4.4. Discussion The derived rules can provide sufficient evidence for OS firms to identify the reasons for satisfactory or unsatisfactory UXs. Domain experts have found these rules beneficial for understanding the UX of new OS designs. Some of these rules concern the demonstration of expertise and others are critical inputs for OS promotion strategies and design aspects, that is, the aspects related to the target users and promotion channels. Users who provide specific personal information expect a unique service from the OS to yield a satisfactory UX. Special projects have been proposed regarding product design and target user promotion in Taiwan and international markets. Although an OS featuring flawless functional abilities is expected to possess a greater market share compared with that of other OSs, satisfying the needs of all users is difficult because of time and cost limitations. Thus, OSs must be revised based on satisfactory or unsatisfactory UXs, including user perceptions of the GUI support, clarity and ease of learning, supporting

architecture, and usefulness. When a new version of OS is launched, OS firms can promote the new product to high-potential customers to satisfy their requirements. Additionally, providing satisfactory UX information to other groups can increase company market share. The proposed framework provides app developers with information for evaluating promotion strategies. According to the target users and their perceptions of OS designs, app developers can determine the ideal OS and segments as the first promotion channel. Previously, app developers selected the most popular OS and segment as the first channel without considering the optimal segment. Apps are drastically challenged by other apps in the most prevalent OS and segment. The advantages of apps can easily be ignored because the competition of numerous competing apps that with little market share of the product. The limited market share of the most prevalent OSs and segment cannot provide sufficient financial resources to develop apps for future OSs. In this study, information regarding satisfactory OS UXs enabled app developers to determine the optimal OS and segment. These app developers selected the OS with the most satisfactory UX as the first platform for developing apps to increase likelihood of market success. The proposed framework provides information that enables device makers to determine future design aspects. User design requirements are not satisfied by the OS design alone, but rather

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the combination of the OS design and hardware device. Thus, hardware devices are critical for product development. Device makers have the ability to create numerous products because of the rapid development of technology. However, device makers encounter excessive design elements, causing challenges to identifying the elements with the greatest potential. Thus, providing UX information facilitates the selection of ideal design elements for enhancing product designs.

83

Acknowledgements This research is supported by The Ministry of Science and Technology, Taiwan (NSC100-2628-E-007-017-MY3, NSC102-2622-E-007013), The Toward World Class University Project of National Tsing Hua University from the Ministry of Education (102N2075E1), and Quanda Computer, Inc. Appendix A. Items in the OS UX questionnaire

5. Concluding remarks

Aspect

Questions

Computer companies constantly introduce new products to foster consumption and enhance revenue in which user preferences over human computer interface including UX of OS affect consumer satisfaction. This paper developed a framework based on the RST to extract potentially useful rules from OS–UX interaction for enhancing new product acceptance according to the UX. Based on the interpretation and discussion of the rules with domain experts, useful marketing strategies and effective design management techniques were developed for assisting in the product design process to identify and allocate the appropriate product to customers with a specific background, to enhance the acceptance of the new product, and to increase the new product revenue. Furthermore, the results inspired the effective improvement of product design management, including empathy understanding, defining, ideating, prototype creating, and testing. Design management has been improved, although differentiating its contribution from that of other design process efforts is difficult. In this paper, the RST was used for data mining to derive rules and enabled an easier understanding of the obtained results, for communicating with the users with an acceptable level of accuracy and reasonable lift. The empirical study demonstrated the practical viability of this framework for extracting useful design management rules and generating marketing strategies. Data-mining approaches have been used in other fields, such as yield enhancement (Chien, Wang, & Cheng, 2007; Chien, Hsu, & Chen, 2013a), fault location (Chien, Chen, & Lin, 2002; Peng, Chien, & Tseng, 2004), pattern recognition (Hsu & Chien, 2007; Chien, Hsu, & Chen, 2013b; Liu & Chien, 2013), demand forecast (Chien, Chen, & Peng, 2010), cycle time reduction (Kuo, Chien, & Chen, 2011; Chien, Hsu, & Hsiao, 2012), machine clustering (Chien & Hsu, 2006), and talent recruiting in the high-tech industry (Chien & Chen, 2007, 2008; Chen & Chien, 2011). Future research can be done to employ data mining approaches for analyzing usage data and user experience feedback to support product design decisions. This paper applied user information data, such as demographic factors, personal character, usage frequency, personal aesthetic, and voluntariness of use, were applied to predict the UX. Future studies can be conducted to investigate other user data (e.g., lifestyle) to improve the accuracy of the user identification information. Alternative data-mining techniques (Mak & Munakata, 2002) can be studied in future studies to compare various approaches for a comprehensive exploration of the complex interrelationships among the user perception variables and UX. Furthermore, the OS phenomena are closely related to high-tech product positioning, which provides information for launching new products based on the marketplace, to meet the needs of each homogenous consumer segment (Kaul & Rao, 1995). Because UX exerts a substantial influence on the development of various technological products, numerous opportunities remain for unique UX research (Partala & Kallinen, 2012). Further studies can be done to apply the proposed methodology to other products to determine their appearance and usability, as well as to identify the key users to whom the product should be promoted, thereby increasing the new product market and revenue.

Performance expectancy

1. I think this OS enables me to accomplish tasks more quickly. 2. I think this OS is useful to me. 3. I think this OS increases my productivity. 4. I think this OS has good functional performance. 5. I think this OS has good connection performance.

Effort expectancy

6. I think this OS is clear and understandable to learn. 7. I think this OS is easy for learning. 8. I think this OS is easy to use. 9. I think this OS is stability to use. 10. I think this OS is fluent to use.

Social influence

11.In using the OS, I will reference to the views of others. 12. In using the OS, I will emotions by the important people of mine. 13. In using the OS, my behaviors affected by others. 14. In using the OS, the brand image is good to me. 15. In using the OS, the product uniqueness is good to me.

Facilitating conditions

16. I think this OS has a good supporting platform. 17. I think this OS has good supporting graphical user interface (GUI). 18. I think this OS has a good supporting architecture.

Behavioral intention

19. I have great usage intention on this OS. 20. I have great purchasing intention on this OS. 21. I plan to use the system in the future.

References Aaker, J. L. (1999). The malleable self: The role of self-expression in Persuasion. Journal of Marketing Research, 36(February), 45–57. Belk, R. W. (1988). Possessions and the extended self. Journal of Consumer Research, 15(2), 139–168. Beynon, M. J. (2004). Stability of continuous value discretisation: An application within rough set theory. International Journal of Approximate Reasoning, 35(1), 29–53. Beynon, M. J., & Peel, M. J. (2001). Variable precision rough set theory and data discretisation: An application to corporate failure prediction. Omega, 29(6), 561–576. Bloch, P. H., Brunel, F. F., & Arnold, T. J. (2003). Individual differences in the centrality of visual product aesthetics: concept and measurement. Journal of Consumer Research, 29(4), 551–565. Chen, L.-F., & Chien, C.-F. (2011). Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries. Flexible Services and Manufacturing Journal, 23(3), 263–289. Chien, C.-F., & Chen, L. (2007). Using rough set theory to recruit and retain highpotential talents for semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 20(4), 528–541.

84

C.-F. Chien et al. / Computers & Industrial Engineering 73 (2014) 75–84

Chien, C.-F., & Chen, L. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(1), 280–290. Chien, C.-F., Chen, S., & Lin, Y. (2002). Using bayesian network for fault location on distribution feeder. IEEE Transactions on Power Delivery, 17(13), 785–793. Chien, C.-F., Chen, Y.-J., & Peng, J.-T. (2010). Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. International Journal of Production Economics, 128(2), 496–509. Chien, C.-F., & Hsu, C. (2006). A novel method for determining machine subgroups and backups with an empirical study for semiconductor manufacturing. Journal of Intelligent Manufacturing, 17(4), 429–439. Chien, C.-F., Hsu, C.-Y., & Chen, P.-L. (2013a). Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence. Flexible Services and Manufacturing Journal, 25(3), 367–388. Chien, C.-F., Hsu, S.-C., & Chen, Y.-J. (2013b). A system for online detection and classification of wafer bin map defect patterns for manufacturing intelligence. International Journal of Production Research, 51(8), 2324–2338. Chien, C.-F., Hsu, C.-Y., & Hsiao, C. (2012). Manufacturing intelligence to forecast and reduce semiconductor cycle time. Journal of Intelligent Manufacturing, 23(6), 2281–2294. Chien, C.-F., Wang, W., & Cheng, J. (2007). Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Systems with Applications, 33(1), 192–198. Dimitras, A. I., Slowinski, R., Susmaga, R., & Zopounidia, C. (1999). Business failure prediction using rough sets. European Journal of Operation Research, 114(2), 263–280. Fischer, G. (2001). User modeling in human–computer interaction. User Modeling and User-Adapted Interaction, 11(1–2), 65–86. Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques. Morgan Kaufmann Publishers. Hassenzahl, M., & Tractinsky, N. (2006). User experience – A research agenda. Behavior & Information Technology, 25(2), 91–97. Howes, A., & Young, R. M. (1997). The role of cognitive architecture in modeling the user: Soar’s learning mechanism. Human–Computer Interaction, 12(4), 311– 343. Hsu, S., & Chien, C.-F. (2007). Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing. International Journal of Production Economics, 107(1), 88–103. Hsu, C. Y., Chien, C.-F., Lin, K., & Chien, C. (2010). Data mining for yield enhancement in TFT-LCD manufacturing and an empirical study. Journal of the Chinese Institute of Industrial Engineers, 27(2), 140–156. Huang, K. L., Chen, K. H., & Ho, C. H. (2014). Promoting in-depth reading experience and acceptance. design and assessment of Tablet reading interfaces. Behaviour & Information Technology, 33(6), 606–618. Kaul, A., & Rao, V. R. (1995). Research for product positioning and design decisions. International Journal of Research in Marketing, 12, 293–320. Kobsa (2001). Generic user modeling systems. User Modeling and User-adapted Interaction, 11, 49–63. Kuniavsky, M. (2007). User experience and HCI. In A. Sears & J. A. Jacko (Eds.), The human–computer interaction handbook: fundamentals, evolving technologies, and emerging applications (pp. 897–916). New York: Lawrence Erlbaum Associates Inc. Kuo, C.-J., Chien, C.-F., & Chen, C.-D. (2011). Manufacturing intelligence to exploit the value of production and tool data to reduce cycle time. IEEE Transactions on Automation Science and Engineering, 8(1), 103–111. Lee, S., Lee, J.-H., & Garrett, T. C. (2013). A study of the attitude toward convergent products: a focus on the consumer perception of functionalities. Journal of Product Innovation Management, 30(1), 123–135.

Liu, C.-W., & Chien, C.-F. (2013). An intelligent system for wafer bin map defect diagnosis: An empirical study for semiconductor manufacturing. Engineering Applications of Artificial Intelligence, 26(5–6), 1479–1486. Liu, H., Hussain, F., Tan, C., & Dash, M. (2002). Discretization: An enabling technique. Data Mining and Knowledge Discovery, 6(4), 393–423. Lin, F., & Ye, W. (2009). Operating System Battle in the Ecosystem of Smartphone Industry. In 2009 International Symposium on Information Engineering and Electronic Commerce (IEEC ’09), pp. 617–621, Los Alamitos: IEEE. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297. Magro, M. C., & Pinceti, P. (2009). A confirmation technique for predictive maintenance using the rough set theory. Computers & Industrial Engineering, 56(4), 1319–1327. Mak, B., & Munakata, T. (2002). Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3. European Journal of Operational Research, 136(1), 212–229. Malär, L., Krohmer, H., Hoyer, W. D., & Nyffenegger, B. (2011). Emotional Brand Attachment and Brand Personality: The Relative Importance of the Actual and the Ideal Self. Journal of Marketing, 75(4), 35–52. Park, J., Han, S. H., Kim, H. K., Oh, S., & Moon, H. (2013). Modeling user experience. A case study on a mobile device. International Journal of Industrial Ergonomics, 43(2), 187–196. Partala, T., & Kallinen, A. (2012). Understanding the most satisfying and unsatisfying user experiences: Emotions, psychological needs, and context. Interacting with Computers, 24(1), 25–34. Pawlak, Z. (1982). Rough Sets. International Journal of Computer and Information Sciences, 11(5), 341–356. Pawlak, Z. (1997). Rough set approach to knowledge-based decision support. European Journal of Operation Research, 99(1), 48–57. Pawlak, Z. (2002). Rough sets, decision algorithms and Bayes’ theorem. European Journal of Operation Research, 136(1), 181–189. Pawlak, Z., & Andrzej, S. (2007). Rough sets and Boolean reasoning. Information Sciences, 177(1), 41–73. Peng, J., Chien, C., & Tseng, B. (2004). Rough set theory for data mining for fault diagnosis on distribution feeder. IEE Proceedings-Generation, Transmission, and Distributions, 151(6), 689–697. Shi, F., Sun, S., & Xu, J. (2012). Employing rough sets and association rule mining in KANSEI knowledge extraction. Information Sciences, 196, 118–128. Shyng, J. Y., Wang, F. K., Tzeng, G. H., & Wu, K. S. (2007). Rough set theory in analyzing the attributes of combination values for the insurance market. Expert Systems with Applications, 32(1), 56–64. Sundar, S. S., Bellur, S., Oh, J., Xu, Q., & Jia, H. (2014). User experience of on-screen interaction techniques: An experimental investigation of clicking, sliding, zooming, hovering, dragging, and flipping. Human-Computer Interaction, 29(2), 109–152. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. Yang, H., Han, S. H., & Park, J. (2010). User interface metaphors for a PDA operating system. International Journal of Industrial Ergonomics, 40(5), 517–529. Yang, C. C., & Shieh, M. D. (2010). A support vector regression based prediction model of affective responses for product form design. Computers & Industrial Engineering, 59(4), 682–689. Zhai, L. Y., Khoo, L. P., & Zhong, Z. W. (2009). A rough set based decision support approach to improving consumer affective satisfaction in product design. International Journal of Industrial Ergonomics, 39(2), 295–302. Zhong, N., Dong, J., & Ohsuga, S. (2001). Using rough sets with heuristics for features selection. Journal of Intelligent Information Systems, 16(3), 199–214.