Cultural Dimensioning of Websites Using Neural

This special assignment was completed in Aalto University Finland under supervision of prof. Timo O. Korhonen in 2012 Cultural Dimensioning of Websites Using Neural Networks Xiaowen Li 244837 S-72.3120 Special Project in Communications 2011 summer & autumn Keywords: cultural dimensioning, service design, cultural service localization, glocal services Abstract Culture based website studies are attracting increasing attention due to heavy globalization and increased diversity of Internet usage. For instance, trade, education, data storage and recovery and communication are reaching new international platforms and forms of service interactions in general. Also, interactions using developing social media are becoming very important. For instance, over 20% of all Internet time is spend with social media in US. There is a heavy international service design development going on over increasing variety of demographics both for individuals and organizations. Relating user-experience issues are now recognized to be a complex matter of culture, technology and economics that needs to be systematically reflected in relating user interfaces and service concepts. In this paper we focus on a website user interface cultural dimensioning taking a user-centric point of view. In practice, we can hence create for instance company websites that offer culturally tailored user experience both for home and foreign users. This enables designers to recognize, choose and adjust website elements to fit to the targeted cultures. An important objective is to investigate if better communication between global and local (glocal) stakeholders could be obtained by a user interface design taking cultural factors systematically into account. Our case study focuses on cultural parameterization of bank websites in Finland and China. The relating mapping is realized by cultural parameterization and extrapolation that is realized by a feed forward neural network. For our knowledge this is first study where cultural parameterization and resulting cultural service localization is investigated by neural networks. Our method opens up new frontiers in i) identification of cultural factors, ii) understanding their meaning and iii) helping web service and user interface designers to create websites where cultural factors are systematically addressed. 1. Background Culture, that reflects itself both in context and functions, affects likely almost every aspect of human life. Cultural Dimensions that are societal, psychological, individual and non-material dimensions can be used to describe a specific culture. Nowadays cultural aspects in user-experience are attracting attention due to increased user diversity and increased need to improve recognition and adaptation to user requirements. Users come with increasing variety of demographics and individual needs. Culturally rooted user interface and service development offers ways to identify, understand, and measure culturally influenced factors. We propose that in realizing a truly applicable business 1

case, user experience based development using culturally based planning is at least as important as the relating strategic and technologic factors. The culture-based design can improve service design in content, service concept, user interface, and brand selling. In this new age of globalization and localization (glocalization), modern technology and commerce enable global distribution of products and services. Successful organizations nowadays start to realize that they need to understand and address the needs of a culturally diverse user base to adapt to the local market. This business challenge of taking into account culture and users in service concepts and user interfaces define the goal characteristics of service design that rely on both market and user awareness. This assists also adaptation to target market helping to achieve both short-term and long-term success with well-recognized business objectives. Our important message is that Website design should be seen as a cultural artifact. Culture based website studies are attracting attention due to heavy globalization reflecting in increased user interactions and diversity of Internet usage for instance for trade, education, and general purpose data recovery and communication. Also, social interactions using fast developing social media are becoming very important. Therefore, there is a tendency for service development over increasing variety of demographics, as well as in individual and organizational needs. Cultural parameterization of websites is a relatively seldom addressed research topic still it can greatly help the web designers to develop more creative and customer tailored websites giving substantial benefits for all parties involved. This paper will introduce an integrated view and respective methodology for designing websites where cultural factors are accounted for meeting the user expectations. This is based on well-established cultural dimensioning methods that we will address here. 2. Theory 2.1 Culture Dimensions So far, literature lacks some consensus in a comprehensive definition of culture. The various definitions cover a large range of aspects and depend strongly on author’s focus and personal preferences. Geerd Hofstede defines culture as “the collective programming of the mind distinguishing the members of one group or category, of people from another” (Hofstede G. , 2011). Another definition approaches culture as “patterns of thinking, feeling, and acting that influence how people communicate with each other and with computers” (Ford, 2003). One way to understand cultures may be found not by trying to develop a universal definition but by structuring elements of cultures into smaller pieces called as “cultural dimensions”. Cultural dimensioning has been developed by Edward Hall, Florence Kluckhohn, Shalom Schwartz, Fons Trompennaars, and Geert Hofstede, among others (Hofstede G. , 1993), (Schwartz, 2004), (Trompenaars, 1998), (Kluchhohn, 1953), (Hall, 1959). Many of these studies have been quite extensive. Especially, Hofstede’s dimensioning studies have covered up to 72 nationalities, 38 occupations, 20 languages, and addressed all in all about 116,000 people. He ended up to five dimensions, namely Power Distance (PDI), Individualism vs. Collectivism (IDV), Femininity vs. Masculinity (MAS), Uncertainty Avoidance (UAI), and Long-Term Orientation (LTO). Fig. 1 2

depicts an example of these cultural indexes for Finland and China (Respective definitions of cultural definitions are discussed in Appendix I). Currently, the most extensive cultural research institute is World Values Survey that in collaboration with EVS (European Values Study) has carried out representative national surveys in 97 societies containing almost 90 percent of the world’s population (World Values Survey, 2012).

120 100 80 Finland

60

China 40 20 0 PDI

IDV

MAS

UAI

LTO

Figure 1: Cultural Indexes for Finland and China following Geert Hofstede’s Cultural Dimensions, (Itim International, Geert Hofstede Cultural Dimensions), PDI: Power Distance; IDV Individualism vs. Collectivism; MAS: Femininity vs. Masculinity; UAI: Uncertainty Avoidance; LTO: Long-Term Orientation In order to focus especially to usability and user interface issues we are going now to follow cultural dimensioning framework investigated by Valentina-Johanna Baumgartner’s in her MSc study in 2003 (Baumgartner, 2003), where 29 Culture Dimensions in 9 sources from 11 authors were presented and ranked by 57 user interface design experts who came from 21 nationalities. Following scale was used in evaluation: “very important” (4), “important” (3), “not sure” (2), “not very important” (1), and “not important” (0) (Table I). Table I: The ranked importance of 29 cultural dimensions (Baumgartner, 2003) Rank Ave. Cultural Dimensions 3,73 Context 01 02

3,30

03 04

3,21 3.18

05

3.14

Environment and Technology Uncertainty Avoidance Technological Development Time perception

Original Defined CD Context Contexting Language, Environment and Technology Uncertainty Avoidance Rate of Technological Development Polychronic or Monochronic Time 3

Author Hall Victor Victor Hofstede Wright Hall

06 07 08

2,86 2.80 2.75

Authority Conception Affective vs. Neutral Space

09 10

2.73 2.73

Face-saving Activity orientation

11

2.71

12 13

2.66 2.66

14

2.55

15

2.45

16

2.43

17

2.43

18

2.41

19 20 21

2.34 2.25 2.11

22 23 24 25

2.07 2.07 1.91 1.84

Nonverbal Communication Specific vs. Diffuse Individualism vs. Collectivism

Instrumental vs. Expressive Time orientation

Long-term vs. Short-term orientation Universalism vs. Particularism International Trade and Communication Gender roles Meaning of life Achievement vs. Ascription Power distance Property Economic progress Internal vs. external control

Sequential vs. Synchronic Cultures Conceptions of Time Authority Conception Affective vs. Neutral Space Space Orientation Face-Saving Human Action Doing vs. Being Nonverbal Communication Specific vs. Diffuse Cultures Individualism vs. Collectivism Relationships to Others The Relationship of People to Their World Instrumental vs. Expressive Time

Trompenaars Victor Victor, Condon&Yousef Parsons, Trompenaars Hall Adler Victor Kluckhohn & Strodtbeck, Condon & Yousef Adler Victor Parsons, Trompenaars Hofstede, Trompenaars, Condon & Yousef Kluckhohn & Strodtbeck Adler Parsons

Kluckhohn &Strodtbeck, Condon & Yousef Adler time Hofstede

Time orientation Long vs. short orientation Time Universalism vs. Particularism Rate of Development of International Trade and Communication Femininity vs. Masculinity Meaning of life Achievement vs. Ascription Power distance Property Rate of Economic Progress Internal vs. external control Relationship to Nature The Relationship of People to Their World 4

Kluckhohn & Strodtbeck Parsons, Trompenaars Wright

Hofstede Condon & Yousef Parsons,Trompenaars Hofstede Condon & Yousef Wright Trompenaars Kluckhohn & Strodtbeck Adler

26 27 28

1.71 1.68 1.59

29

1.45

Resources Degree of power Human nature orientation Political decentralization

Resources Degree of Power Human nature orientation The Nature of the Individual Rate of Political Decentralization

Wright Wright Kluckhohn & Strodtbeck Adler Wright

We will return later in our text to discuss effects of these cultural dimensions on website and user experience design. 2.2 Neural Networks Interactions among neurons within the brain are the causes of some body activities and also vice versa. Certain activities lead to the firing of a certain set of neurons. The connections between some neurons strengthen when activities are repeated, which is at least part of the dynamics behind formation of memory. Neural network theory is a set of bio-inspired technical theories aiming to utilize technical arrangement of artificial “neurons” to support signal processing tasks such as estimation, interpolation, extrapolation and recognition of similarities within data sets (correlation analysis or pattern recognition). A simple overview of neural networks is given by (Siganos, 1996). Generally these networks are called as artificial neural networks (ANNs). For instance, single-layer, feed-forward network is linear, with single direction logic and has only one hidden layer as the one shown in Fig 2. Multilayer networks contain multiple internal processing units. ANNs can also have feedback loops that can give rise to stability problems and are therefore not as common as feed forward networks working without internal feedback.

Figure 2: A simple feed forward – type ANN 5

Figure 3: Basic components surrounding a biological neural cell and the respective ANN So, ANN is formed of interconnected, weighted parallel elements modeling axons and dendrites and addition element modeling the neural network, Fig 3, (Siganos, 1996). In some practical application, ANN needs to be first trained by a large data set (desired input/output variables) to “understand” nature of the data at hand. This sets the weighting coefficients of the network. After this, the ANN can be used for instance to estimate behavior of some multi-dimensional target function also in some unknown area, eg with inputs that were not used for training the network. Generally neural networks work better, the larger the number of weights, and the bigger the training file. A neural network can be defined as a mapping between input and output sets X and Y: [9] 𝑓: 𝑋 → 𝑌, where 𝑓(𝑥) is a composition of functions 𝑔𝑖 (𝑥) that determine for instance convergence properties of the network. For a weighted sum: 𝑓(𝑥) = 𝐾(∑𝑖 𝑤𝑖 𝑔𝑖 (𝑥)). Where K is the activation function and 𝑔𝑖 is the vector denoting the response of the network: 𝑔 = (𝑔1 , 𝑔2 , … , 𝑔𝑛 ). During the learning process, the cost function is defined as 𝐶: 𝐹 → 𝑅, the optimal solution 𝑓 ∗ is defined as: 𝐶(𝑓 ∗ ) ≤ 𝐶(𝑓), ∀𝑓 ∈ 𝐹, which means the cost of optimal solution is the least cost among all the sub optimal solutions. 2.3 Realization of Neural Networks in Matlab Figure 4 shows the mechanism of Matlab Neural Network Toolbox.

Figure 4: Mechanism of Matlab Neural Network Toolbox [13] Let us now discuss the details as applied in our study using Matlab Neural Networks fitting tool (Neural Network Toolbox 7 User’s Guide, 2011): 6

1. Input Data: Import the fitting problems data into the input matrix X and the target matrix T. % [x,t] = to_be_done_dataset 2. Create the neural network (NN): NN will figure out the relationship between the input dataset and target dataset and learn to estimate median values. If the hidden layer is given enough neurons, two-layer forward neural networks can fit any input-output relationship. 3. Train the network: The samples will be automatically divided into training, validation and test sets randomly. The training set is used to teach the network. Training is done when the network goes through all the validation set. The test set aims at providing a completely independent measure of network accuracy.[net,tr] = train(net,x,t); 4. Testing the Neural Network: Performance is measured in terms of mean squared error, and shown in log scale. It decreases rapidly as the network is trained. The mean squared error of the trained neural network can be measured by the testing samples. The respective Matlab script runs as follows: 5. outputs = net ( inputs ); 6. errors = gsubtract ( targets, output); 7. performance = perform ( net, targets, outputs) 8. The regression plot shows the actual network outputs plotted in terms of the associated target values. While the error histogram will show how the magnitudes error are distributed. 9. Evaluate the network: If the network’s performance is not good enough, a possible choice is to train the network again or to increase the number of the neurons or get a larger training data set. Generally, these methods can help increase the performance quite a lot. If you are satisfied with the performance, you can save the network and use it to do the prediction by inputting the new data. The respective Matlab command in our case would be: Pred=net(new input);

2

Method

3.1 Mapping In this paper, targeted user interface design refers especially to culture associated aesthetical elements. These are denoted by categorized cultural indicators (or ’signs’) convoying cultural meanings matching to the culture that the design was made for. This has been earlier addressed by A. Marcus reporting a mobile phone design based on Hoefstead’s cultural dimensioning demonstrated in the annual IBM New Paradigms in Computing conference held in July 2002 (Marcus, 2003). However, details of the design process were not reported. In this paper we are suggesting six typical visual elements for website design to be used as cultural attractors: Structure, Text, Image, Color, Navigation and Multimedia Elements. They together create a ‘look and feel’ to match the cultural expectations of the users. The first step of automated cultural dimensioning of websites using neural networks is to map the website features into the cultural dimensions. Table III defined 16 specific webpage features which represent some visual characteristics of some target websites in the order of importance based on subjects’ personal preferences. These, on the other hand, are based on preliminary division of user interface into five design components which are metaphor, mental model, navigation, interaction, 7

and appearance.” (Baumgartner, 2003)). In our paper we follow a model where service design is composed to i) service concept (or service idea) ii) user interface and iii) business logic. We assume that they all are culturally connected. In this paper we categorize user interface first in general terms of Table I. Then we ask our subjects to rank the sites with respect of aesthetical qualifiers: ” Structure, Text, Image, Color, Navigation and Multimedia Elements.” In addition, we asked the tested pages to be ranked in their primary function, e.g. if they serve a clear purpose and how well in the scale from 0 – “creates disturbing contradictions” to 4 – “greatly helps in passing the message”. Table II – Rating Websites, Survey Design Information content Amount of text Colorfulness Color temperature Image complexity

The amount of information the page contains, Less or More (0 to 100) The Percentage of text in the page, regardless of images, margins, background Degree of colorfulness, Colorless or Colorful (0 to 100) The color temperature, Warm or Cold (0 to 100) Whether the images in the page are Simple/Plain or Complex (0 to 100)

Second, the visual features of the websites are mapped to corresponding supporting cultural dimensions. Let us now consider how some of the respective cultural dimensions reflect themselves into aesthetical features of the web pages: • Uncertainty Avoidance defines the extent to which people feel threatened by uncertain or unknown situations (Hofstede's Cultural Dimensions, Understanding workplace values around the world). Websites with high Uncertain Avoidance use more text and images and clear navigations and clicking steps to demonstrate the explicit massages. However, websites with low Uncertain Avoidance use a more implicit way to imply the massage, which demands a certain pre-knowledge of the situation and the possible results. The concept of high-low context is overlapped here. • The page distribution reflects the structure of the website which can be described as centralized or decentralized. More centralized websites convey farther Power Distance in comparison. • Neutral vs. Affective means whether to display the emotions or not. More affective websites will use more colorful and complicated images, navigations, backgrounds and multimedia elements to express more information with emotions. • Masculinity vs. Feminity reflects on the color temperature, an example might be, young boys prefer black, dark blue or green while girls prefer softer and brighter colors like pink or yellow. • Hommization Element can be treated as a reflection of Individualism vs. Collectivism. On the website for more collectivistic cultures, pictures of joyful human faces or groups of people are always presented to imply that the product is attractive. The definitions of the cultural dimensions that are included in this study are presented in Appendix I. Most of the website features fit the cultural dimension theories very well. However, there still exist some features that need to be considered separately on both cross-culture and cross-industry conditions. In same culture domain, in some certain industries the culture feature is not obvious and even opposite as it should be. (E.g. The front page of Chinese banks contains more information than 8

Finnish banks, opposite to Low-Context and High-Context values for the two countries) While in different cultures but in the same industry it shows a large amount of similarity. (e.g. the structure of the page distribution, the color usage and navigation layout are quite alike.) Considering the reasons above, we should choose the representative dimensions based on the aspect of both the culture and industry. Table III: Mapping from website features to the cultural dimensions(inspired by reference [14]) Website Feature 1. Information Content 2. Page Distribution

Short ICT

3. Text Scale

TS

4. Image Scale

IS

5. Colorfulness

CN

6. Color Temperature 7. Image Complexity 8. Background

CT

9. Navigation Diversity 10. Media Element

NG

PD

IC BG

ME

11. Cultural Element CE HE 12. Hommization Element BC 13. Business Coverage Power 14. Steps Complexity SC

15. Step Storage

SS

Description The amount of information the page contains, less or more Divergent or Distributed Centralized (Hierarchical)Decentralized (Flatter Hierarchies) The Percentage of Text in the page, regardless of images, margins, backgrounds. The Percentage of Images in the page, regardless of text, margins, backgrounds. Degree of colorfulness (how many and what’s the scale of the colors) Colors’ property from Cold to Warm

Cultural Dimensions LowContext vs. High-Context Power Distance

Simple - Complex Static- Dynamic Colorful/ Use of Brevity-Complexity Brevity-Complexity

Neutral vs. Affective pictures

Neutral vs. Affective Masculinity vs. Feminity

, Neutral vs. Affective

Music, Video, Ads, Banners(Graphical text), Detail Decoration, Plain- Colorful Traditional or typical Cultural Element, Few- Many Human faces, human pictures etc bringing in humanistic touch Range of services, Narrow -Wide (different industries / same industry ) Amount of steps needed in order to get to the target sub-page, Few-Many Saving of the username/key and the 9

Uncertainty Avoidance LowContext vs. High-Context Uncertainty Avoidance

Neutral vs. Affective Uncertainty Avoidance Neutral vs. Affective

Long vs. short term orientation Individualism vs. Collectivism Polychronic vs. Monochronic Time Uncertainty Avoidance

Time Orientation

16. Clause Confirmation

CC

favorite links; Availability of shortcuts Pop up of the Clause Confirmation (I Specific vs. Diffuse agree to…) , Few- Many

3.2 Parameterization In order to automate cultural dimensioning of websites, website features need to be parameterized to fit in the tool such as NN and SOM. Inspired by Geert Hofstede’s study which attributed values of cultural dimensions for different cultures, parameterization for the website features is performed as a combination of both automatic and/or manual methods. The features include diverse aspects on multi-level variables like text recognition, pattern recognition, color recognition and so on. For the objective features like information content, page distribution, text scale, image scale, color temperature and Hommization Element (Table III, section 12), tools and algorithms can be used to parameterize automatically. However, for more subjective features like image complexity, colorfulness, and navigation diversity and so on, the opinions may vary between different individuals. Surveys can be done for the target users to parameterize those features in order to fulfill the design requirements for the specific users – a feature that would be useful for web site designers. In addition, there are two kinds of distributions of values: continuous distribution and discrete distribution. For example, the Text Scale and Image Scale can be treated as continuous distribution within the scale range from 0% to 100%. However, Hommization Element can be treated as discrete distribution since it can be judged from how many human images are contained. On this point binary system and multi-system can be imported to deal with the distributions and to fit the simulation tools. One example of the parameterization by manual work on bank sites will be presented in part 4 Case Study. 3.3 Simulation Neural Network Toolbox After the website features be parameterized, datasets can be input into the NNet fitting tool to do the simulation and prediction. By using the Matlab neural networks toolbox, the network can be trained to fit the inner connections of the target website features. Thus the network is trained into a predictor of website design which can deal with both within culture and cross culture website prediction. For example, if N samples of websites with M features to be analyzed, the network can be trained to fit the function of the data connections. When M-n features of the target website are already known, the trained network can be used to predict the rest of its n features. In this way, the neural networks give the designer useful suggestions about how to set the values of the websites features. Thus the degree of how the culture 10

reflects on the design can be analyzed. In addition, the network can also fit the functions of websites from two cultures, thus it can be treated as a cross-culture transformer. 4

Case Study: Finland and China Bank Pages

In the case study, the most famous Finnish and Chinese banks’ home pages are taken as examples to carry out our method and to prove our system. 4.1 Mapping For bank industry, most of the sites choose to use cold colors. Also, another similar phenomenon is that blank areas are popularly used as the backgrounds. According to author’s study for all the homepages of the banks sites, the following 7 elements in Table IV, which can well represent the most important but different aspects of bank websites in Finnish and Chinese cultures, are chosen from the 16 website features listed above. Table IV: Mapping from cultural dimensions to website features for Bank Websites Website Feature Short Information Content ICT Page Distribution Text Scale Image Scale Colorfulness Image Complexity

PD TS IS CN IC

Navigation Diversity

NG

Index Brevity-Complexity Centralized - Decentralized Percentage Percentage Uncolorful -colorful Simple - Complex Static- Dynamic Brevity-Complexity

Cultural Dimensions Low- Context vs. High-Context Power Distance Uncertainty Avoidance Uncertainty Avoidance Neutral vs. Affective Neutral vs. Affective Neutral vs. Affective Uncertainty Avoidance

4.2 Parameterization The idea is based on manual work in which our own standards are set to estimate the bank sites features. In order to prove this manual work makes sense, a questionnaire was carried out in the Cross-Culture Management class in Aalto University among 55 students from all five continents and 17 different countries. The average estimation values for five website features of the Nordea Bank Finland, Bank of Communications China, Agriculture Bank of China are listed in Table V. Table V: Survey result of three banks Nordea Bank Finland ICT TS CN CT 28 Survey 48 46 30 Banks

IC 35

Bank of Communications ICT TS CN CT IC 67 60 35 33 50 11

Agriculture Bank of China ICT TS CN CT IC 81 75 69 54 75

10 30 70 70 35 35 50 85 70 80 60 80 Author 45 55 20 ICT: Information Content; TS: Text Scale; CN: Colorfulness; CT: Color Temperature; IC: Image complexity As seen from the average value estimation by all the survey participants for the 5 website feature demotions, the result is very close to the manual estimation by the author. Therefore, the author continues to apply the value estimation for the 7 chosen features of the Finnish and Chinese bank pages based on her own judgment. Table VI shows the index made by the author for the chosen elements of the homepages: Table VI: Index of website features for Bank Website (evaluated by the Author) Banks • Finland Nordea Bank Finland PLC OP-Pohjola Group Sampo Bank PLC Aktia Savings Bank PLC Tapiola Bank LTD Handelsbanken Säästöpankki POP Bank Group Bank of Åland PLC Citibank International PLC Danske Bank A/S DnB NOR Bank, Helsinki Branch SEB Group Suomen Asuntohypopankki LTD • China Bank of China Agricultural Bank of China Bank of Communications China CITIC Bank China Construction Bank China Development Bank Exim Bank of China Hua Xia Bank Industrial and Commercial Bank of China People's Bank of China Xiamen International Bank Postal Savings Bank of China

Short

ICT

PD

TS

IS

CN

IC

NG

Nordea Pohjola Sampo Aktia Tapiola Handels Säästö POP Åland Citi Danske DnB SEB SAB

45 30 20 40 20 15 40 45 35 35 35 40 40 40

35 35 60 25 40 60 25 35 40 30 35 50 35 30

55 50 40 65 75 70 45 45 75 85 80 45 60 50

45 50 60 35 25 30 55 55 25 15 20 55 40 50

20 40 20 30 35 20 45 45 20 25 20 30 35 40

30 45 35 30 35 20 50 50 20 30 25 50 30 45

10 60 10 10 10 10 40 40 10 10 10 60 10 40

BOC Agricultural Communications CITIC Construction Development Exim Hua Xia I&C

75 85 70 45 45 45 55 75 95

35 35 35 35 35 35 35 35 20

90 70 70 60 70 75 75 80 70

10 30 30 40 30 25 30 20 30

30 80 35 80 60 60 60 75 80

25 80 50 85 60 60 55 70 90

20 70 40 60 40 20 40 40 70

People Xiamen Postal

75 40 45

20 45 35

85 50 55

15 50 45

60 75 80

35 80 85

20 50 60

12

4.3 Results and Discussions 4.3.1

Within culture design: Finnish bank site mapping and simulation.

By using the Matlab Neural Network fitting tool, within culture simulation can be realized. A set of sample data will be trained in the fitting tool so that a network is achieved to compose a fitting function for the connections of the multi-dimension data. In this way, when the incomplete target data is imported, the network can output the prediction of its default values of the missing dimensions. Figure 8 shows the simulation result of the Finnish bank site mapping.

Figure 5: Within culture simulation result: Finnish bank site mapping. Purpose: Train the network into a designer of how to map PD, TS, IS, CN, IC, NG to ICT. Input: PD, TS, IS, CN, IC, NG of 13 Finnish banks. Target: ICT of these 13 banks. There are 9 training samples, 2 validation samples and 2 testing samples. Figures (a) show the result when number of hidden Neurons=10; Figures (b) show the result when number of hidden Neurons=30. Prediction Results: Input: Nordea Bank site’s PD, TS, IS, CN, IC, NG Output: Nordea Bank site’s ICT (The reference ICT=45) number of hidden Neurons=10, ICT= 38.5056; number of hidden Neurons=30, ICT= 42.6851; Figure (a1) to (a4) show the regressions of the trained network for the training samples, validation samples, testing samples and all the samples. As it shown in the figure, there were only 2 samples for 13

validation and testing so that the slope of the line is hardly close to 1. In addition, in NNet fitting tool, large amount of samples is required for ensure the accuracy of the network. 13 sets of data will result in a very random training network. This is proved when the network is retrained, the result of training R varies a lot and even unreasonable negative results appeared sometimes. However, reasonable regressions still appeared most of the time and we can use the network from the reasonable simulations to do the prediction. The network was saved and the Nordea Bank’s PD, TS, IS, CN, IC, NG were input. The respective Matlab script runs like: ICT= net(PD, TS, IS, CN, IC, NG). The prediction goes quite well and it gives very reasonable suggestion which is very close to the original data. In addition, when number of hidden neurons is raised to 30, the training result is more reliable and better. The probability of the reasonable result increased. (Negative R is less) The prediction result is also better in this case. 4.3.2

Cross culture design: Finnish bank sites to Chinese bank sites simulation and mapping.

In addition, by using the Matlab Neural Network fitting tool, cross culture simulation can also be realized. In this case, the input data set will be the values of the original culture websites features (Finland), while the output is the target culture (China). The data will be trained in the fitting tool so that a network is achieved to compose a fitting function to map from input to output according to the connections of the multi-dimension data. In this way, the trained network can be used as a transformer to predict the result based on the input of a new set of target data. Figure 9 shows the simulation result of the Finnish bank sites to Chinese bank sites mapping.

Figure 6: Cross culture simulation result: Finnish bank sites to Chinese bank sites mapping. Purpose: Train the network into a transformer of how to map ICT, PD, TS, IS, CN, IC, NG from 14

Finnish banks to Chinese banks. Input: ICT, PD, TS, IS, CN, IC, NG of the 12 Finnish banks. Target: ICT, PD, TS, IS, CN, IC, NG of the 12 Chinese banks. There are 8 training samples, 2 validation samples and 2 testing samples. Figures (c) show the result when number of hidden Neurons=10; Figures (d) show the result when number of hidden Neurons=30. Prediction Result: Input: Nordea Bank’s value for ICT, PD, TS, IS, CN, IC, NG are 45, 35, 55, 20, 30, 10. Output: Corresponding Chinese bank website design suggestion: number of hidden Neurons=10: 52.8679, 26.0045, 61.6305, 53.8595, 68.1943, 32.1837, 39.2195 number of hidden Neurons=30: 75.0500, 34.3235, 89.4770, 9.2129, 30.1902, 25.7649, 22.3367 Figure (c1) to (c4) show the regressions of the trained network for the training samples, validation samples, testing samples and all the samples. The problem of too small sample number still exists. when the network is retrained, the Taining R still turns out to be quite random and negative R appears now and then. However, reasonable trained networks can still be used in prediction, The network was saved and the Nordea Bank’s ICT, PD, TS, IS, CN, IC, NG were input. The respective Matlab script runs like: Pre= net(ICT, PD, TS, IS, CN, IC, NG). The prediction results are mostly reasonable but sometimes there exist negative values or values over 100. This might because the embedded functions in Matlab NNet fitting tool are not suitable enough for the multi- dimension prediction. Likewise, as shown in Figure (d), when number of hidden neurons is raised to 30, the training and prediction results are more reliable and better. 5

A process model of culture-based website design

Having explored some key theories and case studies within cultural based website design, a process model is now sought to be establishes as a framework for the developing, in which mythologies are defined as representative of the sequence of stages. Thus, a hierarchical structure can be refined: Mapping, parameterization, simulation and applying the results. Figure 11 shows the model of the processing flow. First, when mapping from cultural dimensions to website features, a basic study of the cultural dimension theories is required. In addition, the website features, including the 16 examples that the author reasoned above together with other important case-based features, should be involved. The mapping may not be one to one correspondence since one cultural dimension may reflect different website features while one websites features may convey different cultural characteristics. However, exceptions may exist due to the diversity of different industries and particularity of a single case. Second, the website features will be parameterized with ether tools or in manual methods. With the some tools like programs or algorisms in data mining, pattern recognitions, website features can be scored. Third, the values of the website features can be used in simulation tools like NNet to analyze the inner-similarities of the features, thus to accomplish the prediction or to compose a self-organizing map. Finally, the results can be analyzed and applied into practice. In addition, in this way, the results can contribute to the user interface and user interaction design in order to accomplish a user-friendly design.

15

The framework is a practical and creative method in culture-based design can be not only applied into website design, instead, it can be used into every field of culture-based design, like T-shirt design or poster design and so on, regardless of the industry differences.

Figure 11: Hierarchical model of culture-based website design 6

Conclusion

Multicultural website design asks for a greater understanding of glocalization. Cross-cultural usability aims at enabling real-time communication and interaction between a glocal website and stakeholders. In this study, two kinds of Neural Network based simulations and predictions were applied after the parameterization of sample web sites cultural design features. The network is then used for helping the website designers to properly apply the cultural elements to their designs for the target users. Some main Finnish and Chinese banks’ home pages are used as a case study. Within this paper, a new and practical framework is raised: Mapping the theory – cultural dimensions to the visual elements – website features. It is followed by parameterizing the website features with either tools or manual methods. In the end, use the NNet tools to simulate and predict. It turned out to be a feasible and creative way to do the cross-culture mapping, which will help the designers to choose or adjust the website elements to fit the target culture background. Also, this is a concept which can be carried out in within-culture design, cross-industry design and in many other product and service designs. 16

Future challenges of our research include 1) apply more tools and survey studies to realize website features’ parameterization in order to automate larger scale realization.; 2) increasing sample number for the NNet fitting tool to improve neural network training accuracy. A further verification should be obtained between perceptual designs versus cultural dimensioning that would then reflect in overall accuracy and orthogonality of parameterization.

17

Appendix I: Definitions of the Cultural Dimensions included in the study [4] [7] [15] [16] • Power distance (Geert Hofstede)[4] is the degree of inequality that exists – and is accepted – among people with and without power. Higher PD indicates that society accepts an unequal distribution of power and people understand "their place" in the system. Lower PD means that power is shared and well dispersed. • Individualism vs. Collectivism refers to the strength of the ties people have to others within the community. Individualism indicates a loose connection with people which means a lack of interpersonal connection and little sharing of responsibility, beyond family and perhaps a few close friends. Collectivism society would have strong group cohesion, and there would be a large amount of loyalty and respect for members of the group. The group itself is also larger and people take more responsibility for each other's well being. • Masculinity is described as pertaining to societies in which social gender roles are clearly distinct. Femininity refers to how much a society sticks with, and values, traditional male and female roles. In masculinity culture, men are expected to be tough, to be the provider, to be assertive and to be strong. In femininity culture, the roles are simply blurred. You see women and men working together equally across many professions. Men are allowed to be sensitive and women can work hard for professional success. • Uncertainty avoidance can be defined as the degree of anxiety society members feel when in uncertain or unknown situations. High UAI cultures try to avoid ambiguous situations whenever possible. They are governed by rules and order and they seek a collective "truth". Low UAI cultures indicate the society enjoys novel events and values differences. There are very few rules and people are encouraged to discover their own truth. • Long Term Orientation refers to how much society values long-standing. Delivering on social obligations and avoiding "loss of face" are considered very important. As opposed, Short Term Orientation, stands for the fostering of virtues related to the past and present, in particular, respect for tradition, preservation of ‘face’ and fulfilling social obligations. • Low- Context vs. High-Context (Edward T. Hall)[15] Context is the ‘information that surrounds an event’. In a low-context, low levels of programmed information are used to provide context; therefore, a large amount of explicit information must be present to specify meaning. In a high-context, a high amount of programmed information is used to provide context; therefore, more time is required to program and to abstract meaning from the given set of information. • Polychronic vs. Monochronic Time (Edward T. Hall)[16]The monochronic time concept follows the notion of “one thing at a time”, while the polychronic concept focuses on multiple tasks being handled at one time, and time is subordinate to interpersonal relations. • Neutral vs. Affective (Fons Trompenaars) [7] Affective cultures believe that all relationships with others are human affairs and that people should express their feelings openly – therefore reactions are shown immediately verbally and/or non-verbally by using mimic and gesture in form of body signals. Neutral cultures think that the nature of their relationships with others should be objective and detached; they believe that emotions confuse the issues – members of neutral societies tend to hide their emotions and don't show them in public. • Specific vs. Diffuse measures how far people get involved with other's life space. Specific cultures believe relationships with others should be explicit, delineated and regulated as in a contract. Diffuse cultures emphasize the real and personal contact of the whole person in a relationship. 18

Appendix II: Bank List and Homepages involved in the study Bank Homepage Nordea Bank Finland PLC http://www.nordea.fi/ OP-Pohjola Group https://www.op.fi/ Sampo Bank PLC http://www.sampopankki.fi/ Aktia Savings Bank PLC http://www.aktia.fi/ Tapiola Bank LTD http://www.tapiola.fi/ Handelsbanken http://www.handelsbanken.fi/ Säästöpankki https://www.saastopankki.fi/ POP Bank Group https://www.poppankki.fi/ Bank of Åland PLC http://www.alandsbanken.fi/ Citibank International PLC http://www.citigroup.com/citi/global/fin.htm Danske Bank A/S http://www.danskebank.com/ DnB NOR Bank, Helsinki Branch https://www.dnb.no/en/corporate/ SEB Group http://www.seb.fi/pow/wcp/suomi.asp Suomen Asuntohypopankki LTD http://www.hypo.fi/etusivu Bank of China www.boc.cn Agricultural Bank of China www.abchina.com Bank of Communications www.bankcomm.com China CITIC Bank http://www.ecitic.com/bank/ China Construction Bank www.ccb.com China Development Bank www.cdb.com.cn Exim Bank of China www.eximbank.gov.cn Hua Xia Bank http://www.hxb.com.cn/chinese/index.html Industrial and Commercial Bank of http://www.icbc.com.cn/icbc/default.htm China Different layout for Chinese and English pages People's Bank of China www.pbc.gov.cn Xiamen International Bank www.xib.com.cn/ Postal Savings Bank of China http://www.psbc.com/

References [1] Hofstede, G. (2011). Culture. Retrieved 11.27, 2011, from: http://www.geerthofstede.nl/culture.aspx [2] Hoft, Nancy L.(1996). Developing a Cultural Model, In: Del Galdo, Elisa M. / Nielsen, Jakob: International User-Interfaces, New York: John Wiley & Sons, 41-73. [3] Itim International, Geert Hofstede Cultural Dimensions: Retrieved 12. 27, 2011, from: http://www.geert-hofstede.com, http://geert-hofstede.com/finland.html [4] Hofstede's Cultural Dimensions, Understanding workplace values around the world. http://www.mindtools.com/pages/article/newLDR_66.htm [5] Hofstede, Geert (1991): Cultures and Organizations: Software of the Mind, London: 19

McGraw-Hill [6] Shalom H. Schwartz, (2006). Value Dimensions of Culture and National Difference. [7] Valentina-Johanna Baumgartner (2003). A Practical Set of Cultural Dimensions for Global User-Interface Analysis and Design. [8] Oracle Think Quest. Neural Networks. Retrieved 01.15, 2012, from: http://library.thinkquest.org/C007009/introduction/types/types.html [9] Herve Abdi (1994). A Neural Network Primer. Journal of Biological System, Vol.2(3), pages 247-283. [10] Teuvo Kohonen (1990). The Self-Organizing Map, Proceedings of the IEEE, Vol.78, No.9, September 1990. [11] Natasa Jevtic. Interactive Tutorial on Neural Networks, Retrieved 01.15, (2012), from http://sydney.edu.au/engineering/it/~irena/ai01/nn/som.html [12] Jae-Wook Ahu, Sue Yeon Syn. Interactive System Design. April 27, (2005). Retrieved 01.15, 2012, from: http://www.sis.pitt.edu/~ssyn/som/som.html [13] Matlab, 2011. Neural Network Toolbox 7 User’s Guide. [14] Aaron Marcus and Associates, Inc, (2005). Lecture Note, Cross-Cultural User-Experience Design. http://assets.en.oreilly.com/1/event/3/Cross-Cultural%20User-Experience%20Design%20Presentatio n.pdf [15] Korac-Kakadabse N., Kouzmin A., Korac-Kakadabse A. and Savery L. (2001). Low- and highcontext communication patterns: towards mapping cross-cultural encounters. Cross Cultural Management, vol. 8 (2), pp. 3-24. [16] Intercultural Organizational Development – Tamas Consultants Inc. Geert Hofstede's Dimensions of Culture and Edward T. Hall's Time Orientations

20