The Impact of Language on Website Use and User Satisfaction

Transcription

1 The Impact of Language on Website Use and User Satisfaction Anett Kralisch & Veit Köppen Institute of Information Systems Humboldt-University & Free University Berlin, Germany

2 Motivation: the Tower of Babel on the Internet FRENCH URDU THAI PORTUGUESE QUECHUA ENGLISH 2

3 Motivation: the Tower of Babel on the Internet > 6000 languages spoken worldwide > 6000 languages spoken by potential Internet users 3

4 Motivation: the Tower of Babel on the Internet > 6000 languages spoken worldwide > 6000 languages spoken by potential Internet users website translation / adaptations decisions often have significant financial ramificatiants 4

5 Motivation: the Tower of Babel on the Internet > 6000 languages spoken worldwide > 6000 languages spoken by potential Internet users website translation / adaptations decisions often have significant financial ramifications language as a barrier to accessing information / participation on the Internet (digital divide) 5

6 Motivation: the Tower of Babel on the Internet > 6000 languages spoken worldwide > 6000 languages spoken by potential Internet users website translation / adaptations decisions often have significant financial ramifications language as a barrier to accessing information / participation on the Internet (digital divide) Which languages should be considered for translation? 6

7 Motivation: the Tower of Babel on the Internet > 6000 languages spoken worldwide > 6000 languages spoken by potential Internet users website translation / adaptations are often important investment decisions language as barrier to information access / citizen participation on the Internet (digital divide) Which languages should be considered for translation? How does language affect website use? 7

8 Motivation: the Tower of Babel on the Internet > 6000 languages spoken worldwide > 6000 languages spoken by potential Internet users website translation / adaptations are often important investment decisions language as barrier to information access / citizen participation on the Internet (digital divide) Which languages should be considered for translation? How does language affect website use? How does language affect satisfaction with a website s offer? 8

9 Agenda 1. The model 2. The variables 3. Data & Measures 4. Preliminary Results 5. Outlook 9

11 1. The model Language Proficiency Level + + Website Use. A Website s perceived net value - + Internet Language Offer Satisfaction. 11

13 2. The variables Language Proficiency Level + + Website Use. A Website s perceived net value - + Internet Language Offer Satisfaction. 13

14 2. The variables: net value language-related NET VALUE of a website = VALUE of information gathering - COSTS of information gathering 14

15 2. The variables: antecedents Language Proficiency Level + + Website Use. A Website s perceived net value - + Internet Language Offer Satisfaction. 15

16 2. The variables: antecedents NET VALUE = VALUE - COSTS 16

17 2. The variables: antecedents NET VALUE = VALUE - COSTS Non-native language / Language-proficiency level: 17

18 2. The variables: antecedents NET VALUE = VALUE - COSTS COGNITIVE EFFORT Revised-Hierarchy-Model (Dufour & Kroll 1995) Non-native language / Language-proficiency level: higher cognitive effort required for non-native language processing / at a lower language-proficiency level 18

19 2. The variables: antecedents NET VALUE = VALUE - COSTS Non-native language / Language-proficiency level: COGNITIVE EFFORT Revised-Hierarchy-Model (Dufour & Kroll 1995) higher cognitive effort for non-native language processing / at a lower language-proficiency level TIME e.g. Hahne 2001 slower language processing required in a non-native language / at a lower language-proficiency level 19

20 2. The variables: antecedents NET VALUE = VALUE - COSTS Non-native language / Language-proficiency level: COGNITIVE EFFORT Revised-Hierarchy-Model (Dufour & Kroll 1995) higher cognitive effort for non-native language processing / at a lower language-proficiency level TIME e.g. Hahne 2001 slower language processing in a non-native language / at a lower language-proficiency level H1: A user s perception of a website s language-related net value increases with his/her language proficiency. 20

21 2. The variables: antecedents NET VALUE = VALUE - COSTS Internet Language Offer How much information is offered in the user s native language on the Internet? Higher value if few websites are offered in that language Lower value if many websites are offered in that language 21

22 2. The variables: antecedents NET VALUE = VALUE - COSTS Internet Language Offer How much information is offered on the Internet in the user s native language? Higher value Lower value if few websites are offered in that language if many websites are offered in that language H2: A user s perception of a website s language-related net value decreases with an increase in the perceived amount of information offered on the Internet in the site s language. 22

23 2. The variables Language Proficiency Level + + Website Use. A Website s perceived net value - + Internet Language Offer Satisfaction. 23

24 2. The variables: outcomes Information Foraging Theory (Pirolli, Card 1995): Link is followed if: costs < expected value. 24

25 2. The variables: outcomes Information Foraging Theory (Pirolli, Card 1995): Link is followed if: costs < expected value. Website Use: 25

26 2. The variables: outcomes Information Foraging Theory (Pirolli, Card 1995): Link is followed if: costs < expected value. Website Use: How many pages are accessed? (User decides about following the link before the page is visited.) 26

27 2. The variables: outcomes Information Foraging Theory (Pirolli, Card 1995): : Link is followed if: costs < expected value. Website Use: How many pages are accessed? Which pages are accessed? (User decides about following the link before the page is visited.) (User decides about the length of a page visit while the page is visited.) 27

28 2. The variables: outcomes Information Foraging Theory (Pirolli, Card 1995): Link is followed if: costs < expected value. Website Use: How many pages are accessed? Which pages are accessed? (User decides about following the link before the page is visited.) (User decides about the lenght of a page visit while the page is visited.) H3: The number of pages accessed by a user on a site increases with the perceived language-related net value of that site. 28

29 2. The variables: outcomes Information Foraging Theory (Pirolli, Card 1995): Link is followed if: costs < expected value. Website Use: How many pages are accessed? Which pages are accessed? (User decides about following the link before the page is visited.) (User decides about the lenght of a page visit while the page is visited.) H3: The number of pages accessed by a user on a site increases with the perceived language-related net value of that site. H4: The depth of processing a user performs during a visit to a site increases with the perceived language-related net value of that site. 29

30 2. The variables: satisfaction Technology Acceptance Model (Davis 1993): Perceived usefulness positive attitude 30

31 2. The variables: satisfaction Technology Acceptance Model (Davis 1993): Perceived usefulness positive attitude Net Value Satisfaction 31

32 2. The variables: outcomes Technology Acceptance Model (Davis 1993): Perceived usefulness positive attitude Net Value Satisfaction H5: User satisfaction increases with perceived net value. 32

34 3. Data & Measures WEBPAGE 1. Questionnaire: all perceptual & attitudinal variables Multilingual questionnaire Likert or semantic differential scales 34

35 3. Data & Measures WEBPAGE 1. Questionnaire: all perceptual & attitudinal variables Multilingual questionnaire Likert or semantic differential scales: 2. Logfiles: extent of website use Linked to the questionnaire trough session-ids IP-address ( geographical information) requested page, time of access, etc. 35

36 3. Data & Measures WEBPAGE 1. Questionnaire: all perceptual & attitudinal variables Multilingual questionnaire Likert or semantic differential scales: 2. Logfiles: extent of website use Linked to the questionnaire trough session-ids IP-address ( geographical information) requested page, time of access, etc. 3. Webpage analysis: simplified depth of processing Self-developed software measures web page complexity 36

37 3. Data & Measures WEBPAGE 1. Questionnaire: all perceptual & attitudinal variables Multilingual questionnaire Likert or semantic differential scales: 2. Logfiles: extent of website use Linked to the questionnaire trough session-ids IP-address ( geographical information) requested page, time of access, etc. 3. Webpage analysis: simplified depth of processing Self-developed software measures web page complexity OTHER SOURCES 1. Internet statistics: cross-check with self-reports Information about language offer on the Web e.g. Cyber Census Report; Mikami & Suzuki 2004; DMOZ.org World Statistics 37

38 3. Data & Measures WEBPAGE 1. Questionnaire: all perceptual & attitudinal variables Multilingual questionnaire Likert or semantic differential scales: 2. Logfiles: extent of website use Linked to the questionnaire trough session-ids IP-address ( geographical information) requested page, time of access, etc. 3. Webpage analysis: simplified depth of processing Self-developed software measures web page complexity OTHER SOURCES 1. Internet statistics: cross-check with self-reports Information about language offer on the Web e.g. Cyber Census Report; Mikami & Suzuki 2004; DMOZ.org World Statistics 2. Language Status: cross-check with self-reports Which official language(s) in which region? Self-developed database 38

40 4. Preliminary Results data from a pretest-questionnaire simplified data analysis patients only (due to significant correlations between language and patients/physicians) N=83 40

41 4. Preliminary Results data from a pretest-questionnaire simplified data analysis patients only (due to significant correlations between language and patients/physicians) N=83 Preliminary results: no significant impact Language Proficiency Level Website Use. H1 H3 A Website s perceived net value H2 H5 Internet Language Offer Satisfaction. 41

42 4. Preliminary Results Tendencies: Language Proficiency Level β = (p=0.163) A Website s perceived net value Website Use. β = (p=0.255) Internet Language Offer Satisfaction. 42

43 4. Preliminary Results Tendencies: Language Proficiency Level β = (p=0.462) Website Use. A Website s perceived net value β = (p=0.417) Internet Language Offer Satisfaction. 43

44 4. Preliminary Results Tendencies: Language Proficiency Level Website Use. A Website s perceived net value Internet Language Offer β = (p=0.151) Satisfaction. 44

45 4. Preliminary Results Tendencies: Language Proficiency Level A Website s perceived net value Website Use. Internet Language Offer + Satisfaction. 45

46 4. Preliminary Results Tendencies: Website culturally adapted to the major language groups? Language Proficiency Level A Website s perceived net value Website Use. Internet Language Offer + Satisfaction. 46

48 Outlook Final questionnaire with revised questions is currently posted on the website. Logfiles are registered at the same time. Validation of Internet statistics and language status data for predicting website use and satisfaction. 48

49 Outlook Final questionnaire with revised questions is currently posted on the website. Logfiles are registered at the same time. Validation of Internet statistics and language status data for predicting website use and satisfaction. Future questions to answer: Theroleof language associated values (e.g. Dmoch, 1997) e.g. French elegance; English - modernity Theroleof thenumber of languages on one website. Personal language aspects (language fear / motivation). Data aggregation on national level the role of culture 49

50 Vielen Dank für Ihre Aufmerksamkeit! (native ) Thank you for your attention! (non-native )