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Investigating user’s information needs and attitudes towards

proactivity in mobile tourist guidesAdem Sabic

Markus Zanker

Alpen-Adria-Universität Klagenfurt, Austriaademsabic@gmail.com | mzanker@acm.org

http://www.aau.at

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Agenda

• Motivation and Recent Work• Goals and Design• Results• Hypotheses• Conclusions

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Motivation

User‘s attention is a valuable and

very scarce resource

Credit: Shutterstock.com

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Proactive Computing

“Ability to act on the user’s behalf in anticipation of future goals or problems without the user’s requests, where a proactive system can trigger itself by capturing a priori what the users want with better service quality”. (Kwon, 2006)

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Challenges

(How to) „Act with

the right information,

at the right time,

at the right place,

in the right way,

to the right person”(Fischer, 2012)

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Recent Work 1/2

• Most work done in proactive agents / personal assistant area

• Context-aware systems finally made their way to users (focus mainly on location, time, task etc.)

• Limited range of proactive applications in industry (Google Now, weather warnings, navigation systems etc.)

• Some research prototypes: push-based RS, restaurants/gas stations [Woerndl et al., 2011]

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Recent Work 2/2• Longer term field study on predicting disruptive

smartphone notifications to include problem of concept drift [Smith et al., 2014]

• Model of smartphone use based on qualitative evidence that categorized activities into communication, entertainment, facilitation and information search [Wang et al., 2014]

• Attitude towards proactive recommendations researched with projective techniques showing a majority eager to adopt them and fears / anxieties of others [Tussyadiah & Wang, 2014]

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Goal

• Explorative study to generate hypotheses about the relationships between factors that most probably impact the adoption of proactive systems

• Contribute towards model building

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Factors 1/3• Information need (IN): Different categories of

travel-related information services such as route services, traffic, weather, POIs…

• Tolerance Towards Interruptions (TTI): Degree of disruption for different events such as call, texts

or updates • Openness to share personal data (OP): Which

categories of data the users allows the system to exploit

such as location or personal contacts

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Factors 2/3

• Trust (TR): Based on continuous use of route navigation even if it makes an error (sole proactive system users experienced)

• Willingness to provide feedback (PF): in order to improve its models

• Willingness to pay (WTP): economic dimension to sustainable service provisioning

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Factors 3/3

Similarity-attraction hypothesis [Nass et al., 1995]: people prefer to interact with others who are similar

•Proactive Attitude (PA): “is a personality characteristic, [] belief in rich potential of changes that can be made to improve oneself and one’s environment” [Schmitz & Schwarzer, 1999]

Fear that proactive devices distract and create stressful situations•General Self-Efficacy Scale (SE): perceived self-efficacy in

dealing with daily hassles and stressful life events [Schwarzer & Jersualem, 1995]

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Empirical Study

• Offline paper survey• 65 participants• Aged from 20 to 47 years (M=25.23, SD=5.131)• Females (52.31%)• Highest education level: high-school (50.77%),

bachelor (40.00%) and master degrees (6.15%)• 95.31% of participants use smartphones and

35.94% tablets

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Information needs and trip phase 1/2

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Information needs and trip phase 2/2

• More than one device used for finding information in pre- and on-trip phases (mobile devices dominate the latter phase)

• Types of information delivery mechanisms users prefer (while traveling):– notification-based methods (alerts, warnings etc.)

68.42%– voice-based (22.81%) – widget/UI-based (21.05%)* don’t add to 100% due to multiple-choices

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Events with high priority

• Complementarily intrusiveness is perceived highest for Application updates and lowest for text messages

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Security and Privacy Concerns

• High interest in having the possibility to see and approve all information that the system analyses

• Mediocre openness to share private data with the system

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Economic perspective

• Medium interest in paying for services

• General preference for ad-supported versions

• Willingness-to-pay – 9.63 EUR (AVG) per install or – 2.13 EUR for monthly subscription.

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Personality scales

• The Self-Efficacy Scale (α = .783) (SE)– With Higher SE score users check for more categories of

information in the before and on-trip phases– Trust in proactive systems correlates negatively (p <= 0.01) with

the SE score– For all of the categories of data that can be analysed by the

system, significant negative correlations (mostly at the p < 0.01 level) between openness to allow a system to analyse category data and participant's SE score

• Nicely supports the reasons of the anxious group in [Tussyadiah & Wang, 2014]

• Proactive Attitude (α =.713) (PA)– No correlations with any of the other factors

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Hypotheses 1/2

• Economic perspective:– Users who are more willing

to provide feedback (invest more to make it useful) are willing to pay more

– Users with a lower tolerance towards interruption (i.e. suffer more from being disturbed) are also willing to pay more

WTP

PF

TTI

+

-

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Hypotheses 2/2

• Privacy perspective:– The less diverse users‘

information needs were, the more willing they were to let the system exploit their data

– Users who perceive themselves to be more efficient check for more information categories before travel

OP

IN

+

+GSE

-

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Conclusions• Explorative study with the aim to contribute to hypothesis

and model development

• Similarity-Attraction hypothesis does not seem to apply to proactive system behavior

• For practical system development:– Sensitivity towards privacy concerns of users– Worse experience with status quo makes users more

likely to adopt and pay for proactive systems

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Thank you for your attention!

Questions?

Questions?Questions?

Markus ZankerIntelligent Systems and Business Informatics

Alpen-Adria-Universität Klagenfurt, Austria

M: mzanker@acm.org

P: +43 463 2700 3753

Skype: markuszanker

W: http://www.isbi.at/mzanker

Visit: http://www.recommenderbook.net

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Project OSTAR• Development of an innovative online system for

recommending individual tours and trails in alpine regions– Research partners:

• EURAC research, Bolzano, Italy• Free University Bolzano-Bozen, Italy• Autonomous Province of Bolzano – South Tyrol (Dept. for spatial and

statistical informatics)• Alpen-Adria-Universität Klagenfurt

– Application partners:• Tourism regions in Carinthia and South Tyrol

– Runtime: 2012-2014– Programme:

• Interreg IV Italy-Austria