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Cultural and generational influences on privacy concerns: a qualitative study in seven European countries

  • Empirical Research
  • Published:
European Journal of Information Systems

Abstract

This research examines how European citizens decide to disclose and protect their personal data and thereby reveals cultural and generational divides. Focus group discussions featured either young people, aged 15 to 24 years, or adults, between 25 and 70 years of age, and were conducted in seven EU member states. The results of a computer-aided text analysis with two complementary software packages suggest similarities and differences in participants’ views and privacy concerns (PC). Responsibility is relevant to personal data management, which represents a hotly contested issue. A geographical north–south divide appears for the importance of responsibility as opposed to trust. Moreover, people regard disclosure differently in the south (as a choice) and east (as forced) of Europe. Younger people express more positive attitudes toward data management, feel more responsible, and are more confident in their ability to prevent possible data misuse. Their lower PC and greater protective behaviours (i.e., a potential reversed privacy paradox) may help explain contradictory results in prior literature. These results offer significant and useful theoretical, managerial, and policy implications.

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Acknowledgements

The authors thank Ioannis Maghiros, Wainer Lusoli, and Margherita Bacigalupo from the European Commission IPTS Joint Research Centre for their support and confidence. This study was funded by the European Commission IPTS (Institute for Prospective Technological Studies) Joint Research Centre (EC JRC IPTS Contract No. 151592-2009 A08-FR).

Author information

Authors and Affiliations

  1. GRANEM Research Center, Angers University, Angers, France

    Caroline Lancelot Miltgen & Dominique Peyrat-Guillard

Authors
  1. Caroline Lancelot Miltgen
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  2. Dominique Peyrat-Guillard
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Corresponding author

Correspondence to Caroline Lancelot Miltgen.

Appendices

Appendix A

Table A1

Table A1 Justification for the countries chosen for the study (Eurostat 2008)
Full size table

Appendix B

Table B1

Table B1 Sample characteristics
Full size table

Appendix C

Process of data collection and analysis

Step 1: Data collection

  • Two focus groups organised per country (one with young people and one with adults), 7 countries (14 focus groups), a total of 139 participants (8–12 in each focus group)

  • All moderators are scholars of partner universities, having a very good knowledge of English

  • The same instructions are given to the partners to recruit participants

  • Before the beginning of each focus group, participants completed a questionnaire to indicate their individual characteristics (see Appendix B)

  • The same guide was used by all moderators, trained and briefed in advance

  • The moderator and participants use their common native language during the discussion

  • The same moderator conducts the two focus groups in his or her country

  • All the discussions are audio and video-recorded

  • Each moderator is in charge of the transcription of the focus groups he or she conducted

  • Each moderator is in charge of the translation of the discussions in English

  • All the discussions translated into English are sent by the moderators to the authors

Step 2: Preparation of the corpus by the researchers

A global corpus of all translations of the discussions in the 7 countries is prepared for the analysis: a partition of the corpus is made to separate the discourse of each of the 139 participants. Thus, each sentence pronounced by one participant can be linked to the number of the participant, to the country, to one of the two focus groups of the country, and to the individual characteristics of this participant. With Alceste software, the separation is a row beginning with the number of the participant (four characters) and then the character ‘*’. The individual characteristics are added after the ‘*’.

Example:

0001 *Country_Estonia *Gender_F *Age_19to24 *FG_Young

Text of the focus group participant number 1, an Estonian female, aged between 19 and 24, interviewed in the focus group with young people

In this file, participants 1–19 are from Estonia; participants 20–39 are from France; participants 40–59 are from Romania; participants 60–80 are from Germany; participants 81–100 are from Greece; participants 101–120 are from Poland; participants 121–139 are from Spain.

Seven files (one per country) are also prepared in the same way to run detailed analyses per country.

Step 3: Data analyses by the software packages

Alceste software

  • Segmentation of the corpus and reduction of the words to their roots (lemmatisation): The software identifies the set of lexicometrical base units in the corpus. Each unit is named a graphical form, or word-type (Lebart & Salem, 1994). The software identifies more complex forms and thus regroups into units the graphical forms that correspond to the different ways in which the same lemma can occur (e.g., verbs changed to infinitives, plurals to singular). In the standard analysis, rare words (frequency less than 4) are eliminated.

  • Partition of the corpus by the software: The Alceste software divides the text into CUs that correspond more or less to a small paragraph, depending on the length of the corpus. The entire corpus is separated into different CUs. The lexical table cross-tabulates the lemmatised forms, with the text separated into different CUs. The rows of the data table correspond to the different CUs, and the columns correspond to the different graphical forms (lemmatised words). The cells contain either 0 or 1 (complete disjunctive table), depending on the absence or presence, respectively, of the graphical form in a CU.

illustration

figure b

  • DHC: A DHC is performed on the entire lexical table. A second classification tests the stability of the classes obtained. In this second classification, each CU is longer (minimum of 12 words instead of 10 in the standard configuration). In the classification, all the CUs are first placed together in a single class. Then, at each step, the two most different classes (i.e., with the greatest margin contrast) are identified until all CUs have been either classified or not, depending on their graphical forms

WordMapper software

  • Segmentation of the corpus, identification of ‘significant words’ (meaningful), and reduction of the words to their roots (lemmatisation): any WordMapper analysis must begin with the creation of meaningful words, and during that phase empty words, such as articles, are eliminated. In the standard analysis, rare words (frequency of the word in the corpus less than 3) and non-significant (empty) words (number of letters less than 3) are eliminated. The software establishes the list of all the significant words with a minimum frequency of 3.

  • Construction of the data table by the software: the list of the lemmatised significant words is used by the software to build the data table. The rows of the data table correspond to the different lemmatised significant words, and the columns correspond to the different modalities of the variable chosen.

illustration

figure a

  • A CFA is performed on the data table.

Step 4: Interpretation of the results by the researchers

Results with Alceste software

A dendogram resulting from the DHC shows the hierarchical division of the classes (each class, i.e., a group of co-occurring words, forms a specific lexical world). In practice, what is important is the stability of the classes obtained, and the percentage of CUs globally classified. The individual characteristics of the participants are not taken into account when classifying the responses (they are supplementary elements), but it is possible to describe each class in terms of its population. The software calculates the representativeness of each word and each CU for a specific class, according to the χ2 statistic (see Appendix D).

Results with WordMapper software

The statistics given by the software help the researcher to interpret the results of the CFA. These statistics are the same and the interpretation is the same as a CFA performed on non-textual data: absolute contributions per axis, relative contributions (squared cosine) per axis, coordinates per axes.

Appendix D

Table D1

Table D1 Example calculation of χ 2 with Alceste
Full size table

Appendix E

Examples of interactions during the focus groups

Focus group with young people in France: Discussion about ‘Responsibility’

Participant number 22

If you trust a website you also don’t want that Internet site or company to give out your information either. So the responsibility for the site is not just ours

Participant number 24

At the same time if there’s a problem you're not going to go and complain to the site

Participant number 22

It’s a bit pointless … you can only blame yourself

Participant number 23

Yes because you don't know if behind it there's actually a person at all. So you're not going to get angry … well yeah you get angry with the computer at the time, but the computer is not going to give you an answer. So then you blame yourself. The problem is when you get a call from a call centre, you don't know how to take your information back. If they start rattling off the details of your life you're not going to say: ‘no that's not me’

Participant number 20

Your reflex is to hang up, but a better thing to do would be to ask them how they got your information

Participant number 23

At the same time they use formulaic questions and you just answer them

Participant number 24

Then you can tell them for your landline you don’t want to or you don't agree with that kind of technique

Focus group with young people in Greece: Discussion about ‘Trust’

Moderator

How can companies gain your trust, so that you will give them your data?

Participant number 87

Mainly by being well-known

Participant number 88

I gave it to a company, only because a friend of mine was working there

Participant number 90

To be trustworthy actually. Not just having a good reputation, the company must also have terms of use, and we must be certain that the data will not be used for another purpose

Participant number 81

…If I were asked something irrelevant, I would be suspicious, why is he asking me that? I would question its motives, when I can’t imagine how the company will use it

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Miltgen, C., Peyrat-Guillard, D. Cultural and generational influences on privacy concerns: a qualitative study in seven European countries. Eur J Inf Syst 23, 103–125 (2014). https://doi.org/10.1057/ejis.2013.17

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