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- de Gruyter
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- © 2023 Dariusz Piotrowski, published by Sciendo
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- 2450-0097
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- 10.18559/ebr.2023.1.5
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Abstract
Economics and Business Review Vol. 9 (1), 2023: 119-141 https://www.ebr.edu.pl https://doi.org/10.18559/ebr.2023.1.5 Privacy frontiers in customers’ relations with banks Dariusz Piotrowski Abstract Keywords The widespread use of digital technologies in banking allows • privacy banks to obtain and analyse huge amounts of data from dif- • personal data processing ferent communication channels. While this phenomenon • Artificial Intelligence is conducive to improving the quality of services it also in- • banking ethics creases the risk of privacy breaches. The aim of this study • social media is to identify what factors determine consumer acceptance of banks’ use of public access personal data found on social media accounts. The results indicate the importance of the financial incentive and consumers’ assessment of banks’ information activities regarding the processing of personal data. Determinants relating to the technological sophisti - cation of respondents were also found to be significant, with a particular focus on the ethical evaluation of deci - sions made by Artificial Intelligence algorithms. The results of the work may be used by banks in practice to adapt the area of personal data management to the requirements of e-privacy and Trustworthy Artificial Intelligence. JEL codes: A13, G21, O33 Article received 13 January 2023, accepted 7 April 2023. This research was funded by Nicolaus Copernicus University in Toruń under Grant no. FUTURE/07/2020. Suggested citation: Piotrowski, D. (2023). Privacy frontiers in customers’ relations with banks. Economics and Business Review, 9(1), 119–141. https: //doi.org/10.18559/ebr.2023.1.5 This work is licensed under a Creative Commons Attribution 4.0 International License https: //creativecommons.org/licenses/by/4.0 Department of Financial Management, Faculty of Economic Sciences and Management, Nicolaus Copernicus University, ul. Gagarina 13a, 87-100 Toruń, Poland, darius@umk.pl, https:// orcid.org/0000-0001-8482-8064. 120 Economics and Business Review, Vol. 9 (1), 2023 Introduction The provision of financial services involves the processing of customers’ personal data. This area has changed dramac ti ally as a result of the signic fi ant involvement of banks in the digital transformation of the economy (Rodrigues et al., 2022). Customers’ intensive use of e-banking and in particular mobile banking applications has significantly increased the amount and type of data collected by banks (Wottrich et al., 2019). These institutions also use external sources of customer data such as social media and geo-location (Cambridge Centre for Alternative Finance, 2020). The data is usually collected in digital form, which allows for in-depth analysis using Artificial Intelligence (OECD, 2021). Machine learning algorithms detect consumer behaviour pae tt rns and anomalies that can be used to improve service quality and beer tt match cu - stomer preferences and expectations (Financial Stability Board, 2017; Giza & Wilk, 2021). The implementation of Artificial Intelligence technology by banks has also created an opportunity for banks to offer financial services to customers using chatbots and robo-advisors (Hasal et al., 2021; Waliszewski & Zięba-Szklarska, 2020). However, market regulators have noticed that the processing of personal data can be a source of many irregularities; hence, for instance, banks were obliged to protect customer privacy (Hacker, 2021). The aim of this study is to identify what factors determine consumer ac - ceptance of banks’ use of public access personal data found on social me- dia accounts. It analysed factors relating to customers’ experiences and attitudes towards the use of financial services and digital technologies. In particular it considered the legal and ethical aspects of the processing of personal data and the importance of a financial incentive. The present work makes a significant contribution to information privacy research through the use of original empirical data showing consumers’ perspectives on the processing of personal data by banks. The paper uses a contextual analysis of consumer privacy the advantages of which were pointed out by Acquisti, Brandimarte et al. (2015). The work is novel thanks to the inclusion in the research of variables relating to decisions made by Artificial Intelligence operating within the bank. The uniqueness of the research relates to the ethical aspects of the assessment made by consumers regarding the results of the processing of personal data using Artificial Intelligence. A multilevel ordered logit model was used to identify statistically significant variables aecting ff the willingness to share a wide range of personal data with banks. This model is an appropriate research tool due to the qualitative nature of the dependent variable. The remainder of this paper is organised as follows. In the next section a literature review is conducted focusing on the processing of personal data and privacy issues in the digital world. The subsequent section provides a de- D. Piotrowski, Privacy frontiers in customers’ relations with banks 121 scription of data obtained through the CATI (computer-assisted telephone interviewing) survey, presents the research hypothesis and the research me- thod used in the analysis. Next the estimation results are presented identi - fying determinants of the willingness to share personal data with banks and a discussion is carried out indicating their originality. The main conclusions as well as the theoretical and practical implications are presented in the last part of the paper. 1. Institutional background The issue of personal data processing has been widely regulated in the European Union. This section indicates the most important legal regulations that banks are obliged to comply with. According to Articles 11–14 of the Directive regarding anti-money laundering and countering the financing of terrorism (European Parliament, 2018), when establishing a business rela- tionship a bank is obliged to identify the customer and verify the custo mer’s identity on the basis of documents, data, or information obtained from a re- liable and independent source. By doing so the bank initiates the proces - sing of customers’ personal data. Article 4(1) of the General Data Protection Regulation (GDPR, 2016) defines personal data as any information relating to an identified or identifiable natural person whereby an identifiable natu - ral person is one who can be identified, directly or indirectly and in particu - lar by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identi - ty of that natural person. Banks obtain customers’ personal data through access to official docu - ments proving customers’ identities as well as through the provision of fi - nancial services to them. In addition, to the customer’s name, gender, date and place of birth, place of residence and nationality, banks are able to ob - tain data on the financial situation in the customer’s household, shopping and payment habits and assets by analysing transactions made on perso - nal accounts, credit card accounts and in the process of granting credit and monitoring repayment (Credit Suisse, 2020). When analysing the sources of obtaining data on consumers it is worth mentioning the new opportunities created by Payment Services Directive 2 (2015). Open banking allows for the exchange of data between banks and third parties helping financial in - stitutions to gain a bee tt r understanding of consumer attitudes, behaviour, and preferences. The increasing use of digital technology and in particular mobile devices and applications has given banks access to consumer data 122 Economics and Business Review, Vol. 9 (1), 2023 well beyond the realm of finance, i.e., personal biometric data such as fin - gerprints, face shape, images of the iris or retina which are the source of sensitive and confidential information (Nguyen & Dang, 2019; Piotrowska, Polasik, & Piotrowski, 2017). The acquisition of customers’ personal data is one step within the broader processing of personal data which includes activities such as recording, orga - nisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making availa - ble, alignment or combination, restriction, erasure, or destruction. The GDPR (2016) provisions impose an obligation to process personal data lawfully, fair - ly and in a transparent manner in relation to the data subject. Furthermore, the processing of data should comply with specific principles: ‘purpose li - mitation’—the personal data obtained may be used only for a well-defined purpose; ‘data minimization’—the purpose of processing should be achieved using as little personal data as possible; ‘correctness’—the data used must be accurate and up-to-date; ‘storage limitation’—the manner in which personal data are stored should allow for the identification of the natural person ne - cessary for the specified purpose; ‘integrity and confidentiality’—ensuring adequate security of personal data; ‘accountability’—identifying the person responsible within the entity for the processing of personal data. Detailed requirements also clarify the issue of obtaining consent to process personal data. The data processor should be able to demonstrate that the consumer has given informed consent (Betzing et al., 2020). The fulfilment of this con - dition is facilitated by the clear separation of consent to the processing of personal data from other statements as well as the clear and plain language used in the statement (Muravyeva et al., 2020). Customer contact with the bank through electronic banking channels cre- ates the conditions for the generation of an enormous amount of data, while the electronic form of the data favours their collection, storage and analy - sis to extract hidden information. The banking sector uses AI mainly in areas such as customer service, client acquisition and risk management (Cambridge Centre for Alternative Finance, 2020; Gancarczyk et al., 2022). More specific applicao ti ns include reporting and record management, data analytics, credit scoring, compliance, AML/CFT (Anti-Money Laundering / Counter Financing of Terrorism), KYC (Know Your Customer) checks, anti-fraud, chatbots, robo-ad - visors, biometric authentication and personalised products (Korol & Fotiadis, 2022; OECD, 2021). Processing personal data using digital technologies requires banks to meet additional regulatory requirements. Thus pursuant to Article 13.2f of the GDPR (2016), the controller of personal data that will be used to make automated decisions is obliged to inform the data subject of this together with an indication of the modalities of such decision-making and the envis - aged consequences of such personal data processing for the data subject. D. Piotrowski, Privacy frontiers in customers’ relations with banks 123 With this requirement in mind the European Commission (2019) formulated recommendations addressed to those responsible for the development, de - ployment and use of AI which aim to ensure that Artificial Intelligence oper - ates in accordance with the law and ethical principles. The regulations con - tained in the Ethics Guidelines for Trustworthy AI refer to universal principles and values such as the right to privacy, fairness, transparency, accountabil - ity and confidentiality. The closing years of the 20th century saw a tremen- dous increase in the importance of the Internet in the functioning of societ - ies and economies almost all over the world (Yamin, 2019). The European Union, recognising the need to regulate the area of electronic communica - tions networks and services, adopted the Framework Directive (2002), which applied, inter alia, to electronic banking services. The development of digi - tal technologies and their growing use meant that interpersonal communi- cation services, including Internet telephony, instant messaging and email services were also regulated in subsequent years (Recast, 2018). The indi - cated regulations as well as the Directive on privacy and electronic commu - nications (2002) and the Proposal concerning the respect for private life and the protection of personal data in electronic communications and repealing Directive 2002/58/EC (European Commission, 2017) set out requirements for the processing of personal data in electronic communications to ensure the protection of consumer privacy. However, none of the aforementioned regulations explicitly define the concept of privacy, hence the need to refer to the literature in this regard. 2. Literature review 2.1. Privacy in electronic communications According to Belanger and Crossler (2011) and Yee (2017) privacy refers to a person’s ability to control the processing of data concerning them. Loh (2018) emphasises the importance of autonomy of decisions and life choices in the distribution of information. Pollach (2005) defines informational pri - vacy as the right of individuals to decide when, to whom, how and to what extent information concerning them will be shared. Elsewhere Martin (2016) considers privacy as a kind of social contract obliging individuals to follow rules adopted in the community relating to access to information and how it is used. Nissenbaum (2009) and Martin (2011) present a contextual view of privacy where the situations in which individuals find themselves and the relationships between them aect ff which behaviour is acceptable and which 124 Economics and Business Review, Vol. 9 (1), 2023 is treated as an invasion of privacy. Floridi (2006) points out that the ana - lysis of information privacy should take into account the cultural context, the responsibility of the individual taking into account legal norms, religio - us beliefs and epistemic practices as well as the definition of the adequate scope of the right to privacy so that the processing of any personal data is not defined as an invasion of privacy. According to Margulis (2003) the de - velopment of digital technologies has also made the issue of privacy in the public sphere important. The privacy literature covers the collection, sharing and use of data. Obar and Oeldorf-Hirsch (2020) show that privacy policies of electronic service providers are infrequently read because they are often difficult to compre - hend and too long. With the cognitive problems of consumers in mind Solove (2013) criticises the privacy self-management approach to cookie acceptance. He suggests embracing paternalistic regulations regarding the collection, use and disclosure of personal data. Elsewhere the work of Pentina et al. (2016) analyse the phenomenon of the privacy paradox. It is characterised by a con- tradiction between the stated concern for privacy protection and the actual actions of consumers that pose a risk of privacy violation (Barth & De Jong, 2017). Acquisti and Grossklags (2006) point out that consumers tend to take into account short-term benefits when making decisions about sharing per - sonal data while showing little concern for the protection of privacy that is in fact based on managing information over the long term. Consumers’ beha - viour can also be explained by the fact that they attribute little value to the data they transmit while failing to recognise the serious privacy risks in doing so. Conversely, Tene and Polonetsky (2013) believe that consumers are awa- re of the value that personal data represent. They are therefore prepared to offer access to them in return for a specific consideration which may take the form of money or, for example, access to selected electronic services (Fife & Orjuela, 2012). Moreover, in the Balaban and Mustățea (2021) study respon- dents assessed the said practice as fair given the current practice of data col - lection by providers of electronic communication services. As a counterpo- int to the views presented Roessler (2015) points to the moral limitations of treating personal data as tradable goods. 2.2. Data disclosure in social media The development of digital technologies and in particular the Internet has given society wide and rapid access to large amounts of information. The ad - vent of social media has broadened insights into the lives of others and has additionally created the opportunity to share information from one’s own life with multiple audiences (Shadbolt & Hampson, 2018). Various factors influen - D. Piotrowski, Privacy frontiers in customers’ relations with banks 125 cing the use of social media have been idened in the literature. Muhammad, Dey and Weerakkody (2018) reported privacy and security, personal behavio- ur, social influence and technological solutions. Whiting and Williams (2013) emphasised the importance of relaxation, expression of opinions and intrin - sic psychological needs of entertainment. The study by Trivedi et al. (2018) showed the importance of a desire to connect, communicate and collabora- te with others. The benefits of a social media presence have also been recognised by banks (Kirakosyan, 2015). These institutions use social plao tf rms in the area of marketing, as a communication channel, to find out consumers’ opinions on the services provided and the execution of financial transactions (Parusheva, 2017). Twitter, Facebook, LinkedIn, and YouTube are used to present banking products and to gauge consumer reactions. Information gleaned from social media provides a beer tt understanding of consumer needs and contributes to banks’ competitiveness (Askar et al., 2022). The use of social media is also analysed in the literature in the context of privacy. Research includes users’ willingness to share information online, the extent of disclosure of personal information and how access to information is controlled (Cheung et al., 2015). Some social media users overlook the fact that their activity on the plaorms tf leaves numerous digital footprints. They show a lack of due consideration about what content they post online (Azucar et al., 2018). The willingness to disclose data about oneself and the low as- sessment of privacy risks stems from a false belief in one’s own eectiv ff eness in managing privacy (Chen & Chen, 2015). The attitude presented hampers the management of personal data and significantly increases the risk of pri - vacy breaches (Acquisti, Taylor et al., 2016). The literature also points to more conscious and deliberate actions by social media users in terms of privacy management. The study by Shane- Simpson et al. (2018) showed a different extent of self-disclosure and pu - blic or private profile preference depending on whether the respondent was a Facebook, Twitter, or Instagram user. Elsewhere a study by Xie and Karan (2019) yielded that social media user behaviour varied depending on the type of information. Users displayed greater propensity to share everyday life and entertainment information in contrast to personal contact informa - tion where significant restrictions were applied to its dissemination. The paper by Stutzman, Gross and Acquisti (2013) revealed that Facebook users increased the amount of personal information shared with friends while applying restrictions on this to other users. McGuinness and Simon (2018) additionally highlighted the practice of social media users applying varying online privacy settings. The research also identified factors influencing the intention to share di - gital footprints on social media plaorms. tf Muhammad, Dey, Syed Alwi et al. (2022) analysed constructs such as: perceived relative advantage, perceived tifi 126 Economics and Business Review, Vol. 9 (1), 2023 social influence, perceived control, enjoyment, self-enhancement and trust, highlighting the importance of the laer tt factor. Mutimukwe et al. (2019) fur - ther considered the eect ff of organisational privacy assurances. Keith et al. (2013) examined the impact of variables such as perceived benefits, perceived privacy risks, intent to disclose and awareness of privacy risks. They discove- red that disclosure intentions are determined by perceived privacy risks on a larger scale than by perceived benefits. Elsewhere, Dinev and Hart (2006) proved that there is a negative impact of perceived privacy risk on an indivi - dual’s intent to disclose information through an application while it is positive with perceived benefits. According to Chai and Kim (2012), the propensity to give information may also depend on the ethical culture—the norms and va - lues shared in a social media community. Chai (2020) found that users’ infor- mation-sharing behaviour on social media is positively associated with ethical culture, while information privacy is perceived negatively. Another area of research is related to the ethical aspects of personal data processing (Floridi & Taddeo, 2016). Social media are a source of scores of information about their users contained in photos, videos, and comments. The use of Artificial Intelligence techniques makes it possible to learn abo - ut the behaviour and preferences of specific individuals including sensitive areas such as racial and ethnic origin, religious and worldview beliefs, politi - cal views, as well as feelings and health status (Batrinca & Treleaven, 2015). Culnan and Bies (2003) pointed out that consumers’ willingness to share data depends on the perceived fairness of corporate information practices. The need to consider ethical issues when managing digital privacy was also hi- ghlighted by Sarathy and Robertson (2003). Cai et al. (2020) recognised the threat of advertising aack tt s while Steppe (2017) and Zuiderveen Borgesius and Poort (2017) cited price discrimination as an example of the use of per - sonal data that is incompatible with consumer interests. Behrendt and Loh (2022) pointed to the danger of algorithmic discrimination when using big data and Artificial Intelligence in automated decision-making. The problems of discrimination, unequal treatment and exclusion associated with the use of digital technologies in the field of data processing were also signalled by Royakkers et al. (2018). Artificial Intelligence is increasingly being used in decision-making that includes an ethical component (Bejger & Elster, 2020). This raises the need for moral programming of the technology and the definition of responsibi - lity for decisions made by Artificial Intelligence (Martin, 2019). According to Wernaart (2021), programming should relate to everyday cases where the results of decisions have more subtle consequences going beyond the labo- ratory or life-or-death situations. He also draws aen tt tion to the need to di - stinguish between moral programming and the ethical issues arising from its application. Millar (2017) analyses the maer tt from another angle pointing to different expectations and assessments of AI performance depending on D. Piotrowski, Privacy frontiers in customers’ relations with banks 127 whether the situation / decision involves high or low stake ethical settings. Although artificial morality has been explored in the literature (Misselhorn, 2018), the ultimate assessment of actions, including decisions made using Artificial Intelligence algorithms is made by consumers with varying degrees of moral sensitivity or moral imagination (Reynolds, 2008). 3. Research methodology The use of the computer-assisted telephone interview method allowed the author to obtain the empirical data used in the study. The survey was conducted by a professional research agency—Interactive Research Center Sp. z o.o. in October 2020. It included a sample of 911 Polish citizens aged 18–65. The sample was representative of Polish society in terms of gender, age and place of residence. Table 1 presents the variables used in the analy- sis and the structure of the responses given by the respondents. The depen- dent variable Data Sharing (Y) refers to consumers’ attitudes towards banks’ use of public access personal data found on their social media accounts. This variable reveals consumers’ attitudes towards privacy. The explanatory va - riables relate to the socio-demographic characteristics of the respondents, the experience of using banking services, the respondents’ attitudes towards ICTs and the bank’s activities in the area of personal data processing as well as the ethical aspects of decisions made using Artificial Intelligence techno - logy. The Exchange Rate and Drink variables included in the study were de- signed in such a way that the first indicates the benefits perceived by consu - mers from the operation of Artificial Intelligence in banks, while the second variable allows us to know the respondents’ evaluation of a situation where the decision of Artificial Intelligence generates negative consequences for the individuals whose personal data were analysed. The paper adopts the following research hypothesis: The use of a financial incentive significantly increases consumer acceptance of banks’ use of public access personal data found on social media accounts. A multilevel ordered lo - git model was used to identify the variables most influencing consumers’ de - cisions to provide data to banks for analysis. Such a model is usually applied when the dependent variable takes a number of finite and discrete values that contain ordinal information (Arfan & Khan, 2017; Wooldridge, 2010). It is assumed that the ordered response variable Y can take on any of the J + 1 values 0, 1, …, J, and it is supposed that underlying the observed response is a latent variable (Cottrell & Lucchetti, 2014): Y* = Xβ + ε 128 Economics and Business Review, Vol. 9 (1), 2023 Table 1. Characteristics of variables and the structure of responses obtained in CATI (N = 911) Variable Variable description Responses % Data Willingness to consent to the Definitely not 71.7 Sharing (Y) bank’s analysis of the content Rather not 16.4 posted on a public profile in social It’s hard to say 7.6 media Rather yes 3.0 Definitely yes 1.3 Gender Gender Female 50.2 Male 49.8 Age Group Age group 18–24 8.5 25–34 23.9 35–44 24.7 45–54 20.0 55–65 22.9 Residence Place of residence Village 28.8 Village-suburban area 7.9 City up to 20,000 inhabitants 13.3 City with 20,001–100,000 20.2 inhabitants 17.8 City with 100,001–500,000 12.0 inhabitants City over 500,000 inhabitants Education Education level Primary and below 2.0 Lower secondary 18.5 and basic vocational 40.4 Secondary 39.1 Higher Internet Frequency of using Internet No or less than once a year 3.7 Use Several times a year 1.4 Several times a month 6.4 A few times a week 12.7 Several times a day 75.8 Social Frequency of using social media No or less than once a year 22.5 Media Use Several times a year 1.3 Several times a month 5.9 A few times a week 15.2 Several times a day 55.1 Internet Frequency of using Internet or No or less than once a year 15.5 or Mobile mobile banking services Several times a year 0.9 Banking Several times a month 18.7 Use A few times a week 40.0 Several times a day 24.9 Investment Use of bank advisory services re- Yes 33.5 Advisory lated to savings and investment No 66.5 D. Piotrowski, Privacy frontiers in customers’ relations with banks 129 Variable Variable description Responses % Loan Use of bank advisory services re- Yes 53.9 Advisory lated to obtaining financing in the No 46.1 form of a loan Processing The rules for the processing of Definitely not 5.3 Rules personal data of customers are Rather not 17.6 presented by banks in a concise It’s hard to say 24.2 and understandable way Rather yes 42.6 Definitely yes 10.3 Exchange The bank, using Artificial Definitely not 9.8 Rate Intelligence, analyzed the client’s Rather not 13.5 transactions and noticed that he It’s hard to say 20.5 often travelled abroad, which is Rather yes 38.6 why it offered favorable exchange Definitely yes 17.6 rates. Do you consider the bank’s operation as ethical? Drink The bank, using Artificial Definitely not 41.6 Intelligence, analyzed the custom- Rather not 31.8 er’s transactions and noticed that It’s hard to say 15.9 in recent months he was buying Rather yes 8.3 alcohol very often and therefore Definitely yes 2.4 refused to grant the customer a loan. Do you consider the bank’s operation as ethical? Cash Bonus Consent for the bank to analyze Definitely not 54.3 the content posted on the social Rather not 21.4 media account in return for receiv- It’s hard to say 13.3 ing a one-off amount of PLN 500 Rather yes 8.0 from the bank Definitely yes 3.0 Social Obtaining information about con - Definitely not 42.7 Media sumers by the bank by analyzing Rather not 29.8 Ethics photos, videos and comments It’s hard to say 17.1 posted by them in public access on Rather yes 8.2 social media is ethical Definitely yes 2.2 Source: Own research. The “cut points” α < α < ⋯ < α are defined, such that: 1 2 J Y = 0 if Y *≤ α Y = 1 if α < Y *≤ α 1 2 Y = J if Y *> α Using the Gretl software package for econometric analysis the unknown parameters α and βs were estimated based on the statistical method of ma - j 130 Economics and Business Review, Vol. 9 (1), 2023 ximum likelihood estimation. The backward elimination procedure identified significant variables in the multilevel ordered logit model. In the first phase of the study, all the variables considered were applied to the model. In sub- sequent phases of the iterative procedure the least significant variables were removed from the model. Finally, only the variables for which the z-statistic was significant at the 5% level remained in the model (see Table 2). Table 2. Estimation results of the multilevel ordered logit model for the variable Data Sharing (Y) Standard Variable Coefficient z-statistic Probability error Internet Use 0.287472 0.113337 2.536 0.0112 Loan Advisory −0.544405 0.173809 −3.132 0.0017 Processing Rules 0.262246 0.0716915 3.658 0.0003 Social Media Ethics 0.723483 0.0801545 9.026 <0.0001 Drink 0.251227 0.0821264 3.059 0.0022 Cash Bonus 0.763749 0.0734577 10.40 <0.0001 Cut1 6.60922 0.677397 9.757 <0.0001 Cut2 8.29479 0.705207 11.76 <0.0001 Cut3 9.14571 0.725231 12.61 <0.0001 Cut4 10.6251 0.785624 13.52 <0.0001 Mean dependent variable 1.385291 S.D. dependent variable 0.810483 Log-likelihood −590.0483 Akaike criterion 1200.097 Schwarz criterion 1248.242 Hannan–Quinn criterion 1218.478 Number of cases ‘correctly predicted’ 691 (75.9%) Likelihood ratio test 409.257 [0.0000] Source: Own research. 4. Results A preliminary analysis of the data in Table 1 suggests that the vast majo- rity of respondents are familiar with modern ICTs. As many as 88.5% of re- D. Piotrowski, Privacy frontiers in customers’ relations with banks 131 spondents use the Internet at least a few times a week while 70.3% use social media at least a few times a week. With regard to banking products 64.9% of respondents use Internet or mobile banking at least a few times a week. Less experience among respondents was observed for the other financial services included in the analysis. Advice on savings and investment products was so- ught by 33.5% of respondents while advice on sources of finance was used by 53.9%. The analysis of the empirical data on personal data processing reveals that a clear majority of respondents have a negative attitude towards sharing data publicly available on social media accounts with banks (a total of 88.1% of ‘definitely not’ and ‘rather not’ responses against a total of 4.3% of ‘defini - tely yes’ and ‘rather yes’ responses). Despite the introduction of an element of financial gratification of PLN 500 (approximately EUR 105) in exchange for giving consent only 11% of respondents would be willing to allow banks to analyse data held on social media accounts while the majority of respondents (75.7%) still express a negative stance on this issue. Against this backdrop re - spondents’ assessments of the rules on the processing of personal data ap- pear favourable for banks—52.9% of consumers present the view that the regulations applied by the banks are concise in form and understandable in content. Negative feelings in this respect (answers ‘definitely not’ and ‘rather not’) were expressed by 22.9% of respondents. The next three variables included in the study combine issues of perso- nal data processing including the use of Artificial Intelligence and the ethi - cal evaluation of automated decisions made by banks. For the Social Media Ethics variable 72.5% of respondents perceive as unethical the banks’ actions of extracting customer information from data in the public domain on their social media accounts. Far fewer respondents—10.4%—rate the aforemen- tioned behaviour of banks as ethical, with the answer ‘definitely yes’ given by only 2.2% of respondents. Similarly the vast majority of respondents (a to- tal of 73.4% of ‘definitely not’ and ‘rather not’ responses) rate as unethical the actions of Artificial Intelligence in denying credit to a person suspected of alcohol abuse. The AI decision was met with understanding (answers ‘de- finitely yes’ and ‘rather yes’) by only 10.5% of respondents. Assessments on the ethicality of decisions made by Artificial Intelligence when the outcome of the analyses is satisfactory to consumers are decidedly different. Offering favourable exchange rates was viewed positively by 56.2% of respondents while the decision of Artificial Intelligence was rated as unethical by 23.3% of respondents. The proper aim of the study was to determine the factors signic fi antly inu fl - encing the Data Sharing (Y) variable. The results of the logit model estimation are presented in Table 2. They indicate a statistically significant positive rela - tionship between the variables: Internet Use, Processing Rules, Drink, Social Media Ethics and Cash Bonus against the variable Data Sharing and negative for the variable Loan Advisory. The estimation results can be considered sa - 132 Economics and Business Review, Vol. 9 (1), 2023 tisfactory due to the high percentage of ‘correctly predicted’ cases (75.9%). When evaluating the aforementioned result, it should also be taken into ac - count that, for the Data Sharing variable, respondents were able to choose from five response options. 5. Discussion The multilevel ordered logit model estimation yields that a rise in respon- dents’ Internet usage increases the acceptance of banks’ use of public ac- cess personal data found on their social media accounts. Active Internet us - ers are able to perceive the different forms of presentation of personal data (documents, comments, photos, videos), the different contents of the data (information on family, friends, leisure activities, political leanings), the posi - tive and negative eects ff of their processing (fame, recognition, resentment, jealousy, hatred) and the speed with which information spreads in the mod - ern world. According to the adaptive cognition theory of social network par - ticipation (Hu & Ma, 2010) active users are more predisposed to estimate the positive and negative consequences of their choices in the area of personal data processing due to their acquired knowledge and experience. Apart from rationality in privacy management the result obtained in the study can also be explained in a different way. Consumers’ acceptance of banks’ use of data extracted from social media may be related to the fact that they do not see this decision as a threat to their privacy or rate a privacy breach as highly im- probable (Flender & Müller, 2012). The significance of the Social Media Ethics variable indicates that an incre - ase in the perception that banks’ actions in handling data obtained from so - cial media are ethical has a positive eect ff on the acceptance of banks’ use of such data. As mentioned earlier people using social media are able to choose their account settings regarding access to published data. This access can be limited to a group of friends, or unlimited, public. It is then possible for any person or entity to obtain the data held in the account. Moreover regardless of the choice of settings the account holder essentially loses control over the subsequent processing of this data. It can therefore be assumed that people who knowingly post certain content on their accounts and knowingly set ac- cess to the data as public will accept the bank’s actions regarding the proces - sing of this data and perceive them as ethical. This attitude is in line with the communication privacy management theory (Petronio, 2002) where each in - dividual subjectively sets the boundaries of his or her privacy. Informed consumers should be knowledgeable about the processing of personal data. They can gain this knowledge by observing the online beha- D. Piotrowski, Privacy frontiers in customers’ relations with banks 133 viour of other users but the primary source is knowledge of the regulations governing the processing of personal data in a given institution (in the case of this study—in banks). The significance of the Processing Rules variable indi - cates that familiarity with privacy policies and a positive assessment of their content and form increases respondents’ acceptance of banks’ processing of data obtained from social media. The initial survey found that the use of financial gratification as a means of encouraging consumers to share their personal data is likely to impact just 11% of respondents. Despite this estimation of the multilevel ordered logit model identified the Cash Bonus variable as a significant determinant of con - sumers’ decisions to accept banks’ use of data held in social media accounts. It can therefore be concluded that the result obtained in the study confirms the theory of immediate gratifications (Du et al., 2019). Of the two variables directly related to the operation of Artificial Intelligence in banks only the Drink variable proved statistically significant. Thus, the be - lief in the ethical nature of the operation of Artificial Intelligence in the case Artificial Intelligence makes a decision that benefits the consumer (Exchange Rate) does not sufficiently reinforce the willingness of respondents to share a wider catalogue of personal data with banks. Only the fact that a consumer assesses as ethical a decision made by Artificial Intelligence with adverse con - sequences for the consumer significantly increases the willingness to accept banks’ use of data posted on social media (Ashworth & Free, 2006). However, this assessment depends on ethical sensitivity (Chowdhury, 2019; Toti et al., 2021) which is an individual characteristic of each consumer depending on demographics, psychological and ethical experience factors (Schwartz, 2016). According to Hagerty and Rubinov (2019), when we analyse how the perfor- mance of Artificial Intelligence is assessed we should also take into account the cultural and social context. The last variable that is statistically significant is Loan Advisory. Estimation of the model showed that the fact that respondents receive advice on sources of finance negatively aects ff their willingness to accept banks’ use of data po - sted on social media. The first thing to note when analysing the result obta - ined is the weaker negotiating position of the customer vis-à-vis the bank in the case of loans and borrowings as opposed to savings and investment pro- ducts. Respondents wishing to finance their consumer or investment expen - diture with a loan must take into account a bank’s potential refusal. The se - cond point is that if a consumer declares to have received credit or loan ad- vice they have probably undergone a credit assessment procedure. Having to provide the bank with a signic fi ant amount of personal data as well as waiting for the result of the bank’s assessment of their financial situation may have been a source of negative experience for many respondents. Another mat - ter worth considering is consumers’ perception of inappropriate practices by banks in the area of financial advice. Indeed Piotrowski’s (2022) study found 134 Economics and Business Review, Vol. 9 (1), 2023 a negative impact of the use of financial advisory on the ethical assessment of banks operating in Poland. The last issue that needs to be taken into ac - count when analysing the Loan Advisory variable is consumers’ fear of the bank knowing their true financial situation. Artificial Intelligence algorithms analysing data from social media accounts can detect signs of deterioration in the financial situation of consumers which in turn can lead to the refusal of credit, the introduction of additional collateral, or the demand for immedia - te repayment of already granted credit. However, Artificial Intelligence may fail to see signs of deterioration in the debtor’s financial situation and decide against deferring loan instalments. The respondents’ logic presented above ts fi into the privacy calculus theory (Kehr et al., 2015) where privacy decisions are made rationally by comparing the benefits and costs associated with the dissemination of information. Conclusions Privacy refers to the extent of an individual’s control over the processing of their personal data. In this study, consumers’ attitudes to privacy were ana - lysed in the context of banks’ use of publicly accessible personal data found on social media accounts. The results of the research helped identify factors behind consumers’ acceptance of the loss of privacy to banks. Preliminary analysis showed that the vast majority of Polish residents aged 18–65 do not consent to the use of the aforementioned data by banks. It can therefore be concluded that if banks are to obtain consent to access a wider catalogue of personal data they will have to convince consumers of the potential benefits on the one hand and the absence of serious related risks on the other. This task seems very difficult in the case of those who value privacy but almost impossible in the case of those who are negative towards the banking sec - tor. In the case of these consumers the reluctance to use social media data can be linked to the banks’ unethical practices and their information and ne - gotiating advantage vis-à-vis their customers which would be further streng - thened aer c ft onsenting to the analysis of said data. The main part of the research which was based on the estimation of a mul- tilevel ordered logit model showed the significance of the Cash Bonus varia - ble, thus confirming the research hypothesis. While it is true that the intro - duction of a financial incentive significantly influences consumers’ consent to data analysis this factor only determines the decisions made by a relatively small proportion of respondents. The study yields the conclusion that a high level of knowledge about the processing of personal data and awareness of the consequences of this pro- D. Piotrowski, Privacy frontiers in customers’ relations with banks 135 cess favours consumers’ decision to give up some privacy. The respondents see the ethical aspects of personal data processing as equally important. Consumers with a strong conviction in the ethicality of Artificial Intelligence and guided by ethical principles themselves express a willingness for letting banks use data posted on social media accounts. Consumers also recognise the concerns surrounding the potential for AI algorithms to cause them harm. However, these concerns are not only rela- ted to the potentially harmful eects ff of algorithms but also to the unethi - cal behaviour of consumers themselves. This is because those intending to conceal their actual financial situation from the bank realise that subjecting a broader catalogue of personal data to analysis may be to their disadvantage. In conclusion the research conducted in this article has shown that more aen tt tion needs to be paid to ethical maer tt s in the theoretical and empirical analysis of privacy issues. This aspect assumes particular importance in the context of the increasingly widespread social and economic use of Artificial Intelligence which also extends to the financial services sector. The type of data collected, the manner in which they are acquired and in particular the decisions made by machine learning algorithms will raise difficult ethical di - lemmas concerning consumer privacy in the near future thus prompting fur - ther research work in this area. The results of the study underpin applicative conclusions for banks. The first recommendation relates to the outcome of Artificial Intelligence. Banks should increase consumer awareness of how Artificial Intelligence works, how algorithms make decisions and the eects ff of these decisions on consumers. In general this demand translates into banks implementing the concept of explainable Artificial Intelligence (XAI). Banks should provide customers with an understandable and accessible form of explaining the pathway to AI decisions. Artificial Intelligence is increasingly making decisions with a specific ethi - cal component. Beyond clear-cut cases it can be a problem for Artificial Intelligence to judge specific behaviours as appropriate or undesirable. The difficulty in evaluation stems from the multitude of possible situations gi - ving rise to ethical dilemmas, the influence of context on the behaviour of the actors analysed and the diversity of social norms used in the evaluation. Harmonising banks’ criteria for assessing customers using AI could be a helpful solution. Therefore, the second recommendation was formulated as follows: Ethical standards developed by the bank reflecting the main social norms of the customers served should apply in AI training. The limitations of the research stem from the fact that only representati - ves of the Polish population were included. 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Journal
Economics and Business Review – de Gruyter
Published: Apr 1, 2023
Keywords: privacy; personal data processing; Artificial Intelligence; banking ethics; social media; A13; G21; O33