Dissertation Master in Management
Privacy and Personalization: Consumer’s drivers, deterrents and moderators to
share personal information with firms and in an online context
Nuno Tiago Dias Pereira
Dissertation Report Master in Management
Supervised by
Professor Teresa Maria Rocha Fernandes Silva
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Biographical Note
Nuno Pereira was born on April 28th, 1994. He went to high school at the Paulo VI
High School in Gondomar, attending the Social-Economics course.
In 2012, he decided to enroll in the University of Porto’s School of Economics and Management (FEP), where he began his bachelor’s degree in Economics, finished in 2015. After a brief professional experience at Sonae Sierra, he started his master’s degree in Management at the same school in 2016.
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Acknowledgements
I would like to thank everyone who supported me during this long process.
My first ever so thankful word is to my supervisor, professor Teresa Fernandes, for the round-clock presence and support, always just an e-mail away to provide guidance, comments and recommendations throughout all the stages of the process.
Secondly, I would thank my family for the support provided to accomplish my goals throughout my entire life, as well as the patience to listen to my excited ramblings during the writing process.
Thirdly, I would thank all my friends, from the Medievais to my friends from FEP, as well as all the rest of random groups of friends, for being a mental and emotional support during the entire project.
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Abstract
The digital age is upon us and it has had an impact in how interactions between consumers and companies take place. With consumers now becoming rich sources of information with increasing access to products and services, their loyalty and interest is now fragmented, which creates new challenges to companies as they try to provide extra value to acquire both the business that consumers represent but also their data, highlighting the importance of further studying the way consumers and companies interact in that world.
This study was therefore developed with the aim of filling a research gap that analyses what constructs influence consumers’ willingness to disclose personal information. A research model was created to assess drivers, deterrents and moderators of willingness to disclose personal information, which was done by combining different models used to study technology adoption, such as the Technology Acceptance Model (TAM) and the Unified Theory of Technology Acceptance and Use of Technology (UTAUT), as well as models used to study privacy and consumer behavior, such as the Privacy Calculus Theory, which lead to the consideration of certain constructs as drivers and deterrents of consumers’ willingness to disclose personal information, as well as the consideration of age, gender, past experience and sector of the company as moderators of that same behavior. Data was collected through an online survey that gathered a convenience sample of 956 Portuguese consumers’ who have willingly shared personal information with companies in an online context.
Findings showed that proposed drivers such as Perceived Usefulness, Social Influence, Hedonic Motivation and Previous Habits have a positive impact in explaining consumers’ willingness to share, with Previous Habits being the most significant of the drivers, while deterrents such as Perceived Internet Privacy Risk and Effort Expectancy were confirmed to have a negative impact in consumers’ willingness to disclose personal data. Additionally, data also provided partial or full support to proposed moderators.
The present study attempts to add to the existing literature by contributing to a deeper understanding of consumer willingness to disclose personal data, while also aiming to provide support for future research in this area and information regarding how and what companies can adapt to different expectations consumers might have.
Keywords: Willingness to disclose, Personal Data, Privacy, Personalization, Online Interaction
v Index
1 Introduction ... 1
2 Literature Review ... 4
2.1 The digital and (challenges of) Big Data ... 4
2.2 Personalization ... 7
2.3 Privacy ... 8
2.4 Theoretical models of consumers’ acceptance behavior ... 11
2.4.1 Theory of Reasoned Action ... 11
2.4.2 Technology Acceptance Model ... 12
2.4.3 Theory of Planned Behavior ... 14
2.4.4 Unified Theory of Acceptance and Use of Technology ... 14
2.4.5 Privacy Calculus Theory ... 16
3 Research framework and hypotheses ... 18
4 Empirical Study ... 22 4.1 Methodology ... 22 4.2 Data Collection... 22 4.3 Data analysis ... 25 4.3.1 Sample Description ... 25 4.3.2 Descriptive Analysis ... 27 4.3.3 Factor analysis ... 29
4.3.4 Hypotheses analysis: H1 – H7 analysis ... 32
4.3.5 Hypothesis analysis: H8 analysis ... 36
4.4 Discussion of Results ... 44
5 Conclusions ... 51
5.1 General Considerations ... 51
5.2 Managerial & Theoretical Implications ... 53
5.3 Limitations and Future Research ... 55
Bibliography ... 57
Appendixes ... 70
Appendix 1 – Technology Adoption Models and supporting literature Table ... 70
Appendix 2 – Methodology benchmark ... 71
Appendix 3 – Full Survey ... 72
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Appendix 5 – “Past Experience” mean differences ... 81 Appendix 6 – “Sector” mean differences ... 82
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1 Introduction
With society facing an increasingly faster adoption of new technologies, marked by the rise of social media and the internet of things, the ability to store and analyze massive quantities of data has also developed. With this ability rapidly evolving and growing into several aspects of our daily life, it’s natural to expect a discussion on its implications (Boyd & Crawford, 2012)
On one hand, with access to increasing amounts of data of their customers, it’s possible to argue that companies will be able to better know their customers’ characteristics and needs, allowing them to provide a more personalized consuming experience (Wedel & Kannan, 2016). On the other hand, it’s also reasonable to argue that customers might become wary of the use given to their information in the sake of personalization, more precisely, in the form of information privacy concerns.
This balance becomes even more complex when we take into consideration that “unlike evaluating a product, estimating the value of a person's information privacy preferences remains an open problem” (Hirschprung et al., 2016, p. 450), reinforcing that privacy still remains incalculable, which is logical given the unique preferences of each individual consumer. Hence, even though privacy is not a recent research topic, with cornerstone discussions on the topic dating back as far as the nineteenth century (Warren & Brandeis, 1890), it’s now becoming an even more pertinent and pressing topic. As such, it becomes apparent the need for further reflection on the position adopted by the consumers in light of the privacy-personalization paradox.
Following these concerns, the Marketing Science Institute has elected the trade-off between privacy concerns and the benefits of personalization, customization versus intrusion, annoyance versus effectiveness as a 2016-2018 research priority (MSI, 2016). This recommendation of topic comes at a time where research on the topic is still considered to be lacking and well needed (Buchanan et al., 2007; Zhu et al, 2017). Furthermore, in Deloitte’s Tech Trends 2017 report, we are made aware to the fact that data previously ignored has started to be analyzed by companies in an effort to obtain additional insight into consumer habits (Deloitte, 2017).
Considering the importance attributed to the topic at hand, both in academic and entrepreneurial terms, the research gap that we propose to develop lies on the fact that,
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despite the growth in the number of studies regarding privacy, there still is a gap in research when it comes to consumers’ reactions to disclosure of personal information (Roeber et al, 2015), more precisely their drivers, deterrents and moderators for information disclosure upon request by companies. Additionally, there’s still uncertainty concerning the drivers that lead consumers to share personal data regardless of the privacy risks (White, 2004; Dinev & Hart, 2004 Dinev & Hart, 2006; Norberg, Horne & Horne, 2007; Lopez-Nicolas, Molina-Castillo & Bouwman, 2008; Li, Sarathy & Xu, 2011; Mothersbaugh et al, 2012).
Furthermore, it’s also considered that there is a need to explore if the type of services the customer seeks can be connected with different attitudes regarding disclosure of personal information (Milne & Gordon, 1993; Bart et al, 2005; Norberg, Horne & Horne, 2007; Krafft, Arden & Verhoef, 2017).
As such, this study aims to understand why the customer is willing (or not) to share personal information upon request, and thus also willing to abdicate their privacy for the promise of a more personalized service or experience. Firstly, the primary goal of this research is to determine the drivers of customers’ willingness to share personal information. In fact, consumers, as rational agents, are expected to have motivations for their actions, and the ability to calculate and act upon the perceived risks and benefits (Morosan & DeFranco, 2015). The purpose would lie on understanding what the consumer is expecting to obtain in return of sharing their personal information. For example, does the consumer expect to receive personalized discounts or exclusive promotions and to what degree is it expecting one option more than another?
Additionally, the second goal of this research is to find key drivers and moderators of these motivations. Not only are there different drivers for information disclosure, but the willingness to share information can also be connected with the situational context (Sharma & Crossler, 2014; Chakraborty et al, 2016). Furthermore, it’s reasonable to consider the possibility of demographic characteristics also having an influence on the consumers’ position regarding information sharing. In other words, part of the study will focus on trying to determine if, for example, consumers are more willing to share information with companies of certain industries, such as food retail, fashion, entertainment, under the guise of further service/product personalization (Hand et al, 2009; Chen & Teng, 2013; Derikx, Reuver & Kroesen, 2016), while also establishing and reinforcing possible connections
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between consumers’ behavior towards privacy and personalization, as well as with their age and gender (Krasnova et al, 2010; Jai & King, 2016; Chakraborty et al, 2016).
To accomplish the proposed goals, a research model was developed to assess drivers, deterrents and moderators of willingness to disclose personal information. Data was collected through an online survey that gathered a convenience sample of 956 Portuguese consumers’ who have had online interactions with companies where they willingly disclosed personal information, analyzed under the light of the proposed research model and tested through the use of SPSS.
With this study, it would hopefully be possible to better comprehend the consumers’ perspective and opinions on the privacy/personalization issue, how and when they are willing to share their personal information and what they expect to gain from sharing it.
At the same time, it’s also expected that this study can help provide a better insight for companies to understand how they should gather consumer information and how they should use that same information to better target their consumers, which means it would entail important managerial implications for companies and the market, as the correct use of the big amounts of data available by companies can be of significant financial consequence (McAfee & Brynjolfsson, 2012; Endo et al, 2016).
In the subsequent sections of the study, a literature review of the main concepts that ought to be taken into consideration is offered, followed by a theoretical revision of models developed for the study of technology adoption, privacy and consumer behavior. Afterwards, the proposed hypotheses are presented, followed by the analysis of the gathered data with the main results. Finally, the implications and respective conclusions will be presented.
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2 Literature Review
Society is currently facing an increasingly faster rate of technological innovation, where everything and everyone can be connected, while constantly sharing information and communicating. Such societal evolution is bound to require a reflection about its consequences, benefits and disadvantages.
2.1 The digital and (challenges of) Big Data
With technological advancements, the ability to collect data or information has also increased, which has, in part, led to the rise of the concept of Big Data.
Big Data has been described as enormous amounts of information that have been created and continues to be created (Brown, Chui & Manyika, 2011; Salas-Olmedo et al, 2018).
Nowadays, with the technological advancements and cultural changes that society has been facing, we are left with copious amounts of information being continuously produced, through a diverse scope of sources, starting with the consumer’s smartphone activity, as well as their online and social media activity. Such frenetic activity by the consumers leads to the creation of a lot of information, basically turning everyone into a “walking data generator” (McAfee & Brynjolfsson, 2012, p. 5). But the ability to gather and sustain such amounts of information has only been created thanks to the technological progress that allows the increase of processing power, as well as the increase in storage and battery capacity (Miorandi et al, 2012).
In fact, Big Data represents the creation of so much information that it is commonly associated with three key factors or V’s (Russom, 2011; McAfee & Brynjolfsson, 2012; Sagiroglu & Sinanc, 2013; Salas-Olmedo et al, 2018). Even though some studies point to the existence of five factors or V’s (Akter & Wamba, 2016), the majority refers to the following three:
• Volume – the amount of information available and created daily is increasing • Velocity – the speed at which data is created has also become important, as real-time
information becomes a reality
• Variety – Big Data develops in several formats, as it comes from several sources, some of which are considered new, such as the several new social media platforms.
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Such diversification has also allowed the complementation with information from traditional sources (Salas-Olmedo et al, 2018).
We also know that the complexity of information gathered has increased, as companies are faced with both structured, unstructured and even what is considered semi structured data (Russom, 2011; Chen, Mao & Liu, 2014; Akter & Wamba, 2016).
On one hand, structured data appears to be considered that which can be more easily categorized, such as, for example, demographic characteristics, and can result from the interaction between the consumer and the company. On the other hand, unstructured data appears to correspond to more nuanced, not-as-easily interpreted information, such as text and human language (Russom, 2011), as well as clicks, likes, links, tweets, voices (Akter & Wamba, 2016). Finally, even though semi structured was the category for information such “XML and RSS feeds” (Russom, 2011), a lot of information remains complicated to allocate to one of these categories, such as audio and video (Russom, 2011; Akter & Wamba, 2016). However, the ability to process and analyze such amounts of data has also developed (McAfee and Brynjolfsson, 2012) through ever so sophisticated systems that allow for the storage, management and analysis of Big Data (Akter & Wamba, 2016).
Big Data, as mentioned, has been brought forth by a tidal wave of information, coming from multiple sources. A phenomenon that has assisted the rise of Big Data has been the increasing implementation of the Internet of Things throughout society.
Described as the mobile and internet-enabled devices’ ability to support several operations, all while taking into consideration factors such as location and context (Chen, Chiang & Storey, 2012; Sicari et al, 2015), the Internet of Things (IoT) has also been associated with the increasing number of physical objects with an internet connection and the transformation of these objects from ordinary to smart, interconnecting them through the internet with resource to current technologies (Miorandi et al., 2012; Al-Fuqaha et al, 2015).
The ability of obtaining information from common objects’ surroundings represents several opportunities for all the stakeholders involved (Miorandi et al., 2012) as well as several implications and applications (Atzori, Iera & Morabito, 2010), causing the impact in Big Data to become apparent.
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The relation between Big Data and Internet of Things lies on the fact that IoT implies the existence of a large number of data generators, producing retrievable content, therefore contributing to the production of large amounts of information associated with Big Data, indicating that both concepts are interlinked (Atzori, Iera & Morabito, 2010; Roeber et al, 2015).
While analysis of information and data might not seem like anything new, it’s relevant to mention Big Data Analytics, as they refer to the ability of analyzing massive quantities of information – Big Data (Russom, 2011).
Given the sheer amount of information currently available, it becomes a very important job to be able to analyze it. Nonetheless, Big Data, due to its volume, carries a lot of value in terms analytical insights (Russom, 2011; Chen, Chiang & Storey, 2012) and, due to technological advancements, the resources and methods needed to analyze big amounts are now available, allowing for a better suited analysis.
Taking into account the amount of information held within Big Data, part of which is being generated by consumers, the impact brought upon companies by Big Data Analytics comes as a consequence (Akter & Wamba, 2016). In different streams of research, Big Data Analytics is always considered to add value to companies and organizations, may it be through the “identification of new opportunities” or through “strategy-led analytics” (Akter & Wamba, 2016).
In fact, the use of data-driven decision-making processes can cause an actual impact in regard to helping businesses become more profitable and productive, which would indicate the potential of Big Data Analytics as a competitive advantage (Xu et al, 2007; Lee & Cranage, 2011; McAfee & Brynjolfsson, 2012; Akter & Wamba, 2016; Wedel & Kannan, 2016).
With access to much more information about their consumers, as well as the ability to analyze said information, companies are now able to provide increasingly personalized products and services, almost targeting the customer individually (Schafer, Konstan & Riedl, 2001), contributing to the proliferation of personalization.
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2.2 Personalization
The ability to gather such massive quantities of information and put them to use is of consequence for consumers as well – under the guise of access to personalized services, product offerings, targeted discounts and other formats.
Personalization, sometimes also referred to as customization (Miceli, Risotta & Costabile, 2007), usually alludes to the ability of making products, services and contents individualized, as to match a customer’s personal tastes and tastes (Lee and Cranage, 2011). It can also be argued that personalization refers to the ability of delivering “the right content to the right person in the right format at the right time” (Ho & Tam, 2005, p. 96). Personalization has also been considered the act of learning the customer’s personal tastes, adapting to said tastes and finally learning the effectiveness of such adaptation (Wedel & Kannan, 2016), as well as the process of centering the product or service creation around the customer (Tuli, Kohli & Bharadwaj, 2007).
The ability to provide a personalized product or service based on the Big Data becomes even more relevant when the heterogeneity among consumers is considered (Wedel & Kannan, 2016), given that, with more information about the customer’s habits, companies can try to provide an almost individually tailored product or service experience or, if necessary, varying degrees of personalization (Galvez-Cruz & Renaud, 2006; Lee & Cranage, 2011; Wedel & Kannan, 2016).
There are recognizable advantages and benefits of personalization for both customers and companies. While customers enjoy the benefits of convenience, efficiency, and individualization (Lee & Cranage, 2011), companies are able to reinforce their connection with the customer and improve, besides purchase intention, key indicators, such as sales, customer retention and loyalty (Urban, Amyx & Lorenzon, 2009; Wedel & Kannan, 2016).
However, in discussing the advantages and benefits of personalization, it’s important to examine the consumers perspective. While benefiting of personalized offering, consumers might have different expectations from such personalization (Lee & Rha, 2016; Zhu et al., 2017; Tezinde, Smith & Murphy, 2002). When confronted with the need to share their information, consumers can raise questions regarding what they stand to gain from sharing it (Mani & Chouk, 2017) and if value is actually added to the situation, which can cause a
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feeling of intrusiveness (Nguyen & Simkin, 2017) and influence their inclination to answer truthfully (Poddar, Mosteller, & Ellen, 2009).
The role of customers’ expectations becomes even more relevant when it’s taken into consideration that companies can obtain the customers’ personal information in order to enact personalization in their services and to constitute the “Big Data” available for the companies (Brown, Chui & Manyika, 2011). With access to such amounts of information regarding consumers’ behaviors, preferences and tastes, obtained through an ever-increasing number of sources, new challenges from an ethical standpoint are raised (Sicari et al, 2015), as it becomes necessary to safely manage and secure private information that has been provided by consumers and that is now on the possession of companies (Peltier, Milne & Phelps, 2009).
Therefore, the accumulation of consumers’ information that Big Data represents leads to the need of discussion around the issue of privacy and private information management, without neglecting what type of awareness and expectations the consumers’ might have regarding what use is to be given to their personal information by the companies (Schafer, Konstan & Riedl, 2001; Akter & Wamba, 2016).
2.3 Privacy
As it has been able to understand, technological advancements have allowed for an ever-increasing ability to not only produce, but also store and analyze information. Information that has, in a vast number of cases, been created by consumers. Such situation brings into question the need to discuss the issue of privacy.
According to Warren and Brandeis (1890), considered one of the most quintessential representations of the privacy definition, privacy is connected with what “concerns the private life, habits, acts and relations of an individual” (p. 216). However, they refer to privacy in a different context than the one society is currently facing.
It’s due to the constantly occurring technological innovation that new perspectives on privacy have come to fruition. While information privacy has been characterized as the ability to and the degree of control over one self’s information (Westin, 1967; Fried, 1968), consumer privacy has also been presented as the ability to control “presence of other people in the environment during a market transaction or consumption behavior” and
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“dissemination of information related to or provided during such transactions or behaviors to those who were not present” (Goodwin, 1991, p. 152).
The concepts of information and consumer privacy become increasingly relevant in a day and age where big quantities of data are available, drawing attention from several areas of society (Smith, Dinev & Xu, 2011). Given the amount of personal data that companies are currently able to obtain on consumers, the debate on how to best defend consumers’ privacy right has arisen to the center stage (Wang, Lee & Wang, 1998; Davenport & Harris, 2007), highlighting the importance of fighting the violation of information privacy, that stands as the “the unauthorized collection, disclosure, or other use of personal information” (Wang, Lee & Wang, 1998, p. 64).
Additionally, it has been considered that the ability to retain control over their own personal information is an element of a person’s privacy (Newell, 1995). And that leads to the acknowledgement of the impact that concepts such as Big Data and Internet of Things can have on an individual’s privacy, as the privacy debacle is currently resting precisely on this “newly” found ability to harness data from, for example, mobile technologies (Steijn & Vedder, 2015).
These developments in the conceptualization of privacy, alongside technological advancements that allow data gathering and analysis, have led to the companies renewed abilities to personalize their offering, requiring further reflection on how consumers perceive the use of their personal information by private companies.
The consumers’ information can and will routinely be accessed by third parties, as companies amass great amounts of data (Lewis, Kaufman & Christakis, 2008), data that includes personal information which the consumers often make easily available, which would imply acceptance of privacy risks (Akter & Wamba, 2016; Robinson, 2017).
If consumers accept to voluntarily make their information available to companies when they request it, even though there are risks and privacy concerns, then the consumer might be recognizing benefits in incurring in such situation (Smith, Dinev & Xu, 2011; Sharma & Crossler, 2014), balancing them against the expected risks (Margulis, 2003) and still willingly making the decision to trade-off their privacy (Sharma & Crossler, 20014; Morosan & DeFranco, 2015; Hirschprung et al., 2016), to share their information.
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Consumers, as individuals, might have different degrees of privacy preferences, as they are able to decide which information they wish to make public or not, further indicating the existence of different motivations underlying such information disclosure (Lewis, Kaufman & Christakis, 2008), differences which have become inspiration for privacy related legislation, such as the GDPR.
Adopted in May 2016 and applied since the 15th of May of 2018, the piece of
regulation commonly known as GDPR aimed to strengthen EU citizens rights in the current digital age, while covering any organization that deals with personal data of EU residents (European Commission, 2018; Hsu, 2018).
By providing a clear and transversal definition of personal information as “anything that could directly or indirectly identify a person” (Hsu, 2018), the GDPR provides not only a guidance for companies who want to deal with private data, but also enshrines the individuals right to the protection of their personal information and the access to the data that has been collected, plus the right to rectify it if it so requires (European Commission, 2018).
Such legislation as allowed for an extension of individual rights, as access to data must be unequivocally given, while individuals must also have access to whichever information is held regarding them, granting them more control over what consumer information companies can retain, making organizations liable for violations of the GDPR (Tankard, 2016; Tikkinen-Piri, Rohunen & Markkula, 2018).
With a common framework aimed at regulating the gathering and handling of EU residents’ personal data, not only do the EU countries become better prepared for the future challenges that the digital age presents (Ryz & Grest, 2016; Zerlang, 2017; Gellert, 2018; Tikkinen-Piri, Rohunen, Markkula, 2018), but it also creates new challenges for companies and new roles for consumers, given that, despite potential loss of privacy, consumers have proved open to disclose personal data under certain circumstances. Such information disclosure is vital for companies in the sense of customer personalization, retention and satisfaction, further instigating the need to study what might still lead consumers to share and under what conditions.
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2.4 Theoretical models of consumers’ acceptance behavior
Throughout the years, the motivations behind individuals’ behaviors have been extensively studied and researched, leading to the development of multiple frameworks, models and theories that attempt to structure behavior.
As certain frameworks became more established, the literature grew to reinforce them and assert them as models composed of drivers that ought to be taken into consideration. However, as human behavior is subject to several variables and constant change, several frameworks have been created to explain it and then further broadened in additional studies. Therefore, to better sustain the research of the proposed topic, the use of long-established models must follow, as to guarantee solid theoretical cornerstones that ought to sustain this work.
Taking the importance of strong theoretical frameworks, models from Information Systems, Communication and Social Behavior were examined and taken into account, as to obtain the variables that we aim to regard and put to test, as presented in Appendix 1. 2.4.1 Theory of Reasoned Action
Originated in Fisbein and Ajzen’s work in 1975 (Fishbein & Ajzen, 1975), the Theory of Reasoned Action (TRA) came to be the basis of several other models of study of technology acceptance and adoption, such as the Technology Acceptance Model (Davis, 1985; Davis, Bagozzi & Warshaw, 1989) and the Theory of Planned Behavior (Ajzen, 1985), becoming a prominent theory on human behavior research (Venkatesh et al, 2003).
Founded on the belief that humans make rational decisions and that they make use of the information available (Ajzen, 1985; Kim, Kim & Park, 2010), the TRA focused on two main constructs: the attitude toward behavior and subjective norm (commonly known as social influence).
2.4.1.1 Attitude toward Behavior
Described as “a person’s general feeling of favorableness or unfavorableness toward some stimulus object” (Fishbein & Ajzen, 1975, p. 216), the attitude manifested by an individual towards something is connected with the beliefs the individual has formed around it (Fishbein & Ajzen, 1975).
Further studies around the construct of attitude toward behavior have been conducted to research consumers’ attitudes and have argued for the existence of a
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connection between consumer attitudes and consumer behavior (Ajzen, 1985; Lee & Cranage, 2011; Lee & Chang, 2011; Stutzman, Capra & Thompson, 2011; Krafft, Arden & Verhoef, 2017).
2.4.1.2 Subjective Norm
Characterized as “the person’s perception that most people who are important to him think he should or should not perform the behavior in question” (Fishbein & Ajzen, 1975, p. 302), subjective norm (also labelled social influence) represents the level to which the consumer considers that others (that matter to the individual) think he should adopt a certain behavior, having been well established throughout research into human behavior (Thompson, Higgins & Howell, 1991; Venkatesh et al, 2003; Miltgen, Popovic & Oliveira, 2013; Shibchurn & Yan, 2015; Faqih, 2016).
The construct has gathered attention, not only in the study of technology adoption (Venkatesh et al, 2003; Faqih, 2016), but also in information disclosure on social networking websites (Shichurn & Yan, 2015; Mosteller & Poddar, 2017), even though there are dissenting voices regarding its real impact (Venkatesh, Thong & Xu, 2012; Miltgen, Popovic & Oliveira, 2013).
2.4.2 Technology Acceptance Model
Initially developed in 1989 as an expansion of the Theory of Reasoned Action (Ajzen & Fishbein, 1975), the Technology Acceptance Model (Davis, 1985; Davis, Bagozzi & Warshaw, 1989) has become an extensively used model in the research of information systems, garnering significant popularity in helping to explain technology adoption (Rosen & Sherman, 2006; Faqih, 2016). However, the model has grown to become a framework used to understand consumers’ attitudes towards several topics, ranging from online services (Aldas-Manzano et al, 2009; Lee & Chang, 2011; Dix, Jamieson & Phau, 2011; Lian & Yen, 2014) to information disclosure (Zimmer et al, 2010; Li, Sarathy & Xu, 2011; Ho, 2012; Sharma & Crossler, 2014) and information technology acceptance (Miltgen, Popovic & Oliveira, 2013; Shibchurn & Yan, 2015).
Though the initial version of the Technology Acceptance Model (TAM) focused on two main drivers, perceived usefulness and perceived ease of use (Davis, Bagozzi & Warshaw, 1989), a later developed version came to include perceived enjoyment (Davis, Bagozzi & Warshaw, 1992), receiving further scrutiny (Childers et al, 2001).
13 2.4.2.1 Perceived Usefulness
One of the quintessential elements of the Technology Acceptance Model, Perceived Usefulness, is characterized as the budding user’s personal perception of the level to which a particular tool, system or technology will improve its own performance (Davis, Bagozzi & Warshaw, 1989; Hernandez, Jimenez & Martin, 2009; Aldas-Manzano et al, 2009; Ho, 2012; Faqih, 2016; Mani & Chouk, 2017)
The available literature stresses how, depending on how strongly the user believes the tool or system is significant and relevant, the more likely they are to regard it as useful and wish to adopt it (Hartwick & Barki, 1994), proving to be a valuable construct in researching information technology and its adoption (Miltgen, Popovič & Oliveira, 2013). This construct proves even more pertinent as it has proved to be positively connected with consumers’ reactions toward mass personalization (Lee & Chang, 2011), with their participation in social commerce (Sharma & Crossler, 2014) and information disclosure (Li, Sarathy & Xu, 2011), while also heralded as a construct considered by consumers when considering to engage in information disclosure with companies in exchange for pertinent specific information (Krafft, Arden & Verhoef, 2017).
2.4.2.2 Perceived Ease of Use
Similar to Perceived Usefulness, Perceived Ease of Use was also developed as one of the main components of the Technology Acceptance Model, as one of the fundamental factors that influence the individual’s decision to adopt technology (Davis, Bagozzi & Warshaw, 1989). Described as the level to which the user conjectures that resorting to a certain tool, system or technology would be effortless (Davis, Bagozzi & Warshaw, 1989; Lankton & Wilson, 2007; Aldas-Manzano et al, 2009; Lee & Chang, 2011).
According to the literature encompassing this factor, the user’s perception of ease of use is considered to have the potential to influence the decision to proceed with information disclosure (Krafft, Arden & Verhoef, 2017), even though its actual weight on technology adoption has gathered mixed results and positions (Hernandez, Jimenez & Martin, 2009; Miltgen, Popovič & Oliveira, 2013; Faqih, 2016).
2.4.2.3 Perceived Enjoyment
Included in a later version of the Technology Acceptance Model, Perceived Enjoyment was originally characterized as the degree to which the use of computer is considered enjoyable (Davis, Bagozzi & Warshaw, 1992).
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Later studies surrounding the model led to the consideration of perceived enjoyment (besides the two other previously mentioned variables of TAM) as applicable to the adoption, acceptance and use of technology as a whole (van der Heijden, 2003; Kim, Kim & Park, 2010; Wakefield, 2013; Sharma & Crossler, 2014).
2.4.3 Theory of Planned Behavior
Also built upon the Theory of Reasoned Action (Ajzen & Fishbein, 1975), the Theory of Planned Behavior (TPB) attempts to study variables considered to influence the relation between intention and behavior (Ajzen, 1985), as it argues that the focus of research into human behavior should be in understanding it, not merely predicting it.
Adopting the basis of the Theory of Reasoned Action, which assumes that humans tend to usually adopt behaviors of a sensible nature (Ajzen & Fishbein, 1975), the TPB gained popularity (Wu & Chen, 2005) by adapting the indicators Attitude toward Behavior and Subjective Norm – also called Social Influence – and by dwelling into the construct of Perceived Control (Ajzen, 1985; Armitage & Conner, 2001; Venkatesh et al, 2003).
2.4.3.1 Perceived Behavioral Control
First seriously developed in the Theory of Planned Behavior (Ajzen, 1985) and further supported as a meaningful driver of behavioral intention (Ajzen, 1991; Ajzen, 2002), perceived control has been defined as the level of control an individual perceives to have over factors that might cause interference with the execution of the behavior in question (Ajzen, 1985; Ajzen, 1991; Ajzen, 2002). Later studies with focus on this particular factor led to its reinforcement as a significant driver in consumer attitudes and behaviors (Lee & Chang, 2011; Dix, Jamieson & Phau, 2011; Tucker, 2014; Benson, Saridakis & Tennakoon, 2015; Krafft, Arden & Verhoef, 2017; Mosteller & Poddar, 2017).
2.4.4 Unified Theory of Acceptance and Use of Technology
Established in 2003, the Unified Theory of Acceptance and Use of Technology – UTAUT (Venkatesh & Davis, 2000; Venkatesh & Morris, 2000; Venkatesh et al, 2003; Venkatesh, Thong & Xu, 2012) aims to create a theory that encompasses elements of several other theories used to investigate information technology acceptance.
The initial version of UTAUT included and established four core elements, namely, Performance Expectancy and Effort Expectancy, both adapted from the Perceived Usefulness construct from the Technology Acceptance model (Miltgen, Popovic & Oliveira,
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2013), Social Influence (as a representation of subjective norm) and Facilitating Conditions (serving as proxy for behavioral control) (Venkatest et al, 2003; Venkatesh, Thong & Xu, 2012; Miltgen, Popovič & Oliveira, 2013; Krafft, Arden & Verhoef, 2017).
By 2012, the model was updated to include additional constructs capable of influencing consumer behavior, such as Hedonic Motivation, Price Value and Habit (Venkatesh, Thong & Xu, 2012). The construct of Hedonic Motivation, characterized as the “fun or pleasure derived from using a technology” (Venkatesh, Thong & Xu, 2012, p.5), was proposed in order to take into consideration the research in Information System built upon the concept of Perceived Enjoyment (Davis, Bagozzi & Warshaw, 1992; van der Heijden, 2003; Lee & Chang, 2011; Sharma & Crossler, 2014).
2.4.4.1 Facilitating Conditions
When developing the construct of Facilitating Conditions, the construct was considered as the level to which the individual thinks that an organizational support structure exists to sustain the use of the technology (Venkatest et al, 2003). Even though the construct was based in previous constructs such as Perceived Behavioral Control (Ajzen, 1985; Ajzen, 1991), it was also based in constructs such as Facilitating Conditions (Thompson, Higgins & Howell, 1991), defined as environmental objective factors that facilitate the concretization of a certain behavior, and Compatibility (Moore & Benbasat, 1991), characterized as the level to which a new technology is considered to be consistent with already established values, needs and previous experiences of potential users.
2.4.4.2 Price Value
Defined as the consumer’s subjective trade-off between the perceived benefits of a product/service and the monetary cost for using them, this construct was found to be able to influence technology adoption by consumers, as it represents the need to reflect on the adoption of technology and ponder the actual cost versus its benefits (Dodds, Monroe & Grewal, 1991; Venkatest, Thon & Xu, 2012; Liu et al, 2015). The financial incentives perceived by the consumers when sharing personal data with companies, under the guise of discounts and other incentives, have led to the development of this construct, as companies can use such incentives to persuade consumers (Krafft, Arden & Verhoef, 2017).
2.4.4.3 Habit
The construct of habit aims to introduce two constructs that have been explored in research of technology adoption, experience and habit. Despite conflict in the topic around
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the definition of the two, the model, as presented, advances that it considers experience as “the passage of time from the initial use of a target technology” (Venkatesh, Thong & Xu, 2012, p. 6), reflecting the research in the area that presents past experience as a potential driver for consumer behavior (Culnan & Armstrong, 1999; Pavlou, 2003; Lankton & Wilson, 2007; de Kerviler, Demoulin & Zidda, 2016), operating as a moderator, and habit, considered to be a “a self-reported perception” (Venkatesh, Thong & Xu, 2012, p. 6).
2.4.5 Privacy Calculus Theory
Developed, in its early form, as a model to establish the connection and effect that perceived vulnerability and perceived control could have on perceived privacy concerns, the Privacy Calculus model grew to be applied in the privacy-paradox for e-commerce transactions (Dinev & Hart, 2004; Dinev & Hart, 2006), with its later version including constructs vital in discussing information disclosure: willingness to provide personal information, perceived internet privacy risk and personal internet interest.
Considered to have derived from the Social Exchange Theory (Krasnova et al, 2010), its focus lies on studying the drivers of consumers’ willingness to provide personal information and the connections amongst them, the model has led to ample literature, its focus ranging from e-commerce (Dinev & Hart, 2006) to personalization (Morosan & DeFranco, 2015; Zhu et al, 2017).
2.4.5.1 Perceived Internet Privacy Risk
Given the nature of the study, the consumer’s perception of risk of sharing personal data online comes as a vital part of the discourse of information disclosure (Dinev & Hart, 2006), as it deals with the perception of uncertainty derived from the behavior of information disclosure and its perceived consequences – positive or negative (Littler & Melanthiou, 2006). The perception of risk in information disclosure has gathered widespread attention under the topic in question (H Bauer et al, 2005; Hur, Ko & Valacich, 2007; Youn, 2009; Milne, Labrecque & Cromer, 2009; Dix, Jamieson & Phau, 2011; de Kerviler, Demoulin & Zidda, 2016; Faqih, 2016), supporting its inclusion as a factor of consumers’ intention to share information.
Additionally, the proposed model adds the notions of Internet Privacy Concerns and Internet Trust as being influenced by the Perceived Internet Privacy Risk, as well as capable
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of impacting the willingness to provide personal information in internet transactions (Dinev & Hart, 2006; Krafft, Arden & Verhoef, 2017).
2.4.5.2 Internet Privacy Concerns
Presented as the concerns the consumer or user might have with the occurrence of behavior considered opportunistic regarding the personal information submission made through the internet by the consumer in question, it was the focus of the initial version of the Privacy Calculus Model established in 2003 (Dinev & Hart, 2003; Dinev & Hart, 2006).
However, in the extended version, it was considered as driving factor of willingness to provide personal information and capable of influencing perceived internet privacy risk (Dinev & Hart, 2006).
The driver has been used to explore the consumers attitudes and sensitivities towards personal information disclosure in different industries (Derikx, de Reuver & Kroesen, 2016), as well as in research about coping behaviors in response to privacy-related situations (Youn, 2009), technology adoption (Miltgen, Popovic & Oliveira, 2013) and information disclosure (Krafft, Arden & Verhoef, 2017).
2.4.5.3 Internet Trust
Defined as the beliefs held by consumer regarding its trust on the due process to which its personal information will be submitted (Dinev & Hart, 2006), it was structured as a driver of the willingness to share personal information, but also as a factor affected by perceived internet privacy risk.
The concept of trust has been widely referenced in the internet and e-commerce literature available (Bart et al, 2005; Kim, Kim & Park, 2010; Chen & Teng, 2013; Miltgen, Popovic & Oliveira, 2013; Faqih, 2016; Nguyen & Simkin, 2017), connecting the construct with the higher consumer disposition to disclose information and willingness to interact. 2.4.5.4 Personal Internet Interest
Presented as “the degree of cognitive attraction to internet interactions” (Dinev & Hart, 2006, p.8), Personal Internet Interest was found to influence the willingness to disclose personal information (Dinev & Hart, 2006). This construct allows the understanding of individual interest and perception of what benefits the user stands to gain by interacting with an environment as broad as the internet.
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3 Research framework and hypotheses
Contemplating the several models developed for the study of technology acceptance and adoption, the Unified Theory of Acceptance and Use of Technology appears as the most complete framework, encompassing elements of models and theories that were developed throughout time (Venkatesh et al, 2003; Venkatesh, Thong & Xu, 2012). Although the proposed models could be considered as classical models of study of consumer behavior, they have resisted the test of time, still being used in current studies of the topic (Miltgen, Popovič & Oliveira, 2013; Cheng & Teng, 2013; Faqih, 2016; Mani & Chouk, 2017; Kraft, Arden & Verhoef, 2017).
Bearing in mind the adaptation of the Unified Theory of Acceptance and Use of Technology (UTAUT) to information disclosure, the hypotheses that ought to be studied must be adapted from the context of technology adoption, leading to the creation of the framework proposed in Figure 1 below.
Drawing from the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), it’s possible to argue that, if consumers perceive that, by using a certain technology, they will be able to be better suited to work, they’ll also be more likely to adopt said technology (Davis, Bagozzi & Warshaw, 1989; Davis, Bagozzi & Warshaw, 1992; Venkatesh et al, 2003), while the same could be said in an
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information disclosure situation, as consumers will be more willing to disclose with companies if they see it as useful interaction (Krafft, Arden & Verhoef, 2017). However, considering that Performance Expectancy was developed while focusing in a work-environment, the construct “Perceived Usefulness” (Davis, Bagozzi & Warshaw, 1989) might prove to be better suited to an information disclosure context:
H1. Consumers’ perceived usefulness of information disclosure will have a positive impact
in consumer’s willingness to share personal information.
In the day and age of social networks, where everyone is interconnected and expected to do so (Pasternak, Veloutsou & Morgan-Thomas, 2017), the UTAUT model includes social influence as driver of consumer’s intention to adopt technology (Venkatesh et al, 2003). Additionally, admitting that Social Influence is connected with the individual’s perception of other people’s expectations for its behavior, it can be considered that Social Influence falls under the guise of Perceived Benefits, as its fulfillment would be positive for the individual in question. In doing so, it recognizes the role that this variable has in influencing consumer behavior, allowing its adaptation for information disclosure:
H2. Social Influence of information disclosure will have a positive impact in consumers’
willingness to share personal information.
Taking into account the development of the Perceived enjoyment construct in the Technology Acceptance model (Davis, Bagozzi & Warshaw, 1992; Chen & Teng; 2013) and the Hedonic Motivation construct in the UTAUT model (Venkatesh et al, 2012), it’s possible to assume that the fun and enjoyment incurred by the consumer might impact their willingness to share information, allowing for the partial understanding of how the consumer perceives the benefits of personalization:
H3. Hedonic Motivation of information disclosure will have a positive impact in consumers’
willingness to share personal information.
Developed in the renewed UTAUT model (Venkatesh, Thong & Xu, 2012), the cognitive trade-off between the cost and perceived monetary compensation of adopting technology has also been researched in the area of information disclosure (Roeber et al, 2015, Krafft, Arden & Verhoef, 2017), allowing for the partial understanding of how the consumer perceives the benefits of personalization, through the measurement of the impact that the perception of financial or monetary benefits will have in the consumer’s willingness to
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disclose their private information. More precisely, the construct of Perceived Financial Reward aims to study if certain perceived types of incentives (in this case, of a more financial nature) for information disclosure have or not an impact on the consumers’ willingness to share personal information (Krafft, Arden & Verhoef, 2017):
H4. Perceived financial reward of information disclosure will have a positive impact in
consumers’ willingness to share personal information.
As proposed in the renewed UTAUT model (Venkatesh, Thong & Xu, 2012), and reinforced in privacy research (Milne & Gordon, 1993; Pavlou, 2003; Metzger, 2007), consumers’ prior behaviors and experiences are likely to influence their decisions (Limayem, Hirt & Cheung, 2007). Furthermore, given the potential clash between the construct Personal Internet Interest (Dinev & Hart, 2006) and the construct Habits presented in the UTAUT model (Venkatesh, Thong & Xu, 2012), it could be argued that the consumer’s habits would already indicate a degree of interest in the activity at hand, which led the Personal Internet Interest construct to be abandoned in favor of the Habits construct:
H5. Previous Habits of information disclosure will have a positive impact in consumers’
willingness to share personal information.
Advanced under the Privacy Calculus model (Dinev & Hart, 2006), and applied to information disclosure, consumers’ perceived risk is considered to influence their disposition to disclose information, given that consumers’ fear has been seen to dissuade them from sharing (Krafft, Arden & Verhoef, 2017). Perceived Risk must then be taken into account under the light of the Privacy Calculus Mode, as it portrays the perceived potential negative consequences of incurring in a certain behavior:
H6. Perceived Internet privacy risk of information disclosure will have a negative impact in
consumers’ willingness to share personal information.
Additionally, still in the scope of the TAM and the UTAUT models, the perceived effort allocated to the use of technology is not only considered an influencing driver of consumer adoption (Davis, Bagozzi & Warshaw, 1989; Davis, Bagozzi & Warshaw, 1992; Venkatesh et al, 2003; Venkatesh, Thong & Xu, 2012), but has also been used to study how complicated and long processes could decrease users’ willingness to disclose personal information with companies (Krafft, Arden & Verhoef, 2017), which leads to the use of such a construct to study information disclosure:
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H7. Consumers’ effort expectancy of information disclosure will have a negative impact in
consumer’s willingness to share personal information.
Finally, according to the literature dedicated to technology acceptance and information disclosure, there are reasons to believe that moderators such as gender, age, past experience and sector of the company involved (Milne & Gordon, 1993; Venkatesh & Morris, 2000; Wu & Chen, 2005; Metzger, 2007; Norberg, Horne & Horne, 2007; Waters & Ackerman, 2011; Venkatesh, Thong & Xu, 2012; Jai & King, 2016), will have an impact in the willingness to disclose personal information,though the discussion around the impact of these constructs has received mixed results (Metzger, 2007; Lankton & Wilson, 2007; Youn, 2009; Waters & Ackerman, 2011; Wakefield, 2013; Jai & King, 2016). From the results available, men were considered to have a higher predisposition to share their personal information and younger individuals have also been indicated as demonstrating a higher disposition to disclosing personal information (Jai & King, 2016). Moreover, the inclusion of past experience as moderator becomes useful as the consumer can be argued to already have taken into consideration its own privacy concerns when reflecting upon the risks of sharing personal information. Furthermore, although literature regarding the variation of consumer perception and behavior according to the sector of the company they’re interacting with is scarce, the available literature provides insight into the differences of consumer willingness to share personal information, with consumers indicating a higher willingness to disclose with online shops and lower willingness to share with, for example, banks (Roeber et al, 2015), while also pointing that consumers of industries such as apparel and textile had a higher willingness to share personal data in comparison to what was observed for financial services, retailing and telecommunications (Krafft, Arden & Verhoef, 2017):
H8. The intensity of the drivers and deterrents on consumers’ willingness to share personal
information is moderated by (i) Gender; (ii) age, (iii) past experience and (iv) sector of the company involved.
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4 Empirical Study
In this section, the selected methodology and the results of the proposed study will be presented, portraying the selected methods of measurement and data collection, as well as the analysis of the gathered data.
4.1 Methodology
The methodology selected will be a quantitative analysis through the use of survey by questionnaire for data collection.
To objectively ascertain the best method of data collection that would allow the study of the proposed hypotheses, an analysis of the available literature proved vital, as it provided information regarding the most common practices in the research of technology adoption and information disclosure.
By observing the literature in Appendix 2, it’s possible to understand that online surveys and questionnaires are the most commonly used methods of data collection, as it allows for the gathering of bigger data samples, while also allowing for the use of regression as a statistical method, to test the proposed hypotheses. It’s also possible to ascertain that this method of data collection has been widely used, both throughout time and geographies, reinforcing its pertinence.
The use of surveys and questionnaires for data collection is advantageous in the study of models such as the proposed, given that, when testing and quantifying connections between variables, the results should allow for generalization (Saunders, Lewis & Thornhill, 2016).
Additionally, as quantitative studies appear to be the norm, Likert scales are also commonly used to better developed and standardize answers.
Therefore, the methodology of choice for the study at hand will be developed under the form of an online survey, allowing for a larger data pool, created with resource to Likert scales and analyzed under a quantitative perspective (Saunders, Lewis & Thornhill, 2016).
4.2 Data Collection
The data was obtained resorting to a self-administered online survey, submitted through e-mail to a specific set of respondents, composed of students and staff of the University of Porto.
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Measurement items (questions) created to study the proposed hypotheses were adapted from similar studies, as to reinforce the validity of the study and of its results and verify a possible causal. However, taking into consideration that certain studies were focused on different characteristics of technology adoption, certain items were changed to reflect the purpose of this study.
Additionally, given that the survey targeted a Portuguese population, a translation to the Portuguese language was unavoidable as to guarantee that it was intelligible to individuals answering. A declaration of anonymity was also presented in the beginning of the survey, to assure respondents of the respect for the personal data being provided in the survey and subside any possible fears.
During the creation of the survey, it was submitted to testing with a small pool of diverse test subjects, that were able to provide feedback on respondents’ perception of the survey, before submitting it to a larger audience.
To maintain the focus of the study on the individuals that share personal information with companies in an online context and the factors that influence them, as well as to reinforce the moderator of “Past Experience”, a question that allowed to separate individuals that claimed to have never shared personal data online with companies was placed at the forefront of the survey (“How regularly do you share personal information online with companies?”).
It must also be mentioned that the method used to numerically measure the proposed items – the Likert scale – was adapted from previously mentioned research, as shown in Appendix 2. This measurement method was achieved through the use of a scale ranging from 1 to 5 (1 being “strongly disagree” and 5 “strongly agree”).
Additionally, it was deemed important to shuffle the questions, as respondents have been found to try to keep consistency in their answers (Podsakoff et al, 2003). Therefore, aiming to make it more difficult for respondents to maintain artificial consistency between answers or to search for patterns in the questions, as well as to avoid biased results, the questions were shuffled randomly.
As presented in Table 1 below, it’s possible to see the connection that was established between the questions placed to the respondent, the constructs and the literature:
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Questions regarding the demographic data, as well as habits of online consumption, were left for the end of the survey, which can be found, in its entirety, on Appendix 3.
The survey and the associated data collection was done online, through the online platform provided by Google.
The sample of respondents was obtained from the thousands of students of University of Porto, as well as professors and non-teaching staff of the same University. It must also be noted that the survey was made available to a network of acquaintances present in social
PH1 I'm used to shopping online
PH2 I'm accustomed to using my social networks accounts to login into other websites PH3 I'm used to making payments/transactions online
HM1 I can experience something different de Kerviler, Demoulin & Zidda 2016
HM2 I can have access to something that amuses me Morosan & DeFranco 2015
HM3 I can escape routine Pentina et al 2016
PU1 I can do what I needed in a more pratical way Venkatesh et al 2003
PU2 I can receive recommendations based on previous interactions and purchases Bart et al 2005 PU3 I can obtain more information about products and services López-Nicolás, Molina-Castillo
& Bouwman 2008
PFR1 I can buy personalized products and services that allow me to save money Hur, Ko & Valacich 2009 PFR2 I can have access to personalized discounts Krafft, Arden & Verhoef 2017 PFR3 I can have access to special promotions de Kerviler, Demoulin & Zidda 2016 SI1 I can have access to something people important to me think I should use Venkatesh et al 2003
SI2 I can access comments and reviews given by other consumers Bart et al 2005
SI3 I can use something promoted by celebrities Bart et al 2005
PIPR1 The risk that my personal information will be used for other purposes Dinev & Hart 2006 PIPR2 The risk that my personal information will be revealed to governmental entities Dinev & Hart 2006 PIPR3 The risk that my transactions and banking data will be granted access to Dinev & Hart 2006
PIPR4 The risk of my personal information being made public Dinev et al 2006
PIPR5 The risk of having my personal information made available to third
parties/companies without my authorization Dinev & Hart 2006
EE1 Not being helped when faced with problems or obstacles Bart et al 2005
EE2 Not being able to control which information is used Bart et al 2005
EE3 Losing a lot of time understanding how to operate the system Dinev & Hart 2005 WSPI1 In the future, I plan to share my personal information online with companies
WSPI2 I'm willing to share my personal information online with companies WSPI3 I'm used to sharing my personal information whenever I'm looking for this
product/service Hedonic Motivation
Scale Source of Measure
Previous Habits
Venkatesh, Thong & Xu 2012
Venkatesh, Thong & Xu 2012 Perceived Usefulness
Perceived Financial Reward
Social Influence
Perceived Internet Privacy Risk
Effort Expectancy
Willingness to share personal information
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networks. The survey was made widely available in order to obtain a self-imposed goal of answers of, at least, quintuple the number of questions (Hair et al, 2010).
4.3 Data analysis
Through the application of the online survey, it was possible to obtain 1077 valid answers (i.e. people that submitted the survey) that make up the general sample. However, of those 1077, only 956 claimed to have, to a certain degree and at a certain point, shared their personal information with companies in an online context, as 121 claimed to have never incurred in such behavior.
The following sections will offer three different analyses of the collected data.
The first analysis will a characterization of the sample, with a brief reflection of the respondents’ characteristics. The second analysis will offer a descriptive analysis of the variables, containing the average and the standard deviation. Finally, the third analysis will offer a factor analysis of the obtained results.
4.3.1 Sample Description
Taking into consideration the 956 valid responses that constitute the sample, we now proceed to an analysis of the respondents’ individual characteristics.
Table 2 presented below shows the respondents’ individual characteristics, namely, demographic data and habits of online consumption.
Respondents % Age 17 - 26 326 34,1 27 - 36 155 16,2 37 - 46 202 21,1 47 - 56 161 16,8 57 + 112 11,7 Gender Male 336 35,1 Female 620 64,9
Daily hours spent online
0 - 2h 201 21,0 3h - 5h 400 41,8 6h - 8h 233 24,4 > 8h 122 12,8 Total 956 100 Characteristics Sample
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Considering the individuals that have shared personal information with companies, in an online and commercial context, it’s possible to see that a higher proportion of the respondents (64,9%) are female, as opposed to male (35,1%).
It’s also possible to verify a clear concentration of respondents that have shared their personal information online with companies in the younger age groups, more precisely, in the group between 22 and 26 years old (19,7%).
It’s vital to address the disparity between age groups, given the higher concentration in younger age groups. Such distribution of respondents through the several age groups is understandable when the sample’s origin (University of Porto) is taken into consideration.
Additionally, another characteristic of the respondents that was analyzed was the time the respondents spent online, becoming clear that most of the individuals (41,8%) spend between 3 to 5 hours online daily.
The respondents were also asked about two other characteristics, more precisely, the sector of the last company with whom they shared personal data online with and the frequency with which they share personal data online with companies in a commercial context, as it can be seen in Table 3 below:
Respondents % Sector Retail - Fashion 247 25,8 Entertainment 126 13,2 Healthcare 100 10,5 Tourism 167 17,5 Banking/Finance 147 15,4 Retail - Grocery 47 4,9 Others 122 12,8 Sharing Frequency Rarely 423 44,2 Occasionally 408 42,7 Frequently 125 13,1 Total 956 100 Characteristics Sample
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As it is possible to observe on Table 3 above, there is a fair distribution of respondents throughout the proposed sectors of last interaction of information sharing, with Retail – Fashion appearing as the most significant sector, with 247 answers or 25,8% of the respondents having last shared personal data with companies of these sector, while the smallest group appeared to be Retail – Grocery, with just 47 answers or 4,9% of the respondents having last shared with that sector.
Finally, regarding the frequency with which the respondents share personal information with companies in a transactional and commercial situation, we can see that there’s a clear concentration in two categories of behavior, “Rarely” and “Occasionally” sharing, with 44,2% and 42,7% respectively, with respondents who shared “Frequently” representing 13,1% of the sample.
4.3.2 Descriptive Analysis
After carefully analyzing the answers obtained through the survey, it became possible to provide a descriptive analysis of the variables considered for the intents of this study.
In Table 4, it’s possible to observe the questions’ average and standard deviations. Through Table 4, we can see that, regarding the “Previous habits” variable, question PH2 (“I’m accustomed to using my social networks accounts to login into other websites”) has shown the lowest average answer, averaging 2,373, and a standard deviation of 1,414.
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When taking into consideration the “Hedonic Motivation” variable, we see that, although all three questions had close average answers, question HM1 (“I can experience something different”) had the lowest average, with 1,931, and a standard deviation of 1,088. While analyzing the questions of the “Perceived Usefulness” variable, it’s possible to understand that question PU1 (“I can do what I needed in a more practical way”) achieved the highest average value, 3,783, with a standard deviation of 1,122.
Furthermore, when analyzing the questions related to the “Perceived Financial Reward” variable, it’s clear that, even though the values aren’t all that dispersed, question PFR1 (“I can buy personalized products and services that allow me to save money”) obtained a higher answer average, about 2,854, with a standard deviation of 1,326.
Additionally, after reviewing the questions regarding the variable “Social Influence”, we see that question SI2 (“I can have access comments and reviews given by other consumers”) achieved a significantly higher average in comparison to the other questions, averaging 2,332, and a standard deviation of 1,318.
Average Standard Deviation
PH1 I'm used to shopping online 3,109 1,349
PH2 I'm accostumed to using my social networks accounts to login into other websites 2,373 1,414
PH3 I'm used to making payments/transactions online 3,544 1,349
HM1 I can experience something different 2,086 1,061
HM2 I can have access to something that amuses me 2,195 1,235
HM3 I can escape routine 1,931 1,088
PU1 I can do what I needed in a more pratical way 3,783 1,122
PU2 I can receive recommendations based on previous interactions and purchases 2,730 1,242
PU3 I can obtain more information about products and services 2,688 1,232
PFR1 I can buy personalized products and services that allow me to save money 2,854 1,326
PFR2 I can have access to personalized discounts 2,591 1,339
PFR3 I can have access to special promotions 2,785 1,354
SI1 I can have access to something people important to me think I should use 1,720 0,994
SI2 I can access comments and reviews given by other consumers 2,332 1,318
SI3 I can use something promoted by celebrities 1,350 0,737
PIPR1 The risk that my personal information will be used for other purposes 4,555 0,902 PIPR2 The risk that my personal information will be revealed to governmental entities 3,052 1,374 PIPR3 The risk that my transactions and banking data will be granted access to 4,360 1,045
PIPR4 The risk of my personal information being made public 4,173 1,086
PIPR5 The risk of having my personal information made available to third parties/companies without my authorization 4,431 0,955
EE1 Not being helped when faced with problems or obstacles 3,353 1,293
EE2 Not being able to control which information is used 4,470 0,902
EE3 Losing a lot of time understanding how to operate the system 2,654 1,284
WSPI1 In the future, I plan to share my personal information online with companies 2,492 1,135 WSPI2 I'm willing to share my personal information online with companies 2,344 1,070 WSPI3 I'm used to sharing my personal information whenever I'm looking for this product/service 2,304 1,120
Question
