{"id":541,"date":"2020-12-04T11:02:29","date_gmt":"2020-12-04T10:02:29","guid":{"rendered":"https:\/\/digitaleconomy.org\/?p=541"},"modified":"2020-12-04T11:02:34","modified_gmt":"2020-12-04T10:02:34","slug":"interpreting-the-privacy-paradox","status":"publish","type":"post","link":"https:\/\/fromdatatoimpact.com\/index.php\/2020\/12\/04\/interpreting-the-privacy-paradox\/","title":{"rendered":"Interpreting the Privacy Paradox"},"content":{"rendered":"\n

Factors Influencing Privacy Fatigue in a Mobile Application Environment<\/em><\/p>\n\n\n\n

Paula Heuser & Leonie Gehrmann<\/a><\/h4>\n\n\n\n
<\/div>\n\n\n\n

The smartphone has become an indispensable daily companion and a\npersonal extension of the self. We cannot imagine life without mobile apps anymore,\nas they provide endless opportunities, including unlimited access to\ninformation, round-the-clock connectivity and instantaneous problem solving.\nUser data is thereby the fuel of such apps, whose features and functionalities consider\nvarious types of personal and sometimes sensitive information. While most data are\noften not actively or voluntarily created by consumers, the specific\narchitecture in which apps are embedded allows for large scale data aggregation\nrevealing a fine grained, holistic picture of individual consumers (Buck et al\n2014b, p. 27). These highly personalized insights into \u201ereal lives\u201d are very\nvaluable to marketers and useful for precise targeting, customer service and\nrelationship management. It is known that freemium business models gain revenue\nby selling personal data to third parties, so especially free versions of apps\nrequire a broad scope of permissions to information that is often unrelated to\nthe apps\u2019 functionality (Barth et al 2019, p. 56). Thus, the vast opportunities\nof app usage come with certain risks and raise discussions around informational\nprivacy or the gathering, storage, processing, and dissemination of personal\ndata (Kokolakis 2017, p. 123). <\/p>\n\n\n\n

Theoretical Background <\/h2>\n\n\n\n

With data breaches continuing to rise (De Groot 2019) and consumers\nexpressing a feeling of being \u201ecreeped out\u201d by too much personalization\n(Shklovski et al 2014, p. 2351), surveys show that lack of online privacy is\none of the major concerns of citizens (Kokolakis 2017, p. 122). While data\ndisclosure seems to have become a necessary part of modern life, 70% of\nEuropeans are concerned about the potential misuse of personal information and\nits negative outcomes (Special\nEurobarometer 359 <\/em>2011,\np.2). Yet, numerous studies show that even though people claim to be concerned\nabout their online privacy, they tend to disclose personal data anyways (Kokolakis\n2017, p. 122). This dichotomy between privacy attitudes and behavior is called\nthe privacy paradox<\/strong> and has been studied extensively across\nvarious research areas. In fact, the exploration of the relationship between\nprivacy attitudes, information disclosure, protection behavior and potential\ninfluencing variables has revealed some inconsistencies. This highlights the\ncomplexity of the paradox and the necessity to understand it, as it has significant\nimplications for e-commerce, online social networking, and governmental\nregulations (Kokolakis 2017).<\/p>\n\n\n\n

One of many interpretations and explanations for the paradox is that\npeople perform a rational calculus of benefits and risks of information\ndisclosure. As granting access to personal data is necessary to benefit from\nvarious app features, some researchers conclude that it is reasonable to\ndisclose information despite privacy concerns, if the perceived benefits of app\nusage exceed the expressed concerns (Barth and de Jong 2017, pp. 1044-1045).  Yet, as human decision-making\nis affected by cognitive biases, a large stream of literature considers people\u2019s\nlimited ability, information, time, and emotional strength to make rational\ndecisions within these situations (Li, Sarathy, and Xu 2010, p. 7). Especially the complexity of the mobile app context makes it hard to\ngrasp the consequences of granting access to personal data, so consumers often\nlack all necessary information to make informed judgements (Barth and de Jong\n2017, p. 1046). Indeed, consumers frequently complain about a feeling of loss\nof control and being overwhelmed by the amount of information to consider when\ndeciding what data to disclose or how to protect oneself. Information barriers\nprevent users from knowing the extent of the observation, storage, and\nprocessing of their behavior (Buck et al. 2014a, p. 2). Similarly, the unpredictable consequences of data disclosure are\ncomplex and difficult to calculate so individuals make judgements based on\nincomplete information (Acquisti 2004, p. 23). Furthermore, the continuous habit of buying from the same app store\nand granting access to personal data is accompanied by low levels of\ninvolvement and cognitive control, as people want to proceed quickly with the\nuse of an app (Buck et al. 2014b, p. 31). <\/p>\n\n\n\n

Taking this into consideration, a few researchers started to explore the\nphenomenon of privacy fatigue<\/strong> as an alternative explanation for the\nreduction in decision-making efforts. Fatigue\ngenerally arises from situations in which people are faced with high demands,\nhaving to deal with more things than they can handle (Choi, Park and\nJung 2018, p. 44). As privacy policies have become increasingly complex, people\nare overwhelmed and eventually give up trying to understand them. Thereby, a\nstate of emotional exhaustion is accompanied by attitudes of frustration and\nhopelessness, serving as a cognitive coping mechanism and making people choose\nthe easiest way of simply granting access to personal data (Choi, Park and Jung\n2018, p. 44). <\/p>\n\n\n\n

In their groundbreaking research on cognitive heuristics, Tversky and Kahneman (1974) <\/em>explain that fatigued individuals can fall\nback on heuristics and biases in decision-making. With the aim of minimizing\neffort, they often avoid unnecessary decisions, choose the easiest available\noption, let immediate motivations drive decisions and behave impulsively (Stanton et al. 2016, p. 29). Several studies show that one of the key outcomes of fatigue is\nbehavioral disengagement, leading to withdrawal and giving up of protective\nbehavior. Additionally, fatigued individuals put in less effort to remove\npersonal information and do not bother to provide intentional fake data, to\nengage in negative word-of-mouth or to complain to the company (Choi, Park and\nJung 2018, p. 44).<\/p>\n\n\n\n

Having discussed the consequences of privacy fatigue, some researchers\nhave also considered self-efficacy<\/strong> and privacy literacy<\/strong> as potential drivers of the phenomenon. Highly self-efficacious people\ntend to feel confident that they possess the skills to protect themselves\nagainst online privacy risks (Boehmer et al. 2015; Milne, Labrecque, and Cromer\n2009). Looking at the literature, this seems to\nbe an important predictor since people that don\u2019t believe their protection\nbehavior is effective are less likely to protect themselves (Boerman, Kruikemeier, and Zuiderveen Borgesius 2018,\np.16). <\/p>\n\n\n\n

Alternatively, studies find evidence that consumers\nacross various age groups and countries lack sufficient understanding about\nmarketing surveillance practices and privacy-related functions on mobile phones\n(Park and Mo Jang 2014, pp. 299-301; Trepte and Masur\n2017, p. 6). Therefore, researchers emphasize the importance of\nonline privacy literacy, as skilled individuals are more likely to be aware of\nthreats and are empowered to take \u201cinformed control of their digital\nidentities\u201d (Park 2013, p. 217).<\/p>\n\n\n\n

Empirical Analysis<\/h2>\n\n\n\n

To the best of our knowledge, no scientific study has empirically examined\nthe antecedents of privacy fatigue, so our project takes a closer look at this\nresearch gap and empirically investigates its potential drivers. Thereby, the\nfocus on a mobile setting is of special interest since, compared to e-commerce\nwebsites on a computer for example, the data aggregated via the use of mobile\napps is even more sensible, while at the same time the system is less secure\n(Buck, Kaubisch and Eymann 2016, p. 394). Due to its resource-efficiency and\nability to reach a high number of people, an online questionnaire was chosen to\nempirically investigate the relationship between privacy fatigue, self-efficacy\nand mobile privacy literacy. The questionnaire ran for one month in the spring\nof 2020 and used a snowball technique to recruit participants with a focus on\nthose living in Germany or with a German background. In total, the final sample\nconsists of 283 respondents. Thereby, roughly 2\/3 of participants are female\nand while almost 70% are younger than 40, the age of respondents ranges from 16\nto 85. <\/p>\n\n\n\n

After a brief introduction on the research background, participants\nfirst read a hypothetical scenario about a situation in which they download a\nfitness app and are asked to reveal their location and various types of\npersonal information. Then, with participants given the ability to imagine the\nsetting, the main part consists of the survey questions that measure the three constructs\nof interest discussed previously. Finally, participants are asked to rate the\nfrequency of use of different mobile apps, before sociodemographic questions\nabout age, gender, education, and occupation, as well as a short thank you\nconclude the questionnaire. <\/p>\n\n\n\n

For self-efficacy, respondents report their level of agreement (on a\n5-point Likert scale) with each of four different statements on their\nconfidence in avoiding danger and protecting their personal information online.\nThe items are adapted from Milne, Labrecque, and Cromer (2009, p. 456). As\nexpected, a rather low mean of self-efficacy of 2.48 indicates that overall,\nparticipants do not appear to believe in their ability to avoid danger and\nprotect personal information in a mobile app environment. Men tend to be\nslightly more self-efficacious than women and younger age groups exhibit higher\nself-efficacy than older ones.<\/p>\n\n\n\n

To capture mobile privacy literacy, the objective knowledge of\nparticipants is considered, since self-assessment of literacy might additionally\naddress self-efficacy (Trepte et al. 2015, p. 347). Masur, Teutsch, and Trepte\n(2017) emphasize the multidimensionality of online privacy literacy and develop\na scale that refers to users\u2019 knowledge on technical aspects of online data\nprotection, as well as German regulations and institutional practices. This\nproject focuses on respondents\u2019 knowledge about institutional practices and\ndata protection law. Each of these two dimensions consists of five questions.\nOne point is given for each correct answer while no points are given for wrong\nanswers. The points are then summed up to calculate the raw mobile privacy\nliteracy score for each respondent (Masur, Teutsch, and Trepte 2017, p. 267). A\nmean score of 5.74 out of 10 maximum points in the mobile privacy literacy test\nconfirms that overall participants lack substantial knowledge about practices\nof institutions and legal aspects of data protection. An average of 4 out of 5\npoints regarding institutional practices shows that participants appear to be aware\nof the ways companies and service providers collect data. Yet, a median of 2 correct\nresponses to the 5 questions about data protection law shows that people are\nnot very knowledgeable of their rights. On average men\u2019s scores are about one\npoint higher than women\u2019s, while individuals between the age of 25 and 39\nachieve the highest scores and individuals over 70 years the lowest.<\/p>\n\n\n\n

Finally, the measurement of Choi, Park, and Jung (2018) is adapted for\nprivacy fatigue. Respondents report their level of agreement (on a 5-point\nLikert scale) with six statements concerning their extent of emotional\nexhaustion and cynicism. A mean of 3.31 shows that participants generally feel\na sense of weariness towards privacy issues. While women seem to feel more\nfatigue than men, there are no obvious group differences regarding age or\nfrequency of app usage. <\/p>\n\n\n\n

Results and Discussion<\/h2>\n\n\n\n

As explained in the beginning of this post, the privacy paradox is highly complex with varying explanations developed by different researchers. Findings have indicated that especially self-efficacy plays a role in determining individuals\u2019 sense of weariness towards privacy issues and their motivation to protect themselves. Additionally, studies on awareness and knowledge of online privacy indicate that privacy literacy also influences certain aspects of fatigue and can help to derive a complementary explanation of the phenomenon. The rather low levels of self-efficacy in the empirical analysis indicate that overall, participants are not fully confident they have the skills and abilities to avoid or cope with danger in a mobile app environment. Interestingly, men and younger age groups tend to exhibit comparatively higher levels of self-efficacy. Low scores on mobile privacy literacy show as expected, that participants generally lack substantial knowledge about practices of institutions and especially legal aspects of data protection.<\/p>\n\n\n\n

A further regression analysis suggests that self-efficacy has a significant negative relationship with privacy fatigue. While mobile privacy literacy has a significant negative relationship with privacy fatigue when excluding age, gender and frequency of app usage, the relationship is not significant when including these variables as control. Hence, it seems that sociodemographic characteristics and app usage behavior do not have a significant effect on privacy fatigue. However, overall the results indicate that all of these variables together only explain a small portion of the variance in privacy fatigue, meaning there is room for improvement to find another model that is more successful at predicting this variable of interest. Nonetheless, especially the significant findings for the model\u2019s most important predictor, self-efficacy, are meaningful and help understand how privacy fatigue arises.<\/p>\n\n\n\n

Results from the statistical analysis provide important implications for marketing management and governmental regulations and show that we need to rethink the public\u2019s relationship with privacy issues. It has been widely criticized that privacy laws and security technologies have not kept pace with how rapidly developing technologies collect, store and process data (Wedel and Kannan 2016, p. 113). Germany is known to be a country with a comparatively stricter view on data protection and just in May 2020, the Federal Court passed a law that requires users to actively accept which cookies are tracked (Bundesgerichtshof 2020). While some might think that this is a step in the right direction, this thesis suggests two issues. Firstly, privacy regulations on mobile apps are more complex and less transparent to consumers. As many popular data-sensitive apps follow a \u201ctake-it-or-leave-it\u201d approach and are developed in countries with weaker legal data protection, many consumers exhibit functional confusion and are not able to draw the connection between privacy threats and a mobile environment (Park and Mo Jang 2014, p. 300). Secondly, the phenomenon of fatigue indicates that people fall into their familiar routine of neglect and simply consent whenever they are confronted with a privacy decision. Hence, simply making stricter laws and bombarding consumers with more complicated privacy statements is not the solution but will rather lead to more privacy fatigue.  <\/p>\n\n\n\n

Instead, it is crucial to recognize the existence of fatigue and users\u2019 lack of effort to make rational decisions regarding information disclosure. Understanding the role of self-efficacy and mobile privacy literacy can certainly help in finding actions that reduce the sense of weariness towards mobile privacy issues. The public should be made aware of the threats of information disclosure, but also learn that protection is not an impossible challenge. Increasing knowledge about institutional practices and data law can offer support in helping users understand the complex threats and realize what is actually being done with their data. Obviously, simply being aware of the dangers will not help if people lack the confidence that they can do something to protect themselves. Therefore, researchers suggest a more transparent app design and framing of messages about privacy can facilitate a feeling of control and personal responsibility, increasing protective behavior (Boehmer et al. 2015, p. 1031). While many individuals think they are not tech-savvy enough, it needs to be stressed that there are easy tools and steps to follow that assist protection (Milne, Labrecque, and Cromer 2009, p. 467). <\/p>\n\n\n\n

Marketing managers and app providers face a complicated trade-off between benefiting from vast amounts of customer data, but also keeping customers happy in the long-term. One might argue that privacy fatigue is in marketers\u2019 interest as consumers disclose data without questioning it and thus enable companies to offer personalized services and effectively targeted advertisements. Yet, there are always risks of data breaches or other debacles causing public outcry and especially the dimension of cynicism within privacy fatigue can decrease overall customer satisfaction (Choi, Park and Jung 2018, p. 49). Partial or complete withdrawal of customers is not desired as marketers want to get a clear picture of them. Hence, companies need to find an equilibrium of getting valuable data and maintaining trust. Research suggests that the solution lies within the communication and design of apps. They should have a user-oriented design that decreases information overload and empowers users to make self-determined decisions about privacy protection (Barth and de Jong 2017, p. 1051). Raising privacy awareness on an application-specific level and connecting this with knowledge about essential tools and protection methods can aid marketers in strengthening the relationship with the customer in the long-term and benefiting from an acceptable level of data disclosure (Deuker 2009, p. 281). <\/p>\n\n\n\n

To conclude, some remarks on the limitations of this project. Due to the chosen snowball sampling technique, the sample contains respondents from similar backgrounds. While the age range is quite large and represents a considerable number of participants from younger as well as older generations, the sample is not balanced in terms of gender or education. Furthermore, as research emphasizes the importance of technical skills with regard to understanding the privacy threats in the app context (Buck, Kaubisch, and Eymann 2016, p. 393), there is a strong need for the development of a privacy literacy construct in the mobile app context that covers various dimensions and not only institutional practices and data protection law. Especially, no clear definition of the term privacy fatigue in the academic literature makes it hard to define what exactly should be measured. Therefore, a more distinct definition of the term as well as enhanced development of a measurement construct is necessary. Finally, for a comprehensive interpretation of the privacy paradox it is necessary to assess individuals\u2019 actual disclosure behavior and investigate how far privacy fatigue is connected to the paradoxical behavior. This enables a better understanding of the consequences of fatigue and its implications in practice. Nevertheless, the primary goal of this project is to focus on how privacy fatigue arises and how it relates to other research on the privacy paradox. As it is the first to ever investigate the influencing factors of privacy fatigue, it adds fresh insights into a complementary explanation of the privacy paradox. Especially the finding that higher levels of self-efficacy may decrease privacy fatigue has meaningful implications in practice and can help future researchers to further understand the phenomenon. \u00a0 <\/p>\n\n\n

\n
<\/span>References<\/div>
\n
    \n
  • Acquisti, Alessandro (2004), \u201cPrivacy in Electronic Commerce and the Economics of Immediate Gratification,\u201d in Proceedings of the 5th ACM Conference on Electronic Commerce, 21\u201329.\n<\/li>\n
  • Barth, Susanne and Menno D.T. de Jong (2017), \u201cThe privacy paradox \u2013 Investigating discrepancies between expressed privacy concerns and actual online behavior \u2013 A systematic literature review,\u201d Telematics and Informatics, 34 (7), 1038\u201358.\n<\/li>\n
  • Barth, Susanne, Menno D.T. de Jong, Marianne Junger, Pieter H. Hartel, and Janina C. Roppelt (2019), \u201cPutting the privacy paradox to the test: Online privacy and security behaviors among users with technical knowledge, privacy awareness, and financial resources,\u201d Telematics and Informatics, 41, 55\u201369.\n<\/li>\n
  • Boehmer, Jan, Robert LaRose, Nora Rifon, Saleem Alhabash, and Shelia Cotten (2015), \u201cDeterminants of online safety behaviour: Towards an intervention strategy for college students,\u201d Behaviour and Information Technology, 34 (10), 1022\u201335.\n<\/li>\n
  • Boerman, Sophie C., Sanne Kruikemeier, and Frederik J. Zuiderveen Borgesius (2018), \u201cExploring Motivations for Online Privacy Protection Behavior: Insights From Panel Data,\u201d Communication Research, 00 (0), 1\u201325.\n<\/li>\n
  • Buck, Christoph, Chris Horbel, Claas Christian Germelmann, and Torsten Eyman (2014a), \u201cThe Unconscious App Consumer\u202f: Discovering and Comparing the Information \u2010 Seeking Patterns Among Mobile Application Consumers,\u201d in Twenty Second European Conference on Information Systems, 1\u201314.\n<\/li>\n
  • Buck, Christoph, Chris Horbel, Tim Kessler, and Claas Christian Germelmann (2014b), \u201cMobile Consumer Apps: Big Data Brother is Watching You,\u201d Marketing Review St. Gallen, 31 (1), 26\u201335.\n<\/li>\n
  • Buck, Christoph, Daniela Kaubisch, and Torsten Eymann (2016), \u201cWer wei\u00df was\u202f? \u2013 Digitale Privatsph\u00e4re und App-Literacy aus Nutzerperspektive,\u201d in Multikonferenz Wirtschaftsinformatik (MKWI) 2016., 391\u2013402.\n<\/li>\n
  • Bundesgerichtshof (2020), “Bundesgerichtshof zur Einwilligung in telefonische Werbung und Cookie-Speicherung” (accessed June 4th, 2020), https:\/\/www.bundesgerichtshof.de\/SharedDocs\/Pressemitteilungen\/DE\/2020\/2020067.html\n<\/li>\n
  • Choi, Hanbyul, Jonghwa Park, and Yoonhyuk Jung (2018), \u201cThe role of privacy fatigue in online privacy behavior,\u201d Computers in Human Behavior, 81, 42\u201351.\n<\/li>\n
  • De Groot, Juliana (2019), “The History of Data Breaches” (accessed June 4th, 2020), https:\/\/digitalguardian.com\/blog\/history-data-breaches.\n<\/li>\n
  • Deuker, Andr\u00e9 (2009), \u201cAddressing the Privacy Paradox by Expanded Privacy Awareness – The Example of Context-Aware Services,\u201d in 5th IFIP WG 9.2., 275\u201383.\n<\/li>\n
  • Kokolakis, Spyros (2017), \u201cPrivacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon,\u201d Computers and Security, 64, 122\u201334.\n<\/li>\n
  • Li, Han, Rathindra Sarathy, and Heng Xu (2010), \u201cUnderstanding Situational Online Information Disclosure as a Privacy Calculus,\u201d Journal of Computer Information Systems.\n<\/li>\n
  • Masur, Philipp K., Doris Teutsch, and Sabine Trepte (2017), \u201cEntwicklung und Validierung der Online-Privatheitskompetenzskala (OPLIS),\u201d Diagnostica, 63 (4), 256\u201368.\n<\/li>\n
  • Milne, George R., Lauren I. Labrecque, and Cory Cromer (2009), \u201cToward an Understanding of the Online Consumer\u2019s Risky Behavior and Protection Practices,\u201d Journal of Consumer Affairs, 43 (3), 449\u201373.\n<\/li>\n
  • Park, Yong Jin (2013), \u201cDigital Literacy and Privacy Behavior Online,\u201d Communication Research, 40 (2), 215\u201336.\n<\/li>\n
  • Park, Yong Jin and S. Mo Jang (2014), \u201cUnderstanding privacy knowledge and skill in mobile communication,\u201d Computers in Human Behavior, 38, 296\u2013303.\n<\/li>\n
  • Shklovski, Irina, Scott D. Mainwaring, Halla Hrund Sk\u00falad\u00f3ttir, and H\u00f6skuldur Borgthorsson (2014), \u201cLeakiness and creepiness in app space,\u201d in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2347\u201356.\n<\/li>\n
  • \u201cSpecial Eurobarometer 359: Attitudes on Data Protection and Electronic Identity in the European Union\u201d (2011). TNS Opinion & Social.\n<\/li>\n
  • Stanton, Brian, Mary F. Theofanos, Sandra Spickard Prettyman, and Susanne Furman (2016), \u201cSecurity Fatigue,\u201d IT Professional, 18 (5), 26\u201332.\n<\/li>\n
  • Trepte, Sabine and Philipp K Masur (2017), \u201cPrivacy attitudes, perceptions, and behaviors of the German population,\u201d Forum Privatheit und selbstbestimmtes Leben in der digitalen Welt. Karlsruhe:\n<\/li>\n
  • Trepte, Sabine, Doris Teutsch, Philipp K. Masur, Carolin Eicher, Mona Fischer, Alisa Hennh\u00f6fer, and Fabienne Lind (2015), \u201cDo People Know About Privacy and Data Protection Strategies? Towards the \u2018Online Privacy Literacy Scale\u2019 (OPLIS),\u201d in Reforming European Data Protection Law, 333\u201365.\n<\/li>\n
  • Tversky, Amos and Daniel Kahneman (1974), \u201cJudgment under Uncertainty: Heuristics and Biases Amos,\u201d Science, 185 (4157), 1124\u201331.\n<\/li>\n
  • Wedel, Michel and P.K. Kannan (2016), \u201cMarketing Analytics for Data-Rich Environments,\u201d Journal of Marketing, 80 (6), 97\u2013121.\n<\/li>\n<\/ul>\n<\/div><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

    Factors Influencing Privacy Fatigue in a Mobile Application Environment Paula Heuser & Leonie Gehrmann The<\/p>\n","protected":false},"author":4,"featured_media":545,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5,10],"tags":[],"coauthors":[19],"class_list":["post-541","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-economics-of-data","category-people-analytics"],"jetpack_featured_media_url":"https:\/\/fromdatatoimpact.com\/wp-content\/uploads\/2020\/12\/Cell_withLock2-1.jpg","_links":{"self":[{"href":"https:\/\/fromdatatoimpact.com\/index.php\/wp-json\/wp\/v2\/posts\/541","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fromdatatoimpact.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fromdatatoimpact.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fromdatatoimpact.com\/index.php\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/fromdatatoimpact.com\/index.php\/wp-json\/wp\/v2\/comments?post=541"}],"version-history":[{"count":4,"href":"https:\/\/fromdatatoimpact.com\/index.php\/wp-json\/wp\/v2\/posts\/541\/revisions"}],"predecessor-version":[{"id":549,"href":"https:\/\/fromdatatoimpact.com\/index.php\/wp-json\/wp\/v2\/posts\/541\/revisions\/549"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/fromdatatoimpact.com\/index.php\/wp-json\/wp\/v2\/media\/545"}],"wp:attachment":[{"href":"https:\/\/fromdatatoimpact.com\/index.php\/wp-json\/wp\/v2\/media?parent=541"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fromdatatoimpact.com\/index.php\/wp-json\/wp\/v2\/categories?post=541"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fromdatatoimpact.com\/index.php\/wp-json\/wp\/v2\/tags?post=541"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/fromdatatoimpact.com\/index.php\/wp-json\/wp\/v2\/coauthors?post=541"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}