Classification of literature on level of analysis
Level of analysis | Privacy | Information privacy concern (IPC) |
---|---|---|
Individual | 29 | 10 |
Group | 2 | 0 |
Organization | 11 | 0 |
Societal | 9 | 3 |
Alashoor , T. , Han , S. and Joseph , R.C. ( 2017 ), “ Familiarity with big data, privacy concerns, and self- disclosure accuracy in social networking websites: an APCO model ”, Communications of the Association for Information Systems , Vol. 41 No. 4 , pp. 62 - 96 , doi: 10.17705/1cais.04104 .
Alge , B.J. , Ballinger , G.A. , Tangirala , S. and Oakley , J.L. ( 2006 ), “ Information privacy in organizations: empowering creative and extra-role performance ”, Journal of Applied Psychology , Vol. 91 No. 1 , p. 221 .
Amor , D. ( 2002 ), Internet Future Strategies: how Pervasive Computing Will Change the World , Prentice Hall Professional .
Angst , C.M. and Agarwal , R. ( 2009 ), “ Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion ”, MIS Quarterly , Vol. 33 No. 2 , pp. 339 - 370 .
Awad , N.F. and Krishnan , M.S. ( 2006 ), “ The personalization privacy paradox: an empirical evaluation of information transparency and the willingness to be profiled online for personalization ”, MIS Quarterly , Vol. 30 No. 1 , pp. 13 - 28 .
Belanger , F. and Crossler , R.E. ( 2011 ), “ Privacy in the digital age: a review of information privacy research in information systems ”, MIS Quarterly , Vol. 35 No. 4 , pp. 1017 - 1041 .
Belanger , F. and Hiller , J.S. ( 2006 ), “ A framework for e-government: privacy implications ”, Business Process Management Journal , Vol. 12 No. 1 , pp. 48 - 60 .
Black , S.L. , Stone , D.L. and Johnson , A.F. ( 2015 ), “ Use of social networking websites on applicant’s privacy ”, Employee Responsibilities and Rights Journal , Vol. 27 No. 2 , pp. 115 - 159 .
Brown , M. and Muchira , R. ( 2004 ), “ Investigating the relationship between internet privacy concerns and online purchase behavior ”, Journal of Electronic Commerce Research , Vol. 5 No. 1 , pp. 62 - 70 .
Burton , S.H. , Tanner , K.W. , Giraud-Carrier , C.G. , West , J.H. and Barnes , M.D. ( 2012 ), “ Right time, right place health communication on Twitter: value and accuracy of location information ”, Journal of Medical Internet Research , Vol. 14 No. 6 .
Cao , J. and Everard , A. ( 2008 ), “ User attitude towards instant messaging: the effect of espoused national cultural values on awareness and privacy ”, Journal of Global Information Technology Management , Vol. 11 No. 2 , pp. 30 - 57 .
Chen , K. and Rea , A.I. ( 2004 ), “ Protecting personal information online: a survey of user privacy concerns and control techniques ”, Journal of Computer Information Systems , Vol. 44 No. 4 , pp. 85 - 92 .
Clark , T.D. Jr. , Jones , M.C. and Armstrong , C.P. ( 2007 ), “ The dynamic structure of management support systems: theory development, research focus, and direction ”, MIS Quarterly , Vol. 31 No. 3 , pp. 579 - 615 .
Cranor , L.F. , Guduru , P. and Arjula , M. ( 2006 ), “ User interfaces for privacy agents ”, ACM Transactions on Computer-Human Interaction , Vol. 13 No. 2 , pp. 135 - 178 .
Culnan , M.J. and Armstrong , P.K. ( 1999 ), “ Information privacy concerns, procedural fairness, and impersonal trust: an empirical investigation ”, Organization Science , Vol. 10 No. 1 , pp. 104 - 115 .
Culnan , M.J. and Bies , R.J. ( 2003 ), “ Consumer privacy: balancing economic and justice considerations ”, Journal of Social Issues , Vol. 59 No. 2 , pp. 323 - 342 .
Culnan , M.J. and Williams , C.C. ( 2009 ), “ How ethics can enhance organizational privacy: lessons from the choice point and TJX data breaches ”, MIS Quarterly , Vol. 33 No. 4 , pp. 673 - 687 .
Dillon , T.W. , Hamilton , A.J. , Thomas , D.S. and Usry , M.L. ( 2008 ), “ The importance of communicating workplace privacy policies ”, Employee Responsibilities and Rights Journal , Vol. 20 No. 2 , pp. 119 - 139 .
Dinev , T. and Hart , P. ( 2004 ), “ Internet privacy concerns and their antecedents – measurement validity and a regression model ”, Behaviour and Information Technology , Vol. 23 No. 6 , pp. 413 - 423 .
Dinev , T. and Hart , P. ( 2006 ), “ An extended privacy calculus model for e-commerce transactions ”, Information Systems Research , Vol. 17 No. 1 , pp. 61 - 80 .
Dinev , T. , Bellotto , M. , Hart , P. , Russo , V. , Serra , I. and Colautti , C. ( 2006a ), “ Internet users’ privacy concerns and beliefs about government surveillance: an exploratory study of differences between Italy and the United States ”, Journal of Global Information Management , Vol. 14 No. 4 , pp. 57 - 93 .
Dinev , T. , Bellotto , M. , Hart , P. , Russo , V. , Serra , I. and Colautti , C. ( 2006b ), “ Privacy calculus model in e-commerce – a study of Italy and the United States ”, European Journal of Information Systems , Vol. 15 No. 4 , pp. 389 - 402 .
Dobosz , B. , Green , K. and Sisler , G. ( 2006 ), “ Behavioral marketing: security and privacy issues ”, Journal of Information Privacy and Security , Vol. 2 No. 4 , pp. 45 - 59 .
Donaldson , T. and Dunfee , T.W. ( 1994 ), “ Toward a unified conception of business ethics: integrative social contracts theory ”, Academy of Management Review , Vol. 19 No. 2 , pp. 252 - 284 .
Earp , J.B. , Anton , A.I. , Aiman-Smith , L. and Stufflebeam , W.H. ( 2005 ), “ Examining internet privacy policies within the context of user privacy values ”, IEEE Transactions on Engineering Management , Vol. 52 No. 2 , pp. 227 - 237 .
Fletcher , K.P. and Peters , L.D. ( 1997 ), “ Trust and direct marketing environments: a consumer perspective ”, Journal of Marketing Management , Vol. 13 No. 6 , pp. 523 - 539 .
Floridi , L. ( 2017 ), “ Group privacy: a defence and an interpretation ”, Group Privacy , pp. 83 - 100 .
Fried , C. ( 1968 ), “ Privacy ”, The Yale Law Journal , Vol. 77 No. 3 , pp. 475 - 493 , doi: 10.2307/794941 .
Gonzalez , N. , Miers , C. , Redigolo , F. , Simplicio , M. , Carvalho , T. , Näslund , M. and Pourzandi , M. ( 2012 ), “ A quantitative analysis of current security concerns and solutions for cloud computing ”, Journal of Cloud Computing: Advances, Systems and Applications , Vol. 1 No. 1 , p. 11 .
Gregor , S. ( 2006 ), “ The nature of theory in information systems ”, MIS Quarterly , Vol. 30 No. 3 , pp. 611 - 642 .
Hong , W. and Thong , J.Y.L. ( 2013 ), “ Internet privacy concerns: an integrated conceptualization and four empirical studies ”, MIS Quarterly , Vol. 37 No. 1 , pp. 275 - 298 .
Hsu , C.W. ( 2006 ), “ Privacy concerns, privacy practices, and website categories: toward a situational paradigm ”, Online Information Review , Vol. 30 No. 5 , pp. 569 - 586 .
Hughes , K. ( 2012 ), “ A behavioural understanding of privacy and its implications for privacy law ”, The Modern Law Review , Vol. 75 No. 5 , pp. 806 - 836 .
Jensen , C. and Potts , C. ( 2004 ), “ Privacy policies as decision-making tools: an evaluation of online privacy notices ”, Proceedings of the SIGCHI conference on Human Factors in Computing Systems , pp. 471 - 478 .
Jentzsch , N. ( 2001 ), “ The economics and regulation of financial privacy: a comparative analysis of the United States and Europe ”, Working Paper , John F. Kennedy Institute .
Kharade , J. ( 2016 ), “ G2C E-governance project implementation at local level in a Pune division context, BVIMSR’s ”, Journal of Management Research , Vol. 8 No. 1 , p. 19 .
Klasnja , P. , Consolvo , S. , Jung , J. , Greenstein , B.M. , LeGrand , L. , Powledge , P. and Wetherall , D. ( 2009 ), “ When I am on wi-fi, I am fearless: privacy concerns and practices in everyday wi-fi use ”, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , pp. 1993 - 2002 .
Kufer , J. ( 1987 ), “ Privacy, autonomy, and self-concept ”, American Philosophical Quarterly , Vol. 24 No. 1 , pp. 81 - 89 .
Kyei-Blankson , L. , Iyer , K.S. and Subramanian , L. ( 2016 ), “ Social networking sites: college students' patterns of use and concerns for privacy and trust by gender, ethnicity, and employment status ”, International Journal of Information and Communication Technology Education (IJICTE) , Vol. 12 No. 4 , pp. 62 - 75 .
Laufer , R.S. and Wolfe , M. ( 1977 ), “ Privacy as a concept and a social issue: a multidimensional development theory ”, Journal of Social Issues , Vol. 33 No. 3 , pp. 22 - 42 .
Li , X.B. and Sarkar , S. ( 2006 ), “ Privacy protection in data mining: a perturbation approach for categorical data ”, Information Systems Research , Vol. 17 No. 3 , pp. 254 - 270 .
Liu , C. and Arnett , K.P. ( 2002 ), “ Raising a red flag on global WWW privacy policies ”, Journal of Computer Information Systems , Vol. 43 No. 1 , pp. 117 - 127 .
Loi , M. and Christen , M. ( 2019 ), “ Two concepts of group privacy ”, Philosophy and Technology , Vol. 33 No. 2 , pp. 1 - 18 .
Malhotra , N.K. , Kim , S.S. and Agarwal , J. ( 2004 ), “ Internet users’ information privacy concerns (IUIPC): the construct, the scale, and a causal model ”, Information Systems Research , Vol. 15 No. 4 , pp. 336 - 355 .
Martin , K. ( 2016 ), “ Do privacy notices matter? Comparing the impact of violating formal privacy notices and informal privacy norms on consumer trust online ”, The Journal of Legal Studies , Vol. 45 , pp. S191 - S215 .
Meinert , D.B. , Peterson , D.K. , Criswell , J.R. and Crossland , M.D. ( 2006 ), “ Privacy policy statements and consumer willingness to provide personal information ”, Journal of Electronic Commerce in Organizations , Vol. 4 No. 1 , pp. 1 - 17 .
Milberg , S.J. , Smith , H.J. and Burke , S.J. ( 2000 ), “ Information privacy: corporate management and national regulation ”, Organization Science , Vol. 11 No. 1 , pp. 35 - 57 .
Milne , G.R. and Gordon , M.E. ( 1993 ), “ Direct mail privacy-efficiency trade-offs within an implied social contract framework ”, Journal of Public Policy and Marketing , Vol. 12 No. 2 , pp. 206 - 215 .
Miyazaki , A.D. and Krishnamurthy , S. ( 2002 ), “ Internet seals of approval: effects of online privacy policies and consumer perceptions ”, Journal of Consumer Affairs , Vol. 36 No. 1 , pp. 28 - 49 .
Moores , T.T. and Dhillon , G. ( 2003 ), “ Do privacy seals in e-commerce really work? ”, Communications of the ACM , Vol. 46 No. 12 , pp. 265 - 271 .
Morgeson , F.P. and Hofmann , D.A. ( 1999 ), “ The structure and function of collective constructs: implications for multilevel research and theory development ”, Academy of Management , Vol. 24 No. 2 , pp. 249 - 265 .
Norberg , P.A. and Horne , D.R. ( 2007 ), “ Privacy attitudes and privacy-related behavior ”, Psychology and Marketing , Vol. 24 No. 10 , pp. 829 - 847 .
Normalini , M.K. and Ramayah , T. ( 2017 ), “ Trust in internet banking in Malaysia and the moderating influence of perceived effectiveness of biometrics technology on perceived privacy and security ”, Journal of Management Sciences , Vol. 4 No. 1 , pp. 3 - 26 .
Nov , O. and Wattal , S. ( 2009 ), “ Social computing privacy concerns: antecedents and effects ”, Proceedings of the 27th International Conference on Human Factors in Computing Systems , Boston, MA , April 4-9 , pp. 333 - 336 .
Omariba , Z.B. , Masese , N.B. and Wanyembi , G. ( 2012 ), “ Security and privacy of electronic banking ”, International Journal of Computer Science Issues , Vol. 9 No. 3 , pp. 432 - 446 .
Ovengalt , C.T. , Djouani , K. , Kurien , A.M. and Chibani , A. ( 2017 ), “ A context broker for better access to quality and cost-effective healthcare ”, Procedia Computer Science , Vol. 109 , pp. 988 - 993 .
Posner , R.A. ( 1983 ), The Economics of Justice (5. print ed.) , Harvard University Press . Cambridge, MA , p. 271 .
Raval , D. and Jangale , S. ( 2016 ), “ Cloud-based information security and privacy in healthcare ”, International Journal of Computer Applications , Vol. 150 No. 4 .
Razzouk , N.Y. , Seitz , V. and Nicolaou , M. ( 2008 ), “ Consumer concerns regarding RFID privacy: an empirical study ”, Journal of Global Business and Technology , Vol. 4 No. 1 , p. 69 .
Reagle , J. and Cranor , L.F. ( 1999 ), “ The platform for privacy preferences ”, Communications of the Acm , Vol. 42 No. 2 , pp. 48 - 51 .
Ryker , R. , Lafleur , E. , McManis , B. and Cox , K.C. ( 2002 ), “ Online privacy policies: an assessment of the Fortune E-50 ”, Journal of Computer Information Systems , Vol. 42 No. 4 , pp. 15 - 20 .
Saha , S. , Bhattacharyya , D. , Kim , T.H. and Bandyopadhyay , S.K. ( 2010 ), “ Model-based threat and vulnerability analysis of e-governance systems ”, International Journal of U-& E-Service, Science and Technology , Vol. 3 No. 2 , pp. 7 - 21 .
Schwaig , K.S. , Kane , G.C. and Storey , V.C. ( 2005 ), “ Privacy, fair information practices and the fortune 500: the virtual reality of compliance ”, Acm Sigmis Database: The Database for Advances in Information Systems , Vol. 36 No. 1 , pp. 49 - 63 .
Singh , A.J. and Chauhan , R. ( 2012 ), “ Technology challenges in e-service accessibility ”, Journal of Engineering and Technology , Vol. 2 No. 1 , p. 32 .
Skinner , G. , Han , S. and Chang , E. ( 2006 ), “ An information privacy taxonomy for collaborative environments ”, Information Management and Computer Security , Vol. 14 No. 4 , pp. 382 - 394 .
Smith , H.J. ( 2004 ), “ Information privacy and its management ”, MIS Quarterly Executive , Vol. 3 No. 4 , pp. 291 - 313 .
Smith , H.J. , Dinev , T. and Xu , H. ( 2011 ), “ Information privacy research: an interdisciplinary review ”, MIS Quarterly , Vol. 35 No. 4 , pp. 989 - 1016 .
Smith , H.J. , Milberg , S.J. and Burke , S.J. ( 1996 ), “ Information privacy: measuring individuals’ concerns about organizational practices ”, MIS Quarterly , Vol. 20 No. 2 , pp. 167 - 196 .
Sohail , M.S. and Al-Jabri , I.M. ( 2014 ), “ Attitudes towards mobile banking: are there any differences between users and non-users? ”, Behavior and Information Technology , Vol. 33 No. 4 , pp. 335 - 344 .
Spottswood , E.L. and Hancock , J.T. ( 2017 ), “ Should I share that? Promoting social norms that influence privacy behaviors on a social networking Site ”, Journal of Computer-Mediated Communication , Vol. 22 No. 2 , pp. 55 - 70 .
Straub , D.W. , Jr and Collins , R.W. ( 1990 ), “ Key information liability issues facing managers: software piracy, proprietary databases, and individual rights to privacy ”, MIS Quarterly , Vol. 14 No. 2 , pp. 143 - 156 .
Swani , K. and Brown , B.P. ( 2011 ), “ The effectiveness of social media messages in organizational buying contexts ”, American Marketing Association , Vol. 22 , p. 519 .
Sydell , L. ( 2006 ), “ Google fights request to turn over search records ”, NPR, January 20 , available at: www.npr.org/templates/story/story.php?storyId=5165530
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Thiesse , F. ( 2007 ), “ RFID, privacy and the perception of risk: a strategic framework ”, The Journal of Strategic Information Systems , Vol. 16 No. 2 , pp. 214 - 232 .
Van Slyke , C. , Shim , J.T. , Johnson , R. and Jiang , J. ( 2006 ), “ Concern for information privacy and online consumer purchasing ”, Journal of the Association for Information Systems , Vol. 7 No. 6 , pp. 415 - 444 .
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Home — Essay Samples — Information Science and Technology — Internet — The Importance of Internet Privacy
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Introduction, threats to personal data and privacy, legal and ethical considerations, protecting your digital sanctuary, works cited.
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Acute essay topics on internet privacy: 20 expert suggestions.
Internet privacy is a very important topic nowadays. Many people are afraid that they’re watched by the government through the Internet and these suspicions aren’t always baseless. If your teacher assigned you to write an essay on Internet privacy, there are many narrow topics that you may choose from.
A good topic isn’t enough to get an excellent mark for your work. You should conduct thorough research and present your points or arguments properly. Ask your teacher about sources that you should use to succeed. Acquire these sources and find there, at least, three points that can support your main idea. It’s important to defend your statements using factual evidence rather than just your opinion. Before you start writing your paper, it’s advisable to make a good outline. This will help you structure your text properly. Compose your essay using appropriate writing techniques and transitions between paragraphs to make your text flow smoothly. Proofread your paper to eliminate errors made during the process of writing. Come up with your title after your paper has been complete to make it 100% relevant to the text.
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Introduction, observation, analysis of observation, works cited.
There are various methods of collecting data in research. Some of the methods include the use of interview schedules, where respondents are asked questions and the respondents respond directly while the researcher records the answers. The use of questionnaires is also another method. This is where the researcher drafts questions on paper and distributes them to the respondents so as to answer them. This can either be mail questionnaire whereby questions are mailed to the concerned respondents and they answer them and mail back to the researcher. This however is not a reliable way of collecting data. The letters can get lost on the way or misplaced and may not reach the intended persons. It is also the most expensive method of collecting data since the researcher has to attach stamps which will be used to email back the respondent’s answers. If the researcher does not attach the stamps the respondents would not likely spend his money on another person’s work.
Direct questionnaire is another method where the researchers distribute to the respondents and wait to be filled and collect them for analysis. It is the best method though the respondents may not be willing to fill in the blank spaces. Some of the respondents might be illiterate or have no time to fill the questions asked. It is also an expensive method because the researcher has to print the questions to be asked. Another method of data collection is observation method. It is the most convenient method of collecting data. This is because the researcher does not have to bother anybody when conducting a research. This involves behaviors being observed by researcher and recorded. It does not involve direct contact with the respondents. The researcher establishes the target population where research will be conducted. Every activity that takes place is observed by the researcher (Salehnia, p. 158).
A research was conducted concerning ethical issues of internet privacy. This consists of individuals using internet in a confidential way. It involves not exposing so much information about oneself through the internet. It has become great concern for people to be sensitized on the importance to keep their information private when using the internet services. This involves being private on social networking sites such as Facebook, Twitter and many other chat services. The information of individuals should not be revealed so much. This is done in order to discourage internet crimes. The personal information which is provided should be protected.
This is done by implementing various policies on the information privacy. Personal information should not be used for anything either for criminal intentions or not without his/her consent. This personal information may include bank details and employment details. It may also include health and other personal details. Hackers normally get their way to this information. They then use this information to commit crimes and for malicious purposes. They can attack specific persons using the illegally obtain details. This causes people to fear using certain sites. They fear performing transactions on shopping websites (Kizza, p. 97).
This is because they do not want to send their credit card numbers and other information over the web. Therefore authentication should be done to ensure that only authorized users of any system are allowed. Measures should therefore be put in place to ensure that the private information is transmitted securely. This is to make sure that the information is not intercepted or interrupted. Mailing history can be used to track the activities of company employees. Most the companies use employee monitoring systems. This system keeps a log of all activities performed by each employee. This intrudes on their privacy because even their personal emails are traced. An IP masking software can be used to prevent these intrusions.
A research was conducted in a college setting. It is a social institution where different people behave differently. It’s a college where all the students are in session. There are those who are studying different courses ranging from diplomas, degrees, masters and PhDs. Studies were going on well and everybody is going on with their chores as usual. Some students are running up and down to make sure that they do not miss their classes. Observation method was chosen because it is the simplest method to use hence it would be easier and faster to get the data needed. It also enables the researcher to get first-hand information hence there is no need to rely on others to get the data. Though it also has various demerits to the researcher, it was the best method to use. Some of its demerits include: its time consuming & requires small sample which may not actually give accurate results (Azari, p. 178).
However despite the demerits the researcher was able to overcome them by making sure that he gets the accurate answers as possible and uses the shortest time skillfully. Observation was seen to be the best method of collecting data because it was a school setting where students from different backgrounds were in attendance. The research was carried out in the computer laboratory. The reason why the researcher chose to conduct his research in the computer lab is to enable him to collect data that is relevant to the topic. It is also a place where you can find most of the students and everybody concentrates on whatever they are doing. The researcher cannot be found easily by the students in the computer lab. Students attend labs at intervals because the students are in the lessons differently at different times. The timetable is different for students hence when they are free they attend labs to do their classwork and others browse in different social sites.
The researcher sat on one of the computers in the lab in order to get good climes of what the students are doing in the lab. Everybody was busy in the lab and all the eyes were on the computer. The lab is very quiet but the sounds of some people murmuring were evident. The people in the lab consist of students only. An outsider or anybody who is not a student is not allowed to enter the computer lab unless he/she possesses a college identity card. Since the researcher is not a student in the college, he had to seek permission from the college administration and was granted. The desktops are wide about 17inches wide hence any other person can access what the other person is doing. The computers are in straight rows (Himma, p. 142).
The computer lab is very large and accommodates up to two hundred students. All students have their own computers but some of them share maybe because they were working on research and two of them have to consult. Not all of the computers are occupied but at times they are almost full. The seats in the lab are made of leather but some of them are broken on the backside. The air conditioners generate good fresh air which enables students to be active and not dumpy. And the place smelt nice though.
The researcher observed that the students in the computer lab do various things in the library. The students are of different ages. Some are aged between 19 years old to 45 years approximately. But the majority of students are youths who are partaking in undergraduate studies. Some of the older students are studying the master programs’ or even PhDs. The researcher also observed that the majority of students attend computer labs for social networking. Majority of the students were smiling as they type on the keyboard, which is a clear indication that they are chatting with some people. The assumption the researcher made because a person cannot smile when doing some serious class assignment. While those people of older age are involved in doing some research on the classwork by the seriousness in their faces. It is also shown by the way they are involved in taking short notes from the computer.
The researcher also observed that majority of those in the labs in terms of gender are ladies and they were Christians by the look of mode dress. However there were Muslims who wore scuffs and Muslim gears but in minimal number. Most of the students wore casual wear. This includes the jeans and t-shirts. Majority of ladies also wore trousers and minority wore skirts. Where students imitate one another in the way they wear clothes. Fashion in wear seems to be considered seriously in the college. All the students try to dress to impress especially those on youthful stage. Older people seemed to wear official wear. Some of them put on their ties which is a sign of some of them holding working jobs and only coming to class on part-time basis (Stair, Reynold and Reynolds, p. 109).
All the students appear to be white in terms of race but there were a minimal number of African American students. In about an hour when the researcher was in the library, he noticed that most of the students who are using computers did not mind hiding the information being displayed on the desktop. In fact he noticed one of the students who were shopping online who did not mind hiding her credit numbers. The numbers could be assessed easily. This is because they could not adhere to internet privacy or they have no idea of what they were getting themselves into. The researcher also found out that most students do not log off their social networking sites. Some could find out that their class time was over and they would rush to their class forgetting to end whatever they were doing on the computer.
The researcher also found out that the students of the same age sitting next to each other. He observed one of the students who were friends requesting another one to move to another chair to sit next to his friend. The lab technician was also keener in watching out those who were consulting loudly. It was discovered that middle-aged men were requested at most times to lower their voices more than ladies and older men. In fact, at one point two male students were sent outside for being rude to the lab technician. This however shows that boys were ruder than girls even though girls were more in the computer lab than boys.
Since the middle-aged students outnumbered the older students, focus of the study will only be based on the majority of the students. Analysis will focus on gender, socialization, social learning and ethnicity. Men and women tend to regroup together and do most of the things when they are together. As found out by the researcher, ladies tend to share most of the things when they were browsing in the computer lab. This however enables them to learn from each other in terms of learning and daily activities in school. It was also found out that ladies are the ones who forget most to log off their computers. This however shows that degree to which women tend to forget than men are higher.
The students tend to learn more through peer group socialization. This is seen in terms of fashion in which students tend to wear. Most of the clothes which they wear tend to be the same. Peer group socialization can either lead one to be good or take up bad morals depending on which type of peers we associate with. A group of bodies who were chased away by the lab technicians has learned bad behavior either from one of themselves. The students also learn behaviors through social learning process. This is where individuals learn behaviors through friends they communicate either through social networking on the internet. Some have learned about social sites on the computer through being demonstrated to by their friends. College is a social institution where different students from different backgrounds meet and share various ideas. In the process of being together some students are re-socialized and they learn behaviors that were not in the family institution. The institutional norms enable the students to respect one another and therefore bring order to the college. This is mostly displayed in the computer lab where students are orderly (Kimmel, p. 75).
The college rules also shape the behaviors of the students in the school. Rules act as social control tool which helps shape the behaviors of human beings. Durkheim argued that human being is a social animal and they are bound to make mistakes. There should be rules put in place to guide the behaviors of that individual. Various students have not learned the ethical issues of internet privacy. Internet crimes take place every day because people do not know this policy. However, college students need to be taught more about the importance of keeping the information that reveals much about them to be kept more secret. The researcher’s observation also reveals that people of the same age tend to share various ideas in terms of mode of dress and the way they imitate one another. The people we associate with and those we come across every day shape our behaviors greatly. People behave differently in the presence of others. Behaviors also are observed in society through a way of modeling. We try to imitate our models and in the process, we take up their habits.
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71% of adults say they are very or somewhat concerned about how the government uses the data it collects about them, up from 64% in 2019.
The share of Americans who say they are very or somewhat concerned about government use of people’s data has increased from 64% in 2019 to 71% today. Two-thirds (67%) of adults say they understand little to nothing about what companies are doing with their personal data, up from 59%.
Americans’ understanding of digital topics varies notably depending on the subject. For example, majorities of U.S. adults know what cookies on websites do and can identify a secure password, but far fewer can recognize an example of two-factor authentication.
Test your knowledge of digital topics and terms by taking our nine-question quiz. Then, compare your score with knowledge measured among the American public.
About six-in-ten Americans (59%) see TikTok as a major or minor threat to national security in the United States.
As they watch the splashy emergence of generative artificial intelligence and an array of other AI applications, experts participating in a new Pew Research Center canvassing say they have deep concerns about people’s and society’s overall well-being. At the same time, they expect to see great benefits in health care, scientific advances and education
Experts are split about how much control people will retain over essential decision-making as digital systems and artificial intelligence spread. They agree that powerful corporate and government authorities will expand the role of AI in people’s daily lives in useful ways. But, many worry these systems will diminish individuals’ ability to control their choices.
Black Americans are critical of key aspects of policing and criminal justice. But their views on face recognition technology are more nuanced.
Public views are tied to how these technologies would be used and what constraints would be in place.
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This document presents the findings from the University of Queensland (UQ) component of the November 2011 Dual Frame Omnibus Survey, conducted by the Social Research Centre. The aim of the UQ research is to explore the Australian communities understanding of and attitudes towards online privacy.
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Between 2000 and 2008, access to high-speed, broadband internet grew significantly in the United States, but there is debate on whether access to high-speed internet improves or harms wellbeing. We find that a ten percent increase in the proportion of county residents with access to broadband internet leads to a 1.01 percent reduction in the number of suicides in a county, as well as improvements in self-reported mental and physical health. We further find that this reduction in suicide deaths is likely due to economic improvements in counties that have access to broadband internet. Counties with increased access to broadband internet see reductions in poverty rate and unemployment rate. In addition, zip codes that gain access to broadband internet see increases in the numbers of employees and establishments. In addition, heterogeneity analysis indicates that the positive effects are concentrated in the working age population, those between 25 and 64 years old. This pattern is precisely what is predicted by the literature linking economic conditions to suicide risk.
We are grateful to participants at the Association of Public Policy and Management and the Washington Area Labor Symposium conferences for their helpful comments. Any errors or conclusions are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
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Privacy has emerged as a basic human right across the globe and in India too it has been recognized as a Fundamental Right under Article 21 of the Indian Constitution. Right to Privacy is closely related to the protection of data which in this technological and globalized world, has become very difficult to achieve. Further, violation of privacy rights by the Ruling majority through discriminatory legislation has also become possible due to lack of legal protection to this Right. In India, this Right was not initially recognized as a Fundamental Right, neither any specific law on data protection for securing the Rights of Privacy of the citizens was enacted. At the same time, there had been many allegations regarding violation of privacy rights both by the Government as well as by the Private Commercial Entities from time to time in India. Such allegations were also placed before the Courts of Law where the Courts had given landmark Judgements including guidelines and rulings. It thus becomes very important to analyze all these legal developments relating to the Right to Privacy and Data Protection to understand the extent of security granted by the Indian legal framework to the citizens over Right to Privacy. It has however been found that adequate recognition has been given to the Right to Privacy by the Indian Legal Regime and therefore significant steps were taken to prevent data theft and misutilization of sensitive information, yet a major extent of progressive developments is still needed to enhance the scope of data protection in the contemporary times for securing the Right to Privacy of the Indian citizens.
Keywords: Privacy; Data Protection; Personal Information; Sensitive Information; Confidentiality; and Public Interest
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Internet privacy entails securing personal data that is published on the internet. This kind of privacy encompasses safeguarding of sensitive and private information, data, preferences, and communications. In the current cyberspace, different people have raised complaints regarding loss of personal information whenever they do online transactions, visit social media sites, and participate in online games as well as attending forums. In such circumstances, many people exhibit incidences of compromised passwords, the revelation of victims’ identities, and fraud. Such privacy issues come as a result of negligence by the users or website developers who do not comply with the regulations and standards that curb privacy violation. Thus, internet privacy is under threat due to the inability of the internet service providers who have no explicit permission to gain access or share confidential information. This essay presents a discussion of internet privacy and how online users fall victims due to increased vulnerability of online security.
Internet privacy is a paradoxical and difficult to understand. People are aware of their behaviors online and are bound to use strong passwords when before accessing their own accounts. In the same note, users are supposed to log out their own accounts when ceasing access to the internet so that they leave it safe and secure from intruders. However, regardless of such knowledge and clear understanding of the privacy policy, they do not bother. Instead, they lament of the increased vulnerability due to such negligence. On a separate note, people tend to care about protecting their personal information and normally feel insecure when issuing it on the internet. Therefore, this behavior brings about privacy paradox since they are unable to gain control of their own digital information.
Internet privacy faces numerous risks that include phishing that encompasses hacking activities that are used to obtain secured information, usernames, passwords, security PIN, credit card numbers and bank account numbers. Furthermore, users have been under attack through pharming which covers redirection of legitimate website users to a new and different IP address. Others include the use of offline applications that get information without user’s consent. In such circumstances, a computer that is online can be accessed to get the previously acquired data that is sent to the spyware source. On a separate note, there is a rise in the usage of malware which allows an illegal damage of computers both online and offline through application viruses, Trojan, and spyware.
In addition to the above measures, users should always keep their operating systems and windows up to date to avoid vulnerability. A rise in software vulnerability is enhanced through the use of outdated systems. This increases loopholes for cyber criminals. For example, Flash and Java are normally vulnerable to security threats hence expose their users to cyber attacks. On the same note, users should avoid accessing free Wi-Fi networks since such networks are used by hackers with network sniffers that retrieve crucial information. Lastly, users should always update and change their privacy settings on regular basis.
In conclusion, the essay has elaborated on internet privacy based on the different risks and vulnerability that users are facing besides highlighting the different measures that should be put in place. However, reduction of vulnerability of internet privacy begins with the user’s behavior regarding internet usage. Users should incorporate all the measures and should be careful when revealing their personal information online. Lastly, users should be aware of their privacy rights and should fight for such rights especially in cases when breaches occur.
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Title: gpt-4 technical report.
Abstract: We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.
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If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.
This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.
Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.
Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.
Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).
Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.
Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.
The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.
Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.
As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.
Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).
Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.
In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.
Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.
Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.
The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.
Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.
Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.
To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.
What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.
Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.
In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.
The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.
Alex Singla and Alexander Sukharevsky are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall is an associate partner in the Washington, DC, office.
They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.
This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.
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