Jump to content

Active users

From Wikipedia, the free encyclopedia
(Redirected fromMonthly Active Users)

Active users
Number of new and active Wikipedia users in Indonesia between September 2010 and March 2012
General information
Unit systemProduct metric
Unit ofMedia consumption
SymbolDAU, WAU, MAU

Active usersis a softwareperformance metricthat is commonly used to measure the level of engagement for a particularsoftwareproduct or object, by quantifying the number of active interactions fromusersor visitors within a relevant range of time (daily, weekly and monthly).

The metric has many uses insoftware managementsuch as insocial networking services,online games,ormobile apps,inweb analyticssuch as inweb apps,incommercesuch as inonline bankingand inacademia,such as in user behavior analytics and predictive analytics. Although having extensive uses in digital behavioural learning, prediction and reporting, it also has impacts on the privacy andsecurity,and ethical factors should be considered thoroughly. It measures how many users visit or interact with the product or service over a given interval or period.[1]However, there is no standard definition of this term, so comparison of the reporting between different providers of this metric is problematic. Also, most providers have the interest to show this number as high as possible, therefore defining even the most minimal interaction as "active".[2]Still the number is a relevant metric to evaluate development of user interaction of a given provider.

This metric is commonly assessed per month asmonthly active users(MAU),[3]per week asweekly active users(WAU),[4]per day asdaily active users(DAU)[5]andpeak concurrent users(PCU).[6]

Commercial usage

[edit]

Predictors of success engagement measurement (KPI) and advertisement

[edit]

Active users on any time scale offers a rough overview of the amount of returning customers a product maintains, and comparing the changes in this number can be used to predict growth or decline in consumer numbers. In a commercial context, the success of asocial-networking-siteis generally associated with a growing network of active users (greater volume of site visits), social relationships amongst those users andgenerated contents.Active Users can be used as a keyperformance indicator(KPI),managingandpredictingfuture success, in measuring the growth and current volume of users visiting and consuming the site. The ratio of DAU and MAU offers a rudimentary method to estimatecustomer engagementand retention rate over time.[7]A higher ratio represents a larger retention probability, which often indicates success of a product. Ratios of 0.15 and above are believed to be a tipping point for growth while sustained ratios of 0.2 and above mark lasting success.[8]

Chen, Lu, Chau, and Gupta (2014)[9]argues that greater numbers of users (early adopters) will lead to greateruser-generated content,such as posts of photos and videos, that "promotes and propagates" social media acceptance, contributing to social-networking-site growth. The growth of social media use, characterised as increase of active users in a pre-determined timeframe, may increase an individual'ssocial presence.Social presencecan be defined as the degree to which a social-networking communications medium allows an individual to feel present with others.[10][11]

Moon and Kim's (2001)[12]research results found that individual'senjoymentof web systems have positive impacts on theirperceptionson the system, and thus would form "high behaviour intention to use it". Munnukka (2007)[13]have found strongcorrelationsbetween positive previous experience of related types ofcommunicationsandadoptionof new mobilesite communication services.However, there are also cases where active users and revenue seemed to have a negativecorrelation.For instance, Snap Inc.'s gains in daily active users (DAU) havestabilisedor decreased during theCOVID-19 Pandemic,revenue still exceeded estimates, with strong similar strong trends in the current period.[14]

Number of new articles (red line) and active users (blue line) on Swedish Wikipedia

Greater number active users boost the number of visits on particular sites. With more traffic, moreadvertiserswill be attracted, contributing torevenue generation.[15]In 2014, 88% ofcorporation's purpose of social media usage isadvertising.[16]Active Users increase allowssocial-networking sitesto build and follow more customer profiles, that is based on customer's needs and consumption patterns.[17]Active user data can be used to determine high traffic periods and create behavior models of users to be used for targeted advertising. The increase of customer profiles, due to increase of active users, ensures a more relevantpersonalised and customisedadvertisements. Bleier and Eisenbeiss (2015)[18]found that morepersonalisedandrelevantadvertisements increase "view-throughresponses "and strengthen theeffectivenessof "the advertisedbanner"significantly. DeZoysa (2002)[19]found that consumers are more likely to open and responsive on personalised advertisements that are relevant to them.

External reporting purposes

[edit]

TheFinancial Accounting Standard Boarddefines that objective of financial reporting is provide relevant and material financial information to financial statement users to allow for decision making and ensure an efficient economic |resource allocation.[20]All reporting entities, primarilypublicly listed companiesand largeprivate companiesare required by law to adhere to disclosure and accounting standards requirements. For example, in Australia, companies are required to comply withaccounting standardsset by theAustralian Accounting Standards Board,which is part of theCorporations Act 2001.In social media company's context, there is also reporting of non-financial information, such as the number of users (active users). Examples may include:

Company Non-financial metrics[21]
Facebook Daily Active Users (DAU), Monthly Active Users (MAU)
Twitter Monthly Active Users (MAU), Timeline Views Per MAU
Groupon Active Customer Units

Alternative methods of reporting thesemetricsare through social networks and the web, which have become important part of firm's "information environment" to report financial and non-financial information, according to Frankel (2004),[22]whereby firm relevant information is being spread and disseminated in short spans of time between networks of investors, journalists, and other intermediaries and stakeholders.[23]Investment blogs aggregator, likeSeeking Alpha,has become significant for professionalfinancial analysts,[24]who giverecommendationson buying and selling stocks. Studies by Frieder and Zittrain (2007)[25]have raised new concerns about how digitalcommunications technologiesinformation reportinghave the ability to affectmarket participants.

Admiraal (2009)[26]emphasised that nonfinancial metrics reported bysocial mediacompanies, including active users, may give not desirable assurance in success measurements, as the guidance, and reportingregulationsthat safeguards thereliabilityandqualityof the information are too few and have not yet beenstandardized.Cohen et al. (2012)[27]research on a set of economic performance indicators found that there is a lack of extensivedisclosuresand a materialvariabilitybetween disclosure practices based on industries and sizes. In 2008, the U.S. Securities and Exchange Commission took a cautious approach in revising their public disclosureguidancefor social media companies and claim the information to be "supplementalrather thansufficientby themselves ".[28]Alexander, Raquel, Gendry and James (2014)[29]recommended that executives and managers should take a morestrategicapproach in managinginvestor relationsandcorporate communications,ensuring investor's andanalyst'sneeds are jointly met.

Usage in academia

[edit]

Researching and web-behavioural analysis and prediction

[edit]

The active user metric can be particularly useful inbehavioural analyticsandpredictive analytics.The active user metric in the context ofpredictive analyticscan be applied in a variety of fields includingactuarial science,marketing,finance services,healthcare,online-gaming,andsocial networking.Lewis, Wyatt, and Jeremy (2015),[30]for example, have used this metric conducted a research in the fields ofhealthcareto study quality and impacts of a mobile application and predicted usage limits of these applications.

Active users can also be used in studies that addresses the issue ofmental health problemsthat could cost theglobal economy$16 Trillion U.S. Dollars by 2030, if there is a lack of resource allocated formental health.[31]Through web-behavioural analysis, Chuenphitthayavut, Zihuang, and Zhu (2020)[32]discovered that the promotion of informational, social andemotional supportthat represents media and public perception has positive effects on their research participants behavioural intention to use online mental health intervention. Online psychological educational program, a type of online mental health interventions are found to promote well-being, and decreased suicidal conception.[33]

In the fields of online-gaming, active users is quite useful in behaviour prediction andchurn ratesof online games. For example, active user's features such "active Duration" and "play count" can have inversecorrelationswith churn rates, with "shorter play times and lower play count" associated with higher churn rates.[34]Jia et Al. (2015)[35]showed that there are social structures that transpire or emerge and centred around highly active players, withstructuralsimilarity betweenmultiplayer online-games,such asStarCraft IIandDota.

The Active Users metric can be used to predict one'spersonality traits,which can be classified and grouped into categories. These categories have accuracy that ranges from 84%–92%.[36]Based on the number of user's in a particular group, the internet object associated with it, can be deemed as "trending", and as an "area of interest".

Ethical considerations and limitations

[edit]

With the internet'sevolutioninto a tool used for communications andsocialisation,ethical considerations have also shifted from data-driven to "human-centered", further complicating the ethical issues relating with concepts of public and private on online domains, whereby researchers and subjects do not fully understand theterms and conditions[37]Ethical considerations need to be considered in terms of participative consent,data confidentiality-privacy-integrity,and disciplinary-industry-professionalnormsand acceptedstandardsincloud computingandbig dataresearch. Boehlefeld (1996)[38]noted that researchers usually refer to ethical principals in their respective disciplines, as they seek guidance and recommended the guidelines by theAssociation for Computing Machineryto assist researchers of their responsibilities in their research studies in technological orcyberspace.

Informed consentrefers to a situation that participant voluntarily participates in the research with full acknowledgement of the methods of research, risks and rewards associated. With the rise internet being used as a social networking tool, active users may face unique challenges in gaining informed consents. Ethical considerations may includedegree of knowledgeto the participants andage appropriateness,ways and practicality in which researchers inform, and "when" it is appropriate to waive the consent.[39]Crawford and Schultz (2014)[40]have notedconsentto be "innumerable" and "yet-to-be-determined" before the research is conducted. Grady et al. (2017)[41]pointed out thattechnological advancementscan assist in obtaining consent without the in-person meeting of investigators (researchers) and theresearch participants.

A large number of researches is based on individualised data, that encompass usersonline identity(their clicks, readings, movements) and contents consumed and with data-analytics produced inferences about theirpreferences,social relationships,and movement or work habits. In some cases, individuals may greatly benefit, but in others they can be harmed. Afolabi and García-Basteiro (2017)[42]believed that informed consent to research studies is beyond "clicking blocks or supplying signature", as participants could have feel pressured in to joining the research, without researcher's awareness of the situation. There is yet to be a universally accepted form ofindustrystandardsandnormsin terms of data-privacy,confidentialityand integrity, a critical ethics consideration, but there has been attempts to design a process to oversee the research activities anddata collectionto better meet thecommunityandend-user's expectations.[43]There are also policy debates around ethical issues regarding the integration ofedtech (education technology)intoK-12education environment, asminorchildren are perceived to be most vulnerable segment of the entire population.[44]

Technical limitations and challenges

[edit]

Manysocial mediacompanies have their respective differences definition andcalculationmethods of the active users metric. These differences often cause differences in the variable that the metric is measuring. Wyatt (2008)[45]argues that there is evidence that some metrics reported by social media companies do not appear to bereliable,as it requires categoricaljudgements,but is still value-relevant tofinancial statementusers. Luft (2009)[46]conveyed that non-financial metric, like active users, there presents challenges in measurement accuracy and appropriateness in weighting when coupled withaccountingreporting measures. There has been increasing notice frombusiness pressesand academia. oncorporateconventions of disclosureof these information.[47]

Active users are calculated using the internal data of the specific company. Data is collected based onunique usersperforming specific actions which data collectors deem as a sign of activity. These actions include visiting the home orsplash pageof a website, logging in, commentating, uploading content, or similar actions which make use of the product. The number of people subscribed to a service may also be considered an active user for its duration. Each company has their own method of determining their number of active users, and many companies do not share specific details regarding how they calculate them. Some companies make changes to their calculation method over time. The specific action flagging users as active greatly impacts the quality of the data if it does not accurately reflect engagement with the product, resulting in misleading data.[48]Basic actions such as logging into the product may not be an accurate representation of customer engagement and inflate the number of active users, while uploading content or commenting may be too specific for a product and under-represent user activity.

Weitz, Henry and Rosenthal (2014)[21]suggested that factors that may affect accuracy of metrics like active users include issues relating to definition and calculation, circumstances ofdeceptiveinflation, uncertaintyspecificationand user-shared, duplicate or fake accounts. The authors describesFacebookmonthly active userscriterionasregistered userspast 30 days, have used the messenger, and took action to sharecontentand activity differing fromLinkedInwho uses registered members, page visits and views. For example, a customer who uses the Facebook once, to "comment" or "share content", may also be counted as an "active user".[49]A potential cause for these inaccuracies in measurement is the implementedPay-for-Performance systems,that encourages desired behaviours, included high-performance work system.[50]In social media companies, active users is one of the crucial metric that measures the success of the product. Trueman, Wong, and Zhang (2000)[51]have found that in most cases unique visitors and pageviews as a measurement of web-usage accounts for changes in stock prices, and net income in internet companies. Lazer, Lev and Livnat (2001)[52]found that more popular website generated greater stock returns, in their research analysis of traffic data of internet companies through the division of higher and lower than median traffic data. Yieldingportfoliomore returns may sway investors to vote on a more favourable bonus package forexecutive management.Kang, Lee and Na's (2010)[53]research on the global financial crisis in 2007–2008 highlights the importance of prevention of "expropriationincentives"of investors, that provides very prominent implications oncorporate governance,especially during aneconomic shock.

Active user is limited in examining pre-adoptionand post-adoptionbehavioursof users. Users commitment to a particular onlineproductmay also depend on trust and the alternatives quality.[54]Pre-adoptionbehaviour's effects on post-adoption behaviour, that is predicted by past research has suggested,[55]is found to haveassociationswith factors such as habit, gender and some othersocio-culturaldemographics.[56]Buchanan and Gillies (1990)[57]and Reichheld and Schefter (2000)[58]argues that post-adoption behaviours and continuous usage is "relatively more important than first-time or initial usage" as it shows "the degree ofconsumer loyalty",and that ultimately produces long termproduct value.

References

[edit]
  1. ^Henry TF, Rosenthal DA, Weitz RR (September 2014). "Socially Awkward: Social Media Companies' Nonfinancial Metrics Can Send a Mixed Message".Journal of Accountancy.218(3): 52.GaleA381838689.
  2. ^"Spotify for instance defines monthly active users as"..the total count of Ad-Supported Users and Premium Subscribers that have consumed content for greater than zero milliseconds in the last thirty days from the period-end indicated ""(PDF).RetrievedJanuary 7,2023.
  3. ^"Monthly Active Users (MAU)".AppStore Knowledge Base.AppStoreGrowth. December 11, 2019. Archived fromthe originalon March 8, 2021.RetrievedJanuary 20,2020.
  4. ^Darrow B (September 12, 2017)."How Slack Plans to Make It Easier to Chat With Colleagues at Other Companies".Fortune.RetrievedFebruary 16,2019.
  5. ^Shaban H (February 7, 2019)."Twitter reveals its daily active user numbers for the first time".The Washington Post.RetrievedFebruary 16,2019.
  6. ^"Definition of Peak Concurrent Users".Law Insider.
  7. ^"Understanding repeat playing behavior in casual games using a Bayesian data augmentation approach".Quantitative Marketing and Economics.
  8. ^Lovell N (October 26, 2011)."DAU/MAU = engagement".Gamesbrief.RetrievedDecember 3,2019.
  9. ^Chen, Aihui; Lu, Yaobin; Chau, Patrick Y.K.; Gupta, Sumeet (July 3, 2014). "Classifying, Measuring, and Predicting Users' Overall Active Behavior on Social Networking Sites".Journal of Management Information Systems.31(3): 213–253.doi:10.1080/07421222.2014.995557.S2CID38855806.
  10. ^Fulk, Janet; Steinfield, Charles W.; Schmitz, Joseph; Power, J. Gerard (October 1987). "A Social Information Processing Model of Media Use in Organizations".Communication Research.14(5): 529–552.doi:10.1177/009365087014005005.S2CID145786143.
  11. ^Cyr, Dianne; Hassanein, Khaled; Head, Milena; Ivanov, Alex (January 2007). "The role of social presence in establishing loyalty in e-Service environments".Interacting with Computers.19(1): 43–56.doi:10.1016/j.intcom.2006.07.010.
  12. ^Moon, Ji-Won; Kim, Young-Gul (February 2001). "Extending the TAM for a World-Wide-Web context".Information & Management.38(4): 217–230.CiteSeerX10.1.1.859.5396.doi:10.1016/S0378-7206(00)00061-6.S2CID17709833.
  13. ^Munnukka, Juha (October 30, 2007). "Characteristics of early adopters in mobile communications markets".Marketing Intelligence & Planning.25(7): 719–731.doi:10.1108/02634500710834188.
  14. ^"Snap's Missed User Target Shows Challenge Predicting Growth".Bloomberg.com.July 21, 2020.RetrievedNovember 1,2020.
  15. ^Chen, Rui (February 2013). "Member use of social networking sites — an empirical examination".Decision Support Systems.54(3): 1219–1227.doi:10.1016/j.dss.2012.10.028.
  16. ^Dehghani, Milad; Niaki, Mojtaba Khorram; Ramezani, Iman; Sali, Rasoul (June 2016). "Evaluating the influence of YouTube advertising for attraction of young customers".Computers in Human Behavior.59:165–172.doi:10.1016/j.chb.2016.01.037.
  17. ^Rao, Bharat; Minakakis, Louis (December 2003). "Evolution of mobile location-based services".Communications of the ACM.46(12): 61–65.doi:10.1145/953460.953490.S2CID1330830.
  18. ^Bleier, Alexander; Eisenbeiss, Maik (September 2015). "Personalized Online Advertising Effectiveness: The Interplay of What, When, and Where".Marketing Science.34(5): 669–688.doi:10.1287/mksc.2015.0930.
  19. ^DeZoysa, Sanjima. "Mobile advertising needs to get personal".Telecommunications International.36(2).
  20. ^FASB. (2008). Statement of Financial Accounting Concepts No. 1. Retrieved fromhttp://www.fasb.org/resources/ccurl/816/894/aop_CON1.pdf
  21. ^abWeitz, Rob; Henry, Theresa; Rosenthal, David (January 1, 2014)."Limitations of Nonfinancial Metrics Reported by Social Media Companies".Journal of International Technology and Information Management.23(3).doi:10.58729/1941-6679.1074.S2CID220610972.
  22. ^Frankel, Richard; Li, Xu (June 2004). "Characteristics of a firm's information environment and the information asymmetry between insiders and outsiders".Journal of Accounting and Economics.37(2): 229–259.doi:10.1016/j.jacceco.2003.09.004.
  23. ^Rubin, Amir; Rubin, Eran (July 2010). "Informed Investors and the Internet".Journal of Business Finance & Accounting.37(7–8): 841–865.doi:10.1111/j.1468-5957.2010.02187.x.S2CID59058862.
  24. ^Saxton, Gregory D. (September 2012). "New Media and External Accounting Information: A Critical Review: New Media and External Accounting Information".Australian Accounting Review.22(3): 286–302.doi:10.1111/j.1835-2561.2012.00176.x.
  25. ^Frieder, Laura L.; Zittrain, Jonathan (2007)."Spam Works: Evidence from Stock Touts and Corresponding Market Activity".Berkman Center Working Paper.SSRN920553.
  26. ^Admiraal, Michèl; Nivra, Royal; Turksema, Rudi (July 2009). "Reporting on Nonfinancial Information".International Journal of Government Auditing.36(3): 15–20.ProQuest236822392.
  27. ^Cohen, Jeffrey R.; Holder-Webb, Lori L.; Nath, Leda; Wood, David (March 1, 2012). "Corporate Reporting of Nonfinancial Leading Indicators of Economic Performance and Sustainability".Accounting Horizons.26(1): 65–90.doi:10.2308/acch-50073.S2CID154627046.
  28. ^U.S. Securities & Exchange Commission. (2008). Commission guidance on the use of company web sites (Release No. 34- 58288). Retrieved fromhttp://www.sec.gov/rules/interp/%202008/34-58288.pdf.
  29. ^Alexander, Raquel Meyer; Gentry, James K. (March 2014). "Using social media to report financial results".Business Horizons.57(2): 161–167.doi:10.1016/j.bushor.2013.10.009.
  30. ^Lewis, Thomas Lorchan; Wyatt, Jeremy C (August 19, 2015)."App Usage Factor: A Simple Metric to Compare the Population Impact of Mobile Medical Apps".Journal of Medical Internet Research.17(8): e200.doi:10.2196/jmir.4284.PMC4642395.PMID26290093.
  31. ^Patel, Vikram; Saxena, Shekhar; Lund, Crick; Thornicroft, Graham; Baingana, Florence; Bolton, Paul; Chisholm, Dan; Collins, Pamela Y; Cooper, Janice L; Eaton, Julian; Herrman, Helen; Herzallah, Mohammad M; Huang, Yueqin; Jordans, Mark J D; Kleinman, Arthur; Medina-Mora, Maria Elena; Morgan, Ellen; Niaz, Unaiza; Omigbodun, Olayinka; Prince, Martin; Rahman, Atif; Saraceno, Benedetto; Sarkar, Bidyut K; De Silva, Mary; Singh, Ilina; Stein, Dan J; Sunkel, Charlene; UnÜtzer, JÜrgen (October 2018)."The Lancet Commission on global mental health and sustainable development".The Lancet.392(10157): 1553–1598.doi:10.1016/s0140-6736(18)31612-x.PMID30314863.S2CID52976414.
  32. ^Chuenphitthayavut, Krittipat; Zihuang, Tang; Zhu, Tingshao (June 2020). "The prediction of behavioral intention to use online mental health interventions".PsyCh Journal.9(3): 370–382.doi:10.1002/pchj.333.PMID31957241.S2CID210832011.
  33. ^Hoffmann, Willem A. (January 2006). "Telematic Technologies in Mental Health Caring: A Web-Based Psychoeducational Program for Adolescent Suicide Survivors".Issues in Mental Health Nursing.27(5): 461–474.doi:10.1080/01612840600599978.PMID16613799.S2CID34925001.
  34. ^Kim, Seungwook; Choi, Daeyoung; Lee, Eunjung; Rhee, Wonjong (July 5, 2017)."Churn prediction of mobile and online casual games using play log data".PLOS ONE.12(7): e0180735.Bibcode:2017PLoSO..1280735K.doi:10.1371/journal.pone.0180735.PMC5498062.PMID28678880.
  35. ^Jia, Adele Lu; Shen, Siqi; Bovenkamp, Ruud Van De; Iosup, Alexandru; Kuipers, Fernando; Epema, Dick H. J. (October 26, 2015)."Socializing by Gaming: Revealing Social Relationships in Multiplayer Online Games".ACM Transactions on Knowledge Discovery from Data.10(2): 1–29.doi:10.1145/2736698.S2CID207224445.
  36. ^Li, Lin; Li, Ang; Hao, Bibo; Guan, Zengda; Zhu, Tingshao (January 22, 2014)."Predicting Active Users' Personality Based on Micro-Blogging Behaviors".PLOS ONE.9(1): e84997.Bibcode:2014PLoSO...984997L.doi:10.1371/journal.pone.0084997.PMC3898945.PMID24465462.
  37. ^Buchanan, E., & Zimmer, M. (2018). Internet Research Ethics. In The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University.https://plato.stanford.edu/entries/ethics-internet-research/
  38. ^Boehlefeld, Sharon Polancic (June 1996). "Doing the Right Thing: Ethical Cyberspace Research".The Information Society.12(2): 141–152.doi:10.1080/713856136.
  39. ^Hudson, James M.; Bruckman, Amy (April 2004). "'Go Away': Participant Objections to Being Studied and the Ethics of Chatroom Research ".The Information Society.20(2): 127–139.CiteSeerX10.1.1.72.635.doi:10.1080/01972240490423030.S2CID18558685.
  40. ^Crawford, Kate; Schultz, Jason (January 2014)."Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms".Boston College Law Review.55(1): 93–128.ProQuest1664533162.
  41. ^Grady, Christine; Cummings, Steven R.; Rowbotham, Michael C.; McConnell, Michael V.; Ashley, Euan A.; Kang, Gagandeep (March 2, 2017)."Informed Consent".New England Journal of Medicine.376(9): 856–867.doi:10.1056/nejmra1603773.PMID28249147.
  42. ^"Informed Consent"(PDF).New England Journal of Medicine.376(20): e43. May 18, 2017.doi:10.1056/NEJMc1704010.
  43. ^Jackman, Molly; Kanerva, Lauri (June 14, 2016)."Evolving the IRB: Building Robust Review for Industry Research".Washington and Lee Law Review Online.72(3): 442.
  44. ^Regan, Priscilla M.; Jesse, Jolene (September 2019). "Ethical challenges of edtech, big data and personalized learning: twenty-first century student sorting and tracking".Ethics and Information Technology.21(3): 167–179.doi:10.1007/s10676-018-9492-2.S2CID54220346.
  45. ^Wyatt, Anne (January 2008). "What financial and non-financial information on intangibles is value-relevant? A review of the evidence".Accounting and Business Research.38(3): 217–256.doi:10.1080/00014788.2008.9663336.S2CID219594306.
  46. ^Luft, Joan (September 1, 2009). "Nonfinancial Information and Accounting: A Reconsideration of Benefits and Challenges".Accounting Horizons.23(3): 307–325.doi:10.2308/acch.2009.23.3.307.
  47. ^Cohen, Jeffrey; Holder-Webb, Lori; Nath, Leda; Wood, David (January 1, 2011). "Retail Investors' Perceptions of the Decision-Usefulness of Economic Performance, Governance, and Corporate Social Responsibility Disclosures".Behavioral Research in Accounting.23(1): 109–129.doi:10.2308/bria.2011.23.1.109.S2CID145264864.
  48. ^Park, Patrick; Macy, Michael (December 27, 2015)."The paradox of active users".Big Data & Society.2(2): 205395171560616.doi:10.1177/2053951715606164.
  49. ^Sorkin, Andrew Ross (February 6, 2012)."Those Millions on Facebook? Some May Not Actually Visit".DealBook.
  50. ^Jenkins, G. Douglas Jr.; Mitra, Atul; Gupta, Nina; Shaw, Jason D. (1998). "Are financial incentives related to performance? A meta-analytic review of empirical research".Journal of Applied Psychology.83(5): 777–787.doi:10.1037/0021-9010.83.5.777.S2CID55875563.
  51. ^Trueman, Brett; Wong, M. H. Franco; Zhang, Xiao-Jun (2000). "The Eyeballs Have It: Searching for the Value in Internet Stocks".Journal of Accounting Research.38:137–162.CiteSeerX10.1.1.195.103.doi:10.2307/2672912.JSTOR2672912.
  52. ^Lazer, Ron; Lev, Baruch; Livnat, Joshua (May 2001). "Internet Traffic and Portfolio Returns".Financial Analysts Journal.57(3): 30–40.doi:10.2469/faj.v57.n3.2448.S2CID153506314.
  53. ^Kang, Jun-Koo; Lee, Inmoo; Na, Hyun Seung (June 2010). "Economic shock, owner-manager incentives, and corporate restructuring: Evidence from the financial crisis in Korea".Journal of Corporate Finance.16(3): 333–351.doi:10.1016/j.jcorpfin.2009.12.001.S2CID153441435.
  54. ^Li, Dahui; Browne, Glenn J.; Chau, Patrick Y. K. (August 2006). "An Empirical Investigation of Web Site Use Using a Commitment-Based Model".Decision Sciences.37(3): 427–444.doi:10.1111/j.1540-5414.2006.00133.x.
  55. ^Kim, Sung S.; Malhotra, Naresh K. (February 2005). "Predicting System Usage from Intention and Past Use: Scale Issues in the Predictors".Decision Sciences.36(1): 187–196.doi:10.1111/j.1540-5915.2005.00070.x.
  56. ^Venkatesh, Viswanath; Morris, Michael G.; Davis, Gordon B.; Davis, Fred D. (2003). "User Acceptance of Information Technology: Toward a Unified View".MIS Quarterly.27(3): 425–478.doi:10.2307/30036540.JSTOR30036540.S2CID14435677.
  57. ^Buchanan, Robin W.T.; Gillies, Crawford S. (December 1990). "Value managed relationships: The key to customer retention and profitability".European Management Journal.8(4): 523–526.doi:10.1016/0263-2373(90)90115-M.
  58. ^Reichheld, Frederick F; Schefter, Phil (2000). "E-loyalty: Your secret weapon on the Web".Harvard Business Review.78(4): 105–113.ProQuest227807543.
Retrieved from "https://en.wikipedia.org/w/index.php?title=Active_users&oldid=1243261888"