|Unit system||Product Metric|
|Unit of||Media consumption|
|Symbol||DAU, WAU, MAU|
Active users is a measurement metric that is commonly used to measure the level of engagement for a particular product or object, by quantifying the number of active interactions from visitors within a relevant range of time (daily, weekly and monthly). The metric has many uses in both commerce and academia, such as on social networking services, online games, or mobile apps. Although having extensive uses in digital behavioural learning, prediction and reporting, it also has impacts on the privacy and security, and ethical factors should be considered thoroughly. Like any metric, active users may have limitations and criticisms. Active Users is relatively new or neologistic in nature, that became important with the rise of the commercialised internet, with uses in communication and social-networking. It measures how many users visit or interact with the product or service over a given interval or period. This metric is commonly assessed per month as monthly active users (MAU), per week as weekly active users (WAU), per day as daily active users (DAU) or peak concurrent users (PCU).
Predictors of success engagement measurement (KPI) and advertisement
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 a social-networking-site is generally associated with a growing network of active users (greater volume of site visits), social relationships amongst those users and generated contents. Active Users is can be used as a performance indicator, managing and predicting future 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 estimate customer engagement and retention rate over time. 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. Chen, Lu, Chau, and Gupta (2014) argues that greater numbers of users (early adopters) will lead to greater user-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's social presence. Social presence can be defined as the degree to which a social-networking communications medium allows an individual to feel present with others, Moon and Kim's (2001) research results found that individual's enjoyment of web systems have positive impacts on their perceptions on the system, and thus would form "high behaviour intention to use it". Munnukka (2007) have found strong correlations between positive previous experience of related types of communications and adoption of new mobile site communication services. However, there are also cases where active users and revenue seemed to have a negative correlation. For instance, Snap Inc.'s gains in daily active users (DAU) have stabilised or decreased during the COVID-19 Pandemic, revenue still exceeded estimates, with strong similar strong trends in the current period.
Greater number active users boost the number of visits on particular sites. With more traffic, more advertisers will be attracted, contributing to revenue generation. In 2014, 88% of corporation's purpose of social media usage is advertising. Active Users increase allows social-networking sites to build and follow more customer profiles, that is based on customer's needs and consumption patterns. 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 relevant personalised and customised advertisements. Bleier and Eisenbeiss (2015) found that more personalised and relevant advertisements increase "view-through responses" and strengthen the effectiveness of "the advertised banner" significantly. DeZoysa (2002) found that consumers are more likely to open and responsive on personalised advertisements that are relevant to them.
External reporting purposes
The Financial Accounting Standard Board defines 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. All reporting entities, primarily publicly listed companies and large private companies are required by law to adhere to disclosure and accounting standards requirements. For example, in Australia, companies are required to comply with accounting standards set by the Australian Accounting Standards Board, which is part of the Corporations 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:
|Daily Active Users (DAU), Monthly Active Users (MAU)|
|Monthly Active Users (MAU), Timeline Views Per MAU|
|Groupon||Active Customer Units|
Source: Weitz, Henry, Rosenthal (2014)
Alternative methods of reporting these metrics are 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), whereby firm relevant information is being spread and disseminated in short spans of time between networks of investors, journalists, and other intermediaries and stakeholders. Investment blogs aggregator, like Seeking Alpha, has become significant for professional financial analysts, who give recommendations on buying and selling stocks, and influence stock volume and prices. Studies by Frieder and Zittrain (2007) have raised new concerns about how digital communications technologies information reporting have the ability to affect market participants.
Admiraal (2009) emphasised that nonfinancial metrics reported by social media companies, including active users, may give not desirable assurance in success measurements, as the guidance, and reporting regulations that safeguards the reliability and quality of the information are too few and have not yet been standardized. Cohen et al. (2012) research on a set of economic performance indicators found that there is a lack of extensive disclosures and a material variability between disclosure practices based on industries and sizes. In 2008, the U.S Securities and Exchange Commission took a cautious approach in revising their public disclosure guidance for social media companies and claim the information to be "supplemental rather than sufficient by themselves". Alexander, Raquel, Gendry and James (2014) recommended that executives and managers should take a more strategic approach in managing investor relations and corporate communications, ensuring investor's and analyst's needs are jointly met.
Usage in academia
Researching and web-behavioural analysis and prediction
The active user metric can be particularly useful in behavioural analytics and predictive analytics. The active user metric in the context of predictive analytics can be applied in a variety of fields including actuarial science, marketing, finance services, healthcare, online-gaming, and social networking. Lewis, Wyatt, and Jeremy (2015), for example, have used this metric conducted a research in the fields of healthcare to 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 of mental health problems that could cost the global economy $16 Trillion U.S Dollars by 2030, if there is a lack of resource allocated for mental health. Through web-behavioural analysis, Chuenphitthayavut, Zihuang, and Zhu (2020) discovered that the promotion of informational, social and emotional support that 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.
In the fields of online-gaming, active users is quite useful in behaviour prediction and churn rates of online games. For example, active user's features such "active Duration" and "play count" can have inverse correlations with churn rates, with "shorter play times and lower play count" associated with higher churn rates. Jia et Al. (2015) showed that there are social structures that transpire or emerge and centred around highly active players, with structural similarity between multiplayer online-games, such as StarCraft II and Dota.
The Active Users metric can be used to predict one's personality traits, which can be classified and grouped into categories. These categories have accuracy that ranges from 84% - 92%. 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
With the internet's evolution into a tool used for communications and socialisation, 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 the terms and conditions Ethical considerations need to be considered in terms of participative consent, data confidentiality-privacy-integrity, and disciplinary-industry-professional norms and accepted standards in cloud computing and big data research. Boehlefeld (1996) noted that researchers usually refer to ethical principals in their respective disciplines, as they seek guidance and recommended the guidelines by the Association for Computing Machinery to assist researchers of their responsibilities in their research studies in technological or cyberspace.
Informed consent refers 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 include degree of knowledge to the participants and age appropriateness, ways and practicality in which researchers inform, and "when" it is appropriate to waive the consent. Crawford and Schultz (2014) have noted consent to be "innumerable" and "yet-to-be-determined" before the research is conducted. Grady et al. (2017) pointed out that technological advancements can assist in obtaining consent without the in-person meeting of investigators (researchers) and the research participants.
A large number of researches is based on individualised data, that encompass users online identity (their clicks, readings, movements) and contents consumed and with data-analytics produced inferences about their preferences, 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) 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 of industry standards and norms in terms of data-privacy, confidentiality and integrity, a critical ethics consideration, but there has been attempts to design a process to oversee the research activities and data collection to better meet the community and end-user's expectations. There are also policy debates around ethical issues regarding the integration of edtech (education technology) into K-12 education environment, as minor children are perceived to be most vulnerable segment of the entire population.
Technical limitations and challenges
Many social media companies have their respective differences definition and calculation methods of the active users metric. These differences often cause differences in the variable that the metric is measuring. Wyatt (2008) argues that there is evidence that some metrics reported by social media companies do not appear to be reliable, as it requires categorical judgements, but is still value-relevant to financial statement users. Luft (2009) conveyed that non-financial metric, like active users, there presents challenges in measurement accuracy and appropriateness in weighting when coupled with accounting reporting measures. There has been increasing notice from business presses and academia. on corporate conventions of disclosure of these information.
Active users are calculated using the internal data of the specific company. Data is collected based on unique users performing specific actions which data collectors deem as a sign of activity. These actions include visiting the home or splash page of 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. 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) suggested that factors that may affect accuracy of metrics like active users include issues relating to definition and calculation, circumstances of deceptive inflation, uncertainty specification and user-shared, duplicate or fake accounts. The authors describes Facebook monthly active users criterion as registered users past 30 days, have used the messenger, and took action to share content and activity differing from LinkedIn who 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". A potential cause for these inaccuracies in measurement is the implemented Pay-for-Performance systems, that encourages desired behaviours, included high-performance work system. In social media companies, active users is one of the crucial metric that measures the success of the product. Trueman, Wong, and Zhang (2000) 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) 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. Yielding portfolio more returns may sway investors to vote on a more favourable bonus package for executive management. Kang, Lee and Na's (2010) research on the global financial crisis in 2007-2008 highlights the importance of prevention of "expropriation incentives" of investors, that provides very prominent implications on corporate governance, especially during an economic shock.
Active user is limited in examining pre-adoption and post-adoption behaviours of users. Users commitment to a particular online product may also depend on trust and the alternatives quality. Pre-adoption behaviour's effects on post-adoption behaviour, that is predicted by past research has suggested, is found to have associations with factors such as habit, gender and some other socio-cultural demographics. Buchanan and Gillies (1990) and Reichheld and Schefter (2000) argues that post-adoption behaviours and continuous usage is "relatively more important than first-time or initial usage" as it shows "the degree of consumer loyalty", and that ultimately produces long term product value.
- 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. Gale A381838689.
- "Monthly Active Users (MAU)". AppStore Knowledge Base. AppStoreGrowth. December 11, 2019.
- Darrow B (September 12, 2017). "How Slack Plans to Make It Easier to Chat With Colleagues at Other Companies". Fortune. Retrieved February 16, 2019.
- Shaban H (February 7, 2019). "Twitter reveals its daily active user numbers for the first time". The Washington Post. Retrieved February 16, 2019.
- "Definition of Peak Concurrent Users". Law Insider.
- Hui, Sam K. (March 2017). "Understanding repeat playing behavior in casual games using a Bayesian data augmentation approach". Quantitative Marketing and Economics. 15 (1): 29–55. doi:10.1007/s11129-017-9180-2. S2CID 157211197.
- Lovell N (October 26, 2011). "DAU/MAU = engagement". Gamesbrief. Retrieved December 3, 2019.
- 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. S2CID 38855806.
- 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. S2CID 145786143.
- 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.
- Moon, Ji-Won; Kim, Young-Gul (February 2001). "Extending the TAM for a World-Wide-Web context". Information & Management. 38 (4): 217–230. CiteSeerX 10.1.1.859.5396. doi:10.1016/S0378-7206(00)00061-6.
- Munnukka, Juha (October 30, 2007). "Characteristics of early adopters in mobile communications markets". Marketing Intelligence & Planning. 25 (7): 719–731. doi:10.1108/02634500710834188.
- "Snap's Missed User Target Shows Challenge Predicting Growth". Bloomberg.com. July 21, 2020. Retrieved November 1, 2020.
- 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.
- 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.
- 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. S2CID 1330830.
- 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.
- DeZoysa, Sanjima. "Mobile advertising needs to get personal". Telecommunications International. 36 (2).
- FASB. (2008). Statement of Financial Accounting Concepts No. 1. Retrieved from http://www.fasb.org/resources/ccurl/816/894/aop_CON1.pdf
- Weitz, 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).
- 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.
- 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. S2CID 59058862.
- 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.
- Frieder, Laura L.; Zittrain, Jonathan (2007). "Spam Works: Evidence from Stock Touts and Corresponding Market Activity". Berkman Center Working Paper. SSRN 920553.
- Admiraal, Michèl; Nivra, Royal; Turksema, Rudi (July 2009). "Reporting on Nonfinancial Information". International Journal of Government Auditing. 36 (3): 15–20. ProQuest 236822392.
- 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. S2CID 154627046.
- U.S. Securities & Exchange Commission. (2008). Commission guidance on the use of company web sites (Release No. 34- 58288). Retrieved from http://www.sec.gov/rules/interp/%202008/34-58288.pdf.
- 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.
- 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. PMC 4642395. PMID 26290093.
- 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. PMID 30314863. S2CID 52976414.
- 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. PMID 31957241.
- 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. PMID 16613799. S2CID 34925001.
- 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. PMC 5498062. PMID 28678880.
- 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. S2CID 207224445.
- 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. PMC 3898945. PMID 24465462.
- 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/
- Boehlefeld, Sharon Polancic (June 1996). "Doing the Right Thing: Ethical Cyberspace Research". The Information Society. 12 (2): 141–152. doi:10.1080/713856136.
- 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. CiteSeerX 10.1.1.72.635. doi:10.1080/01972240490423030. S2CID 18558685.
- 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. ProQuest 1664533162.
- 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. PMID 28249147.
- "Informed Consent" (PDF). New England Journal of Medicine. 376 (20): e43. May 18, 2017. doi:10.1056/NEJMc1704010.
- 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.
- 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. S2CID 54220346.
- 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. S2CID 219594306.
- 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.
- 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. S2CID 145264864.
- Park, Patrick; Macy, Michael (December 27, 2015). "The paradox of active users". Big Data & Society. 2 (2): 205395171560616. doi:10.1177/2053951715606164.
- Weitz, 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).
- Sorkin, Andrew Ross (February 6, 2012). "Those Millions on Facebook? Some May Not Actually Visit". DealBook.
- 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. S2CID 55875563.
- 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. CiteSeerX 10.1.1.195.103. doi:10.2307/2672912. JSTOR 2672912.
- 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. S2CID 153506314.
- 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.
- 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.
- 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.
- 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. JSTOR 30036540. S2CID 14435677.
- 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.
- Reichheld, Frederick F; Schefter, Phil (2000). "E-loyalty: Your secret weapon on the Web". Harvard Business Review. 78 (4): 105–113. ProQuest 227807543.