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Journal articles on the topic 'User understanding'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Klaus, Tim. "Understanding User Dissatisfaction." Journal of Organizational and End User Computing 23, no. 3 (July 2011): 1–25. http://dx.doi.org/10.4018/joeuc.2011070101.

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This paper examines the role of fairness and how it shapes a user’s view in IT-enabled change. Drawing from several fairness theories, components of fairness are identified and examined in two studies. The first study examines the role of fairness through user interviews and finds that all five components of fairness are considered by users in enterprise system implementations. The second study operationalizes and analyzes the components of fairness through a questionnaire distributed to users. This second study finds that fairness is comprised of all five components that were proposed and a significant relationship exists with user dissatisfaction. The two studies lead to a new theoretical perspective and provide practical implications regarding the role of fairness in IT-enabled change and their strategic implications.

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2

Patton, Jeff. "Understanding User Centricity." IEEE Software 24, no. 6 (November 2007): 9–11. http://dx.doi.org/10.1109/ms.2007.181.

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3

ter Mors, Rob. "Understanding the user." Advanced Materials 5, no. 10 (October 1993): 701–3. http://dx.doi.org/10.1002/adma.19930051002.

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4

Akbay, Saadet. "Understanding of user evaluations on fun products." New Trends and Issues Proceedings on Humanities and Social Sciences 3, no. 5 (July 7, 2017): 74–82. http://dx.doi.org/10.18844/prosoc.v3i5.1966.

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5

Quilici, Alex. "Forming user models by understanding user feedback." User Modeling and User-Adapted Interaction 3, no. 4 (1994): 321–58. http://dx.doi.org/10.1007/bf01099299.

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6

Ndumbaro, Faraja. "Understanding user-system interactions." Information Development 34, no. 3 (February 15, 2017): 297–308. http://dx.doi.org/10.1177/0266666917693885.

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This paper presents findings of a study that examines how the Online Public Access Catalogue (OPAC) of the University of Dar es Salaam library is used; the extent to which users succeed in locating information; and the reasons behind search failure. OPAC transaction logs were collected unobtrusively between January and December 2015, which were then subjected to transaction log analysis. The results indicate a relatively low use of OPAC, with default keyword search, author, title and subject terms being the most preferred access points. Only 102,037 (19.2%) of the search queries received zero hits. Reasons for search failure were multiple. They included spelling mistakes, use of wrong syntax, searching in inappropriate search fields, users’ lack of knowledge of Library of Congress Subject Headings (LCSH) and materials unavailability. Thus, the paper suggests redesigning OPAC interface to include features of the ‘next generation’ catalogue and exposing users to evidence-based information literacy.

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7

Kujala, S., T. Walsh, P. Nurkka, and M. Crisan. "Sentence Completion for Understanding Users and Evaluating User Experience." Interacting with Computers 26, no. 3 (July 12, 2013): 238–55. http://dx.doi.org/10.1093/iwc/iwt036.

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8

Munro, Malcolm C., Sid L. Huff, Barbara L. Marcolin, and Deborah R. Compeau. "Understanding and measuring user competence." Information & Management 33, no. 1 (November 1997): 45–57. http://dx.doi.org/10.1016/s0378-7206(97)00035-9.

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9

Murukannaiah, Pradeep K., and Munindar P. Singh. "Understanding Location-Based User Experience." IEEE Internet Computing 18, no. 6 (November 2014): 72–76. http://dx.doi.org/10.1109/mic.2014.127.

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10

Shaw, Nancy, Joo-Eng Lee-Partridge, and James S. Ang. "Understanding the Hidden Dissatisfaction of Users toward End-User Computing." Journal of Organizational and End User Computing 15, no. 2 (April 2003): 1–22. http://dx.doi.org/10.4018/joeuc.2003040101.

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11

Brown, David, and Bernard Dumouchel. "Understanding user behaviour and its metrics." Information Services & Use 27, no. 1-2 (July 4, 2007): 3–34. http://dx.doi.org/10.3233/isu-2007-271-202.

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12

Zhou, Tao. "Understanding User Social Commerce Usage Intention." Information Resources Management Journal 32, no. 4 (October 2019): 56–71. http://dx.doi.org/10.4018/irmj.2019100104.

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The integration of social media and e-commerce leads to the emergence of social commerce. Although previous research has examined social commerce user behaviour from multiple perspectives, it has focused on the effect of instrumental beliefs, such as perceived value, and has seldom examined the effect of emotional factors, such as sense of community on user behaviour. The purpose of this research is to draw on the stimulus-organism-response (SOR) model to examine the effect of sense of community on users' social commerce usage intention. The results indicate that both social support and service quality (stimulus) affect the sense of community (organism), which in turn affects users' sharing and participation intention (response). The results imply that service providers need to develop the user's sense of community in order to facilitate his or her social commerce usage intention.

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13

Albrecht, Astrid. "Understanding the issues behind user acceptance." Biometric Technology Today 9, no. 1 (January 2001): 7–8. http://dx.doi.org/10.1016/s0969-4765(01)00124-2.

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14

Pons, Alexander P., and Peter Polak. "Understanding user perspectives on biometric technology." Communications of the ACM 51, no. 9 (September 2008): 115–18. http://dx.doi.org/10.1145/1378727.1389971.

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15

Bracci, Margherita, Oronzo Parlangeli, Michele Mariani, and Sebastiano Bagnara. "Understanding the User in Electronic Commerce." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 12 (July 2000): 2–563. http://dx.doi.org/10.1177/154193120004401232.

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Electronic commerce has rapidly become a reality giving rise to significant changes in the relationship between vendor/client and thus deserving a deep analysis. In the present scenario, businesses are called upon to provide products that are becoming more and more like services, and the client is invited to enter into a form of relationship marketing. In reference to this aspect of the development of electronic commerce, the present paper proposes a taxonomy of user/client behavior with reference to three factors a) the behavior of users in regard to the supplier of products b) the behavior of users within the Net c) the way in which users process the information content of messages elaborated by product supplier.

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16

Hale, Gregory. "Hazyspace: towards re‐understanding the user." British Journal of Educational Technology 29, no. 2 (April 1998): 185–86. http://dx.doi.org/10.1111/1467-8535.00061.

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17

Pichl, Martin, Eva Zangerle, and Günther Specht. "Understanding User-Curated Playlists on Spotify." International Journal of Multimedia Data Engineering and Management 8, no. 4 (October 2017): 44–59. http://dx.doi.org/10.4018/ijmdem.2017100103.

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Music streaming platforms enable people to access millions of tracks using computers and mobile devices. However, users cannot browse manually millions of tracks to find music they like. Building recommender systems suggesting music fitting the current context of a user is a challenging task. A deeper understanding for the characteristics of user-curated playlists naturally contributes to more personalized recommendations. To get a deeper understanding of how users organize music nowadays, we analyze user-curated playlists from the music streaming platform Spotify. Based on the audio features of the tracks, we find an explanation of differences in the playlists using a PCA and are able to group playlists using spectral clustering. Our findings about playlist characteristics can be exploited in a SVD-based music recommender system and our proposed clustering approach for finding groups of similar playlists is easy to integrate into a recommender system using pre- or post-filtering techniques.

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18

Goodhue, Dale L. "Understanding User Evaluations of Information Systems." Management Science 41, no. 12 (December 1995): 1827–44. http://dx.doi.org/10.1287/mnsc.41.12.1827.

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19

Zhou, Tao. "Understanding online knowledge community user continuance." Data Technologies and Applications 52, no. 3 (July 2, 2018): 445–58. http://dx.doi.org/10.1108/dta-10-2017-0077.

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PurposeThe purpose of this paper is to draw on the social cognitive theory to identify the determinants of online knowledge community user continuance, which reflects a user’s continued use.Design/methodology/approachBased on the 271 valid responses collected from a survey, structural equation modelling was employed to examine the research model.FindingsThe results indicated that the cognitive factors of outcome expectation and the environmental factors of system quality and knowledge quality significantly affect a user’s continuance intention, which, in turn, affects continuance usage.Research limitations/implicationsThe results imply that service providers need to enhance community platforms and improve knowledge quality in order to retain users and facilitate their continuance.Originality/valueAlthough previous research has examined online knowledge community user behaviour from multiple perspectives such as the social exchange theory and the motivational theory, it has seldom explored the relative effects of personal cognitions and environmental factors on user behaviour. This research fills the gap.

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20

Darke, P., and G. Shanks. "User viewpoint modelling: understanding and representing user viewpoints during requirements definition." Information Systems Journal 7, no. 3 (July 1997): 213–19. http://dx.doi.org/10.1046/j.1365-2575.1997.d01-19.x.

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21

Romero Leguina, Jesús, Ángel Cuevas Rumín, and Rubén Cuevas Rumín. "Digital Marketing Attribution: Understanding the User Path." Electronics 9, no. 11 (November 2, 2020): 1822. http://dx.doi.org/10.3390/electronics9111822.

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Digital marketing is a profitable business generating annual revenue over USD 200B and an inter-annual growth over 20%. The definition of efficient marketing investment strategies across different types of channels and campaigns is a key task in digital marketing. Attribution models are an instrument used to assess the return of investment of different channels and campaigns so that they can assist in the decision-making process. A new generation of more powerful data-driven attribution models has irrupted in the market in the last years. Unfortunately, its adoption is slower than expected. One of the main reasons is that the industry lacks a proper understanding of these models and how to configure them. To solve this issue, in this paper, we present an empirical study to better understand the key properties of user-paths and their impact on attribution models. Our analysis is based on a large-scale dataset including more than 95M user-paths from real advertising campaigns of an international hoteling group. The main contribution of the paper is a set of recommendation to build accurate, interpretable and computationally efficient attribution models such as: (i) the use of linear regression, an interpretable machine learning algorithm, to build accurate attribution models; (ii) user-paths including around 12 events are enough to produce accurate models; (iii) the recency of events considered in the user-paths is important for the accuracy of the model.

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22

Rao, V. V. R. Maheswara, Dr V. Valli Kumari, and Dr KVSVN Raju. "Understanding User Behavior using Web Usage Mining." International Journal of Computer Applications 1, no. 7 (February 25, 2010): 55–64. http://dx.doi.org/10.5120/162-286.

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23

Jae Hyung Byun and Seo JongHwan. "Understanding Fun Factors in Product-User Interaction." Journal of Digital Design 8, no. 3 (July 2008): 21–30. http://dx.doi.org/10.17280/jdd.2008.8.3.003.

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24

Whyte, Grafton, Andy Bytheway, and Chris Edwards. "Understanding user perceptions of information systems success." Journal of Strategic Information Systems 6, no. 1 (March 1997): 35–68. http://dx.doi.org/10.1016/s0963-8687(96)01054-2.

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25

Schlagkamp, Stephan, Rafael Ferreira da Silva, Ewa Deelman, and Uwe Schwiegelshohn. "Understanding User Behavior: From HPC to HTC." Procedia Computer Science 80 (2016): 2241–45. http://dx.doi.org/10.1016/j.procs.2016.05.397.

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26

Benbunan-Fich, Raquel, and Alberto Benbunan. "Understanding user behavior with new mobile applications." Journal of Strategic Information Systems 16, no. 4 (December 2007): 393–412. http://dx.doi.org/10.1016/j.jsis.2007.08.002.

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27

Buscher, Georg, Jacek Gwizdka, Jaime Teevan, Nicholas J. Belkin, Ralf Bierig, Ludger van Elst, and Joemon Jose. "SIGIR 2009 workshop on understanding the user." ACM SIGIR Forum 43, no. 2 (December 14, 2009): 57–62. http://dx.doi.org/10.1145/1670564.1670574.

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28

Hanson, E. J., A. Edberg, S. Dahlin Ivanoff, and S. Iwarsson. "UNDERSTANDING USER PARTICIPATION IN RESEARCH: METHODOLOGICAL CONSIDERATIONS." Innovation in Aging 1, suppl_1 (June 30, 2017): 749. http://dx.doi.org/10.1093/geroni/igx004.2709.

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29

Solomon, Jacob, and Rick Wash. "Human-What Interaction? Understanding User Source Orientation." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 58, no. 1 (September 2014): 422–26. http://dx.doi.org/10.1177/1541931214581088.

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30

Jansen, Bernard J. (Jim). "Understanding User-Web Interactions via Web Analytics." Synthesis Lectures on Information Concepts, Retrieval, and Services 1, no. 1 (January 2009): 1–102. http://dx.doi.org/10.2200/s00191ed1v01y200904icr006.

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31

Carole, William A., and Thomas J. Colacot. "Understanding Palladium Acetate from a User Perspective." Chemistry - A European Journal 22, no. 23 (April 29, 2016): 7686–95. http://dx.doi.org/10.1002/chem.201601450.

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32

Carole, William A., and Thomas J. Colacot. "Understanding Palladium Acetate from a User Perspective." Chemistry - A European Journal 22, no. 23 (May 23, 2016): 7649. http://dx.doi.org/10.1002/chem.201602072.

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33

Cocciolo, Anthony, and Debbie Rabina. "Does place affect user engagement and understanding?" Journal of Documentation 69, no. 1 (January 11, 2013): 98–120. http://dx.doi.org/10.1108/00220411311295342.

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34

Salaba, Athena. "End-User Understanding of Indexing Language Information." Cataloging & Classification Quarterly 47, no. 1 (January 2009): 23–51. http://dx.doi.org/10.1080/01639370802451983.

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35

McGee, Mick, Aaron Rich, and Joe Dumas. "Understanding the Usability Construct: User-Perceived Usbility." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 48, no. 5 (September 2004): 907–11. http://dx.doi.org/10.1177/154193120404800535.

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36

Roebuck, Peter, Roger Simnett, and Hui Lin Ho. "Understanding assurance services reports: A user perspective." Accounting & Finance 40, no. 3 (November 2000): 211–32. http://dx.doi.org/10.1111/1467-629x.00045.

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37

Zhang, Thomas, Jason H. D. Cho, and Chengxiang Zhai. "Understanding User Intents in Online Health Forums." IEEE Journal of Biomedical and Health Informatics 19, no. 4 (July 2015): 1392–98. http://dx.doi.org/10.1109/jbhi.2015.2416252.

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38

Abdallah, Sherief, and Wesam Alnusairat. "Understanding user demographics using public transportation data." International Journal of Business Intelligence and Systems Engineering 1, no. 3 (2019): 251. http://dx.doi.org/10.1504/ijbise.2019.098868.

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Alnusairat, Wesam, and Sherief Abdallah. "Understanding user demographics using public transportation data." International Journal of Business Intelligence and Systems Engineering 1, no. 3 (2019): 251. http://dx.doi.org/10.1504/ijbise.2019.10020308.

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40

Macaulay, Linda. "Cooperation in understanding user needs and requirements." Computer Integrated Manufacturing Systems 8, no. 2 (May 1995): 155–65. http://dx.doi.org/10.1016/0951-5240(95)00010-q.

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41

Garnar, Martin, and Joel Tonyan. "Library as place: Understanding contradicting user expectations." Journal of Academic Librarianship 47, no. 5 (September 2021): 102391. http://dx.doi.org/10.1016/j.acalib.2021.102391.

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42

Beaudry and Pinsonneault. "Understanding User Responses to Information Technology: A Coping Model of User Adaptation." MIS Quarterly 29, no. 3 (2005): 493. http://dx.doi.org/10.2307/25148693.

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43

Shao, Guosong. "Understanding the appeal of user‐generated media: a uses and gratification perspective." Internet Research 19, no. 1 (January 30, 2009): 7–25. http://dx.doi.org/10.1108/10662240910927795.

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44

Dervin, Brenda, and Peter Shields. "Adding the missing user to policy discourse: understanding US user telephone privacy concerns." Telecommunications Policy 23, no. 5 (June 1999): 403–35. http://dx.doi.org/10.1016/s0308-5961(99)00022-1.

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45

Fisch, Eric A. "Understanding and improving the user interface design process." ACM SIGSOFT Software Engineering Notes 18, no. 2 (April 1993): 27–29. http://dx.doi.org/10.1145/159420.155835.

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46

Tan, Victor. "Adding Value to Parks Through Understanding User Needs." CITYGREEN 01, no. 03 (2011): 76. http://dx.doi.org/10.3850/s238258121101043x.

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47

Kang, Sangwoo, and Jungyun Seo. "Two-phase reanalysis model for understanding user intention." Pattern Recognition Letters 42 (June 2014): 35–39. http://dx.doi.org/10.1016/j.patrec.2013.12.015.

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48

Senarath, Awanthika R., and Nalin Asanka Gamagedara Arachchilage. "Understanding user privacy expectations: A software developer’s perspective." Telematics and Informatics 35, no. 7 (October 2018): 1845–62. http://dx.doi.org/10.1016/j.tele.2018.05.012.

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49

Zheng, Zhixiong, Haibo Cheng, Zijian Zhang, Yiming Zhao, and Ping Wang. "An Alternative Method for Understanding User-Chosen Passwords." Security and Communication Networks 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/6160125.

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We present in this paper an alternative method for understanding user-chosen passwords. In password research, much attention has been given to increasing the security and usability of individual passwords for common users. Few of them focus on the relationships between passwords; therefore we explore the relationships between passwords: modification-based, similarity-based, and probability-based. By regarding passwords as vertices, we shed light on how to transform a dataset of passwords into a password graph. Subsequently, we introduce some novel notions from graph theory and report on a number of inner properties of passwords from the perspective of graph. With the assistance of Python Graph-tool, we are able to visualize our password graph to deliver an intuitive grasp of user-chosen passwords. Five real-world password datasets are used in our experiments to fulfill our thorough experiments. We discover that (1) some passwords in a dataset are tightly connected with each other; (2) they have the tendency to gather together as a cluster like they are in a social network; (3) password graph has logarithmic distribution for its degrees. Top clusters in password graph could be exploited to obtain the effective mangling rules for cracking passwords. Also, password graph can be utilized for a new kind of password strength meter.

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50

Callanan, Cormac, and Borka Jerman-Blazic. "User Understanding of Privacy In Emerging Mobile Markets." IEEE Technology and Society Magazine 33, no. 4 (2014): 48–56. http://dx.doi.org/10.1109/mts.2014.2363977.

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