Journal articles: 'Users information behavior'

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A. Kalisdha, A. Kalisdha. "Information Need and Information Seeking Behavior of Users in a Library and Information System." International Journal of Scientific Research 2, no. 11 (June 1, 2012): 274–76. http://dx.doi.org/10.15373/22778179/nov2013/87.

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Chiang, I. Ping. "Exploring Smartphone Users’ Social Information Behavior." Contemporary Management Research 15, no. 1 (March 1, 2019): 53–67. http://dx.doi.org/10.7903/cmr.18461.

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Huang, Chun-Yao, Yung-Cheng Shen, I.-Ping Chiang, and Chen-Shun Lin. "Characterizing Web users' online information behavior." Journal of the American Society for Information Science and Technology 58, no. 13 (2007): 1988–97. http://dx.doi.org/10.1002/asi.20669.

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Wu, Dan, Rui Qiao, and Yi Li. "A study on location-based mobile map search behavior." Program 50, no. 3 (July 4, 2016): 246–69. http://dx.doi.org/10.1108/prog-11-2015-0074.

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Purpose – Mobile users increasingly employ location-based map searches in their daily lives. However, it is still relatively unknown about mobile users’ map related search behaviors. The purpose of this paper is to discover the interactions between the users and mobile map search systems, to reveal the shortcomings of existing mobile map search functions, and to propose improvement suggestions. Design/methodology/approach – Based on a set of controlled user experiments performed on the Baidu mobile phone map, this paper empirically examines users’ location-based mobile search behaviors, such as timing, metering, judging and so on. This paper also conducts statistical correlation tests to generate relation tables and diagrams regarding each variable, for example, the relation between the retrieval time and the retrieval steps. Findings – The results indicate that mobile map users have two important characteristics in their search behaviors: first, mobile map users always follow the single search path. Second, the mobile map search efficiency of users is always low. Research limitations/implications – The situation simulation testing method is mainly used for the construction of a mobile information search behavior environment, which may make the users be nervous and have some effect on the search efficiency. Practical implications – Based on the identification of user behaviors, this paper provides suggestions to optimize and improve mobile map search systems. Originality/value – This paper studies users’ mobile map search behavior based on location and explores the features of user behavior from the perspective of human-computer interaction.

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Sun, Yuan, Shuyue Fang, and Yujong Hwang. "Investigating Privacy and Information Disclosure Behavior in Social Electronic Commerce." Sustainability 11, no. 12 (June 15, 2019): 3311. http://dx.doi.org/10.3390/su11123311.

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Social e-commerce has steadily emerged as a current trend for an enormous amount of Internet users. Despite the popularity and prevalence of social e-commerce, many users hesitate to disclose their information due to privacy concerns. This resistance from users impedes the development of social e-commerce enterprises. In order to help enterprises collect more user information and establish better development strategies, this research builds on the Privacy Antecedent-Privacy Concern-Outcomes (APCO) model and the theory of privacy calculus. This research investigates how the privacy antecedents of hot topic interactivity and group buying experience influence users’ privacy concerns and perceived benefits as well as how to further influence users’ information disclosure behavior. The results from 406 questionnaire responses indicate that hot topic interactivity and group buying experience have significant negative impacts on privacy concerns and significant positive impacts on perceived benefits. Privacy concerns negatively influence the behavior of information disclosure while perceived benefits positively influence the behavior of information disclosure. Based on these results, social e-commerce enterprises should promote users’ behaviors of hot topic interactivity and group buying to stimulate users’ information disclosure behavior.

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Liu, Shuangji, Yongzhong Yang, and Yiwei Wang. "Integration of Museum User Behavior Information Based on Wireless Network." Mobile Information Systems 2021 (July 9, 2021): 1–8. http://dx.doi.org/10.1155/2021/6847144.

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Online museum information resource systems are getting popular these days which allow the users to get detailed information about the objects of their interest, and the user preferences are stored to search for related artifacts considering his/her online behavior. The behavior of users browsing online is integrated to capture relevant information which is integrated into museum information resources. Unfortunately, present implementations have errors in integration and optimization system, so a wireless network-based museum user behavior information integration system is proposed to calculate the user’s interest in museum’s cultural relics. The user behavior information resource model is developed based upon the degree of user interest, and forgetting functions with different decay rates are employed to describe changes in the interest level. This information is then used to construct users’ interest matrices. This matrix also contains information regarding the cultural relics that users have not yet visited. The system will introduce the interest weights of feature words to take the top features of the user behavior information for the integration of the users’ behavior and to combine the feature vectors that can represent the overall trajectory. Moreover, those feature vectors are described that can represent the local trajectory into feature vector to identify the slow-moving sparse targets, which is then utilized for the integration of users’ behavior information. The simulation tests prove that the proposed method can achieve low error in the integration process of user behavior information resources, thereby yielding good results.

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Ranganathan, C., DongBack Seo, and Yair Babad. "Switching behavior of mobile users: do users' relational investments and demographics matter?" European Journal of Information Systems 15, no. 3 (June 2006): 269–76. http://dx.doi.org/10.1057/palgrave.ejis.3000616.

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Zhang, Pei Ying, Ya Jun Du, and Chang Wang. "Clustering Users According to Common Interest Based on User Search Behavior." Advanced Materials Research 143-144 (October 2010): 851–55. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.851.

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The paper presents a novel method to cluster users who share the common interest and discover their common interest domain by mining different users’ search behaviors in the user session, mainly the consecutive search behavior and the click sequence considering the click order and the syntactic similarity. The community is generated and this information will be used in the recommendation system in the future. Also the method is ‘content-ignorant’ to avoid the storage and manipulation of a large amount of data when clustering the web pages by content. The experiment proved it an available and effective way.

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Ruiz, Miguel E., and Pok Chin. "Users' seeking behavior and multilingual image tags." Proceedings of the American Society for Information Science and Technology 47, no. 1 (November 2010): 1–2. http://dx.doi.org/10.1002/meet.14504701407.

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Fagan, Derek, Brian Caulfield, and René Meier. "Analyzing the Behavior of Smartphone Service Users." International Journal of Ambient Computing and Intelligence 5, no. 2 (April 2013): 1–16. http://dx.doi.org/10.4018/jaci.2013040101.

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This paper reports the findings of a study into the behavior of the users of a mobile service. The study analyses the behavior of travelers using a Smartphone application to access real-time transit information and contrasts such user behavior with that of users accessing a transit information service from a website. Previous research in this field has tended to focus upon the perceived benefits of providing real-time transit information and without investigating when and how often passengers would use such real-time transit information services. This paper specifically explores the behavior patterns of travelers using a Smartphone service and those of using a website to provide real-time transit information. Based on empirical data derived from real information services, the impact on user behavior of providing a mobile service is analyzed and contrasted to traditional Web-based service provision. The Smartphone service is furthermore used to conduct a passenger survey to obtain information on the individuals using the mobile service. The results of the analysis presented demonstrate that the demand for information from the website is constant throughout the working week whereas demand for Smartphone information increases during the week peaking during late afternoons and on Fridays. The results of the passenger survey demonstrate that over 80 percent of Smartphone application users are between 18 and 49 years of age and perhaps most importantly, that Smartphone survey questions are twice as likely to be answered compared to the response rates for Web or mail surveys.

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Chen, Wanyu, Zepeng Hao, Taihua Shao, and Honghui Chen. "Personalized query suggestion based on user behavior." International Journal of Modern Physics C 29, no. 04 (April 2018): 1850036. http://dx.doi.org/10.1142/s0129183118500365.

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Query suggestions help users refine their queries after they input an initial query. Previous work mainly concentrated on similarity-based and context-based query suggestion approaches. However, models that focus on adapting to a specific user (personalization) can help to improve the probability of the user being satisfied. In this paper, we propose a personalized query suggestion model based on users’ search behavior (UB model), where we inject relevance between queries and users’ search behavior into a basic probabilistic model. For the relevance between queries, we consider their semantical similarity and co-occurrence which indicates the behavior information from other users in web search. Regarding the current user’s preference to a query, we combine the user’s short-term and long-term search behavior in a linear fashion and deal with the data sparse problem with Bayesian probabilistic matrix factorization (BPMF). In particular, we also investigate the impact of different personalization strategies (the combination of the user’s short-term and long-term search behavior) on the performance of query suggestion reranking. We quantify the improvement of our proposed UB model against a state-of-the-art baseline using the public AOL query logs and show that it beats the baseline in terms of metrics used in query suggestion reranking. The experimental results show that: (i) for personalized ranking, users’ behavioral information helps to improve query suggestion effectiveness; and (ii) given a query, merging information inferred from the short-term and long-term search behavior of a particular user can result in a better performance than both plain approaches.

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Voordijk, Hans, and Ruth Sloot. "BIM mediation and users’ behavior." International Journal of Managing Projects in Business 13, no. 7 (August 28, 2019): 1561–77. http://dx.doi.org/10.1108/ijmpb-11-2018-0255.

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Purpose With building information modeling’s increasing influence, it becomes important to analyze building information model (BIM)’s impact on users’ behavior. Therefore, the purpose of this paper is to explore BIM’s influence on users’ behavior, using the innovative philosophy of technical mediation. This philosophy implies that perceptions and actions are always, to some degree, constituted and transformed by technologies. The question in this study is how the perceptions and actions of users are mediated by BIM. Design/methodology/approach A framework developed by Dorrestijn to assess the impacts of technology is used to explore the different types of impact that BIM has on the perceptions and actions of its users. Through a literature review, this framework is used to categorize the mediating effects of BIM. Following this, expert interviews, a workshop and user interviews explore these effects in practice. Findings Based on Dorrestijn’s framework, it is concluded that guidance and persuasion are important mediating effects of BIM. BIM also impacts the human decision-process through coercive pressures to implement BIM and to embody BIM through acquiring skills. Originality/value With the increasing influence of BIM, analyzing its impact on users’ behavior becomes increasingly relevant. This is the first study to use the technical mediation approach to analyze this impact. In this approach, humans and technologies are seen as interacting with and co-shaping each other.

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Hong, Ying, Meng Wan, and Zheng Li. "Understanding the Health Information Sharing Behavior of Social Media Users." Journal of Organizational and End User Computing 33, no. 5 (September 2021): 180–203. http://dx.doi.org/10.4018/joeuc.20210901.oa9.

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Studies have focused on elucidating the sharing behavior of media users. However, few studies have specifically investigated users' health information sharing behavior in the social media context, especially WeChat. This study proposes a theoretical research model that integrates social capital and user gratification with the theory of planned behavior to explore health information sharing behavior of WeChat users. Based on online survey data collected from 616 WeChat users, correlation analysis and structural equation modeling were sequentially performed. It was found that both social capital and gratification factors play important roles in influencing WeChat users' health information sharing. Social interaction, acting both as social capital and gratification factor directly and indirectly generated positive effects on health information sharing intention. In conclusion, this study revealed the key determinants of health information sharing intention among WeChat users and examined the mediation effects to effectively understand users' health information sharing behavior.

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Halilovic, Semina, and Muris Cicic. "Segmentation of Information Systems Users." Journal of Organizational and End User Computing 25, no. 4 (October 2013): 1–26. http://dx.doi.org/10.4018/joeuc.2013100101.

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The Expectation-Confirmation Model of Information Systems Continuance (ECM-IS) explains antecedents that influence IS users’ behavior and affect their decision whether to continue or discontinue information system (IS) using. ECM-IS emphasizes differences between initial acceptance and IS continuance. For companies that deal with the design and software development, IS continuance is retaining of existing customers of product and services. This study extends the ECM-IS by accounting for unobserved heterogeneity. The Finite Mixture Partial Least Squares (FIMIX-PLS) methodology is applied for identification of distinctive customer segments. Segmentation of IS users was made on the basis of cognitive beliefs and affect influencing one’s intention to continue using IS and two different segments of users were derived. The first segment comprises 65.6%, and the other one 34.4% users. The ECM-IS explained 51.9% of IS continuance intention and 20.7% of satisfaction for the first segment, while for the second segment the ECM-IS explained 98.1% of IS continuance intention and 91.3% of satisfaction.

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Ma, Liang, Xin Zhang, and Xiao Yan Ding. "Social media users’ share intention and subjective well-being." Online Information Review 42, no. 6 (October 8, 2018): 784–801. http://dx.doi.org/10.1108/oir-02-2017-0058.

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Purpose The rise of social media has gained increasing attention in recent years; however, few studies have focused on social media users’ specific behavior and subjective well-being. To fill this research gap, the purpose of this paper is to develop an integrated model to investigate factors that affect social media user’s share intention and the relationship between user’s share intention and subjective well-being. Design/methodology/approach Structural equation model is used in this study. A field survey with 398 WeChat users is conducted to test the research model and hypotheses. Findings The empirical results show that: utilitarian value, hedonic value, user satisfaction and information source credibility are important factors affecting users’ share intention; users’ share intention positively affects user’s subjective well-being; moderating effects show that relative significance positively moderates the relationship between utilitarian value and users’ share intention; and users’ demographic characteristics differences actually exist in users’ share intention. Originality/value First, the authors clear that factors affect social media users’ share intention from the perspective of customer-perceived value. The results deepen our understanding about the factors that affect WeChat users’ share intention. Second, the authors focus on the effect of users’ specific behavior on users’ subjective well-being and found that users’ share intention is one of the important aspects that affect user’s subjective well-being. More importantly, the authors tested users’ characteristic differences in social media users’ share intention, which have previously received limited attention.

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Zhao, Yueshu, and Mingsheng Zhao. "WeChat Users’ Information Protection Behavior Based on Prospect Theory." International Journal of Information and Education Technology 9, no. 6 (2019): 390–95. http://dx.doi.org/10.18178/ijiet.2019.9.6.1233.

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Chen, Yang, Chulu Liang, and Danqing Cai. "Understanding WeChat Users’ Behavior of Sharing Social Crisis Information." International Journal of Human–Computer Interaction 34, no. 4 (January 24, 2018): 356–66. http://dx.doi.org/10.1080/10447318.2018.1427826.

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Frank, Jonathan, Boas Shamir, and Warren Briggs. "Security-related behavior of PC users in organizations." Information & Management 21, no. 3 (October 1991): 127–35. http://dx.doi.org/10.1016/0378-7206(91)90059-b.

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Orso, Valeria, Tuukka Ruotsalo, Jukka Leino, Luciano Gamberini, and Giulio Jacucci. "Overlaying social information: The effects on users’ search and information-selection behavior." Information Processing & Management 53, no. 6 (November 2017): 1269–86. http://dx.doi.org/10.1016/j.ipm.2017.06.001.

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Chen, Hao, and Wenli Li. "Mobile device users’ privacy security assurance behavior." Information & Computer Security 25, no. 3 (July 10, 2017): 330–44. http://dx.doi.org/10.1108/ics-04-2016-0027.

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Purpose Recently, the spread of malicious IT has been causing serious privacy threats to mobile device users, which hampers the efficient use of mobile devices for individual and business. To understand the privacy security assurance behavior of mobile device users, this study aims to develop a theoretical model based on technology threat avoidance theory (TTAT), to capture motivation factors in predicting mobile device user’s voluntary adoption of security defensive software. Design/methodology/approach A survey is conducted to validate the proposed research model. A total of 284 valid survey data are collected and partial least square (PLS)-based structural equation modeling is used to test the model. Findings Results highlight that both privacy concern and coping appraisal have a significant impact on the intention to adopt the security defensive software. Meanwhile, privacy security awareness is a crucial determinant to stimulate mobile device user’s threat and coping appraisal processes in the voluntary context. The results indicate that emotional-based coping appraisal of anticipated regret is also imperative to arouse personal intention to adopt the security tool. Practical implications This result should be of interest to practitioners. Information security awareness training and education programs should be developed in a variety of forms to intensify personal security knowledge and skills. Besides, emotion-based warnings can be designed to arouse users’ protection behavior. Originality/value This paper embeds TTAT theory within the mobile security context. The authors extent TTAT by taking anticipated regret into consideration to capture emotional-based coping appraisal, and information security awareness is employed as the antecedent factor. The extent offers a useful starting point for the further empirical study of emotion elements in the information security context.

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Liu, Guo-qi, Yi-jia Zhang, Ying-mao Fu, and Ying Liu. "Behavior Identification Based on Geotagged Photo Data Set." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/616030.

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The popularity of mobile devices has produced a set of image data with geographic information, time information, and text description information, which is called geotagged photo data set. The division of this kind of data by its behavior and the location not only can identify the user’s important location and daily behavior, but also helps users to sort the huge image data. This paper proposes a method to build an index based on multiple classification result, which can divide the data set multiple times and distribute labels to the data to build index according to the estimated probability of classification results in order to accomplish the identification of users’ important location and daily behaviors. This paper collects 1400 discrete sets of data as experimental data to verify the method proposed in this paper. The result of the experiment shows that the index and actual tagging results have a high inosculation.

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Maity, Moutusy, Kallol Bagchi, Arunima Shah, and Ankita Misra. "Explaining normative behavior in information technology use." Information Technology & People 32, no. 1 (February 4, 2019): 94–117. http://dx.doi.org/10.1108/itp-11-2017-0384.

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PurposeThe purpose of this paper is to identify a model that provides explanations for normative behavior in information technology (IT) use, and to test the model across two different types of normative behavior (i.e. green information technology (GIT), and digital piracy (DP)).Design/methodology/approachThe proposed model is based on the norm activation model (NAM) and the unified theory of acceptance and use of technology model (UTAUT). A total of 374 and 360 usable responses were obtained for GIT and DP, respectively. The authors use the SEM technique in order to test the proposed model on the two sub-samples.FindingsFindings from the proposed model show that DP users’ personal norm (PN) negatively impacts behavioral intention and actual behavior. These findings indicate that users of IT who indulge in DP understand that use of pirated software may not be a socially approved behavior but they still indulge in it because their PNs are not aligned with social expectations. GIT users’ PN positively impacts behavioral intention and actual behavior, and the relationship is stronger for behavioral intention than for actual behavior.Research limitations/implicationsThe sample consists of college students and working professionals based in India who may be savvy with respect to internet use. Future work may evaluate whether the pattern of results that the authors report for normative behavior does hold across other types of normative behavior.Practical implicationsThese findings hint at a gap between the moral compass and the final “action” taken by DP users. What managers need to do is to create awareness among their customers about the implementation of DP/GIT and help users engage in normative behavior.Originality/valueThis research contributes to the literature by integrating the UTAUT and the NAM to explain normative behavior of IT use. The authors propose and test a model that identifies cognitive as well as social-psychological motivations to explain normative behavior in IT use, which have been sparingly studied in extant literature, and provides a holistic understanding of the phenomenon. As such, this research contributes to the existing knowledge of understanding of normative IT behavior.

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Liu, Li, Xin Su, Umair Akram, and Muhammad Abrar. "The User Acceptance Behavior to Mobile Digital Libraries." International Journal of Enterprise Information Systems 16, no. 2 (April 2020): 38–53. http://dx.doi.org/10.4018/ijeis.2020040103.

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The rapid development of mobile digital libraries is an inevitable trend in online library services in recent years. The effect of the new digital service is closely related to the continuous improvements and innovation of online library, as well as users' experiences and acceptance behaviors. Based on the UTAUT model, we conducted a standardized questionnaire survey and collected a dataset with a sample of digital library users. Then, we use SPSS to analyze the dataset and demonstrate the reliability and validity of the sample. Besides, we also use AMOS to conduct structural equation model, examine the fitting degree, and provide theoretical contributions and practical implications for both academia and practice. The results show that the factors influencing a user's behavior include social influence, user innovation, payment value, performance expectancy, facilitating condition, and intention to use, which are all positive. However, effort expectancy and service mobility had no effect on reception behavior.

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Feng, Y., D. S. Liu, Chun Hua Ju, and Hao Tian. "Manufacturing Information Recommendation Model Based on Pattern Mining." Advanced Materials Research 121-122 (June 2010): 294–99. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.294.

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Existing enterprise information systems seldom consider different requirements from different users. To remedy this problem, the idea of manufacturing information active recommendation is put forward in this paper to deliver proper information to proper users correctly and timely. A new model called Sequence Behavior Access Pattern tree (SBAP-tree) is constructed based on the different requirements of user's identity, location, behavior habit and business needs for manufacturing information in Web environment. Using this SBAP-tree, historical situations similar to current situation could be sorted by their values, and the behavior could then be determined and output based on the association between the highly similar historical situations. Finally, an example is provided to demonstrate the effectiveness of SBAP-tree and the manufacturing information recommendation model based on SBAP-tree.

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Zhitomirsky-Geffet, Maayan, and Maya Blau. "Cross-generational analysis of information seeking behavior of smartphone users." Aslib Journal of Information Management 69, no. 6 (November 20, 2017): 721–39. http://dx.doi.org/10.1108/ajim-04-2017-0083.

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Purpose The purpose of this paper is to investigate the predictive factors of information seeking behavior of smartphone users from the cross-generational perspective. Based on existing literature, the two most popular types of information seeking behavior of smartphone users were determined: social information seeking behavior; and functional/cognitive information seeking behavior. Design/methodology/approach A questionnaire comprising 66 questions was administered online to 216 smartphone users of three age groups according to three generations: generation X, Y (millennials) and Z. Several predictive factors were examined for each of these information seeking behavior types: generation, gender, personality traits (the Big Five), daily usage time, period of ownership, various application utilization and the level of emotional gain from smartphones. Findings There is a trade-off between the two types of information seeking behavior. Also, men exhibited significantly more functional/cognitive information seeking behavior than women, and younger generations reported significantly higher emotional gain and social information seeking behavior than older generations. Interestingly, significant differences in smartphone apps’ utilization, information seeking behavior types and their predictive factors were found among users from different generations. Extraversion was positively related to social information seeking behavior only for generations X and Y, while WhatsApp usage was one of the strongest predictive factors only for generation Z. Practical implications This research has practical implications for information system design, education, e-commerce and libraries. Originality/value This is a first study that systematically examines predictive factors of the two prominent types of information seeking behavior on smartphones from the cross-generational perspective.

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Harati, Hadi, Fatemeh Nooshinfard, Alireza Isfandyari-Moghaddam, Fahimeh Babalhavaeji, and Nadjla Hariri. "Factors affecting the unplanned use behavior of academic libraries users." Aslib Journal of Information Management 71, no. 2 (March 18, 2019): 138–54. http://dx.doi.org/10.1108/ajim-04-2018-0092.

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Purpose The purpose of this paper is to identify and design the axial coding pattern of the factors affecting the unplanned use behavior of users of the academic libraries and information centers. Design/methodology/approach The study as an applied research with a qualitative approach employed the grounded theory. The data collection tool was a deep and semi-structured interview. The interviews data were analyzed and coded during three stages of open, axial and selective coding using the MAXQDA 10 qualitative analysis software. The research population consisted of faculty members and experts in three areas of library and information science, management and psychology. Using the combined targeted sampling method (targeted and then the snowball), 12 subjects were selected as the sample size. Findings According to the research findings, the factors affecting the unplanned behavior of users in the use of academic libraries resources and services were identified as factors related to technology, environmental factors, information resources, information services, human resources, individual features, time position factor, cultural factors and social factors. Accordingly, the axial coding pattern of this type of behaviors was designed. Research limitations/implications The research limitations include the lack of theoretical basis related to the unplanned behavior issue in the field of library and information science and the lack of full familiarity of most of the experts in the field of library and information science with this topic. These factors lead to the necessity of explaining the subject under discussion. Originality/value The unplanned behavior of clients can be utilized to persuade users to use the information resources and library services so that the costs spent on their preparation and collection will be justifiable. The current research addressed this aspect of the unplanned information-seeking behavior.

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Jovanović, Tamara, Sanja Božić, Bojana Bodroža, and Uglješa Stankov. "Influence of users’ psychosocial traits on Facebook travel–related behavior patterns." Journal of Vacation Marketing 25, no. 2 (April 24, 2018): 252–63. http://dx.doi.org/10.1177/1356766718771420.

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The principal aim of the article was to explore the psychological aspects of Facebook (FB) users’ travel-related behavior on FB. This especially refers to the time they post their travel-related information, the type of information they post (photos, videos, comments, etc.), when they watch the photos of other people, are they keen to post the information when their impressions are positive or negative, and what level of privacy they keep (with whom they are sharing travel-related information). A total of 804 general FB users from Serbia were included in the study. Analysis of the relationship between travel-related behavior on FB and the different psychosocial aspects of FB use (PSAFU) can be of great importance for online destination marketing. This could help in identifying the patterns of tourists’ FB behavior that result in sharing their travel experiences via electronic word-of-mouth as well as in predicting the FB behavior of future tourists. The study revealed that all analyzed dimensions of PSAFU are related to certain travel-related behavior on FB, explained from 1.9% to 13.7% of these behaviors. The strongest and most consistent predictor of travel-related FB behaviors was ‘Virtual self’ dimension. On contrary, the study showed that Compensatory use of FB is not related to travel-related behavior on FB to a great extent, thus is not of much interest to destination marketers. The further implications are discussed in the article.

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Hariri, Nadjla, Maryam Asadi, and Yazdan Mansourian. "The impact of users’ verbal/imagery cognitive styles on their Web search behavior." Aslib Journal of Information Management 66, no. 4 (July 15, 2014): 401–23. http://dx.doi.org/10.1108/ajim-02-2013-0019.

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Purpose – The purpose of this paper is to investigate the effects of verbal-imagery cognitive styles of information searching behavior of users in using the Web. Design/methodology/approach – In all, 44 participants were recruited for this study. The participants’ cognitive styles were measured by using Riding's Cognitive Style Analysis test. Three search tasks were designed based on Kim's search task definitions. Moreover, an individual lab session was arranged and then participants’ memos were analyzed using content analysis. Findings – In all, 48 strategies in four categories of behaviors in searching the Web were identified. There were associations between users’ cognitive styles and their information searching behavior. The participants’ selection of the search initiation behaviors varied, so that imagers suffered from more varied initial behavior than verbalizers. The verbalizers tended to search in a narrow area, then broadening the area and following structured navigation and reading behavior to process information, while imagers tended to search in a general area, then narrowing down the search and adopting mixed navigational styles and mixed behaviors to process information. This study revealed that there was a difference in search performance of verbalizers and imagers descriptively, as verbalizers spent more time compared to imagers and imagers visited more nodes than verbalizers for the tasks completion. In addition, the task was an important variable influencing the search performance. Based on the key findings (search initiation behaviors, formulating search queries, navigational behaviors, information processing behaviors), a conceptual pattern of Web searching and cognitive styles is presented. Research limitations/implications – The study provides a new understanding of Web users’ information search behavior based on cognitive styles which contributes to the theoretical basis of Web search research. It also raises various questions within the context of user studies Originality/value – The paper adopted a mixed approach in the area of information searching on the Web. A valuable contribution lies in the methods developed.

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Xia, Rong Ze, Yan Jia, Wang Qun Lin, and Hu Li. "Mining Information Spreading Based on Users' Retweet Behavior in Twitter." Applied Mechanics and Materials 380-384 (August 2013): 2866–70. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2866.

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Twitter is one of the largest social networks in the world. People could share contents on it. When we interact with each other, the information spreads. And its users retweet behavior that makes information spread so fast. So there comes an important question: Whats about users retweet behavior? Could we simulate information spreading in twitter by retweeting behavior? We crawl twitter and mine information spreading based on users retweet behavior in it. Through our dateset, we verify the power-law distribution of the retweet-width and retweet-depth. At the same time, we study the correlation between retweet-width and retweet-depth. Finally, we propose an information spreading model to simulate the information spreading process in twitter and get a good result.

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Wang, Chen-Ya, Yi-Chun Lin, Hsia-Ching Chang, and Seng-cho T. Chou. "Consumer Sentiment in Tweets and Coupon Information-Sharing Behavior." International Journal of Online Marketing 7, no. 3 (July 2017): 1–19. http://dx.doi.org/10.4018/ijom.2017070101.

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The authors aim to explore the correlation between coupon information-sharing behavior and consumer sentiment by analyzing tweets. They used Twitter application programming interface to retrieve users' tweets, and took a machine learning approach for sentiment analysis. After the data pre-processing procedure, the authors then examined the correlation between sentiments in tweets and coupon information sharing. More than half of the most active users showed that their coupon information-sharing behavior correlated to both positive and negative sentiments. The results also showed that the response, coupon information sharing, for positive/negative sentiment had no significant time shifting pattern for most of the users. This study preliminary verifies the assumption that there is a correlation between users' sentiments in tweets and coupon information-sharing behavior, and indicates some interesting findings. The authors' findings may shed light on whether sentiment plays a role in social media communication concerning the sharing of coupon information.

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Shi, Juan, Ping Hu, Kin Keung Lai, and Gang Chen. "Determinants of users’ information dissemination behavior on social networking sites." Internet Research 28, no. 2 (April 4, 2018): 393–418. http://dx.doi.org/10.1108/intr-01-2017-0038.

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Purpose As a new communication paradigm, social networking sites (SNS) have boosted information diffusion and viral marketing. Prior researchers have identified various factors affecting information dissemination on SNS. However, they often focus on limited factors and there is a lack of an integrated theoretical framework that explains aspects of relevant factors. Besides, the research on the impacts of relationships on individual retweeting behavior is still controversial. The purpose of this paper is to propose a theoretical framework to systematically investigate the determinants of individual dissemination behavior on SNS based on the elaboration likelihood model (ELM). Moreover, the authors also examine the relative importance of those relevant factors. Design/methodology/approach The authors randomly selected 1,250 members of Twitter and crawled posts published by each member since he/she created the Twitter account using Twitter API. The authors processed the data to create panel data and tested hypotheses with the panel logit model. Findings Factors both on the central route and on the peripheral route of ELM have positive impacts on individual dissemination behavior. Among them, information receiver-related factor and relationships-related factors are the most influential. Contrastingly, source-related factors are the least influential. Furthermore, the authors find that social tie strength mediates almost 50 percent of the effect of value homophily on individual dissemination behavior. Originality/value The authors are the first to directly apply ELM to examine individual dissemination behavior on SNS. By integrating factors into the two information processing routes, They incorporate relevant factors into the model and systematically analyze their impacts on individual retweeting behavior on SNS. The research offers at least one explanation for the contradictory findings about the effect of homophily on individual sharing behavior in previous research. The authors propose new variables that gauge topical relevance and interpersonal value homophily on SNS.

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Spiller, Joan, Anthony Vlasic, and Philip Yetton. "Post-adoption behavior of users of Internet Service Providers." Information & Management 44, no. 6 (September 2007): 513–23. http://dx.doi.org/10.1016/j.im.2007.01.003.

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Alohali, Manal, Nathan Clarke, Fudong Li, and Steven Furnell. "Identifying and predicting the factors affecting end-users’ risk-taking behavior." Information & Computer Security 26, no. 3 (July 9, 2018): 306–26. http://dx.doi.org/10.1108/ics-03-2018-0037.

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Purpose The end-user has frequently been identified as the weakest link; however, motivated by the fact that different users react differently to the same stimuli, identifying the reasons behind variations in security behavior and why certain users could be “at risk” more than others is a step toward protecting and defending users against security attacks. This paper aims to explore the effect of personality trait variations (through the Big Five Inventory [BFI]) on users’ risk level of their intended security behaviors. In addition, age, gender, service usage and information technology (IT) proficiency are analyzed to identify what role and impact they have on behavior. Design/methodology/approach The authors developed a quantitative-oriented survey that was implemented online. The bi-variate Pearson two-tailed correlation was used to analyze survey responses. Findings The results obtained by analyzing 538 survey responses suggest that personality traits do play a significant role in affecting users’ security behavior risk levels. Furthermore, the results suggest that BFI score of a trait has a significant effect as users’ online personality is linked to their offline personality, especially in the conscientiousness personality trait. Additionally, this effect was stronger when personality was correlated with the factors of IT proficiency, gender, age and online activity. Originality/value The contributions of this paper are two-fold. First, with the aid of a large population sample, end-users’ security practice is assessed from multiple domains, and relationships were found between end-users’ risk-taking behavior and nine user-centric factors. Second, based upon these findings, the predictive ability for these user-centric factors were evaluated to determine the level of risk a user is subject to from an individual behavior perspective. Of 28 behaviors, 11 were found to have a 60 per cent or greater predictive ability, with the highest classification of 92 per cent for several behaviors. This provides a basis for organizations to use behavioral intent alongside personality traits and demographics to understand and, therefore, manage the human aspects of risk.

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Yi-wen, ZHANG, BAI Yan-qi, and YANG An-ju. "Statistical analysis of city and the villages Internet users based on user logs." MATEC Web of Conferences 176 (2018): 03011. http://dx.doi.org/10.1051/matecconf/201817603011.

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In recent years, with the rapid increase of users active on the Internet, Internet users access log is also increasing rapidly. According to the user's Internet access log analysis of the characteristics of user behavior on the Internet. In this paper, we classify the statistical analysis of the behavior of Internet users by collecting information and data on urban and rural Internet user behavior. This result may provide a basis for guiding the behavior of Internet software manufacturers or government.

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Hsu, Chin-Lung, and Judy Chuan-Chuan Lin. "An Empirical Study of Smartphone User Behavior." International Journal of Mobile Human Computer Interaction 7, no. 1 (January 2015): 1–24. http://dx.doi.org/10.4018/ijmhci.2015010101.

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This study investigates determinants of the adoption behavior of smartphone users. Despite the increasing number of smartphone users, the literature on information technology usage has paid little attention to the motivation behind smartphone adoption. This study identifies three determinants of smartphone adoption behavior: innovative characteristics, brand equity and social influences. Data were collected from 293 smartphone users. The analytical results have indicated that users choose to use smartphone not only for its usefulness, enjoyment and compatibility to their lifestyle (i.e. innovative characteristics), but also for its cost effectiveness (i.e. brand equity). Additionally, users will search for related information for the suitability of their adoption decisions (i.e. social influence). Together, the above factors account for over 60 percent of adoption behaviors. Moreover, the findings also indicate that perceptions of use varied over the innovation diffusion stage. Implications and suggestions for practitioners are also discussed.

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Yu, Dingguo, Nan Chen, and Xu Ran. "Computational modeling of Weibo user influence based on information interactive network." Online Information Review 40, no. 7 (November 14, 2016): 867–81. http://dx.doi.org/10.1108/oir-12-2015-0391.

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Purpose With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users. Findings Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter. Originality/value This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.

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Oh, Se-Na, and Jee-Yeon Lee. "A Study on Information Searching Behavior of Smart Phone Users." Journal of the Korean Society for information Management 29, no. 1 (March 30, 2012): 191–209. http://dx.doi.org/10.3743/kosim.2012.29.1.191.

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Rajawat, Kumkum. "Information Seeking Behavior of Users of Pharmacy Colleges in Rajasthan." Pearl : A Journal of Library and Information Science 13, no. 3 (2019): 263. http://dx.doi.org/10.5958/0975-6922.2019.00033.0.

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Maisiak, Richard, Sandra Koplon, and Louis W. Heck. "Subsequent behavior of users of an arthritis information telephone service." Arthritis & Rheumatism 33, no. 2 (February 1990): 212–18. http://dx.doi.org/10.1002/art.1780330209.

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Tuhina Choudhury, Tuhina Choudhury. "Information Needs and Seeking Behavior of Assam University Library Users." IOSR Journal of Humanities and Social Science 11, no. 6 (2013): 10–23. http://dx.doi.org/10.9790/0837-1161023.

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Sheremeta, Roman M., and Timothy W. Shields. "Deception and reception: The behavior of information providers and users." Journal of Economic Behavior & Organization 137 (May 2017): 445–56. http://dx.doi.org/10.1016/j.jebo.2017.03.019.

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Al-Samarraie, Hosam, Atef Eldenfria, and Husameddin Dawoud. "The impact of personality traits on users’ information-seeking behavior." Information Processing & Management 53, no. 1 (January 2017): 237–47. http://dx.doi.org/10.1016/j.ipm.2016.08.004.

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Oh, Dong-Geun. "Complaining behavior of public library users in South Korea." Library & Information Science Research 25, no. 1 (March 2003): 43–62. http://dx.doi.org/10.1016/s0740-8188(02)00165-2.

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Zhang, Xiangmin. "Expertise, search behavior, and search performance of engineering users." Proceedings of the American Society for Information Science and Technology 40, no. 1 (January 31, 2005): 546–47. http://dx.doi.org/10.1002/meet.14504001117.

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Shi, Yancui, Jianhua Cao, Congcong Xiong, and Xiankun Zhang. "A Prediction Method of Mobile User Preference Based on the Influence between Users." International Journal of Digital Multimedia Broadcasting 2018 (July 19, 2018): 1–12. http://dx.doi.org/10.1155/2018/8081409.

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User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is introduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction behavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social network and mobile user behavior. Second, the influence between users is calculated according to the user’s influence, the interaction behavior between users, and the similarity of user preferences. When calculating the influence based on the interaction behavior, the context information is considered; the context information and the order of user preferences are considered when calculating the influence based on the similarity of user preferences. The improved collaborative filtering method is then employed to predict mobile user preferences based on the obtained influence between users. Finally, the experiment is executed on the real data set and the integrated data set, and the results show that the proposed method can obtain more accurate mobile user preferences than those of existing methods.

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Zhang, Xiao Juan, Zhenzhen Li, and Hepu Deng. "Information security behaviors of smartphone users in China: an empirical analysis." Electronic Library 35, no. 6 (November 6, 2017): 1177–90. http://dx.doi.org/10.1108/el-09-2016-0183.

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Purpose Understanding user behavior is increasingly critical for information security in the use of smartphones. There is, however, lack of empirical studies about the behavior of smartphone users for information security in China. The purpose of this paper is to present an empirical analysis of the behavior of smartphone users in China in relation to information security. Design/methodology/approach A review of the related literature is conducted, leading to the development of a questionnaire for investigating the behavior of smartphone users. An online survey of the smartphone users in China is conducted. The collected data are analyzed with the use of descriptive analysis and Pearson’s chi-square test to better understand the behavior of smartphone users on information security. Findings The paper shows that there are serious concerns about information security in the use of smartphones in China including the ignorance of security information in downloading and using applications, inadequate phone settings, inappropriate enabling of add-on utilities and lack of proper disaster recovery plans. The study also reveals that there is a significant difference between different groups of users on information security in smartphone use. Research limitations/implications This paper is based on a purposeful sample of smartphone users in China. It is exploratory in nature. Practical implications The paper can lead to a better understanding of the behavior of smartphone users and information security in China and provide relevant government departments and institutions with useful information for developing appropriate strategies and policies and designing specific training programs to improve information security in the smartphone use. Originality/value This paper is the first of this kind to collect quantitative data from users in China for better understanding the behavior of smartphone users on information security. It provides insight towards the adoption of various measures for information security from the perspective of smartphone users in China.

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Subahi, Alanoud, and George Theodorakopoulos. "Detecting IoT User Behavior and Sensitive Information in Encrypted IoT-App Traffic." Sensors 19, no. 21 (November 3, 2019): 4777. http://dx.doi.org/10.3390/s19214777.

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Many people use smart-home devices, also known as the Internet of Things (IoT), in their daily lives. Most IoT devices come with a companion mobile application that users need to install on their smartphone or tablet to control, configure, and interface with the IoT device. IoT devices send information about their users from their app directly to the IoT manufacturer’s cloud; we call this the ”app-to-cloud way”. In this research, we invent a tool called IoT-app privacy inspector that can automatically infer the following from the IoT network traffic: the packet that reveals user interaction type with the IoT device via its app (e.g., login), the packets that carry sensitive Personal Identifiable Information (PII), the content type of such sensitive information (e.g., user’s location). We use Random Forest classifier as a supervised machine learning algorithm to extract features from network traffic. To train and test the three different multi-class classifiers, we collect and label network traffic from different IoT devices via their apps. We obtain the following classification accuracy values for the three aforementioned types of information: 99.4%, 99.8%, and 99.8%. This tool can help IoT users take an active role in protecting their privacy.

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Chen, Zhenguo, Liqin Tian, and Chuang Lin. "Trust evaluation model of cloud user based on behavior data." International Journal of Distributed Sensor Networks 14, no. 5 (May 2018): 155014771877692. http://dx.doi.org/10.1177/1550147718776924.

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In the process of using the cloud platform, how to ensure the safety of users is a matter we must concern. The user authentication can provide a certain degree of security, but when the user information was leaked, this method will not be effective. Therefore, this article proposes a trust evaluation model based on user behavior data. In this model, the user’s historical behavior will be used to construct a set of trusted behavior of the cloud users. On this basis, the direct trust of the user’s behavior can be obtained. Then, the recommendation trust can be calculated by the interaction between the users and other cloud users. Given the current historical trust, the comprehensive trust can be obtained using the weighted average method. Among them, the initial value of historical trust is set to a constant and then updated by the comprehensive trust. In order to control the user’s abnormal behavior more effectively, the suspicious threshold value and the abnormal threshold value were defined, which are used to punish the historical trust. Through the simulation of the virtual digital library cloud platform, the method can effectively evaluate the cloud users.

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Singal, Himani, and Shruti Kohli. "Mitigating Information Trust." International Journal of Technoethics 7, no. 1 (January 2016): 16–33. http://dx.doi.org/10.4018/ijt.2016010102.

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Trusting any information on web is psychosomatic and subliminal by nature. The decision is left on the requestor to assess, judge and corroborate the contents contained in the websites before perceiving it. This is of acute concern when websites deal with sensitive issues like health. There is no standard mechanism that embodies or characterizes how to make these ‘trust' decisions. Although all the web users make these decisions on a frequent basis, there is no method to comply with the rationale to take such decisions. This paper is an attempt to provide a solution to the problem of ‘how much the content, typically provided by any health related website should be trusted?' A probing has been done to study the users' behavior on these websites. This cram makes use of real-time analytical data collected from similarweb.com for hundred health related websites to analyze web users' behavior. The goalmouth is to develop a novel technique to re-rank search results using TRUST as a deciding factor so that more trustworthy web links appears higher in the results list. The aim is to determine and discern the users' attitudinal factors that can be captured in practice without user interaction and also capitalize on the quality of the trust estimates.

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Shwartz-Asher, Daphna, Soon Ae Chun, and Nabil R. Adam. "Knowledge behavior model of e-government social media users." Transforming Government: People, Process and Policy 11, no. 3 (August 21, 2017): 456–75. http://dx.doi.org/10.1108/tg-02-2017-0014.

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Purpose A social media user behavior model is presented as a function of different user types, i.e. light and heavy users. The users’ behaviors are analyzed in terms of knowledge creation, framing and targeting. Design/methodological approach Data consisting of 160,000 tweets by nearly 40,000 twitter users in the city of Newark (NJ, USA) were collected during the year 2014. An analysis was conducted to examine the hypothesis that different user types exhibit distinct behaviors driven from different motivations. Findings There are three important findings of this study. First, light users reuse existing content more often, while heavy and automated users create original content more often. Light users also use more sentiments than the heavy and automated users. Second, automated users frame more than heavy users, who frame more than light users. Third, light users tend to target a specific audience, while heavy and automated users broadcast to a general audience. Research implications Decision-makers can use this study to improve communication with their customers (the public) and allocate resources more effectively for better public services. For example, they can better identify subsets of users and then share and track specialized content to these subsets more effectively. Originality/value Despite the broad interest, there is insufficient research on many aspects of social media use, and very limited empirical research examining the relevance and impact of social media within the public sector. The social media user behavior model was established as a framework that can provide explanations for different social media knowledge behaviors exhibited by various subsets of users, in an e-government context.

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Journal articles on the topic 'Users information behavior'

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