Search results for " Information systems"
showing 10 items of 940 documents
From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets
2021
Abstract In recent years, strategies focused on data-driven innovation (DDI) have led to the emergence and development of new products and business models in the digital market. However, these advances have given rise to the development of sophisticated strategies for data management, predicting user behavior, or analyzing their actions. Accordingly, the large-scale analysis of user-generated data (UGD) has led to the emergence of user privacy concerns about how companies manage user data. Although there are some studies on data security, privacy protection, and data-driven strategies, a systematic review on the subject that would focus on both UGD and DDI as main concepts is lacking. There…
Learning Automata-based Misinformation Mitigation via Hawkes Processes
2021
AbstractMitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Processes (MHP) have become a fundamental tool because they model social network dynamics, which facilitates execution and evaluation of mitigation policies. In this paper, we propose a novel light-weight intervention-based misinformation mitigation framework using decentralized Learning Automata (LA) to control the MHP. Each automaton is associated with a single user and learns to what degree that user should be involved in the mitigation strategy by interacting with a corresponding MHP, and performing a joint ra…
Multimodal data as a means to understand the learning experience
2019
Most work in the design of learning technology uses click-streams as their primary data source for modelling & predicting learning behaviour. In this paper we set out to quantify what, if any, advantages do physiological sensing techniques provide for the design of learning technologies. We conducted a lab study with 251 game sessions and 17 users focusing on skill development (i.e., user's ability to master complex tasks). We collected click-stream data, as well as eye-tracking, electroencephalography (EEG), video, and wristband data during the experiment. Our analysis shows that traditional click-stream models achieve 39% error rate in predicting learning performance (and 18% when we perf…
Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing
2021
Abstract The increasing interest in fuzzy-set Qualitative Comparative Analysis (fsQCA) in Information Systems and marketing raises the need for a tutorial paper that discusses the basic concepts and principles of the method, provide answers to typical questions that editors, reviewers, and authors would have when dealing with a new tool of analysis, and practically guide researchers on how to employ fsQCA. This article helps the reader to gain richer information from their data and understand the importance of avoiding shallow information‐from‐data reporting. To this end, it proposes a different research paradigm that includes asymmetric, configurational‐focused case‐outcome theory construc…
Identifying the combinations of motivations and emotions for creating satisfied users in SNSs: An fsQCA approach
2020
Social Networking Sites (SNSs) play an important role in our daily lives and the number of their users increases regularly. To understand how users can be satisfied in the complex digital environment of SNSs, this study examines how motivations and emotions combine with each other to explain high satisfaction. Users’ motivations comprise four attributes, entertainment, information, social-psychological, and convenience. Emotions are divided into their two main categories, that is positive and negative emotions. We draw on complexity and configuration theories, present a conceptual model along with propositions and perform a fuzzy-set qualitative comparative analysis (fsQCA). Through an empi…
Segmenting the audience of a cause-related marketing viral campaign
2021
Abstract This paper analyses the attitudinal effect of a cause-related marketing campaign which becomes viral through social networks. This attitudinal response is observed in three Internet user segments with different affinity levels: i) strong (familiar with the sponsoring brand and the promoted cause); ii) intermediate (familiar with the brand or the cause); and iii) weak (without previous experiences of either the brand or the social cause). To develop our experiment, 360 Internet surfers agree to participate. Their attitudes were measured before and after the showing of a viral spot in which a pet food brand encourages pet adoption. Our results show that a viral campaign works perfect…
User behaviours after critical mobile application incidents: the relationship with situational context
2015
Users occasionally have critical incidents with information systems IS. A critical IS incident is an IS product or service experience that a user considers to be unusually positive or negative. Critical IS incidents are highly influential in terms of users' overall perceptions and customer relationships; thus, they are crucial for IS product and service providers. Therefore, it is important to study user behaviours after such incidents. Within IS, the relationships between the situational context and user behaviours after critical incidents have not been addressed at all. Prior studies on general mobile use as a related research area have recognized the influence of the situational context,…
DRESS: A Distributed RMS Evaluation Simulation Software
2020
Distributed environments consist of a huge number of entities that cooperate to achieve complex goals. When interactions occur between unknown parties, intelligent techniques for estimating agent reputations are required. Reputation management systems (RMS's) allow agents to perform such estimation in a cooperative way. In particular, distributed RMS's exploit feedbacks provided after each interaction and allow prediction of future behaviors of agents. Such systems, in contrast to centralized RMSs, are sensitive to fake information injected by malicious users; thus, predicting the performance of a distributed RMS is a very challenging task. Although many existing works have addressed some c…
Information Abstraction from Crises Related Tweets Using Recurrent Neural Network
2016
Social media has become an important open communication medium during crises. The information shared about a crisis in social media is massive, complex, informal and heterogeneous, which makes extracting useful information a difficult task. This paper presents a first step towards an approach for information extraction from large Twitter data. In brief, we propose a Recurrent Neural Network based model for text generation able to produce a unique text capturing the general consensus of a large collection of twitter messages. The generated text is able to capture information about different crises from tens of thousand of tweets summarized only in a 2000 characters text.
Ranking-Oriented Collaborative Filtering: A Listwise Approach
2016
Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking accuracy, being able to estimate a precise preference ranking of items for each user rather than the absolute ratings (as rating-oriented CF algorithms do). Conventional memory-based ranking-oriented CF can be referred to as pairwise algorithms. They represent each user as a set of preferences on each pair of items for similarity calculations and predictions. In this study, we propose ListCF, a novel listwise CF paradigm that seeks improvement in bot…