Project

Information Quality in User-generated Content (focusing on crowdsourcing and citizen science)

Goal: User-generated content (UGC) is becoming a valuable organizational resource, as it is seen in many cases as a way to make more information available for analysis. To make effective use of UGC, it is necessary to understand information quality (IQ) in this setting.

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Roman Lukyanenko
added 2 research items
Introductory courses play a special role in pedagogy. They provide students with a broad overview of the discipline, instill general concepts, vocabulary and methods, and encourage further exploration of the discipline. When a discipline is technology-oriented, it commonly introduces a focal technology as part of the intro curriculum. Thus, Introduction to Information Systems (IS) courses commonly expose students to such technologies as spreadsheets or databases. While these technologies remain valuable, the question is, can the curriculum be enriched with another tech-machine learning (ML). Machine learning consists of a constellation of methods and tools by which computer programs "learn" from data by building models that improve performance on a defined task. We assume, a candidate technology for intro courses needs to meet the following criteria: (1) advantage over alternative technologies, (2) relevance to practice, (3) versatility and generality, (4) connectivity to other knowledge, and (5) accessibility. Below we assess ML based on these factors. First, incorporating ML may require shrinking content dedicated to spreadsheets or databases. This may be a justifiable change. First, spreadsheets have become ubiquitous and are now part of bridge courses for college. Second, despite the importance of databases, database practice has become fragmented (e.g., SQL and NoSQL), with no clear winner and a continuous shrinking of the share of relational databases-the ones typically taught in intro courses. In the age of big data and social media, it is no longer clear which approach to data management should be emphasized in the introductory IS courses. Second, as digitalization of human society continues, ML has emerged as a paramount approach for extracting useful information from data, as well as for powering other transformational technologies, such as driverless vehicles, conversational interfaces, and industrial or consumer robots. This technology can introduce the students very early to one of the most cutting-edge skills desired by companies today. Third, much like databases and spreadsheets, ML is a general-purpose technology, making it well positioned to augment a variety of topics covered in Intro to IS. For example, with the help of ML, students can learn how to optimize supply chains, personalize web experience for customers, streamline customer relations management, automate processes, and conduct advanced analytics and business intelligence. In all these cases, ML is not only now widely used in practice, but has been delivering groundbreaking results. Fourth, effective use of ML involves proficiency in other skills, such as data management, file management, statistics, spreadsheets, data visualization, and also problem-solving and conceptual modeling. Thus, ML in the curriculum re-enforces many foundational and background skills desired of students of IS. Finally, with the evolution in software, ML is becoming more accessible for the undergraduates. In my own experience, I used graphical point-and-click RapidMiner (rapidminer.com) in the Introduction to IS at two North American universities with second-and third-year business students. The intuitive nature of the tool meant that students did not struggle with syntax and we focused on core concepts in ML (e.g., training, overfitting, data quality), resulting in rapid (in a matter of 4 to 6 hours) acquisition of conceptual and hands-on ML proficiency by the students. I introduced ML in the middle of the course, and then students used this tool to gain hands-on experience with other topics (e.g., social media, privacy, logistics, and e-commerce). The constant change of IS landscape necessitates periodic adjustments to IS curriculum. Machine learning is a strong candidate for the new foundational technology in Intro to IS courses. We hope our initial effort fuels the debate within the academic community on the value of having ML in Intro to IS courses and motivates explorations of most effective avenues for integrating machine learning into IS pedagogy.
Roman Lukyanenko
added 2 research items
Conventional wisdom holds that expert contributors provide higher quality user-generated content (UGC) than novices. Using the cognitive construct of selective attention, we argue that this may not be the case in some crowd-sourcing UGC applications. We argue that crowdsourcing systems that seek participation mainly from contributors who are experienced or have high levels of proficiency in the crowdsourcing task will gather less diverse and therefore less repurposable data. We discuss the importance of the information diversity dimension of information quality for the use and repurposing of UGC and provide a theoretical basis for our position, with the goal of stimulating empirical research.
While many conceptual modelling grammars have been developed since the 1970s, they share the general assumption of representation by abstraction; that is, representing generalised knowledge about the similarities among phenomena in a domain (classes) rather than about domain objects (instances). This assumption largely ignores the fundamental role that instances play in the constitution of reality and in human psychology. In this paper, we argue there is a need for a grammar that explicitly recognises the primary role of instances. We examine the limitations of traditional class-based approaches to conceptual modelling, especially for modern information environments. We then explore theoretical and practical motivations for instance-based modelling, and show how such an approach can address the limitations of traditional modelling approaches. We conclude by calling for the engineering of instance-based grammars as an important direction for conceptual modelling research to address the limitations of traditional approaches, and articulate five challenges to overcome in such efforts.
Roman Lukyanenko
added 2 research items
The rapid proliferation of online content producing and sharing technologies resulted in an explosion of user-generated content (UGC), which now extends to scientific data. Citizen science, in which ordinary people contribute information for scientific research, epitomizes UGC. Citizen science projects are typically open to everyone, engage diverse audiences, and challenge ordinary people to produce data of highest quality to be usable in science. This also makes citizen science a very exciting area to study both traditional and innovative approaches to information quality management. With this paper we position citizen science as a leading information quality research frontier. We also show how citizen science opens a unique opportunity for the information systems community to contribute to a broad range of disciplines in natural and social sciences and humanities.
The emergence of crowdsourcing as an important mode of information production has attracted increasing research attention. In this article, the authors review crowdsourcing research in the data management field. Most research in this domain can be termed tasked-based, focusing on micro-tasks that exploit scale and redundancy in crowds. The authors' review points to another important type of crowdsourcing – which they term observational – that can expand the scope of extant crowdsourcing data management research. Observational crowdsourcing consists of projects that harness human sensory ability to support long-term data acquisition. The authors consider the challenges in this domain, review approaches to data management for crowdsourcing, and suggest directions for future research that bridges the gaps between the two research streams.
Roman Lukyanenko
added a research item
As crowdsourced user-generated content becomes an important source of data for organizations, a pressing question is how to ensure that data contributed by ordinary people outside of traditional organizational boundaries is of suitable quality to be useful for both known and unanticipated purposes. This research examines the impact of different information quality management strategies, and corresponding data collection design choices, on key dimensions of information quality in crowdsourced user-generated content. We conceptualize a contributor-centric information quality management approach focusing on instance-based data collection. We contrast it with the traditional consumer-centric fitness-for-use conceptualization of information quality that emphasizes class-based data collection. We present laboratory and field experiments conducted in a citizen science domain that demonstrate trade-offs between the quality dimensions of accuracy, completeness (including discoveries), and precision between the two information management approaches and their corresponding data collection designs. Specifically, we show that instance-based data collection results in higher accuracy, dataset completeness and number of discoveries, but this comes at the expense of lower precision. We further validate the practical value of the instance-based approach by conducting an applicability check with potential data consumers (scientists, in our context of citizen science). In a follow-up study, we show, using human experts and supervised machine learning techniques, that substantial precision gains on instance-based data can be achieved with post-processing. We conclude by discussing the benefits and limitations of different information quality and data collection design choice for information quality in crowdsourced user-generated content.
Roman Lukyanenko
added a research item
The rise and increased ubiquity of online interactive technologies such as social media or crowdsourcing (Barbier et al. 2012; de Boer et al. 2012; Doan et al. 2011; Whitla 2009) creates a fertile environment for field experimentation, affording researchers the opportunity to develop, test and deploy innovative design solutions in a live setting. In this research, we use a real crowdsourcing project as an experimental setting to evaluate innovative approaches to conceptual modeling and improve quality of user-generated content (UGC). Organizations are increasingly looking to harness UGC to better understand customers, develop new products, and improve quality of services (e.g., healthcare or municipal) (Barwise and Meehan 2010; Culnan et al. 2010; Whitla 2009). Scientists and monitoring agencies sponsor online UGC systems -citizen science information systems -that allow ordinary users to provide observations of local wildlife, report on weather conditions, track earthquakes and wildfires, or map their neighborhoods (Flanagin and Metzger 2008; Haklay 2010; Hand 2010; Lukyanenko et al. 2011). Despite the growing reliance on UGC, a pervasive concern is the quality of data produced by ordinary people. Online users are typically volunteers, resulting in a user base with diverse motivations and variable domain knowledge (Arazy et al. 2011; Coleman et al. 2009). When
Roman Lukyanenko
added a research item
Crowdsourcing is increasingly used to engage people to contribute data for a variety of purposes to support decision-making and analysis. A common assumption in many crowdsourcing projects is that experience leads to better contributions. In this research, we demonstrate limits of this assumption. We argue that greater experience in contributing to a crowdsourcing project can lead to a narrowing in the kind of data a contributor provides, causing a decrease in the diversity of data provided. We test this proposition using data from two sources-comments submitted with contributions in a citizen science crowdsourcing project, and three years of online product reviews. Our analysis of comments provided by contributors shows that the length of comments decreases as the number of contributions increases. Also, we find that the number of attributes reported by contributors decreases as they gain experience. These finding support our prediction, suggesting that the diversity of data provided by contributors declines over time.
Roman Lukyanenko
added a research item
Data collected by organizations is typically used for tactical purposes-solving a business need. In this study we show the relationship between inferential utility and institutional practices in repurposing unstructured electronic documentation. Our aim is to (1) understand the underpinnings of unstructured-data-entry formats in the data collected by an organization; and (2) study the impact unstructured-data-entry formats have in solving a task or tactical need. We study this phenomenon in the context of case management in foster care. Our findings have important implications both to theory and practice. Unstructured data accounts for more than 80% of the organizational data. Our research analyzes the implications of different unstructured data-entry formats when capturing user input.
Monica Chiarini Tremblay
added a research item
With the growth of machine learning and other computationally intensive techniques for analyzing data, new opportunities emerge to repurpose organizational information sources. In this study, we explore the effectiveness of unstructured data entry formats in repurposing organizational data in solving new tasks and drawing novel business insights. Unstructured data accounts for more than 80% of the organizational data. Our research analyzes the implications of using unstructured data entry formats for propagation of organizational styles. We study this phenomenon in the context of case management in foster care. Using natural language processing and machine learning, we show that unstructured data formats foster entrenchment and propagation of individual organizational styles and deviations from the industry norms. Our findings have important implications both to theory and practice of business analytics, conceptual modeling, organizational theory and general data management.
Roman Lukyanenko
added an update
Short position paper on challenges of IQ of data created by patients online
 
Roman Lukyanenko
added a research item
Healthcare is evolving towards patient-centered care. Of particular interest is Shared Healthcare Decision Making (SHDM) defined here as a collaborative process of patients and physicians making healthcare decisions together, taking into account the best scientific evidence available, as well as the patients’ knowledge and preferences [Oshima Lee and Emanuel 2013]. Compared to traditional decision making based on authoritarian role of physicians, SHDM tends to be more sensitive to complex trade-offs of healthcare. Indeed, few healthcare decisions involve a clear optimal choice. For example, a man diagnosed with localized prostate cancer faces several choices. Surgery produces better urine flow at the risk of incontinence and impotence. Radiation therapy is nonsurgical, but it can cause long-term side effects and is also more expensive. Watchful waiting, while less expensive, can be catastrophic if cancer progresses rapidly. Relaying the clinical consequences of different options is traditionally in the hands of doctors, who generally steer people toward more aggressive treatments [Cutler 2014]. With access to appropriate information about the tradeoffs, it is believed, patients can take a more active role in their own care [Oshima Lee and Emanuel 2013]. Information quality (IQ) is key to empowering patients to make informed decisions. The internet and social media give patients access to more information, including information from patients themselves [Kallinikos and Tempini 2014; Lukyanenko and Parsons 2015]. However, the progress on SHDM is impeded by the many unresolved IQ challenges arising in this novel domain.
Roman Lukyanenko
added an update
The paper talks about the need to increase precision in modern conceptual modeling grammars in order to better support applications such as NoSQL databases, data analytics and crowdsourcing and user generated content environments. The connection to information quality is also discussed.
 
Roman Lukyanenko
added a research item
Online user-generated content has the potential to become a valuable social and economic resource. To make effective use of user-generated contributions, understanding and improving information quality in this environment is critical. Traditional information quality research offers limited guidance forunderstanding informationquality issues in user-generated content. This thesis analyzes the concept of user-generated information quality, considers the limits and consequences of traditional approaches, andoffers an alternative path for improving information quality. Using three laboratory experiments the thesisprovidesempirical evidence of the negative impact of class-based conceptual modeling approacheson information accuracy.In view of the negative consequences of class-based conceptual modeling approaches, the thesisinvestigatesthe information quality implications of instance-based data management. To this extent thisthesisproposesprinciples formodeling user-generated content based on individual instances rather than classes. The application of the proposed principles is demonstratedin the form of an ISartifact-a real system designed to capture user-generated content. The principles are further evaluated in a field experiment. This thesis concludes by summarizingcontributions for research and practiceof information/conceptual modeling, information qualityand user-generated contentand provides directions for future research.
Roman Lukyanenko
added a research item
The increasing reliance of organizations on externally produced information, such as online user-generated content (UGC) and crowdsourcing, challenges common assumptions about conceptual modeling in information systems (IS) development. We demonstrate the societal importance of UGC, analyze the distinguishing characteristics of UGC and identify specific conceptual modeling challenges in this setting, evaluate traditional and recently proposed approaches to modeling UGC, propose a set of conceptual modeling guidelines to be used in development of IS that harness structured UGC; and demonstrate how to implement and evaluate the proposed using a case of development of a real crowdsourcing (citizen science) IS. We conclude by considering implications for conceptual modeling research and practice.
Roman Lukyanenko
added a research item
With the explosive growth in online citizen science and crowdsourcing in general, a major challenge is how to ensure that data provided by diverse and often anonymous crowds is of sufficient quality to be used by experts (e.g., scientists) in analysis and decision making. Facing the diversity of backgrounds, interests and expertise within crowds, the prevailing approach to is to channel volunteer efforts and data input into a form that best addresses the needs of data consumers. For example, many projects are organized around classes that are useful to scientists in their analysis – biological species. This approach, however, has limitations. If members of the general public do not have the required species-identification expertise, they may resort to guessing or abandon a project out of concern about providing incorrect classifications 1–3. Further, this approach may limit initiative and inquisitiveness within crowds 4,5. We advocate an alternative, instance-based model of citizen science, in which projects do not constrain citizens to the classes of interest to scientists and, instead, encourage them to provide any classes and attributes of observed organisms (instances) irrespective of the classification structures needed by scientists. In previous research, we have shown that this approach results in higher accuracy of information provided by the crowd and increases participation. In addition, such an open design facilitates the collection of data that goes beyond the initial defined objectives of a project. However, the resulting data is sparse and the classification labels, although accurate, are typically are not specific enough for traditional analysis, as non-experts overwhelmingly report high-level classes, such as bird, tree or fish. More recently, we have been exploring machine learning and natural language processing to translate heterogeneous attributes and classes to more useful classification levels for experts (e.g., species). To guide or design work, we draw on theories from cognitive psychology (classification theory, theory of biases and heuristics) and philosophy (general ontology). We use a variety of methods, including laboratory and field experimentation, design science and machine learning. We have also developed a real citizen science project, NLNature (www.nlnature.com), a website that gathers sightings of flora and fauna in Newfoundland and Labrador by enlisting citizens as data providers. Our laboratory and NLNature data suggests that data quality and user participation can be improved by relaxing constraints on what data can be provided without necessarily sacrificing data utility – thereby paving the way to data collection processes that are easier for ordinary people to use.
Jeffrey Parsons
added 2 research items
Non-scientists are now participating in research in ways that were previously impossible, thanks to more web-based projects to collect and analyse data. Here we suggest a way to encourage broader participation while increasing the quality of data. Participation may be passive, as when someone donates their computer's 'downtime' to projects such as SETI@home, or active, as when someone uses eBird to log birds they have spotted. Unfortunately, the prevailing data-collection and storage practices for active projects inhibit participation by non-experts.
With the proliferation of unstructured data sources and the growing role of crowdsourcing, new data quality challenges are emerging. Traditional approaches that investigated quality in the context of structured relational databases viewed users as data consumers and quality as a product of an information system. Yet, as users increasingly become information producers, a reconceptualization of data quality is needed. This paper contributes by exploring data quality challenges arising in the era of user-supplied information and defines data quality as a function of conceptual modeling choices. The proposed approach can better inform the practice of crowdsourcing and can enable participants to contribute higher quality information with fewer constraints.
Roman Lukyanenko
added a research item
Online citizen science is a form of crowdsourcing that has received increased attention from researchers. Despite significant potential, a key challenge in leveraging citizens to provide scientific information is the quality of citizen-generated data, a form of user-generated content (UGC). In this work, we present a study in which domain experts in biology were asked to infer classes based on attributes of observed organisms generated by citizen scientists. In addition, because domain expertise is a scarce resource and does not scale in large datasets, we also investigated the potential for classification using machine learning. The results demonstrate that experts generally are able to leverage the non-expert attributes to infer classes that are more specific than those familiar to the non-expert participants. Our work provides evidence of the potential usefulness of the novel instance-based approach to citizen science and suggests several strategies for refining this citizen science model.
Roman Lukyanenko
added 5 research items
User-generated content (UGC) is becoming a valuable organizational resource, as it is seen in many cases as a way to make more information available for analysis. To make effective use of UGC, it is necessary to understand information quality (IQ) in this setting. Traditional IQ research focuses on corporate data and views users as data consumers. However, as users with varying levels of expertise contribute information in an open setting, current conceptualizations of IQ break down. In particular, the practice of modeling information requirements in terms of fixed classes, such as an Entity-Relationship diagram or relational database tables, unnecessarily restricts the IQ of user-generated data sets. This paper defines crowd information quality (crowd IQ), empirically examines implications of class-based modeling approaches for crowd IQ, and offers a path for improving crowd IQ using instance-and-attribute based modeling. To evaluate the impact of modeling decisions on IQ, we conducted three experiments. Results demonstrate that information accuracy depends on the classes used to model domains, with participants providing more accurate information when classifying phenomena at a more general level. In addition, we found greater overall accuracy when participants could provide freeform data compared to a condition in which they selected from constrained choices. We further demonstrate that, relative to attribute-based data collection, information loss occurs when class-based models are used. Our findings have significant implications for information quality, information modeling, and UGC research and practice.
This paper investigates the impact of conceptual modeling on the information completeness dimension of information quality in the context of user-generated content. We propose a theoretical relationship between conceptual modeling approaches and information completeness and hypothesize that traditional class-based conceptual modeling negatively affects information completeness. We conducted a field experiment in the context of citizen science in biology. The empirical evidence demonstrates that users assigned to an instantiation that is based on class-based conceptual modeling provide fewer observations than users assigned to an instance-based condition. Users in the instance-based condition also provided a greater number of new classes of organisms. The findings support the proposed hypotheses and establish that conceptual modeling is an important factor in evaluating and increasing information completeness in user-generated content.
Jeffrey Parsons
added a research item
The rise of user-generated content (UGC, or crowdsourced data) has created important avenues to use new kinds of data in business decision making. This paper examines information quality (IQ) in crowdsourced data. Traditionally, IQ research has focused on the fitness of data for particular uses within an organization, implying the intended uses and data requirements are known prior to use (Lee 2003, Lee et al. 2004, Wang and Strong 1996, Zhu and Wu 2011). In traditional settings, IQ can be maintained using approaches such as access restrictions, training, and input controls (Redman 2001). UGC breaks down organizational boundaries, challenging traditional approaches to IQ by opening information collection to the general public. Access restrictions may severely inhibit the amount of UGC that can be collected (Parsons et al., 2011). Training is sometimes used to maintain accuracy (Dickinson et al. 2010, Foster-Smith and Evans 2003), but presumes both high motivation among crowd contributors and clearly defined information requirements. In UGC applications, the motivation level of potential contributors can be low and information requirements may evolve. Input controls are widely used in UGC applications. For example, in citizen science (Hand, 2010), the task of the crowd is often to classify phenomena, such as galaxies (www.galaxyzoo.org) or birds (www.ebird.org), according to a predetermined schema (e.g., galaxy types or bird species). We argue such an approach (1) often requires a level of domain knowledge that members of the general public generally lack (resulting in contributed data of dubious accuracy), and (2) constrains data collection based on the predetermined schema, thereby excluding potentially useful, but unpredictable, data that contributors may wish to report. In view of these issues, we seek to answer the following general research questions: 1) Does constraining crowdsourced data using a fixed schema affect data accuracy and completeness? 2) How can these effects be mitigated? Research Approach Our work is grounded in two theoretical frames. First, we adopt the ontological position that reality is comprised of " things " that can be described in terms of properties (Bunge, 1977). Things do not inherently belong to predetermined classes, and no class fully describes the properties of any particular thing. Second, psychological research on classification holds that classes are useful abstractions of phenomena that support communication and reasoning (Posner, 1993, Rosch, 1978). Alternative classification schemes are equally valid and can be useful for different purposes (Parsons and Wand, 2008). Additionally, basic-level classes are generally preferred by non-experts in a domain (Rosch et al., 1976). The basic level is typically the first category non-experts think of when they encounter an instance (Jolicoeur et al. 1984) and are the most common categories in ordinary speech (Wisniewski and Murphy, 1989). For example, in biology the basic level is a more general level (e.g., bird or fish) than the species level that is of interest to biologists. Two general implications follow from these theoretical principles. First, a schema-based approach to collecting UGC may lead to information loss, as crowd members will observe and be capable of reporting information about observations beyond what is implied by any particular schema. Second, requiring crowd members to report data according to a fixed set of classes that does not match contributors' conceptual schemata of a domain will negatively affect classification accuracy. We tested these propositions in two laboratory experiments in a citizen science project. In these experiments, biology non-experts were exposed to a set of images of plants and animals.
Roman Lukyanenko
added 2 research items
A major challenge in crowdsourcing is ensuring that data are of acceptable quality to be useful in analysis and to inform decision making. This is a difficult task, as data contributors in these projects are typically unpaid volunteers, often with variable levels of domain expertise and motivations for participating and contributing content. In this research, we propose a general solution to data quality in surveillance-focused crowdsourcing - the alignment between task design and human capabilities as a key factor shaping the quality of user-generated content. To evaluate our theoretical proposal, we conducted two experiments in the context of citizen science.
The role of citizen science in research and natural resource monitoring and management is increasing, as evidenced by the growing number of peer-reviewed publications (including a special section in this journal) and calls for involving citizens in monitoring and governance (through, for example, “participatory research” [Danielsen et al. 2014] and “participatory monitoring” [Kennett et al. 2015]). Citizen science projects can be targeted to a specific research question (and thus involve very specific data-collection protocols) or can be more open-ended (giving rise to a need to collect data for which the uses may be unknown or changing) (Wiersma 2010). Advances in online content production and sharing technologies (i.e., Web 2.0), mobile computing, and sensor-equipped devices have contributed to a dramatic rise in online citizen science projects, in which citizens contribute sightings (e.g., eBird [Sullivan et al. 2009]), transcribe data (e.g., Old Weather [Eveleigh et al. 2013]), or classify phenomena (e.g., Galaxy Zoo [Hopkin 2007]). It is these online projects, also referred to as crowdsourcing (Franzoni & Sauermann 2014), that have been the focus of our research and that inform the opinions presented here.
Shawn Ogunseye
added a research item
It is not uncommon for projects that collect crowdsourced data to be commissioned with incomplete knowledge of data contributors, data consumers, and/or the purposes for which the data collected are going to be used. Such unanticipated uses and users of data form the basis for open information environments (OIEs), and the information collected through systems designed to gather content from users have high quality when they are complete, accurate, current and provided in an appropriate format. However, as it is assumed that experts provide higher quality information, many types of OIEs have been designed for experts. In this paper, we question the appropriateness of this assumption in the context of citizen science systems – an exemplary category of OIE. We begin by arguing that experts are primarily efficient rule-based classifiers, which implies that they selectively focus only on attributes relevant to their classification task and ignore others. Drawing from existing literature, we posit that experts’ focus on only diagnostic features of an entity leads to a learned inattention to non-diagnostic attributes. This may improve the accuracy of the information provided, but at the expense of its completeness, currency, format and ultimately the novelty (for unanticipated uses) of information provided. On the other hand, we predict that non-experts and amateurs may use rules to a lesser extent, resulting in less selective attention and leading them to provide more novel information with less trade-off of one dimension of information quality for another. We propose hypotheses derived from this view, and outline two experiments we have designed to test them across four dimensions of information quality. We conclude by discussing the potential implications of this work for the design of crowdsourcing platforms and the recruitment of experts, amateurs, or novice data contributors in studies of data quality in crowdsourcing settings.
Roman Lukyanenko
added a research item
Roman Lukyanenko
added a project goal
User-generated content (UGC) is becoming a valuable organizational resource, as it is seen in many cases as a way to make more information available for analysis. To make effective use of UGC, it is necessary to understand information quality (IQ) in this setting.