Conference Paper

Modeling the Information Quality of Object Tracking Systems.

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... IS research also applies QCA to topics on the individual level, such as negative (Tan et al., 2016), the use of mobile government services (Liu et al., 2017), or user satisfaction with information on traveler websites (Kourouthanassis et al., 2017). A noteworthy paper used QCA to evaluate different configurations of object tracking systems and cannot be classified into organizational or individual level (Bardaki et al., 2013). ...
... QCA studies with different mode of inquiry Qualitative comparative analysis qualitative data, such as from interviews (Fedorowicz et al., 2018), as well. Only a few studies (around 22%) rely entirely on crisp set QCA (Bardaki et al., 2013;Dawson et al., 2016aDawson et al., , 2016bLevallet and Chan, 2015;Nishant and Ravishankar, 2020;Tan et al., 2016;van de Weerd et al., 2016). Some studies show (around 16%) that when having binary variables such as pricing strategies (Bui et al., 2019) it is also possible to combine fuzzy and crisp sets (see Table 12). ...
... only crisp setsBardaki et al. (2013),Dawson et al. (2016a),Dawson et al. (2016b),Levallet and Chan (2015),Nishant and Ravishankar (2020),Tanet al. (2016), van de Weerd et al. (2016) QCA with only fuzzy sets Bukvova (2012), Fedorowicz et al. (2015), Fedorowicz et al. (2018), Iannacci and Cornford (2018), Koo et al. (2019), Kourouthanassis et al. (2017), Lasrado et al. (2016), Lee et al. (2019), Leischnig et al. (2016), Leonhardt et al. (2018), Liu et al. (2017), Mattke et al. (2020a), M€ uller et al. (2017), Pappas et al. (2019), Park et al. (2017), Park and Mithas (2020), Sch€ obel et al. (2019), Stanko (2016), Tuo et al. (2019), Wang et al. (2020) QCA with crisp sets and fuzzy sets Bui et al. (2019), Maier et al. (2020a), Mattke et al. (2018), Mattke et al. (2020b), Mikalef and Krogstie (2020) Criterion Source Small sample size (N < 50) Bui et al. (2019), Bukvova (2012), Dawson et al. (2016a), Fedorowicz et al. (2018), Nishant and Ravishankar (2020), van de Weerd et al. (2016) Medium sample size (50 ≤ N ≤ 99) Dawson et al. (2016b), Fedorowicz et al. (2015), Iannacci and Cornford (2018). Koo et al. (2019), Lasrado et al. (2016), Levallet and Chan (2015) Large sample size (N ≥ 100) Bardaki et al. (2013), Kourouthanassis et al. (2017), Lee et al. (2019), Leischnig et al. (2016), Leonhardt et al. (2018), Liu et al. (2017), Maier et al. (2020a), Mattke et al. (2018), Mattke et al. (2020a), Mattke et al. (2020b), Mikalef and Krogstie (2020), M€ uller et al. (2017), Pappas et al. (2019), Park et al. (2017), Park and Mithas (2020), Sch€ obel et al. (2019), Stanko (2016), Tan et al. (2016), Tuo et al. (2019), Wang et al. (2020) ...
Article
Purpose Qualitative Comparative Analysis (QCA) is a promising, powerful method that is increasingly used for IS research. However, the Information Systems (IS) discipline still lacks a shared understanding of how to conduct and report QCA. This paper introduces the fundamental concepts of QCA, summarizes the status quo, and derives recommendations for future research. Design/methodology/approach A descriptive literature review in major IS outlets summarizes how and why QCA has been used in the IS discipline, critically evaluates the status quo, and derives recommendations for future QCA studies. Findings The literature review reveals 32 empirical research articles in major IS journals that have used the QCA method. Articles applied QCA to a broad range of research topics at the individual and organizational levels, mainly as a standalone analysis for theory development, elaboration and testing. The authors also provide evidence that most published IS research articles do not take full advantage of the potential QCA, such as analyzing necessary causal conditions or testing the robustness of QCA results. The authors provide seven actionable recommendations for future IS research using QCA. Originality/value The literature review assesses the status quo of QCA’s application in the IS discipline and provides specific recommendations on how IS researchers can leverage the full potential of QCA.
... Indeed, most of the contributions to assessing the completeness of IoT data focus on relational completeness. Assuming that the number of real-world entities is known, many of them apply the relational completeness metric of Batini and Scannapieco (2006) as well as Pipino et al. (2002) in specific IoT contexts such as RFID chips in the logistics industry (van der Togt et al. 2011), contextual information processes (Anagnostopoulos and Kolomvatsos 2016), process mining systems (Janssenswillen and Depaire 2019), object tracking systems (Bardaki et al. 2010), or metrological weather measuring stations (Sicari et al. 2016(Sicari et al. , 2018. Overall, however, since in the IoT context the actual number of real-world entities is typically unknown and thus associated with uncertainty (Bansal et al. 2021;Karkouch et al. 2016;Liu et al. 2020), the methodical applicability of these metrics and approaches is rather limited and only given for selected (special) cases. ...
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The Internet of Things (IoT) is one of the driving forces behind Industry 4.0 and has the potential to improve the entire value chain, especially in the context of industrial manufacturing. However, results derived from IoT data are only viable if a high level of data quality is maintained. Thereby, completeness is especially critical, as incomplete data is one of the most common and costly data quality defects in the IoT context. Nevertheless, existing approaches for assessing the completeness of IoT data are limited in their applicability because they assume a known number of real-world entities or that the real-world entities appear in regular patterns. Thus, they cannot handle the uncertainty regarding the number of real-world entities typically present in the IoT context. Against this background, the paper proposes a novel, probability-based metric that addresses these issues and provides interpretable metric values representing the probability that an IoT database is complete. This probability is assessed based on the detection of outliers regarding the deviation between the estimated number of real-world entities and the number of digital entities. The evaluation with IoT data from a German car manufacturer demonstrates that the provided metric values are useful and informative and can discriminate well between complete and incomplete IoT data. The metric has the potential to reduce the cost, time, and effort associated with incomplete IoT data, providing tangible benefits in real-world applications.
... We also excluded articles that did not provide information about the number of cases. As illustrated in Table 2, there is only one instance of bad practice concerning the ratio of cases and conditions (Bardaki et al., 2013). Since the referred study has seven conditions, the truth table has 128 rows. ...
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Qualitative Comparative Analysis (QCA) has increasingly become popular in Information Systems (IS) research. However, there are several pitfalls and bad practices when applying QCA. Therefore, we aim at providing an extensive overview of (1) how QCA has been applied so far in IS research and (2) how future QCA-based IS research can be improved. To do so, we review articles from IS journals and conferences using an extensive coding scheme based on methodological literature and QCA reviews from other research disciplines. First, our results show standards of reporting and justification, well established in other disciplines, are often not fulfilled. Second, we find that extant research is predominantly based on large-N analyses, which limits some of the key capabilities of QCA. Third, we show that necessity analysis is under- and sometimes even misused. Lastly, extant research suffers from low solution coverage values that are not adequately discussed and sensitivity analyses that are not employed frequently. Our findings represent the current state of QCA in IS research and highlight the potential for improvement in future QCA studies.
... The topic domain with the least number of models in the given sample was logistics (with zero models in two publications). The only publication that applied a formal model was in the data science domain and the model was used for the simulation of information quality characteristics [19]. The majority of the models used in the IoT publications were informal and were applied to describe the system or process structure. ...
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Being a complex topic Internet of Things (IoT) involves multiple disciplines and approaches. In this paper an initial overview of the modeling techniques within the IoT by the Information Systems researchers is provided. These descriptive results offer a basis for discussion about the research topics that are important in IoT for the Information Systems Research community as well as the first overview of the keywords that the authors use to describe their work in IoT- related context. Publications from the IoT context, including some of the topic areas in smart environment from the AIS electronic library were analyzed towards their application of models within their result presentation. The focus of this preliminary analysis is the description of the application and dissemination of modeling in IoT research. Additionally, the results offer insights into the purpose of the model usage by the topic areas under analysis. The findings indicate that the publications that put themselves directly into the IoT context by mentioning it in the paper title, abstract or keywords frequently provide a general overview on the area and mostly do not involve formal modeling, while a more specific specialization requires the usage of formal modeling tools.
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