Towards a Conceptualization of Data and Information Quality in Social Information Systems

Tilly, Roman; Posegga, Oliver; Fischbach, Kai; Schoder, Detlef

doi:10.1007/s12599-016-0459-8

Cited by 37 publications

(23 citation statements)

References 43 publications

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“…I aimed to add clarity by proposing that data is a subcategory of IT resources and has unique characteristics that leverage synergistic effects more than other resources. I built the basis for my conceptual framework by means of a structured literature review in which I identified crucial characteristics of data and grouped them according to the general model of synergistic relationships used by Nevo and Wade [13] in an inductive way [30].…”

Section: Methodsmentioning

confidence: 99%

Interconnectivity of Big Data Sources: Exploring Synergistic Relationships among Data

Weibl

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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In recent years, organizations manage an increasing amount of data in order to make better decisions, personalize products, or sell data. By data being combined from various sources, data assets interact with each other. When the interactions are synergistic, they create greater benefits than the sum of the value of the individual data assets. This study explores enablers, mechanisms, and potential outcomes of synergistic interactions among data assets. Based on systems theory and a synthesis of relevant synergy literature, I developed an initial synergy framework in a data context. On this basis, I conducted 14 qualitative interviews to assess the validity of my initial framework. The interview results assisted me in refining and contextualizing a unified conceptual framework of data synergies. The paper reveals that compatibility and contextual relatedness as enablers and informational complementary as a mechanism can lead to super-additive information value among data assets.

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Section: Methodsmentioning

confidence: 99%

Interconnectivity of Big Data Sources: Exploring Synergistic Relationships among Data

Weibl

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…The definition of quality, as noted in De Feo and Juran's quality handbook, is stated both as "fitness for use" and as "conformance to specification" [9]. This generic understanding of quality could help compare data quality and information quality conceptually [10]. For example, Tilly et al [10] attempted to differentiate the two from definition of data and information: data is objective to present a phenomenon unrelated to an IS, while information is subjective to put the data into context using an IS that users can understand.…”

Section: Definition Of Data Completenessmentioning

confidence: 99%

“…This generic understanding of quality could help compare data quality and information quality conceptually [10]. For example, Tilly et al [10] attempted to differentiate the two from definition of data and information: data is objective to present a phenomenon unrelated to an IS, while information is subjective to put the data into context using an IS that users can understand. Thus, data quality is defined as data's "fitness for use" and can be described by a set of dimensions (e.g.…”

Section: Definition Of Data Completenessmentioning

confidence: 99%

Empirical Evaluation of the Influence of EMR Alignment to Care Processes on Data Completeness

Liu

Zowghi

Talaei-Khoei

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Data completeness is an important dimension of data quality in electronic medical records (EMR). There are many constructs that influence data completeness in EMR. In this paper, we investigate three of these constructs: Clinical staff participation, EMR integration, and EMR alignment to care processes. We use these constructs from related studies as theoretical support to propose a conceptual model of factors influencing data completeness in EMR. The conceptual model is empirically validated using a survey with clinical staff participants. The results reveal that a high level of clinical staff's participation influences the data completeness in EMR. Furthermore, the alignment of EMR to the care processes has an impact on the data completeness in EMR.

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“…Thus to find a standard definition of e-recruitment, conceptualizations of erecruitment need to be known. To our knowledge, no research paper in e-recruitment has focused on conceptualization of e-recruitment, however there are studies in other areas of information systems (IS) that have focused on conceptualization [10][11][12][13][14][15][16][17][18][19]. Most view conceptualization as the formulation of a view about the nature of a phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Conceptualizations of E-recruitment: A Literature Review and Analysis

Abia¹,

Brown²

2020

Lecture Notes in Computer Science

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There is diversity in understanding of electronic recruitment (e-recruitment) which results in confusion on the meaning and use of the term. The purpose of this paper is to bring conceptual clarity by investigating the alternative conceptualizations of e-recruitment in academic literature. Using Grounded Theory Methodology (GTM) techniques we analyzed literature to reveal five alternative conceptualizations; these being: (1) E-recruitment as a Technology Tool, (2) E-recruitment as a System, (3) E-recruitment as a Process, (4) E-recruitment as a Service, and (5) E-recruitment as a Proxy. The conceptualizations map to the scope of the definition and utilization of e-recruitment. Identifying conceptualizations of e-recruitment sets a platform for further research. Further research may include determining the relationships between the conceptualizations and determining conceptualizations in different settings among many other possible research focus topics.

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Towards a Conceptualization of Data and Information Quality in Social Information Systems

Cited by 37 publications

References 43 publications

Interconnectivity of Big Data Sources: Exploring Synergistic Relationships among Data

Interconnectivity of Big Data Sources: Exploring Synergistic Relationships among Data

Empirical Evaluation of the Influence of EMR Alignment to Care Processes on Data Completeness

Conceptualizations of E-recruitment: A Literature Review and Analysis

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