Cost-based analysis of the impact of data completeness and representational consistency

2023 | Decision Support Systems | Citations: 0

Authors: Timmerman, Yoram; Nasfi, Rihem; De Tré, Guy; Pattyn, Filip; Bronselaer, Antoon

Abstract: Data quality is an important topic for businesses and therefore requires appropr ... Expand

Abstract: Data quality is an important topic for businesses and therefore requires appropriate analysis tools. Although several rule-based systems exist today for quality measurement, their results do not always reflect the real impact of quality issues on practical data usability and are therefore not well-suited to base economic decisions on. This work practically implements and evaluates an alternative, cost-based approach for data quality analysis starting from a ‘fitness for use’-perspective. The practical impact of completeness and representational consistency of data stored in an integrated relational database is investigated in an experiment with 218 volunteers. Two alternative versions of this database are then prepared by manually improving their data quality. Participants are randomly assigned to one of three databases and are given a set of questions to resolve by means of SQL. As questions are resolved, we measure several cost-based indicators such as ability to solve, time to solve and number of attempts. Results indicate that the impact of data quality issues can differ significantly from what would be expected when using rule-based measurement. Effects range from almost no impact to a 65% reduction in time needed to solve tasks. Effect sizes up to 0.43 using one-way ANCOVA tests are observed. Collapse

Topics: database system data quality structured query language usability missing data

Methods: experiment analysis of covariance binomial logistic regression path analysis moderation analysis

Paths to open government data reuse: A three-dimensional framework of information need, data and government preparation

2023 | Information & Management | Citations: 0

Authors: Wang, Fang; Zhang, Zhaoqi; Ma, Xin; Zhang, Yichen; Li, Xuguang; Zhang, Xiaofei

Abstract: With the increase of open government data (OGD) supply in many countries, promot ... Expand

Abstract: With the increase of open government data (OGD) supply in many countries, promoting its reuse becomes an important issue to a variety of stakeholders. Considering that most citizens have never used OGD, this study adopted a mixed methods design that includes an experiment and a survey to explore the factors that affect OGD reuse intention. The findings include: (1) user information need, significantly affected by user digital capability, triggers OGD reuse intention. (2) Perceived data value, affected by perceived data quality, information need, and government preparation, is the core influencing factor of OGD reuse intention and user satisfaction. (3) Gov­ ernment preparation has a significant effect on perceived data value, perceived data quality, and user satisfac­ tion. (4) Different from previous studies on other products, perceived data quality and user satisfaction have no direct significant effects on user reuse intention, revealing the dual attributes of OGD as technical products and monopolistic public goods. This study reveals the mechanism of OGD value co-creation by integrating factors related to data, user, and the government, providing novel empirical evidence and theoretical insights to the literature of Customer Satisfaction Index, Technology Acceptance Model, and data reuse. Collapse

Topics: information technology capability data quality user satisfaction public good website

Methods: experiment survey mixed method qualitative coding data transformation

Theories: technology acceptance model

Deep Learning-Based Imputation Method to Enhance Crowdsourced Data on Online Business Directory Platforms for Improved Services

2023 | Journal of Management Information Systems | Citations: 0

Authors: Xu, Da; Hu, Paul Jen-Hwa; Fang, Xiao

Abstract: Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, ... Expand

Abstract: Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, depend on voluntarily user-submitted data about various businesses to assist consumers in finding appropriate options for transactions. Yet the crowdsourced nature of such data restricts the availability of attribute values for many businesses on the platform. Crowdsourced data often suffer serious completeness and timeliness constraints, with negative implications for key stakeholders such as users, businesses, and the platform. We thus develop a novel, deep learning–based imputation method, premised in institutional theory, to estimate missing attribute values of individual businesses on an OBD platform. The proposed method leverages a deep model architecture and considers both inter-business and inter-attribute relationships for imputations. An application to a Yelp data set reveals our method's greater imputation effectiveness relative to prevalent methods. To illustrate the method's practical utilities and values, we further examine the efficacy of business recommendations empowered by its imputed business attribute values, in comparison with those enabled by data imputed by benchmark methods. The results affirm that the proposed method substantially outperforms benchmarks for imputing missing attribute values and empowers more effective business recommendations. This study addresses crucial, prominent completeness and timeliness constraints in crowdsourced data on OBD platforms and offers insights for downstream applications that can improve user experiences, firm performance, and platform services. Collapse

Topics: missing data crowdsourcing data completeness wireless LAN healthcare data

Methods: data imputation autoencoder data transformation deep learning multivariate imputation with chained equations

Theories: institutional theory social learning theory

Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems

2022 | Information Systems Frontiers | Citations: 0

Authors: Heinrich, Bernd; Hopf, Marcus; Lohninger, Daniel; Schiller, Alexander; Szubartowicz, Michael

Abstract: The rapid development of e-commerce has led to a swiftly increasing number of co ... Expand

Abstract: The rapid development of e-commerce has led to a swiftly increasing number of competing providers in electronic markets, which maintain their own, individual data describing the offered items. Recommender systems are popular and powerful tools relying on this data to guide users to their individually best item choice. Literature suggests that data quality of item content data has substantial influence on recommendation quality. Thereby, the dimension completeness is expected to be particularly important. Herein resides a considerable chance to improve recommendation quality by increasing completeness via extending an item content data set with an additional data set of the same domain. This paper therefore proposes a procedure for such a systematic data extension and analyzes effects of the procedure regarding items, content and users based on real-world data sets from four leading web portals. The evaluation results suggest that the proposed procedure is indeed effective in enabling improved recommendation quality. Collapse

Topics: missing data recommender system data quality electronic commerce decision support system

Methods: computational algorithm data imputation literature study binary classification literature sample

Generalized Trust, External Sourcing, and Firm Performance in Economic Downturns

2022 | Organization Science | Citations: 0

Authors: Vanneste, Bart S.; Gulati, Ranjay

Abstract: Going beyond prior research that has focused on dyadic, party-specific trust, th ... Expand

Abstract: Going beyond prior research that has focused on dyadic, party-specific trust, this study investigates the importance of generalized trust, which is not specific to a counterparty and originates from a broader context. We analyze how generalized trust at the regional level affects the extent to which a firm relies on external suppliers and the performance effects of doing so. Furthermore, we assess how these relationships are impacted by an economic downturn. We exploit differences in generalized trust across 145 regions in 12 European countries and use data on more than a million small- and medium-sized enterprises (SMEs) before and during the Great Financial Crisis (from 2008 to 2010). Control variables are selected via a double-selection procedure based on machine learning. We find that firms in high generalized trust regions, compared with those in low generalized trust regions, source more externally (but do not reduce external sourcing less in an economic downturn) and benefit more from external sourcing during an economic downturn. Collapse

Topics: interorganizational relationship organizational productivity asset specificity moral hazard missing data

Methods: lasso regression survey robustness check cluster analysis ordinary least square

Theories: organizational behavior theory

How Employees Learn Information Security Policy Compliance Behavior: Toward a Social Learning Perspective

2022 | European Conference On Information Systems | Citations: 5

Authors: Hengstler, Sebastian; Pryazhnykova, Natalya; Kühnel, Stephan

Abstract: Information security attacks typically exploit the weakest link in the chain, w ... Expand

Abstract: Information security attacks typically exploit the weakest link in the chain, which is in most cases is the IT end user at the workplace. While great strides have been made in understanding and explaining information security behavior, little is known about how such behavior is acquired by individuals in the first place. This research approaches the phenomenon through the lens of social learning theory. We argue that a new employee's behavior is initially learned through differential associations within the social network, rather than through knowledge of formal policies and associated sanctions. We used a scenario-based experimental approach and collected data from new employees with five years or less of work experience. Our results show that employee's behavior changes over time. Reinforcement through sanctions becomes more important in the maintenance phase, while imitation of others becomes less relevant. Collapse

Topics: data security cybersecurity behavior data encryption data completeness human resource management

Methods: survey statistical power analysis hierarchical linear modeling experimental group experiment

Theories: social learning theory theory of planned behavior

Profiling Online Social Network Platforms: Twitter vs. Instagram

2021 | HICSS | Citations: 0

Authors: Ayora, Veruska; Horita, Flávio; Kamienski, Carlos

Abstract: Online Social Networks (OSN) have been increasingly used as sources of informati ... Expand

Abstract: Online Social Networks (OSN) have been increasingly used as sources of information for different applications, ranging from business, politics, and public services. However, there is a lack of information on OSN platforms' behavior that may impact big data processing and real-time services. In this paper, two of the most widely used social networks, Instagram and Twitter, are investigated to broaden the understanding of how each platform's message characteristics influence data completeness and latency. We performed a series of experiments to emulate data posting and collection automatically. Our results increase the level of transparency of the platforms' internal behavior, showing that both can deliver data with reasonably low latencies and high completeness, but Twitter can be up to eight times faster when it comes to multimedia messages. Collapse

Topics: Twitter Instagram data completeness application programming interface mobile application

Methods: experiment computational algorithm literature study

Theories: set theory

An empirical study of the antecedents of data completeness in electronic medical records

2020 | International Journal of Information Management | Citations: 0

Authors: Liu, Caihua; Zowghi, Didar; Talaei-Khoei, Amir

Abstract: There is a body of research that highlights the role of data management to impro ... Expand

Abstract: There is a body of research that highlights the role of data management to improve the quality of data, which in return improves organizational performance. The literature in data management has indicated the five theoretical constructs used to understand the factors influencing data quality, including top management support, capability on the regulation and process management, business-IT alignment, staff participation, and integration of information systems. However, it is unclear how these theoretical constructs can be utilized to understand the antecedents of data completeness as a dimension of data quality. Following that stream of research, the current paper examines the factors influencing data completeness in electronic medical records (EMR). The scope of this study is by only surveying medical professionals at healthcare settings in northern Nevada. The empirical results reveal that resources should be added as one of the antecedents of data completeness in EMR. Collapse

Topics: data completeness data quality quality management data management electronic health record

Methods: conceptual modelling survey partial least squares regression survey design statistical power analysis

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

2020 | HICSS | Citations: 0

Authors: Liu, Caihua; Zowghi, Didar; Talaei-Khoei, Amir

Abstract: Data completeness is an important dimension of data quality in electronic medica ... Expand

Abstract: 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. Collapse

Topics: data completeness data quality data management quality management health information system

Methods: conceptual modelling survey data transformation factor analysis statistical power analysis

Factors influencing effective use of big data: A research framework

2020 | Information & Management | Citations: 26

Authors: Surbakti, Feliks P. Sejahtera; Wang, Wei; Indulska, Marta; Sadiq, Shazia

Abstract: Information systems (IS) research has explored “effective use” in a variety of c ... Expand

Abstract: Information systems (IS) research has explored “effective use” in a variety of contexts. However, it is yet to specifically consider it in the context of the unique characteristics of big data. Yet, organizations have a high appetite for big data, and there is growing evidence that investments in big data solutions do not always lead to the derivation of intended value. Accordingly, there is a need for rigorous academic guidance on what factors enable effective use of big data. With this paper, we aim to guide IS researchers such that the expansion of the body of knowledge on the effective use of big data can proceed in a structured and systematic manner and can subsequently lead to empirically driven guidance for organizations. Namely, with this paper, we cast a wide net to understand and consolidate from literature the potential factors that can influence the effective use of big data, so they may be further studied. To do so, we first conduct a systematic literature review. Our review identifies 41 factors, which we categorize into 7 themes, namely data quality; data privacy and security and governance; perceived organizational benefit; process management; people aspects; systems, tools, and technologies; and organizational aspects. To explore the existence of these themes in practice, we then analyze 45 published case studies that document insights into how specific companies use big data successfully. Finally, we propose a framework for the study of effective use of big data as a basis for future research. Our contributions aim to guide researchers in establishing the relevance and relationships within the identified themes and factors and are a step toward developing a deeper understanding of effective use of big data. Collapse

Topics: big data data quality privacy business process management database system

Methods: literature sample case study literature study qualitative content analysis synthesis

Theories: theory of effective use theory of bounded rationality information systems success model