Data and information quality (DIQ) have been defined traditionally in an organizational context and with respect to traditional information systems (IS). Numerous frameworks have been developed to operationalize traditional DIQ accordingly. However, over the last decade, social information systems (SocIS) such as social media have emerged that enable social interaction and open collaboration of voluntary prosumers, rather than supporting specific tasks as do traditional IS in organizations. Based on a systematic literature review, the paper identifies and categorizes prevalent DIQ conceptualizations. The authors differentiate the various understandings of DIQ in light of the unique characteristics of SocIS and conclude that they do not capture DIQ in SocIS well, nor how it is defined, maintained, and improved through social interaction. The paper proposes a new conceptualization of DIQ in SocIS that can explain the interplay of existing conceptualizations and provides the foundation for future research on DIQ in SocIS.
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The combination of multiple DIQ conceptualizations should not be confused with the combination of multiple DIQ dimensions/metrics; the latter is common to most of the DIQ definitions (see Sect. 2.3 on the levels of DIQ definitions) and also occurs without a DIQ conceptualization (category “only dimensions”).
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Agarwal R, Gupta AK, Kraut R (2008) The interplay between digital and social networks. Inf Syst Res 19:243–252
Ali-Hassan H, Nevo D (2009) Identifying social computing dimensions: a multidimensional scaling study. In: Proceedings of the international conference on information systems (ICIS) 2009
Arazy O, Nov O, Patterson R, Yeo L (2011) Information quality in Wikipedia: the effects of group composition and task conflict. J Manag Inf Syst 27:71–98
Association for Information Systems (2011) Senior scholars’ basket of journals. In: Res. – Assoc. Inf. Syst. http://aisnet.org/?SeniorScholarBasket. Accessed 28 Apr 2015
Bagozzi RP, Dholakia UM (2002) Intentional social action in virtual communities. J Interact Mark 16:2–21
Ballou D, Madnick S, Wang R (2003) Special section: assuring information quality. J Manag Inf Syst 20:9–11
Bansal A, Kauffman RJ, Weitz RR (1993) Comparing the modeling performance of regression and neural networks as data quality varies: a business value approach. J Manag Inf Syst 10:11–32
Bardaki C, Kourouthanassis P, Pramatari K, Doukidis G (2013) An information quality evaluation framework of object tracking systems. In: Proceedings of the international conference on information systems (ICIS) 2013
Barnes S, Vidgen R (2009) An evaluation of user acceptance of a corporate intranet. In: Proceedings of the european conference on information systems (ECIS) 2009
Boell SK, Cecez-Kecmanovic D (2015) What is an information system? In: Proceedings of the annual hawaii international conference on system sciences (HICSS) 2015
Butler BS (2001) Membership size, communication activity, and sustainability: a resource-based model of online social structures. Inf Syst Res 12:346–362
Chai K, Potdar V, Dillon T (2009) Content quality assessment related frameworks for social media. In: Proc Int Conf Comput Sci its Appl ICCSA 09, pp. 791–805
Cheong LK, Chang V (2007) The need for data governance: a case study. In: Proceedings of the australasian conference on information systems (ACIS) 2007
DeLone WH, McLean ER (1992) Information systems success: the quest for the dependent variable. Inf Syst Res 3:60–95
DeLone WH, McLean ER (2003) The DeLone and McLean model of information systems success: a ten-year update. J Manag Inf Syst 19:9–30
Doll WJ, Torkzadeh G (1988) The measurement of end-user computing satisfaction. MIS Q 12:259–274
English LP (1999) Improving data warehouse and business information quality: methods for reducing costs and increasing profits. Wiley, New York
Floridi L (2011) The philosophy of information. Oxford University Press, Oxford
Fung R, Lee M (1999) EC-Trust (trust in electronic commerce): exploring the antecedent factors. In: Proceedings of the Americas conference on information systems (AMCIS) 1999
Giles J (2005) Internet encyclopaedias go head to head. Nature 438:900–901
Glowalla P, Sunyaev A (2014) Process-driven data quality management: a critical review on the application of process modeling languages. ACM J Data Inf Qual 5:1–7
Gu B, Konana P, Rajagopalan B, Chen H-WM (2007) Competition among virtual communities and user valuation: the case of investing-related communities. Inf Syst Res 18:68–85
Hansen P, Järvelin K (2000) The information seeking and retrieval process at the Swedish patent- and registration office. Moving from lab-based to real life work-task environment. In: Proceedings of the SIGIR 2000 workshop on patent retrieval, pp 43–53
Hansen P, Järvelin K (2004) Collaborative information searching in an information-intensive work domain: preliminary results. J Digit Inf Manag 2:26–30
Hirschheim R, Klein HK (2012) A glorious and not-so-short history of the information systems field. J Assoc Inf Syst 13:188–235
Hoyle D (2006) ISO 9000 Quality systems handbook, 5th edn. Butterworth-Heinemann, Oxford
Illari P (2014) IQ: Purpose and DIMENSIONS. In: Floridi L, Illari P (eds) The philosophy of information quality. Springer, Cham, pp 281–301
Jayawardene V, Sadiq S, Indulska M (2013) An analysis of data quality dimensions. IEEE Tech Rep No. 2013–01
Juran JM, Godfrey AB (eds) (1999) Juran’s quality handbook, 5th edn. McGraw-Hill, New York
Juran JM, Gryna FM, Bingham RS (eds) (1974) Quality control handbook, 3rd edn. McGraw-Hill, New York
Kahn BK, Strong DM (1998) Product and service performance model for information quality: an update. In: Proceedings of the 1998 conference on information quality
Kahn BK, Strong DM, Wang RY (2002) Information quality benchmarks: product and service performance. Commun ACM 45:184–192
Kane GC, Ransbotham S (2012) Codification and collaboration: information quality in social media. In: Proceedings of the international conference on information systems (ICIS) 2012
Kane GC, Alavi M, Labianca GJ, Borgatti SP (2014) What’s different about social media networks? A framework and research agenda. MIS Q 38:275–304
Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of social media. Bus Horiz 53:59–68
Kitchenham B (2004) Procedures for performing systematic reviews. Department of Computer Science, Keele University and National ICT Australia Ltd, Keele
Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. Keele University and Durham University Joint Report, Keele and Durham
Lee AS (2010) Retrospect and prospect: information systems research in the last and next 25 years. J Inf Technol 25:336–348
Lee YW, Strong DM, Kahn BK, Wang RY (2002) AIMQ: a methodology for information quality assessment. Inf Manag 40:133–146
Lili L, Rong D (2013) Roles of community commitment and community atmosphere: an empirical study of online community success. In: Proceedings of the Wuhan international conference on E-business (WHICEB) 2013
Link S, Memari M (2013) Static analysis of partial referential integrity for better quality SQL data. In: Proceedings of the americas conference on information systems (AMCIS) 2013
Lukyanenko R, Parsons J, Wiersma YF (2014a) The IQ of the crowd: understanding and improving information quality in structured user-generated content. Inf Syst Res 25:669–689
Lukyanenko R, Parsons J, Wiersma YF (2014b) The impact of conceptual modeling on dataset completeness: a field experiment. In: Proceedings of the international conference on information systems (ICIS) 2014
Ma M, Agarwal R (2007) Through a glass darkly: information technology design, identity verification, and knowledge contribution in online communities. Inf Syst Res 18:42–67
MacInnis DJ (2011) A framework for conceptual contributions in marketing. J Mark 75:136–154
Madnick SE, Wang RY, Lee YW, Zhu H (2009) Overview and framework for data and information quality research. ACM J Data Inf Qual 1:1–2
Mason RO, Mitroff II (1973) A program for research on management information systems. Manag Sci 19:475–487
McKenzie P (2003) A model of information practices in accounts of everyday-life information seeking. J Doc 59:19–40
Morris CW (1938) Foundations of the theory of signs. In: International encyclopedia of unified science. University of Chicago Press, London
Nickerson RC, Varshney U, Muntermann J (2012) A method for taxonomy development and its application in information systems. Eur J Inf Syst 22:336–359
Orr K (1998) Data quality and systems theory. Commun ACM 41:66–71
Ou CXJ, Davison RM, Cheng NCK (2011) Why are social networking applications successful: an empirical study of Twitter. In: Proceedings of the Pacific Asia conference on information system (PACIS) 2011
Parameswaran M, Whinston AB (2007a) Social computing: an overview. Commun Assoc Inf Syst 19:762–780
Parameswaran M, Whinston AB (2007b) Research issues in social computing. J Assoc Inf Syst 8:336–350
Peirce CS (1931) Collected papers. Harv Univ Press, Cambridge
Prestipino M, Aschoff F-R, Schwabe G (2006) What’s the use of guidebooks in the age of collaborative media? Empirical evaluation of free and commercial travel information. In: Proceedings of the Bled eConference (BLED) 2006
Price R, Shanks G (2005) A semiotic information quality framework: development and comparative analysis. J Inf Technol 20:88–102
Rai A, Lang SS, Welker RB (2002) Assessing the validity of is success models: an empirical test and theoretical analysis. Inf Syst Res 13:50–69
Rheingold H (1993) The virtual community: homesteading on the electronic frontier. Addison-Wesley, Reading
Rotchanakitumnuai S (2006) Developing the electronic service acceptance model from internet securities trading system. In: Proceedings of the australian conference on information systems (ACIS) 2006
Sadiq S, Yeganeh NK, Indulska M (2011) 20 years of data quality research: themes, trends and synergies. In: Proceedings of the Australasian database conference (ADC) 2011, pp 153–162
Schlagwein D, Schoder D, Fischbach K (2011) Social information systems: review, framework, and research agenda. In: Research-in-progress, proceedings of the international conference on information systems (ICIS) 2011
Scholz M, Dorner V (2013) The recipe for the perfect review? An investigation into the determinants of review helpfulness. Bus Inf Syst Eng 5:141–151
Seddon PB (1997) A respecification and extension of the DeLone and McLean model of IS success. Inf Syst Res 8:240
Shanks G, Corbitt B (1999) Understanding data quality: SOCIAL and cultural aspects. In: Proceedings of the Australasian conference on information systems (ACIS) 1999. pp 785–797
Shanks G, Darke P (1998) Understanding data quality in data warehousing: a semiotic approach. In: Proceedings of the international conference on information quality (ICIQ) 1998
Stamper R (1992) Signs, organisations, norms and information systems. In: Proceedings of the Australian conference on information systems (ACIS) 1992
Strong DM, Lee YW, Wang RY (1997) Data quality in context. Commun ACM 40:103–110
Talja S (2002) Information sharing in academic communities: types and levels of collaboration in information seeking and use. New Rev Inf Behav Res 3:143–160
Talja S, Hansen P (2006) Information sharing. In: Cole C (ed) Spink A. Springer, Dordrecht, pp 113–134
Valecha R, Oh O, Rao R (2013) An exploration of collaboration over time in collective crisis response during the Haiti 2010 earthquake. In: Research-in-progress, proceedings of the international conference on information system (ICIS) 2013
van der Heijden H (2004) User acceptance of hedonic information systems. MIS Q 28:695–704
van der Pijl G (1994) Measuring the strategic dimensions of the quality of information. J Strateg Inf Syst 3:179–190
Wand Y, Wang RY (1996) Anchoring data quality dimensions in ontological foundations. Commun ACM 39:86–95
Wang RY (1998) A product perspective on total data quality management. Commun ACM 41:58–65
Wang RW, Strong DM (1996) Beyond accuracy: what data quality means to data consumers. J Manag Inf Syst 12:5–33
Weber D, Leone S, Norrie M (2013) Constraint-based data quality management framework for object databases. In: Proceedings of the European conference on information systems (ECIS) 2013
Webster J, Watson RT (2002) Analyzing the past to prepare for the future: writing a literature review. MIS Q 26:xiii–xxiii
Wilson TD (2000) Human information behavior. Inf Sci J 3:49–55
Winter S, Berente N, Howison J, Butler B (2014) Beyond the organizational “container”: conceptualizing 21st century sociotechnical work. Inf Organ 24:250–269
Wixom BH, Todd PA (2005) A theoretical integration of user satisfaction and technology acceptance. Inf Syst Res 16:85–102
Xiao Y, Lu LYY, Liu JS, Zhou Z (2014) Knowledge diffusion path analysis of data quality literature: a main path analysis. J Informetr 8:594–605
Xu YC, Yang Y, Cheng Z, Lim J (2014) Retaining and attracting users in social networking services: an empirical investigation of cyber migration. J Strateg Inf Syst 23:239–253
Zach O (2011) Exploring ERP system outcomes in SMEs: a multiple case study. In: Proceedings of the european conference on information systems (ECIS) 2011
Zheng Y, Zhao K, Stylianou A (2009) Information quality and system quality in online communities: an empirical investigation. In: Proceedings of the special interest group on human computer interaction (SIGHCI) 2009
The authors thank three anonymous reviewers, the guest editors of the special issue on human information behavior, as well as the discussants at the International Conference on Information Systems 2015 for their input and valuable comments on earlier drafts of this article.
Accepted after three revisions by the editors of the special issue.
A1 Structured Literature Search
To develop a taxonomy of existing DIQ conceptualizations, we first conducted a structured literature search of the DIQ domain in general. We identified relevant DIQ conceptualizations and definitions we used to develop a taxonomy. Our literature review followed the best-practice approaches of the IS discipline (Webster and Watson 2002; Kitchenham and Charters 2007).
A1.1 Search Process
We identified relevant articles by searching systematically the titles, keywords, and abstracts of all articles published in the Senior Scholars’ Basket (Association for Information Systems 2011) that is, European Journal of Information Systems, Information Systems Journal, Information Systems Research, Journal of AIS, Journal of Information Technology, Journal of MIS, Journal of Strategic Information Systems, and MIS Quarterly.
We conducted a keyword-based search (Kitchenham 2004; Kitchenham and Charters 2007) using two combinations of keywords: “information AND quality” and “data AND quality.” We also searched the titles, keywords, and abstracts of all articles archived in the AIS Electronic Library (AISeL) for the keywords “information quality” and “data quality.” We collected all papers published before 20 April 2015 that matched these keywords.
We screened the results manually, removed duplicates, and excluded articles that did not cover at least one of the concepts ‘data quality’, ‘information quality’, and DIQ. We included only articles that stated explicitly or referred to a definition of data quality and/or information quality.
Our search process resulted in a set of 730 articles. After removing duplicates and all articles that did match our exclusion criteria (see above), we identified 342 potentially relevant papers. We then screened each of these articles for their respective definitions of DIQ and decided to submit 249 articles to further analysis (see Table 1).
A2.1 Process of Taxonomy Development after Nickerson et al. (2012)
Nickerson et al. 2012 propose an iterative method to develop taxonomies. Briefly summarized, the method proceeds as follows: (1) based on the purpose of the taxonomy, determine a meta-characteristic that informs the selection of characteristics in later stages; (2) determine objective and subjective ending conditions for the iterative cycle to stop; and (3) choose whether to proceed “empirical-to-conceptual” or “conceptual-to-empirical.” Then, in “empirical-to-conceptual”: (4e) identify objects; (5e) identify their common characteristics; and (6e) group characteristics into dimensions and create/revise the taxonomy. In “conceptual-to-empirical,” the process is: (4c) deduce characteristics and dimensions from prior knowledge, experience, or theory; (5c) examine whether objects for characteristics and dimensions; and (6c) create/revise the taxonomy. The process then continues as follows: (7) evaluate objective and subjective ending conditions and either enter into the next iteration (step 3) or terminate, if all conditions are met. Note that in this method, it is possible to alternate between “empirical-to-conceptual” and “conceptual-to-empirical” iterations.
A2.2 Resulting Taxonomy
Table 2 presents an overview of the existing DIQ conceptualizations that were identified through the process of taxonomy building including short descriptions each conceptualization, key publications or examples for this conceptualization, how it maps to the TAD framework, its main points of conflict with SocIS characteristics, and how many studies were assigned to each DIQ conceptualization.
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Tilly, R., Posegga, O., Fischbach, K. et al. Towards a Conceptualization of Data and Information Quality in Social Information Systems. Bus Inf Syst Eng 59, 3–21 (2017). https://doi.org/10.1007/s12599-016-0459-8
- Social information systems
- Social media
- Data quality
- Information quality
- Socio-technical processes