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Information quality, user satisfaction, and the manifestation of workarounds: a qualitative and quantitative study of enterprise content management system users

Abstract

In this paper, we focus on a critical aspect of work in organizations: using information in work tasks which is provided by information systems (IS) such as enterprise content management (ECM) systems. Our study based on the IS success model, 34 interviews, and an empirical study of 247 ECM system users at a financial service provider indicates that it is appropriate to differentiate between contextual and representational information quality as two information quality dimensions. Furthermore, we reveal that in addition to system quality, the two information quality dimensions are important in determining end-user satisfaction, which in turn influences the manifestation of workarounds. Our study also finds that employees using workarounds to avoid an ECM system implemented several years is negatively related to individual net benefits of the ECM system. Hence, we conclude that when investigating large-scale IS such as ECM systems, it is important to differentiate among information quality dimensions to more deeply understand end-user satisfaction and the resulting manifestation of workarounds. Moreover, this research guides organizations in implementing the most appropriate countermeasures based on the importance of either contextual or representational information quality.

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References

  • Alalwan JA (2012) Enterprise content management research: a comprehensive review. Journal of Enterprise Information Management 25(5), 441–461, doi: 10.1108/17410391211265133.

    Article  Google Scholar 

  • Alter S (2006) The work system method: connecting people, processes, and IT for business results. Work System Press.

  • Alter S (2013) Work system theory: overview of core concepts, extensions, and challenges for the future. Journal of the Association for Information Systems 14(2), 1.

    Google Scholar 

  • Alter S (2014) Theory of workarounds. Communications of the Association for Information Systems 34(1), 55.

    Google Scholar 

  • Ansari SM, Fiss PC and Zajac EJ (2010) Made to fit: How practices vary as they diffuse. Academy of Management Review 35(1), 67–92.

    Article  Google Scholar 

  • Ash JS, Berg M and Coiera E (2004) Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. Journal of the American Medical Informatics Association 11(2), 104–112.

    Article  Google Scholar 

  • Azad B and King N (2008) Enacting computer workaround practices within a medication dispensing system. European Journal of Information Systems 17(3), 264–278, doi: 10.1057/ejis.2008.14.

    Article  Google Scholar 

  • Azad B and King N (2011) Institutionalized computer workaround practices in a Mediterranean country: an examination of two organizations. European Journal of Information Systems 21(4), 358–372, doi: 10.1057/ejis.2011.48.

    Article  Google Scholar 

  • Bagozzi RP (1979) The role of measurement in theory construction and hypothesis testing: toward a holistic model. Conceptual and theoretical developments in marketing 8, 15–32.

    Google Scholar 

  • Baron RM and Kenny DA (1986) The moderator‐mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51(6), 1173.

    Article  Google Scholar 

  • Beath C, Becerra-Fernandez I, Ross J and Short J (2012) Finding value in the information explosion. MIT Sloan Management Review 53(4), 18.

    Google Scholar 

  • Bhattacherjee A (2001) Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly 25(3), 351–370.

    Article  Google Scholar 

  • Bhattacherjee A and Hikmet N (2007) Physicians’ resistance toward healthcare information technology: a theoretical model and empirical test. European Journal of Information Systems 16(6), 725–737.

    Article  Google Scholar 

  • Böhn M (2014) The market for ECM software. In Enterprise Content Management in Information Systems Research (Vom Brocke J and Simons A, Eds), pp. 23–36, Springer, Berlin.

  • Boudreau M-C and Robey D (2005) Enacting integrated information technology: a human agency perspective. Organization Science 16(1), 3–18.

  • Brazel JF and Dang L (2008) The effect of ERP system implementations on the management of earnings and earnings release dates. Journal of Information Systems 22(2), 1–21.

    Article  Google Scholar 

  • Brislin RW (1970) Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology 1(3), 185–216, doi: 10.1177/135910457000100301.

    Article  Google Scholar 

  • Broadhurst K, Wastell D, White S, Hall C, Peckover S, Thompson K, Pithouse A and Davey D (2009) Performing ‘initial assessment’: identifying the latent conditions for error at the front-door of local authority children’s services. British journal of social work.

  • Campbell DT and Fiske DW (1959) Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin 56(2), 81–105, doi: 10.1037/h0046016.

    Article  Google Scholar 

  • Carmines EG and Zeller RA (1979) Reliability and Validity Assessment. Sage Publications, Beverly Hills, Calif.

    Book  Google Scholar 

  • Chang JC-J and King WR (2005) Measuring the Performance of Information Systems: A Functional Scorecard. Journal of Management Information Systems 22(1), 85.

  • Chin WW (1998a) Commentary: issues and opinion on structural equation modeling. MIS Quarterly 22(1), 7–16, doi: 10.2307/249674.

    Google Scholar 

  • Chin WW (1998b) The partial least squares approach to structural equation modeling. In Modern Methods for Business Research (Marcoulides GA, Ed), pp. 295–336, Erlbaum, Mahwah, NJ.

    Google Scholar 

  • Chin WW, Gopal A and Salisbury WD (1997) Advancing the Theory of adaptive structuration: the development of a scale to measure faithfulness of appropriation. Information Systems Research 8(4), 342–367, doi: 10.1287/isre.8.4.342.

    Article  Google Scholar 

  • Chin WW, Thatcher JB and Wright RT (2012) Assessing common method bias: problems with the ULMC technique. MIS Quarterly 36(3), 1003.

  • Cohen J (1988) Statistical Power Analysis for the Behavioral Sciences. L. Erlbaum Associates, Hillsdale, N.J.

    Google Scholar 

  • Cvach M (2012) Monitor alarm fatigue: an integrative review. Biomedical Instrumentation & Technology 46(4), 268–277.

    Article  Google Scholar 

  • Davison RM and Ou CXJ (2013) Sharing knowledge in technology deficient environments: individual workarounds Amid corporate restrictions. Proceedings of the 2013 European Conference on Information Systems.

  • Delone W and Mclean E (1992) Information systems success: the quest for the dependent variable. Information Systems Research 3(1), 60–95, doi: 10.1287/isre.3.1.60.

    Article  Google Scholar 

  • Delone W and Mclean E (2003) The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems 19(4), 9–30.

    Article  Google Scholar 

  • Dwivedi YK, Wastell D, Laumer S, Henriksen HZ, Myers MD, Bunker D, Elbanna A, Ravishankar MN and Srivastava SC (2014) Research on information systems failures and successes: status update and future directions. Information Systems Frontiers, doi: 10.1007/s10796-014-9500-y.

    Google Scholar 

  • Eckhardt A, Laumer S and Weitzel T (2009) Who influences whom? Analyzing workplace referents’ social influence on IT adoption and non-adoption. Journal of Information Technology 24(1), 11–24, doi: 10.1057/jit.2008.31.

    Article  Google Scholar 

  • Ferneley EH and Sobreperez P (2006) Resist, comply or workaround? An examination of different facets of user engagement with information systems. European Journal of Information Systems 15(4), 345–356, doi: 10.1057/palgrave.ejis.3000629.

    Article  Google Scholar 

  • Flanagan JC (1954) The critical incident technique. Psychological Bulletin 51(4), 327.

    Article  Google Scholar 

  • Fornell C and Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18(1), 39–50, doi: 10.2307/3151312.

    Article  Google Scholar 

  • Gable GG, Sedera D and Chan T (2008) Re-conceptualizing information system success: the IS-impact measurement model. Journal of the Association for Information Systems 9(7), 1–32.

    Google Scholar 

  • Gasparas J and Monteiro E (2009) Cross-contextual use of integrated information systems. Proceedings of the 2009 European Conference on Information Systems.

  • Gasser L (1986) The integration of computing and routine work. ACM Transactions on Information Systems 4(3), 205–225, doi: 10.1145/214427.214429.

    Article  Google Scholar 

  • Gilbert MR, Shegda KM, Chin K, Tay G and Koehler-Kruener H (2013) Magic quadrant for enterprise content management.

  • Grahlmann KR, Helms RW, Hilhorst C, Brinkkemper S and Van Amerongen S (2011) Reviewing enterprise content management: a functional framework. European Journal of Information Systems 21(3), 268–286, doi: 10.1057/ejis.2011.41.

    Article  Google Scholar 

  • Gremler DD (2004) The critical incident technique in service research. Journal of Service Research 7(1), 65–89, doi: 10.1177/1094670504266138.

    Article  Google Scholar 

  • Hair JF (2010) Multivariate Data Analysis. Prentice Hall, Upper Saddle River, NJ.

    Google Scholar 

  • Hair JF (2014) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage, Thousand Oaks (California) [etc.].

  • Halbesleben JRB, Savage GT, Wakefield DS and Wakefield BJ (2010) Rework and workarounds in nurse medication administration process. Health Care Management Review 35(2), 124–133, doi: 10.1097/HMR.0b013e3181d116c2.

    Article  Google Scholar 

  • Harman HH (1976) Modern Factor Analysis. University of Chicago Press, Chicago.

  • Henseler J, Ringle CM and Sarstedt M (2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 1–21.

  • Hulland J (1999) Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal 20(2), 195–204, doi: 10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7.

    Article  Google Scholar 

  • Ignatiadis I and Nandhakumar J (2009) The effect of ERP system workarounds on organizational control: An interpretivist case study. Scandinavian Journal of Information Systems 21(2), 3.

    Google Scholar 

  • Iivari J (2005) An empirical test of the DeLone-McLean model of information system success. ACM SIGMIS Database 36(2), 8–27, doi: 10.1145/1066149.1066152.

    Article  Google Scholar 

  • Joshi K (1991) A model of users’ perspective on change: the case of information systems technology implementation. MIS Quarterly 15(2), 229, doi: 10.2307/249384.

    Article  Google Scholar 

  • Kim H-W and Kankanhalli A (2009) Investigating user resistance to information systems implementation: A status quo bias perspective. MIS Quarterly 33(3), 567–582.

  • Kim SS (2009) The integrative framework of technology use: an extension and test. MIS Quarterly 33(3), 513–538.

    Google Scholar 

  • Klaus T and Blanton JE (2010) User resistance determinants and the psychological contract in enterprise system implementations. European Journal of Information Systems 19(6), 625–636, doi: 10.1057/ejis.2010.39.

    Article  Google Scholar 

  • Klaus T, Wingreen SC and Blanton JE (2010) Resistant groups in enterprise system implementations: a Q-methodology examination. Journal of Information Technology 25(1), 91–106, doi: 10.1057/jit.2009.7.

    Article  Google Scholar 

  • Koopman P and Hoffman R (2003) Work-arounds, make-work, and kludges. IEEE Intelligent Systems 18(6), 70–75, doi: 10.1109/MIS.2003.1249172.

    Article  Google Scholar 

  • Koplowitz R, Rymer JR, Hammond JS and Brown V (2013) SharePoint enters its awkward teenage years. Customer struggles with social, cloud, and mobile signal A transition.

    Google Scholar 

  • Koppel R, Wetterneck T, Telles JL and Karsh B-T (2008) Workarounds to barcode medication administration systems: their occurrences, causes, and threats to patient safety. Journal of the American Medical Informatics Association 15(4), 408–423.

  • Landis JR and Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1), 159, doi: 10.2307/2529310.

    Article  Google Scholar 

  • Lapointe L and Rivard S (2005) A multilevel model of resistance to information technology implementation. MIS Quarterly 29(3).

    Google Scholar 

  • Laumer S (2016) Information quality dimensions: two exploratory case studies with enterprise content management system users. In 24th European Conference on Information Systems, Paper 141, Istanbul, Turkey.

  • Laumer S, Beimborn D, Maier C and Weinert C (2013) Enterprise Content Management. Business & Information Systems Engineering 5(6), 449–452, doi: 10.1007/s12599-013-0291-3.

    Article  Google Scholar 

  • Laumer S, Maier C, Eckhardt A and Weitzel T (2014) Why are they grumbling about my new system? Theoretical foundation and empirical evidence of employee grumbling as a user resistance behavior. Proceedings of the 2014 International Conference on Information Systems.

  • Laumer S, Maier C, Eckhardt A and Weitzel T (2015a) User personality and resistance to mandatory information systems in organizations. A theoretical model and empirical test of dispositional resistance to change. Journal of Information Technology 31(1), 67–82, doi: 10.1057/jit.2015.17.

    Article  Google Scholar 

  • Laumer S, Maier C, Eckhardt A and Weitzel T (2016) Work routines as an object of resistance during information systems implementations. Theoretical foundation and empirical evidence. European Journal of Information Systems 25(4), 317–343, doi: 10.1057/ejis.2016.1.

    Article  Google Scholar 

  • Laumer S, Maier C and Weitzel T (2015b) Successfully implementing enterprise content management: lessons learnt from a financial service provider. In Proceedings of the 36th International Conference on Information Systems (ICIS 2015), Fort Worth, Tx, USA.

  • Lee YW, Strong DM, Kahn BK and Wang RY (2002) AIMQ: a methodology for information quality assessment. Information & Management 40(2), 133–146, doi:10.1016/S0378-7206(02)00043-5 .

    Article  Google Scholar 

  • Liang H, Saraf N, Hu Q and Xue Y (2007) Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management. MIS Q 31(1), 59–87.

    Google Scholar 

  • Maier C, Laumer S, Eckhardt A and Weitzel T (2013) Analyzing the impact of HRIS implementations on HR personnel’s job satisfaction and turnover intention. The Journal of Strategic Information Systems 22(3), 193–207, doi: 10.1016/j.jsis.2012.10.003.

    Article  Google Scholar 

  • Maier C, Laumer S, Eckhardt A and Weitzel T (2015a) Giving too much social support: social overload on social networking sites. European Journal of Information Systems 24(5), 447–464, doi: 10.1057/ejis.2014.3.

    Article  Google Scholar 

  • Maier C, Laumer S, Weinert C and Weitzel T (2015b) The effects of technostress and switching stress on discontinued use of social networking services: a study of Facebook use. Information Systems Journal 25(3), 275–308, doi: 10.1111/isj.12068.

    Article  Google Scholar 

  • Marakas GM and Hornik S (1996) Passive resistance misuse: overt support and covert recalcitrance in IS implementation. European Journal of Information Systems 5(3), 208–219, doi: 10.1057/ejis.1996.26.

    Article  Google Scholar 

  • Markus ML (1983) Power, politics, and MIS implementation. Communications of the ACM 26(6), 430–444, doi: 10.1145/358141.358148.

    Article  Google Scholar 

  • Martinko MJ, Zmud RW and Henry JW (1996) An attributional explanation of individual resistance to the introduction of information technologies in the workplace. Behaviour & Information Technology 15(5), 313–330, doi: 10.1080/014492996120085a.

    Article  Google Scholar 

  • Mcgann ST and Lyytinen K (2008) The improvisation effect: a case study of user improvisation and its effects on information system evolution. Proceedings of the 2008 International Conference on Information Systems.

  • Mckinney V, Yoon K and Zahedi FM (2002) The measurement of web-customer satisfaction: an expectation and disconfirmation approach. Information Systems Research 13(3), 296–315, doi: 10.1287/isre.13.3.296.76.

    Article  Google Scholar 

  • Nahm AY, Rao SS, Solis-Galvan LE and Ragu-Nathan TS (2002) The Q-sort method: assessing reliability and construct validity of questionnaire items at a pre-testing stage. Journal of Modern Applied Statistical Methods 1(1 (Article 15)).

  • Nordheim S and Päivärinta T (2006) Implementing enterprise content management: from evolution through strategy to contradictions out-of-the-box. European Journal of Information Systems 15(6), 648–662, doi: 10.1057/palgrave.ejis.3000647.

    Article  Google Scholar 

  • Nunnally JC (1967) Psychometric Theory. McGraw-Hill, New York.

    Google Scholar 

  • Paivarinta T and Munkvold B (2005) Enterprise content management: an integrated perspective on information management. In Proceedings of the 38th Annual Hawaii International Conference on System Sciences, p. 96, IEEE.

  • Patterson ES, Rogers ML, Chapman RJ and Render ML (2006) Compliance with intended use of bar code medication administration in acute and long-term care: an observational study. Human Factors: The Journal of the Human Factors and Ergonomics Society 48(1), 15–22.

    Article  Google Scholar 

  • Pavlou PA, Liang H and Xue Y (2007) Understanding and mitigating uncertainty in online exchange relationships: a principal-agent perspective. MIS Quarterly 31(1), 105–136.

    Google Scholar 

  • Petrides LA (2004) Costs and benefits of the workaround: inventive solution or costly alternative. International Journal of Educational Management 18(2), 100–108, doi: 10.1108/09513540410522234.

    Google Scholar 

  • Petter S, Delone W and Mclean E (2008) Measuring information systems success: models, dimensions, measures, and interrelationships. European Journal of Information Systems 17(3), 236–263, doi: 10.1057/ejis.2008.15.

    Article  Google Scholar 

  • Petter S, Delone W and Mclean E (2012) The past, present, and future of “IS success”. Journal of the Association for Information Systems 13(5), 341–362.

    Google Scholar 

  • Petter S, Delone W and Mclean E (2013) Information systems success: the quest for the independent variables. Journal of Management Information Systems 29(4), 7–62, doi: 10.2753/MIS0742-1222290401.

    Article  Google Scholar 

  • Pfaffenberger B (1992) Technological dramas. Science, Technology & Human Values 17(3), 282–312.

    Article  Google Scholar 

  • Pitt LF, Watson RT and Kavan CB (1995) Service quality: a measure of information systems effectiveness. MIS Quarterly 19(2), 173, doi: 10.2307/249687.

    Article  Google Scholar 

  • Podsakoff PM, Mackenzie SB, Lee J-Y and Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology 88(5), 879–903, doi: 10.1037/0021-9010.88.5.879.

    Article  Google Scholar 

  • Polites GL and Karahanna E (2012) Shackled to the status quo: the inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Quarterly 36(1), 21.

    Google Scholar 

  • Polites GL, Roberts N and Thatcher J (2012) Conceptualizing models using multidimensional constructs: a review and guidelines for their use. European Journal of Information Systems 21(1), 22–48.

    Article  Google Scholar 

  • Pollock N (2005) When is a work-around? Conflict and negotiation in computer systems development. Science, Technology & Human Values 30(4), 496–514.

    Article  Google Scholar 

  • Ragu-Nathan TS, Tarafdar M, Ragu-Nathan BS and Tu Q (2008) The consequences of technostress for end users in organizations: conceptual development and empirical validation. Information Systems Research 19(4), 417–433, doi: 10.1287/isre.1070.0165.

    Article  Google Scholar 

  • Rai A, Lang SS and Welker RB (2002) Assessing the validity of IS success models: an empirical testand theoretical analysis. Information Systems Research 13(1), 50–69, doi: 10.1287/isre.13.1.50.96.

    Article  Google Scholar 

  • Richins ML (1987) A multivariate analysis of responses to dissatisfaction. Journal of the Academy of Marketing Science 15(3), 24–31.

    Article  Google Scholar 

  • Ringle CM, Wende S and Becker J-M (2015) SmartPLS 3. SmartPLS GmbH.

  • Rivard S and Lapointe L (2012) Information technology implementers’ responses to user resistance: nature and effects. MIS Quarterly 36(3), 897.

    Google Scholar 

  • Russell B (2007) ‘You Gotta lie to IT’: software applications and the management of technological change in a call centre. New Technology, Work and Employment 22(2), 132–145.

    Article  Google Scholar 

  • Safadi H and Faraj S (2010) The role of workarounds during an opensource electronic medical record system implementation. Proceedings of the 2010 International Conference on Information Systems.

  • Salisbury WD, Chin WW, Gopal A and Newsted PR (2002) Research report: Better theory through measurement-developing a scale to capture consensus on appropriation. Information Systems Research 13(1), 91–103, doi: 10.1287/isre.13.1.91.93.

    Article  Google Scholar 

  • Selander L and Henfridsson O (2012) Cynicism as user resistance in IT implementation. Information Systems Journal 22(4), 289–312, doi: 10.1111/j.1365-2575.2011.00386.x.

    Article  Google Scholar 

  • Strong DM and Miller SM (1995) Exceptions and exception handling in computerized information processes. ACM Transactions on Information Systems (TOIS) 13(2), 206–233.

    Article  Google Scholar 

  • Strong DM and Volkoff O (2010) Understanding organization-enterprise system fit: a path to theorizing the information technology artifact. MIS Quarterly 34(4), 731–756.

    Google Scholar 

  • Truex D, Baskerville R and Travis J (2000) Amethodical systems development: the deferred meaning of systems development methods. Accounting, management and information technologies 10(1), 53–79.

    Article  Google Scholar 

  • Turel O (2014) Quitting the use of habituated hedonic information systems. European Journal of Information Systems.

  • Tyrväinen P, Päivärinta T, Salminen A and Iivari J (2006) Characterizing the evolving research on enterprise content management. European Journal of Information Systems 15(6), 627–634, doi: 10.1057/palgrave.ejis.3000648.

    Article  Google Scholar 

  • Venkatesh V, Morris M, Davis G and Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Quarterly 27(3), 425–478.

    Google Scholar 

  • Vogelsmeier AA, Halbesleben JR and Scott-Cawiezell JR (2007) Technology implementation and workarounds in the nursing home. Journal of the American Medical Informatics Association 15(1), 114–119, doi: 10.1197/jamia.M2378.

    Article  Google Scholar 

  • Volkoff O, Strong DM and Elmes MB (2007) Technological embeddedness and organizational change. Organization Science 18(5), 832–848, doi: 10.1287/orsc.1070.0288.

    Article  Google Scholar 

  • Vom Brocke J, Simons A, Herbst A, Derungs R and Novotny S (2011) The business drivers behind ECM initiatives: a process perspective. Business Process Management Journal 17(6), 965–985, doi: 10.1108/14637151111182710.

    Article  Google Scholar 

  • Vom Brocke J, Sonnenberg C and Buddendick C (2014) Justifying ECM investments with the return on process transformation: the case of an ECM-driven transformation of sales processes at Hilti Corporation. In Enterprise Content Management in Information Systems Research (Vom Brocke J and Simons A, Eds), pp. 255–277, Springer, Berlin.

    Chapter  Google Scholar 

  • Williams LJ, Edwards JR and Vandenberg RJ (2003) Recent advances in causal modeling methods for organizational and management research. Journal of Management 29(6), 903–936, doi: 10.1016/S0149-2063_03_00084-9.

    Article  Google Scholar 

  • Williams MD, Dwivedi YK, Lal B and Schwarz A (2009) Contemporary trends and issues in IT adoption and diffusion research. Journal of Information Technology 24(1), 1–10, doi: 10.1057/jit.2008.30.

    Article  Google Scholar 

  • Wixom BH and Todd PA (2005) A theoretical integration of user satisfaction and technology acceptance. Information Systems Research 16(1), 85–102, doi: 10.1287/isre.1050.0042.

    Article  Google Scholar 

  • Wright RT, Campbell DE, Thatcher JB and Roberts N (2012) Operationalizing multidimensional constructs in structural equation modeling: recommendations for IS Research. Communications of the Association for Information Systems 30(1), 23.

    Google Scholar 

  • Zeelenberg M and Pieters R (2004) Beyond valence in customer dissatisfaction: a review and new findings on behavioral responses to regret and disappointment in failed services. Journal of Business Research 57(4), 445–455.

    Article  Google Scholar 

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Acknowledgement

Some parts of the qualitative study have been presented at the European Conference of Information Systems (ECIS) 2016 in Istanbul, Turkey (Laumer, 2016).

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Correspondence to Sven Laumer.

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Associate Editor: Yogesh K. Dwivedi

Editor: Pär Ågerfalk

Appendices

Appendix A: Item development for workarounds

We develop and validate measurement items for workarounds in four steps in line with methods used in prior research developing new scales and developing new items systematically and rigorously (e.g., Chin et al, 1997; Maier et al, 2015a; Ragu-Nathan et al, 2008; Salisbury et al, 2002).

Step 1: Item development

In a first step, we scanned the recent literature discussing user resistance behavior (see Table A9 for an overview). In this step, we identified some user resistance behaviors related to IS implementations and developed an initial set of items for the proposed variable. We focused especially on those papers taking qualitative approaches to identify workarounds and used the example statements provided to define a first set of items for a variable that can capture the manifestation of workarounds in an empirical study.

In parallel, we also interviewed 20 employees in the target organization (see methodology section) to (a) identify examples of users’ workarounds in the organization and (b) identify the drivers and consequences of workaround behavior. Based on our analysis of the literature and the interviews, we developed a pool of items as illustrated in Table A1.

Table A1 Items of workarounds and q-sorting results (items below 0.61 are removed, here: WA-5)

Step 2: Assessing reliability and construct validity of the new items

Using q-sorting, we tested the reliability and validity of the new proposed items (Landis and Koch, 1977; Nahm et al, 2002) inviting 39 students from our university to participate in a q-sorting test. We developed a list of items including the newly developed ones for workarounds, items proposed by Kim & Kankanhalli (2009) and by Laumer et al (2014) for different user resistance behaviors. We included the existing ones as it is recommended by q-sorting to include items of similar constructs. Hence, items of two established user resistance constructs were included alongside the newly developed ones for workarounds. Moreover, we created an introductory statement defining each variable and instructions how to proceed. In the test, each individual read the introductory statement and assigned each item to one of the three constructs. Based on the assignment, we calculated ratios to evaluate the number of individuals matching the items to the correct variable. Using these results, we removed each item (WA-5) which was assigned by less than 61%, as suggested by prior research (Landis and Koch, 1977).

Step 3: Exploratory and confirmatory factor analysis

Using the remaining items of step 2, we conducted an additional survey in an organization focusing on another ECM system, as described above. The purpose of this survey was to collect data for an exploratory and confirmatory factor analysis to further evaluate the validity and reliability of the items for the new constructs. Therefore, a questionnaire was developed focusing on ECM usage, workarounds, user resistance, and several perceptions of the ECM in the organization. The data collected were used to conduct an exploratory factor analysis using SPSS 22. For this test, we used the newly developed items and the ones proposed for user resistance (Kim & Kankanhalli, 2009) and employee grumbling (Laumer et al, 2014). Our results reveal a three-factor structure. In a second step, the dataset was used to perform a confirmatory factor analysis using SPSS 23. Both steps revealed the same factor structured as illustrated in Table A2.

Table A2 Factor analysis
Table A3 q-sorting results

Step 4: Construct reliability and discriminant validity

In a next step, we focused on the reliability and discriminant validity of the newly proposed measurement model of workarounds, calculating Cronbach’s alpha for the remaining variables of step 3. The resulting value of 0.82 indicates a good construct reliability of the newly developed measurement model for workarounds (Hair, 2010; Nunnally, 1967). Furthermore, we performed again an explorative factor analysis to ensure convergent and discriminant validity. In this step, each item was assigned to the intended construct, confirming convergent and discriminant validity.

In summary, the measurement development process resulted in four items, which were used for the newly proposed variable of workarounds in validating the proposed research model.

Appendix B: Discriminant validity of contextual and representational information quality

To further test the discriminant validity of contextual and representational information quality, additional studies were conducted. The purpose of these studies was to collect data for both a q-sorting study and an exploratory and confirmatory factor analysis to further evaluate the validity and reliability of the two information quality dimensions.

Using q-sorting, we tested the reliability and validity of the proposed information quality dimensions (Landis & Koch, 1977; Nahm et al, 2002) inviting 28 students from our university to participate in a q-sorting test. We included the characteristics identified in our qualitative study. We created an introductory statement defining the two dimensions and instructions how to proceed. In the test, each individual read the introductory statement and assigned each characteristic to one of the two dimensions. Based on the assignment, we calculated ratios to evaluate the number of individuals matching the items to the correct dimensions. As no characteristic was assigned by less than 61%, we conclude that the assignment of the characteristics to the two information quality dimensions is reliable and valid as suggested by prior research (Landis & Koch, 1977).

Furthermore, we also ran a factor analysis with the data collected in the main study of this paper. Also these tests reveal a two-factor structure using the characteristics identified in our qualitative study. Consequently, we include this structure in the main study to analyze the effect of representational and contextual information quality on the manifestation of workarounds.

Appendix C: Measurement items

Table A4 summarizes the definition for each quality characteristic used in our survey instrument.

Table A4 Definition of quality characteristics

Table A5 illustrates the measurement items used and the respecting loadings of each item for the respective construct.

Table A5 Measurement items and loadings

Appendix D: Measurement model validation

Table A6 illustrates the reliability of the first-order constructs and the correlations between them.

Table A6 First-order reliability, AVEs, and correlation of constructs

Table A7 illustrates the cross-loadings of the items of the first-order constructs. The items are shown in Table A5 and are used in Table A7 in the same order as illustrated in Table A5.

Table A7 Cross-loadings (first-order constructs)

Appendix E: ECM system vendors

Table A8 provides an overview of ECM software vendors.

Table A8 ECM software vendor's overview based on Gartner’s magic ECM quadrant 2013 (Gilbert et al, 2013)

Appendix F: User resistance studies

Workaround is one example of user resistance behavior observed in relation to the usage of enterprise systems such as ECM. There are other forms of user resistance behaviors which have already been discussed by prior research (see Table A9), whereas only few articles have explicitly focused on workarounds. Studies focusing on workarounds as user resistance behavior have used interviews to identify and describe potential ways users can work around an IS. They have not provided and applied an instrument for further empirical analysis in this area.

Table A9 User resistance behavior

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Laumer, S., Maier, C. & Weitzel, T. Information quality, user satisfaction, and the manifestation of workarounds: a qualitative and quantitative study of enterprise content management system users. Eur J Inf Syst 26, 333–360 (2017). https://doi.org/10.1057/s41303-016-0029-7

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Keywords

  • user acceptance
  • information systems success
  • workarounds
  • enterprise content management
  • case study
  • field study