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Encourage autonomy to increase individual work performance: the impact of job characteristics on workaround behavior and shadow IT usage

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Abstract

IT users are increasingly experienced at adapting technologies to their needs; resulting in the widespread use of workarounds and shadow IT. To ascertain the impact of job characteristics on this behavior, a survey was conducted among 415 IT users. The collected data underwent Reliability Analysis and Exploratory Factor Analysis in SPSS software. Subsequently, Confirmatory Factor Analysis and Structural Equation Modeling were conducted with the SmartPLS software. The main results indicate that autonomy is strongly related to workaround behavior and shadow IT usage. Workaround behavior and shadow IT use have also been proven to be strongly related. However, the level of skill variety and task identity do not seem to significantly affect workaround behavior and shadow IT usage. Finally, this study’s findings demonstrate that both workaround behavior and shadow IT use are positively related to individual performance. Organizations are therefore encouraged to increase job autonomy in order to achieve enhanced individual performance by presenting workers with opportunities to adapt technologies in the form of workarounds and shadow IT. The use of such alternative solutions provides for faster and more dynamic communication and thus boosts collaboration among co-workers, external partners, and clients.

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References

  1. Bozan K, Berger A (2018) The effect of unmet expectations of information quality on post-acceptance workarounds among healthcare providers. In: Proceedings of the 51st Hawaii international conference on system sciences

  2. Li Y, Haake P, Mueller B (2017) Explaining the influence of workarounds on effective use—the case of a supply chain management system. In: ECIS

  3. Sillic M (2019) Critical impact of organizational and individual inertia in explaining non-compliant security behavior in the Shadow IT context. Comput Secur 80:108–119

    Article  Google Scholar 

  4. Alter S (2014) Theory of workarounds. Commun Assoc Inf Syst 34:1041–1066

    Google Scholar 

  5. Ejnefjäll T, Ågerfalk PJ (2019) Conceptualizing workarounds: meanings and manifestations in information systems research. Commun Assoc Inf Syst 45(1):20

    Google Scholar 

  6. Boudreau MC, Robey D (2005) Enacting integrated information technology: a human agency perspective. Organ Sci 16(1):3–18. https://doi.org/10.1287/orsc.1040.0103

    Article  Google Scholar 

  7. Gasparas J, Monteiro E (2018) Cross-contextual use of integrated information systems. In: 17th European conference on information systems

  8. Globalscape. Be afraid of your shadow: what is “shadow IT” and how to reduce it, 2016. Disponível em: https://www.globalscape.com/resources/whitepapers/shadow-it-guide. Accessed 5 Mar 2018

  9. Brooks J, Oshri I, Mayasandra-Nagaraja R (2018) Information brokering in globally distributed work: a workarounds perspective. In: ICIS

  10. Weinzierl S, Wolf V, Pauli T, Beverungen D, Matzner M (2020) Detecting workarounds in business processes-a deep learning method for analyzing event logs. In: ECIS

  11. Rentrop C, Zimmermann S (2012) Shadow IT-management and control of unofficial IT. In: Proceedings of the 6th international conference on digital society, pp 98–102

  12. Lund-Jensen R, Azaria C, Permien FH, Sawari J, Bækgaard L (2016) Feral information systems, shadow systems, and workarounds—a drift in IS terminology. Procedia Comput Sci 100:1056–1063. https://doi.org/10.1016/j.procs.2016.09.281

    Article  Google Scholar 

  13. Goodhue DL, Thompson RL (1995) Task-technology fit and individual performance. MIS Q 19:213–236

    Article  Google Scholar 

  14. Malaurent J, Avison D (2015) From an apparent failure to a success story: ERP in China—post implementation. Int J Inf Manag 35(5):643–646. https://doi.org/10.1016/j.i**fomgt.2015.06.004

    Article  Google Scholar 

  15. Keller R, Ollig P, Fridgen G (2019) Decoupling, information technology, and the tradeoff between organizational reliability and organizational agility. In: ECIS

  16. Vaezi R, Mills A, Chin W, Zafar H (2016) User satisfaction research in information systems: historical roots and approaches. CAIS 38:27. https://doi.org/10.17705/1CAIS.03827

    Article  Google Scholar 

  17. Hauff S, Richter NF, Tressin T (2015) Situational job characteristics and job satisfaction: the moderating role of national culture. Int Bus Rev 24(4):710–723

    Article  Google Scholar 

  18. Hackman JR, Oldham GR (1976) Motivation through the design of work: test of a theory. Organ Behav Hum Perform 16(2):250–279

    Article  Google Scholar 

  19. Bhuiyan SR, Setia P (2018) Exploring the influence of job characteristics: a comparison between open source and proprietary is development. In: International research workshop on IT project management

  20. Carpenter D, Young DK, McLeod Michele A (2019) IT career counseling: are occupational congruence and the job characteristics model effective at predicting IT job satisfaction? J Inf Syst Educ 29(4):3

    Google Scholar 

  21. Koppel R, Smith S, Blythe J, Kothari V (2015) Workarounds to computer access in healthcare organizations: you want my password or a dead patient? In: Driving quality in informatics: fulfilling the promise. IOS Press, pp 215–220

  22. KamelBoulos MN, Giustini DM, Wheeler S (2016) Instagram and WhatsApp in health and healthcare: an overview. Future Internet 8(3):37

    Article  Google Scholar 

  23. Debono DS, Greenfield D, Travaglia JF, Long JC, Black D, Johnson J, Braithwaite J (2013) Nurses’ workarounds in acute healthcare settings: a sco** review. BMC Health Serv Res 13(1):1–16

    Article  Google Scholar 

  24. Beerepoot I, Koorn JJ, van de Weerd I, van den Hooff B, Leopold H, Reijers H (2019) Working around health information systems: the role of power. In: ICIS

  25. Berente N, Yoo Y (2012) Institutional contradictions and loose coupling: postimplementation of NASA’s enterprise information system. Inf Syst Res 23(2):376–396

    Article  Google Scholar 

  26. Parker SK, Sprigg CA (1999) Minimizing strain and maximizing learning: the role of job demands, job control, and proactive personality. J Appl Psychol 84(6):925

    Article  Google Scholar 

  27. Liang H, Peng Z, Xue Y, Guo X, Wang N (2015) Employees’ exploration of complex systems: an integrative view. J Manag Inf Syst 32(1):322–357

    Article  Google Scholar 

  28. Shao Z, Huang Q (2018) Transformational leadership and IS extended use—the mediating role of job autonomy and moderating role of IT innovativeness. In: PACIS, p 9

  29. Fries VC, Wiesche M, Krcmar H (2016) The Dualism of workarounds: effects of technology and mental workload on improvement and noncompliant behavior within organizations. In: ICIS

  30. Dulipovici A, Vieru D (2016) BYOD-enabled workarounds: a process perspective. In: Proceedings of the 22nd Americas conference on information systems. Association for Information Systems, San Diego

  31. Haag S, Eckhardt A (2017) Shadow IT. Bus Inf Syst Eng 59:1–5

    Article  Google Scholar 

  32. Herzberg F, Mausner B, Snyderman BB (2011) The motivation to work. Transaction Publishers, Piscataway

    Google Scholar 

  33. Shamir B, Salomon I (1985) Work-at-home and the quality of working life. Acad Manag Rev 10(3):455–464

    Article  Google Scholar 

  34. Petter S, DeLone W, McLean ER (2013) Information systems success: the quest for the independent variables. J Manag Inf Syst 29(4):7–62. https://doi.org/10.2753/MIS0742-1222290401

    Article  Google Scholar 

  35. Ali SAM, Said NA, Kader SFA, Ab Latif DS, Munap R (2014) Hackman and Oldham’s job characteristics model to job satisfaction. Procedia Soc Behav Sci 129:46–52

    Article  Google Scholar 

  36. Ketchain L (2003) Happiness at work (in press)

  37. Igbaria M, Guimaraes T (1993) Antecedents and consequences of job satisfaction among information center employees. J Manag Inf Syst 9(4):145–174

    Article  Google Scholar 

  38. Moore JE (2000) One road to turnover: an examination of work exhaustion in technology professionals. MIS Q 24:141–168

    Article  Google Scholar 

  39. Ahuja MK, Chudoba KM, Kacmar CJ, McKnight DH, George JF (2007) IT road warriors: balancing work-family conflict, job autonomy, and work overload to mitigate turnover intentions. MIS Q 31:1–17

    Article  Google Scholar 

  40. Ang S, Slaughter SA (2001) Work outcomes and job design for contract versus permanent information systems professionals on software development teams. MIS Q 25:321–350

    Article  Google Scholar 

  41. Morris MG, Venkatesh V (2010) Job characteristics and job satisfaction: understanding the role of enterprise resource planning system implementation. MIS Q 34:143–161

    Article  Google Scholar 

  42. Tripp JF, Riemenschneider C, Thatcher JB (2016) Job satisfaction in agile development teams: agile development as work redesign. J Assoc Inf Syst 17(4):267

    Google Scholar 

  43. Liere-Netheler K, Vogelsang K, Hoppe U, Steinhüser M (2017) Towards the user: extending the job characteristics model to measure job satisfaction for ERP based workplaces—a qualitative approach. In: CONF-IRM, p 37

  44. Brooks S, Califf C (2017) Social media-induced technostress: its impact on the job performance of it professionals and the moderating role of job characteristics. Comput Netw 114:143–153

    Article  Google Scholar 

  45. Laumer S, Maier C, Weitzel T (2017) 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(4):333–360. https://doi.org/10.1057/s41303-016-0029-7

    Article  Google Scholar 

  46. Györy AAB, Cleven A, Uebernickel F, Brenner W (2012) Exploring the shadows: IT governance approaches to user-driven innovation. In: 20th European conference on information systems (ECIS). Barcelona, Spain

  47. Carpenter D, Young DK, Maasberg M, McLeod A (2017) Predicting IT job satisfaction: occupational congruence and the job characteristics model. In: AMCIS

  48. Piccolo RF, Colquitt JA (2006) Transformational leadership and job behaviors: the mediating role of core job characteristics. Acad Manag J 49(2):327–340

    Article  Google Scholar 

  49. Coelho F, Augusto M (2010) Job characteristics and the creativity of frontline service employees. J Serv Res 13(4):426–438

    Article  Google Scholar 

  50. Tombu M, Jolicœur P (2003) A central capacity sharing model of dual-task performance. J Exp Psychol Hum Percept Perform 29(1):3

    Article  Google Scholar 

  51. Jenkins JL, Anderson BB, Vance A, Kirwan CB, Eargle D (2016) More harm than good? How messages that interrupt can make us vulnerable. Inf Syst Res 27(4):880–896

    Article  Google Scholar 

  52. Kettenbohrer J, Beimborn D, Eckhardt A (2015) Analyzing the impact of job characteristics on employees' acceptance of process standardization. In: ECIS

  53. Haag S, Eckhardt A (2014) Normalizing the shadows—the role of symbolic models for individuals’ shadow IT usage. In: The proceedings of the thirty-fifth international conference on information systems, Auckland

  54. Klotz S, Kopper A, Westner M, Strahringer S (2019) Causing factors, outcomes, and governance of shadow IT and business-managed IT: a systematic literature review. Int J Inf Syst Proj Manag 7(1):15–43

    Google Scholar 

  55. Mallmann GL, Maçada ACG, Montesdioca GPZ (2019) The social side of shadow IT and its impacts: investigating the relationship with social influence and social presence. In: Hawaii international conference on system sciences (52: Grand Wailea, Hawaii). Proceedings. University of Hawaii at Manoa, Honolulu

  56. Van de Weerd I, Vollers P, Beerepoot I, Fantinato M (2019) Workarounds in retail work systems: prevent, redesign, adopt or ignore?. In: European conference on information systems (ECIS)

  57. Pinto AV, Macada ACG, Mallmann GL (2018) Impacto do Comportamento Workaround e do Uso de Shadow IT no Desempenho Individual. In: 18.ª Conferência da Associação Portuguesa de Sistemas de Informação (CAPSI?2018), 2018, Santarém. A Indústria 4.0 e os Sistemas de Informação

  58. Arduin PE, Vieru D (2017) Workarounds as means to identify insider threats to information systems security. Association for information systems. In: Proceedings of the twenty-third Americas conference on information systems

  59. Baysari MT, Hardie RA, Lake R, Richardson L, McCullagh C, Gardo A, Westbrook J (2018) Longitudinal study of user experiences of a CPOE system in a pediatric hospital. Int J Med Inform 109:5–14

    Article  Google Scholar 

  60. Mallmann G, Maçada AC (2016) Behavioral drivers behind shadow IT and its outcomes in terms of individual performance. In: AMCIS

  61. Silic M, Back A (2014) Shadow IT—a view from behind the curtain. Comput Secur 45:274–283. https://doi.org/10.1016/j.cose.2014.06.007

    Article  Google Scholar 

  62. Hair JF Jr, Black W, Babin B, Anderson R (2010) Multivariate data analysis, 7th edn. Prentice, New Jersey

    Google Scholar 

  63. Hair JF Jr, Hult GTM, Ringle C, Sarstedt M (2016) A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications, London

    Google Scholar 

  64. Kline RB (2015) Principles and practice of structural equation modeling. Guilford Publications, New York

    Google Scholar 

  65. Hair JF Jr, Sarstedt M, Hopkins L, Kuppelwieser VG (2014) Partial least squares structural equation modeling (PLS-SEM): an emerging tool in business research. Eur Bus Rev 26(2):106–121

    Article  Google Scholar 

  66. Hair JF Jr, Black WC, Babin BJ, Anderson RE, Tatham RL (2009) Análise multivariada de dados. Bookman Editora

    Google Scholar 

  67. Koufteros XA (1999) Testing a model of pull production: a paradigm for manufacturing research using structural equation modeling. J Oper Manag 17(4):467–488

    Article  Google Scholar 

  68. Fornell C, Larcker DF (1981) Structural equation models with unobservable variables and measurement error: Algebra and statistics. J Market Res 18:382–388

    Article  Google Scholar 

  69. Hair JF Jr, Sarstedt M, Ringle CM, Gudergan SP (2017) Advanced issues in partial least squares structural equation modeling. SAGE Publications, London

    Google Scholar 

  70. Henseler J, Hubona G, Ray PA (2016) Using PLS path modeling in new technology research: updated guidelines. Ind Manag Data Syst 116(1):2–20

    Article  Google Scholar 

  71. Hair JF, Ringle CM, Sarstedt M (2011) PLS-SEM: indeed a silver bullet. J Mark Theory Pract 19(2):139–152

    Article  Google Scholar 

  72. Cohen J (1988) Statistical power analysis for the behavioral sciences. Psychology Press, New York

    Google Scholar 

  73. Ringle CM, Wende S, Becker JM (2015) SmartPLS 3. SmartPLS GmbH, Boenningstedt. Available at: www.smartpls.de

  74. Rathert C, Williams ES, Lawrence ER, Halbesleben JR (2012) Emotional exhaustion and workarounds in acute care: cross sectional tests of a theoretical framework. Int J Nurs Stud 49(8):969–977

    Article  Google Scholar 

  75. Baskerville R (2011) Individual information systems as a research arena. Eur J Inf Syst 20(3):251

    Article  Google Scholar 

  76. Wolf M, Sims J, Yang H (2019) Social media use in HR management; rule making, rule breaking and workarounds: a sociomaterial view. In: UK academy for information systems conference proceedings

  77. Davison RM, Wong LH, Alter S, Ou CX (2019) Adopted globally but unusable locally: what workarounds reveal about adoption, resistance, compliance and noncompliance. In: 27th European conference on information systems: information systems for a sharing society, ECIS 2019. Association for Information Systems, p 16

  78. Azad B, King N (2012) Institutionalized computer workaround practices in a Mediterranean country: an examination of two organizations. Eur J Inf Syst 21(4):358–372. https://doi.org/10.1057/ejis.2011.48

    Article  Google Scholar 

  79. Reiz A, Gewald H (2016) Physicians' resistance towards information systems in healthcare: the case of workarounds. In: PACIS, p 12

  80. Beerepoot I, Van De Weerd I (2018) Prevent, redesign, adopt or ignore: improving healthcare using knowledge of workarounds. In: 26th European conference on information systems, ECIS 2018

  81. Silic M, Barlow JB, Back A (2017) A new perspective on neutralization and deterrence: predicting shadow IT usage. Inf Manag 54(8):1023–1037

    Article  Google Scholar 

  82. Barker S, Fiedler B (2011) Developers, decision makers, strategists or just end-users? Redefining end-user computing for the 21st century: a case study. J Organ End User Comput 23(2):1–14

    Article  Google Scholar 

  83. Kopper A (2017) Perceptions of IT managers on shadow IT. In: AMCIS

  84. Lunardi GL, Maçada ACG, Becker JL, Van Grembergen W (2017) Antecedents of IT governance effectiveness: an empirical examination in Brazilian firms. J Inf Syst 31(1):41–57

    Google Scholar 

  85. Morgeson FP, Humphrey SE (2006) The Work Design Questionnaire (WDQ): develo** and validating a comprehensive measure for assessing job design and the nature of work. J Appl Psychol 91(6):1321

    Article  Google Scholar 

  86. Hackman JR, Lawler EE (1971) Employee reactions to job characteristics. J Appl Psychol 55(3):259

    Article  Google Scholar 

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Funding

This study was financed in part by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES) e do Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

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Correspondence to Aline de Vargas Pinto.

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de Vargas Pinto, A., Beerepoot, I. & Maçada, A.C.G. Encourage autonomy to increase individual work performance: the impact of job characteristics on workaround behavior and shadow IT usage. Inf Technol Manag 24, 233–246 (2023). https://doi.org/10.1007/s10799-022-00368-6

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