IDEAS home Printed from https://ideas.repec.org/a/eee/ijoais/v17y2015icp16-36.html
   My bibliography  Save this article

The effects of decision aid structural restrictiveness on cognitive load, perceived usefulness, and reuse intentions

Author

Listed:
  • Mălăescu, Irina
  • Sutton, Steve G.

Abstract

As accounting environments become increasingly automated through information technology support systems, the underlying systems are increasingly restrictive in an effort to direct user behavior and decision making. However, consistent with the theory of technology dominance, restrictive systems have been found to dominate users' decision processes and to have a detrimental effect when decisions require knowledge from outside the system's capability. This study expands upon this research through an examination of users' preferences for more (less) restrictive systems based on their own level of domain knowledge. Incorporating theory on task technology fit, we theorize that users with less knowledge will prefer to be dominated by the system, while users with greater levels of knowledge will prefer a system that provides the user with a level of control over the decision process rather than submitting entirely to the decision aid's control. These theorizations are empirically tested through an experimental design that varies the level of systems restrictiveness across groups of novice and experienced participants. The results confirm that novice (experienced) participants find a highly restrictive system substantially (minimally) reduces cognitive load, increases (decreases) usefulness of the decision aid, and strengthens (weakens) the intention to reuse the system in the future. The results add an important piece to understanding the effect of restrictive systems in that the users that are most susceptible to dominance by decision aids are the users most willing to adopt a restrictive system that reduces the effort they must put forth and in turn reduces the knowledge they accrue from using the system.

Suggested Citation

  • Mălăescu, Irina & Sutton, Steve G., 2015. "The effects of decision aid structural restrictiveness on cognitive load, perceived usefulness, and reuse intentions," International Journal of Accounting Information Systems, Elsevier, vol. 17(C), pages 16-36.
    • Handle: RePEc:eee:ijoais:v:17:y:2015:i:c:p:16-36
    DOI: 10.1016/j.accinf.2014.02.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1467089514000165
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.accinf.2014.02.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vicky Arnold & Philip A. Collier & Stewart A. Leech & Steve G. Sutton, 2004. "Impact of intelligent decision aids on expert and novice decision‐makers’ judgments," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 44(1), pages 1-26, March.
    2. Seow, Poh-Sun, 2011. "The effects of decision aid structural restrictiveness on decision-making outcomes," International Journal of Accounting Information Systems, Elsevier, vol. 12(1), pages 40-56.
    3. Viswanath Venkatesh, 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, INFORMS, vol. 11(4), pages 342-365, December.
    4. Kieran Mathieson, 1991. "Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior," Information Systems Research, INFORMS, vol. 2(3), pages 173-191, September.
    5. Curtis, Mary B. & Payne, Elizabeth A., 2008. "An examination of contextual factors and individual characteristics affecting technology implementation decisions in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 9(2), pages 104-121.
    6. Stephen K. Asare & Arnold M. Wright, 2004. "The Effectiveness of Alternative Risk Assessment and Program Planning Tools in a Fraud Setting," Contemporary Accounting Research, John Wiley & Sons, vol. 21(2), pages 325-352, June.
    7. James Hunton & Vicky Arnold & Jacqueline L. Reck, 2010. "Retracted: Decision Aid Reliance: A Longitudinal Field Study Involving Professional Buy†Side Financial Analysts," Contemporary Accounting Research, John Wiley & Sons, vol. 27(4), pages 997-1023, December.
    8. Goodwin, Paul & Fildes, Robert & Lawrence, Michael & Stephens, Greg, 2011. "Restrictiveness and guidance in support systems," Omega, Elsevier, vol. 39(3), pages 242-253, June.
    9. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    10. Griffin, Dale W. & Varey, Carol A., 1996. "Towards a Consensus on Overconfidence," Organizational Behavior and Human Decision Processes, Elsevier, vol. 65(3), pages 227-231, March.
    11. Mark S. Silver, 1990. "Decision Support Systems: Directed and Nondirected Change," Information Systems Research, INFORMS, vol. 1(1), pages 47-70, March.
    12. Bernadette Szajna, 1996. "Empirical Evaluation of the Revised Technology Acceptance Model," Management Science, INFORMS, vol. 42(1), pages 85-92, January.
    13. Rose, Jacob M. & Wolfe, Christopher J., 2000. "The effects of system design alternatives on the acquisition of tax knowledge from a computerized tax decision aid," Accounting, Organizations and Society, Elsevier, vol. 25(3), pages 285-306, April.
    14. Gonzalez, George C. & Sharma, Pratyush N. & Galletta, Dennis F., 2012. "The antecedents of the use of continuous auditing in the internal auditing context," International Journal of Accounting Information Systems, Elsevier, vol. 13(3), pages 248-262.
    15. Andreas I. Nicolaou & D. Harrison McKnight, 2006. "Perceived Information Quality in Data Exchanges: Effects on Risk, Trust, and Intention to Use," Information Systems Research, INFORMS, vol. 17(4), pages 332-351, December.
    16. Kim, Hyo-Jeong & Mannino, Michael & Nieschwietz, Robert J., 2009. "Information technology acceptance in the internal audit profession: Impact of technology features and complexity," International Journal of Accounting Information Systems, Elsevier, vol. 10(4), pages 214-228.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shin, Won & Han, Jeongyun & Rhee, Wonjong, 2021. "AI-assistance for predictive maintenance of renewable energy systems," Energy, Elsevier, vol. 221(C).
    2. Desai, Vikram & Bucaro, Anthony C. & Kim, Joung W. & Srivastava, Rajendra & Desai, Renu, 2023. "Toward a better expert system for auditor going concern opinions using Bayesian network inflation factors," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    3. Arnold, Vicky & Collier, Philip A. & Leech, Stewart A. & Rose, Jacob M. & Sutton, Steve G., 2023. "Can knowledge based systems be designed to counteract deskilling effects?," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).
    4. Alewine, Hank C. & Allport, Christopher D. & Shen, Wei-Cheng Milton, 2016. "How measurement framing and accounting information system evaluation mode influence environmental performance judgments," International Journal of Accounting Information Systems, Elsevier, vol. 23(C), pages 28-44.
    5. Sutton, Steve G. & Arnold, Vicky & Holt, Matthew, 2023. "An extension of the theory of technology dominance: Capturing the underlying causal complexity," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmad Almagrashi & Abdulwahab Mujalli & Tehmina Khan & Osama Attia, 2023. "Factors determining internal auditors’ behavioral intention to use computer-assisted auditing techniques: an extension of the UTAUT model and an empirical study," Future Business Journal, Springer, vol. 9(1), pages 1-19, December.
    2. Agarwal, Reeti & Rastogi, Sanjay & Mehrotra, Ankit, 2009. "Customers’ perspectives regarding e-banking in an emerging economy," Journal of Retailing and Consumer Services, Elsevier, vol. 16(5), pages 340-351.
    3. Nan Zhang & Xunhua Guo & Guoqing Chen, 2011. "Why adoption and use behavior of IT/IS cannot last?—two studies in China," Information Systems Frontiers, Springer, vol. 13(3), pages 381-395, July.
    4. Nedra, Bahri-Ammari & Hadhri, Walid & Mezrani, Mariem, 2019. "Determinants of customers' intentions to use hedonic networks: The case of Instagram," Journal of Retailing and Consumer Services, Elsevier, vol. 46(C), pages 21-32.
    5. Arnold, Vicky & Collier, Philip A. & Leech, Stewart A. & Rose, Jacob M. & Sutton, Steve G., 2023. "Can knowledge based systems be designed to counteract deskilling effects?," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).
    6. Escobar-Rodríguez, Tomás & Bartual-Sopena, Lourdes, 2015. "Impact of cultural factors on attitude toward using ERP systems in public hospitals," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 18(2), pages 127-137.
    7. Nistor, Cristian, 2013. "A conceptual model for the use of social media in companies," MPRA Paper 44224, University Library of Munich, Germany.
    8. Viswanath Venkatesh, 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, INFORMS, vol. 11(4), pages 342-365, December.
    9. Gao, Tao (Tony) & Rohm, Andrew J. & Sultan, Fareena & Pagani, Margherita, 2013. "Consumers un-tethered: A three-market empirical study of consumers' mobile marketing acceptance," Journal of Business Research, Elsevier, vol. 66(12), pages 2536-2544.
    10. Johannes Putzke & Detlef Schoder & Kai Fischbach, 2010. "Adoption of Mass-Customized Newspapers: An Augmented Technology Acceptance Perspective," Journal of Media Economics, Taylor & Francis Journals, vol. 23(3), pages 143-164.
    11. Yoon, Jeewhan & Vonortas, Nicholas S. & Han, SungWon, 2020. "Do-It-Yourself laboratories and attitude toward use: The effects of self-efficacy and the perception of security and privacy," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    12. Irfan Bashir & C. Madhavaiah, 2014. "Determinants of Young Consumers’ Intention to Use Internet Banking Services in India," Vision, , vol. 18(3), pages 153-163, September.
    13. Nripendra P. Rana & Yogesh K. Dwivedi & Banita Lal & Michael D. Williams & Marc Clement, 2017. "Citizens’ adoption of an electronic government system: towards a unified view," Information Systems Frontiers, Springer, vol. 19(3), pages 549-568, June.
    14. Wong Lai Soon & Bobby Chai Boon Hui & Wong Kee Luen, 2013. "Joining the New Band: Factors Triggering the Intentions of Malaysian College and University Students to Adopt 4G Broadband," Information Management and Business Review, AMH International, vol. 5(2), pages 58-65.
    15. Kathrin Dudenhöffer, 2013. "Why electric vehicles failed," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 24(2), pages 95-124, July.
    16. Hasnan Baber & N M Baki Billah, 2022. "Fintech and Islamic Banks - an integrative model approach to predict the intentions," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 24(2), pages 24-45, December.
    17. Gartner, Johannes & Fink, Matthias & Maresch, Daniela, 2022. "The Role of Fear of Missing Out and Experience in the Formation of SME Decision Makers’ Intentions to Adopt New Manufacturing Technologies," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    18. Naresh K. Malhotra & Sung S. Kim & Ashutosh Patil, 2006. "Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research," Management Science, INFORMS, vol. 52(12), pages 1865-1883, December.
    19. Hsiao, Chun Hua & Yang, Chyan, 2011. "The intellectual development of the technology acceptance model: A co-citation analysis," International Journal of Information Management, Elsevier, vol. 31(2), pages 128-136.
    20. Hamed Taherdoost, 2019. "Importance of Technology Acceptance Assessment for Successful Implementation and Development of New Technologies," Post-Print hal-02557395, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ijoais:v:17:y:2015:i:c:p:16-36. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-accounting-information-systems/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.