Skip to main content Skip to main navigation menu Skip to site footer
Articles
DOI: 10.14254/1795-6889.2022.18-2.4
Submitted: 2022-06-30
Published: 2022-10-03

Analysis of the Conditions Influencing the Assimilation of the Robotic Process Automation by Enterprises

Collegium of Economic Analysis, Warsaw School of Economics
Robotic Process Automation TOE model Assimilation of innovative IT

Abstract

More and more companies are implementing the RPA (Robotic Process Automation) tools that belong to the newly emerging category of IT solutions used to automate business processes and enable the development of the so-called software robots. The term robot has a metaphorical meaning here – it is a special kind of software, not a device. Due to the fact that this is a new product category and many companies do not have extensive experience in this area yet, the use of the RPA tools is associated with many risks. At the same time, due to the increasingly competitive environment, it seems that there is no turning back from the implementation thereof. For this reason, two goals have been set in the article. The first is to build and verify a research model based on the TOE model (Technology-Organization-Environment), allowing for the identification of the determinants (drivers) influencing the assimilation of the robotic process automation by enterprises. The second goal is to develop recommendations for the managers responsible for implementing the RPA tools that will allow for increasing the assimilation of the robotic process automation. The following methods were used to accomplish these goals: literature research, a survey (conducted on 267 Polish enterprises) and statistical analysis (with the use of the structural equation models).

Metrics

Metrics Loading ...

References

  1. Agarwal, N., & Brem, A. (2015). Strategic business transformation through technology convergence: implications from General Electric's industrial internet initiative. International Journal of Technology Management, 67(2-4), pp. 196-214. DOI: https://doi.org/10.1504/IJTM.2015.068224
  2. Agostinelli, S., Marrella, A., & Mecella, M. (2019). Research Challenges for Intelligent Robotic Process Automation. Lecture Notes in Business Information Processing (pp. 12-18). Berlin: Springer. DOI: https://doi.org/10.1007/978-3-030-37453-2_2
  3. Aguirre, S., & Rodriguez, A. (2017). Automation of a Business Process Using Robotic Process Automation (RPA): A Case Study. Applied Computer Sciences in Engineering (pp. 65-71). Berlin: Springer. doi:10.1007/978-3-319-66963-2_7 DOI: https://doi.org/10.1007/978-3-319-66963-2_7
  4. Ali, O., Shrestha, A., Osmanaj, V., & Muhammed, S. (2020). Cloud computing technology adoption: an evaluation of key factors in local governments. Information Technology & People, 34(2), pp. 666-703. doi:10.1108/ITP-03-2019-0119 DOI: https://doi.org/10.1108/ITP-03-2019-0119
  5. Alshamaila, Y., Papagiannidis, S., & Li, F. (2013). Cloud Computing Adoption by SMEs in the Norh East of England: A multi-perspective framework. Journal of Enterprise Information Management, 26(3), pp. 250–275. doi:10.1108/17410391311325225 DOI: https://doi.org/10.1108/17410391311325225
  6. Amelina, D., Hidayanto, A. N., Budi, N. F., Sandhyaduhita, P. I., & Shihab, R. (2016). Investigating critical factors of social CRM adoption using technology, organization, and environment (TOE) framework and analytical hierarchy process (AHP). 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), (pp. 233-238). Piscataway, USA. doi:10.1109/ICACSIS.2016.7872745 DOI: https://doi.org/10.1109/ICACSIS.2016.7872745
  7. Anagnoste, S. (2018). Robotic Automation Process – The operating system for the digital enterprise. Proceedings of the 12th International Conference on Business Excellence (pp. 54–69). Warszawa, Poland: Sciendo. doi:10.2478/picbe-2018-0007 DOI: https://doi.org/10.2478/picbe-2018-0007
  8. Anagnoste, S. (2018). Setting Up a Robotic Process Automation Center of Excellence. Management Dynamics in the Knowledge Economy, 6(2), pp. 307-322. DOI: https://doi.org/10.25019/MDKE/6.2.07
  9. Asatiani, A., & Penttinen, E. (2016, 11 1). Turning Robotic Process Automation into Commercial Success — Case OpusCapita. Journal of Information Technology Teaching Cases, pp. 67-74. doi:10.1057/jittc.2016.5 DOI: https://doi.org/10.1057/jittc.2016.5
  10. Asiaei, A., & Rahim, N. Z. (2019). A multifaceted framework for adoption of cloud computing in Malaysian SMEs. Journal of Science and Technology Policy Management, 10(3), pp. 708–750. doi:10.1108/JSTPM-05-2018-0053 DOI: https://doi.org/10.1108/JSTPM-05-2018-0053
  11. Bican, P. M., & Brem, A. (2020). Digital Business Model, Digital Transformation, Digital Entrepreneurship: Is There A Sustainable “Digital”? Sustainability, 12(13). doi:10.3390/su12135239 DOI: https://doi.org/10.3390/su12135239
  12. Boritz, J., Efendi, J., & Lim, J.-H. (2018). The Impact of Senior Management Competencies on the Voluntary Adoption of an Innovative Technology. Journal of Information System, 32(2), pp. 25-46. DOI: https://doi.org/10.2308/isys-51685
  13. Cassel, C., Hackl, P., & Westlund, A. (1999). Robustness of partial least-squares method for estimating latent variable quality structures. Journal of Applied Statistics, pp. 435-446. doi:10.1080/02664769922322 DOI: https://doi.org/10.1080/02664769922322
  14. Chatzoglou, P., & Chatzoudes, D. (2016). Factors affecting e-business adoption in SMEs: an empirical research. Journal of Enterprise Information Management, 29(3), pp. 327-358. doi:10.1108/JEIM-03-2014-0033 DOI: https://doi.org/10.1108/JEIM-03-2014-0033
  15. Chin, W. (1998). The partial least squares approach to structural equation modeling. In G. Marcoulides (Ed.), Modern methods for business research (pp. 295-358). Mahwah, USA: Lawrence Erlbaum.
  16. Chin, W., & Newsted, P. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. Hoyle (Ed.), Statistical strategies for small sample research (pp. 307-341). Thousand Oaks, USA: Sage Publications.
  17. Chong, J., & Olesen, K. (2017). A Technology-Organization-Environment Perspective on Eco-effectiveness: A Meta-analysis. Australasian Journal of Information Systems, 21. doi:10.3127/ajis.v21i0.1441 DOI: https://doi.org/10.3127/ajis.v21i0.1441
  18. Chuttur, M. (2009). Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. Sprouts: Working Papers on Information Systems, 9(37). Retrieved from https://aisel.aisnet.org/sprouts_all/290
  19. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Mahwah, USA: Lawrence Erlbaum.
  20. Cooper, V., & Molla, A. (2014). Absorptive Capacity and Contextual Factors that Influence Green IT Assimilation. Australasian Journal of Information Systems, 18(3), pp. 271-288. doi:10.3127/ajis.v18i3.1099 DOI: https://doi.org/10.3127/ajis.v18i3.1099
  21. Cruz-Jesus, F., Pinheiro, A., & Oliveira, T. (2019). Understanding CRM adoption stages: empirical analysis building on the TOE framework. Computers in Industry, 109, pp. 1-13. doi:10.1016/j.compind.2019.03.007 DOI: https://doi.org/10.1016/j.compind.2019.03.007
  22. DeLone, W., & McLean, E. (2002). Information Systems Success Revisited. Proceedings of the 35th Hawaii International Conference on System Sciences (HICSS 02), (pp. 238-249). Big Island, Hawaii, USA. DOI: https://doi.org/10.1109/HICSS.2002.994345
  23. DeLone, W., & McLean, E. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), pp. 9-30. doi:10.1080/07421222.2003.11045748 DOI: https://doi.org/10.1080/07421222.2003.11045748
  24. Dijkstra, T. K., & Henseler, J. (2015). Consistent and Asymptotically Normal PLS Estimators for Linear Structural Equations. Computational Statistics & Data Analysis, 81(1), pp. 10-23. DOI: https://doi.org/10.1016/j.csda.2014.07.008
  25. Eikebrokk, T., & Olsen, D. (2020). Robotic Process Automation and Consequences for Knowledge Workers; a Mixed-Method Study. In M. Hattingh, M. Matthee, H. Smuts, I. Pappas, Y. Dwivedi, & M. Mäntymäki (Eds.), Responsible Design, Implementation and Use of Information and Communication Technology. doi:10.1007/978-3-030-44999-5_10 DOI: https://doi.org/10.1007/978-3-030-44999-5_10
  26. Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2014). Online Purchasing Tickets for Low Cost Carriers: An Application of the Unified Theory of Acceptance and Use of Technology (UTAUT) Model. Tourism Management(43), pp. 70–88. DOI: https://doi.org/10.1016/j.tourman.2014.01.017
  27. Fernandez, D. i Aman, A. (2018). Impacts of Robotic Process Automation on Global Accounting Services. Asian Journal of Accounting and Governance(9). doi:10.17576/AJAG-2018-09-11 DOI: https://doi.org/10.17576/AJAG-2018-09-11
  28. Fillis, I., Johansson, U., & Wagner, B. (2004). A qualitative investigation of smaller firm e-business development. Journal of Small Business and Enterprise Development, 11, pp. 349-361. doi:10.1108/14626000410551609 DOI: https://doi.org/10.1108/14626000410551609
  29. Fornell, C. (1987). A Second Generation of Multivariate Analysis: Classification of Methods and Implications for Marketing Research. In M. Houston (Ed.), Review of Marketing (pp. 407-450). Chicago, USA: American Marketing Association.
  30. Foroudi, P., Gupta, S., Sivarajah, U., & Broderick, A. (2018). Investigating the Effects of Smart Technology on Customer Dynamics and Customer Experience. Computers in Human Behavior, 80, pp. 271-282. doi:10.1016/j.chb.2017.11.014 DOI: https://doi.org/10.1016/j.chb.2017.11.014
  31. Geisser, S. (1974). A predictive approach to the random effects model. Biometrika, 61(1), pp. 101-107. doi:10.2307/2334290 DOI: https://doi.org/10.1093/biomet/61.1.101
  32. Gölpek, F. (2015). Service Sector and Technological Developments. Procedia - Social and Behavioral Sciences, 181, pp. 125-130. doi:10.1016/j.sbspro.2015.04.873 DOI: https://doi.org/10.1016/j.sbspro.2015.04.873
  33. Grandon, E., & Pearson, J. (2004). Electronic commerce adoption: an empirical study of small and medium US businesses. Communications of the Association for Information System, 42(1), pp. 197-216. DOI: https://doi.org/10.1016/j.im.2003.12.010
  34. Hair, J., Hult, G., Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles, USA: Sage Publications.
  35. Hair, J., Ringle, C., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19, pp. 139-151. DOI: https://doi.org/10.2753/MTP1069-6679190202
  36. Hallikainen, P., Bekkhus, R. i Pan, S. (2018). How OpusCapita Used Internal RPA Capabilities to Offer Services to Clients. MIS Quarterly Executive, 17(1), strony 41–52.
  37. Henderson, J., & Venkatraman, N. (1993). Strategic Alignment: Leveraging Information Technology for Transforming Organizations. IBM Systems Journal, 32(1), pp. 472 - 484. doi:10.1147/sj.382.0472 DOI: https://doi.org/10.1147/sj.382.0472
  38. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. New Challenges to International Marketing, 20. doi:10.1108/S1474-7979(2009)0000020014 DOI: https://doi.org/10.1108/S1474-7979(2009)0000020014
  39. Hiran, K., & Henten, A. (2020). An integrated TOE–DoI framework for cloud computing adoption in the higher education sector: case study of Sub-Saharan Africa, Ethiopia. International Journal of System Assurance Engineering and Management, 11, pp. 441–449. doi:10.1007/s13198-019-00872-z DOI: https://doi.org/10.1007/s13198-019-00872-z
  40. Höck, M., & Ringle, C. (2010). Local strategic networks in the software industry: An empirical analysis of the value continuum. International Journal of Knowledge Management Studies, 4(2), pp. 132-151. doi:10.1504/IJKMS.2010.030789 DOI: https://doi.org/10.1504/IJKMS.2010.030789
  41. Hsing Wu, C., Kao, S., & Lin, H. (2013). Acceptance of enterprise blog for service industry. Internet Research, 23(3), pp. 260-297. doi:10.1108/10662241311331736 DOI: https://doi.org/10.1108/10662241311331736
  42. Hui, B., & Wold, H. (1982). Consistency and consistency at large of Partial Least Squares estimates. Amsterdam. Netherlands: North-Holland Publ.
  43. Hwang, B.-N., Huang, C.-Y., & Wu, C.-H. (2016). A TOE Approach to Establish a Green Supply Chain Adoption Decision Model in the Semiconductor Industry. Sustainability, 8(2). doi:10.3390/su8020168 DOI: https://doi.org/10.3390/su8020168
  44. Ivanov, S. (2017). Robonomics – Principles, Benefits, Challenges, Solutions. Yearbook of Varna University of Management, 10, strony 283-293.
  45. Janz, B., Pitts, M., & Otondo, R. (2005). Information Systems and Health Care-II: Back to the Future with RFID: Lessons Learned - Some Old, Some New. Communications of the Association for Information Systems, 15(1), p. 7. doi:10.17705/1CAIS.01507 DOI: https://doi.org/10.17705/1CAIS.01507
  46. Kang, D., & Kim, S. (2015). Process of big data analysis adoption: Defining big data as a new IS innovation and examining factors affecting the process. 48th Hawaii international conference on system sciences, (pp. 4792-4801). Kauai, USA. doi:10.1109/HICSS.2015.569 DOI: https://doi.org/10.1109/HICSS.2015.569
  47. Kedziora, D., & Kiviranta, H. M. (2018). Digital Business Value Creation with Robotic Process Automation (RPA) in Northern and Central Europe. Management, 13(2), pp. 161-174. doi:10.26493/1854-4231.13.161-174 DOI: https://doi.org/10.26493/1854-4231.13.161-174
  48. Kedziora, D., & Penttinen, E. (2020). Governance models for robotic process automation: The case of Nordea Bank. The Journal of Information Technology Teaching Cases, 11(1). doi:10.1177/2043886920937022 DOI: https://doi.org/10.1177/2043886920937022
  49. Kedziora, D., Leivonen, A., Piotrowicz, W., & Öörni, A. (2021). Robotic Process Automation (RPA) Implementation Drivers: Evidence of Selected Nordic Companies. Issues in Information Systems, 22(2), pp. 21-40. doi:10.48009/2_iis_2021_21-40 DOI: https://doi.org/10.48009/2_iis_2021_21-40
  50. Lacity, M. i Willcocks, L. (2017). Robotic Process Automation and Risk Mitigation: The Definitive Guide. Stratford-upon-Avon, UK: Steve Brookes Publishing.
  51. Lacity, M., & Willcocks, L. (2018). Robotic Process and Cognitive Automation: The Next Phase. Stratford-upon-Avon, UK: Steve Brookes Publishing.
  52. Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation. The International Journal of Logistics Management, 29(2), pp. 676-703. doi:10.1108/IJLM-06-2017-0153 DOI: https://doi.org/10.1108/IJLM-06-2017-0153
  53. Maroufkhani, P., Tseng, M.-L., Iranmanesh, M., & Khairuzzaman, W. W. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International Journal of Information Management, 54(102190). doi:10.1016/j.ijinfomgt.2020.102190 DOI: https://doi.org/10.1016/j.ijinfomgt.2020.102190
  54. Mason, C. H., & Perreault, W. D. (1991). Collinearity, Power, and Interpretation of Multiple Regression Analysis. Journal of Marketing Research, 28(3), pp. 268-280. doi:10.1177/002224379102800302 DOI: https://doi.org/10.1177/002224379102800302
  55. Mooi, E. A., & Sarstedt, M. (2011). A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics. Berlin, Heidelberg, Germany: Springer. doi:10.1007/978-3-642-12541-6 DOI: https://doi.org/10.1007/978-3-642-12541-6
  56. Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology–Organisation–Environment (TOE) Framework. 12(2). doi:10.3390/buildings12020090 DOI: https://doi.org/10.3390/buildings12020090
  57. Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017). Digital innovation management: reinventing innovation management research in a digital world. MIS Quarterly, 41(1), pp. 223-238. doi:10.25300/MISQ/2017/41:1.03 DOI: https://doi.org/10.25300/MISQ/2017/41:1.03
  58. Nauwerck, G., & Cajander, A. (2019). Automatic for the People: Implementing Robotic Process Automation in Social Work. Proceedings of the 17th European Conference on Computer-Supported Cooperative Work: The International Venue on Practice-centred Computing and the Design of Cooperation. doi:10.18420/ecscw2019_p04
  59. Nkhoma, M., & Dang, D. (2013). Contributing factors of cloud computing adoption: a technology-organisation-environment framework approach. International Journal of Information Systems and Engineering, 1(1), pp. 38-49. DOI: https://doi.org/10.24924/ijise/2013.04/v1.iss1/30.41
  60. Nugroho, M., Susilo, A., Fajar, M., & Rahmawati, D. (2017). Exploratory study of SMEs technology adoption readiness factors. Procedia Computer Science, 124, pp. 329-336. doi:10.1016/j.procs.2017.12.162 DOI: https://doi.org/10.1016/j.procs.2017.12.162
  61. Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), pp. 497-510. doi:10.1016/j.im.2014.03.006 DOI: https://doi.org/10.1016/j.im.2014.03.006
  62. Osterwalder, A., & Pigneur, Y. (2010). Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. Hoboken, USA: Wiley.
  63. Osterwalder, A., Pigneur, Y., & Tucci, C. (2005). Clarifying Business Models: Origins, Present, and Future of the Concept. Communications of the Association for Information Systems, 16, pp. 1-25. doi:10.17705/1CAIS.01601 DOI: https://doi.org/10.17705/1CAIS.01601
  64. Pan, M.-J., & Jang, W.-Y. (2008). Determinants of the Adoption of Enterprise Resource Planning within the Technology-Organization-Environment Framework: Taiwan's Communications Industry. Journal of Computer Information Systems, 48(3), pp. 94-102. doi:10.1080/08874417.2008.11646025
  65. Park, J.-H., Kim, M.-K., & Paik, J.-H. (2015). The Factors of Technology, Organization and Environment Influencing the Adoption and Usage of Big Data in Korean Firms. 26th European Regional ITS Conference. Madrid, Spain: International Telecommunications Society. Retrieved from https://econpapers.repec.org/RePEc:zbw:itse15:127173
  66. Pateli, A., & Giaglis, G. (2005). Technology innovation-induced business model change: A contingency approach. Journal of Organizational Change Management, 18, pp. 167-183. doi:10.1108/09534810510589589 DOI: https://doi.org/10.1108/09534810510589589
  67. Pool, J. A., Asadi, M. S., & Ansari, M. A. (2015). RFID acceptance in SMEs using TOE framework: an empirical investigation on Iranian SMEs. International Journal of Logistics Systems and Management, 21(3), pp. 335-347. doi:10.1504/IJLSM.2015.069731 DOI: https://doi.org/10.1504/IJLSM.2015.069731
  68. Premkumar, G., & Roberts, M. (1999). Adoption of new information technologies in rural small businesses. Omega, 27(4), pp. 467-484. doi:10.1016/S0305-0483(98)00071-1 DOI: https://doi.org/10.1016/S0305-0483(98)00071-1
  69. Puklavec, B., Oliveira, T., & Popovič, A. (2017). Understanding the determinants of business intelligence system adoption stages: An empirical study of SMEs. Industrial Management & Data Systems, 118(1), pp. 236-261. doi:10.1108/IMDS-05-2017-0170 DOI: https://doi.org/10.1108/IMDS-05-2017-0170
  70. Reinartz, W., Haenlein, M., & Henseler, J. (2009). An Empirical Comparison of the Efficacy of Covariance-Based and Variance-Based SEM. International Journal of Research in Marketing, 26(4), pp. 332-344. doi:10.1016/j.ijresmar.2009.08.001 DOI: https://doi.org/10.1016/j.ijresmar.2009.08.001
  71. Riemenschneider, C., & McKinney, V. (2002). Exploring Beliefs Regarding e-Commerce: What Do Small Business Executives Think? Decision Sciences, 33(4), pp. 9-11.
  72. Rifkin, J. (1995). The End of Work: The Decline of the Global Labor Force and the Dawn of the Post-Market Era. New Youk, USA: G.P. Putnam's Sons.
  73. Ringle, C. G., & Wilson, B. (2009). On the use of formative measurement specifications in structural equation modeling: A Monte Carlo simulation study to compare covariance-based and partial least squares model estimation methodologies. Maastricht, The Netherlands: Maastricht University. doi:10.26481/umamet.2009014 DOI: https://doi.org/10.2139/ssrn.2394054
  74. Rogers, E. (1995). Lessons for guidelines from the diffusion of innovations. The Joint Commission Journal on Quality Improvement(21), pp. 324–328. doi:10.1016/s1070-3241(16)30155-9 DOI: https://doi.org/10.1016/S1070-3241(16)30155-9
  75. Rouhani, S., Ravasan, A., & Ashrafi, A. A. (2018). Business Intelligence Systems Adoption Model: An Empirical Investigation. Journal of Organizational and End User Computing, 30(2), pp. 43-70. doi:10.4018/JOEUC.2018040103 DOI: https://doi.org/10.4018/JOEUC.2018040103
  76. Shaw, R. D., Ch., H. P., Kawalek, P., Snowdon, B., & Warboys, B. (2007). Elements of a business process management system: theory and practice. Business Process Management Journal, 13(1), pp. 91-107. doi:10.1108/14637150710721140 DOI: https://doi.org/10.1108/14637150710721140
  77. Smith, T. W., & Kim, J. (2015). A Review of Survey Data-Collection Modes: With a Focus on Computerizations. Sociological Theory and Method, 30(2), pp. 185-200.
  78. Soares-Aguiar, A., & Palma-Dos-Reis, A. (2008). Why do firms adopt e-procurement systems? Using logistic regression to empirically test a conceptual model. IEEE Transactions on Engineering Management, 55(1), pp. 120-133. doi:10.1109/TEM.2007.912806 DOI: https://doi.org/10.1109/TEM.2007.912806
  79. Sobczak, A. (2021). Robotic Process Automation implementation, deployment approaches and success factors – an empirical study. Entrepreneurship and Sustainability Issues, 8(4), pp. 122-147. doi:10.9770/jesi.2021.8.4(7) DOI: https://doi.org/10.9770/jesi.2021.8.4(7)
  80. Spector, P. E. (1992). Summated Rating Scale Construction: An Introduction. Newbury Park, USA: Sage Publications. doi:10.4135/9781412986038 DOI: https://doi.org/10.4135/9781412986038
  81. Stjepić, A.-M., Ivančić, L., & Vugec, D. S. (2020). Mastering Digital Transformation Through Business Process Management: Investigating Alignments, Goals, Orchestration, and Roles. Journal of Entrepreneurship, Management and Innovation, 16(1), pp. 41-74. doi:10.7341/20201612 DOI: https://doi.org/10.7341/20201612
  82. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36, pp. 111-147. DOI: https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
  83. Sztemberg-Lewandowska, M. (2008). Analiza czynnikowa w badaniach marketingowych. Wrocław, Poland: Uniwersytet Ekonomiczny we Wrocławiu.
  84. Takayama, L. (2009). Toward a Science of Robotics: Goals and Standards for Experimental Research. Proceedings of Workshop on Good Experimental Methodology in Robotics. Seattle: RSS 2009.
  85. This, L. S., & Eam, L. (2011). Estimating the determinants of B2B E-commerce adoption among small & medium enterprises. International Journal of Business and Society, 12, pp. 15-30.
  86. Thurstone, L. (1931). The Measurement of Social Attitudes. The Journal of Abnormal and Social Psychology, 26, strony 249-269. doi:10.1037/h0070363 DOI: https://doi.org/10.1037/h0070363
  87. Tornatzky, L. G., & Fleischer, M. (1990). The Processes of Technological Innovation. Lexington, USA: Lexington Books.
  88. Valdebenito, J., & Quelopana, A. (2019). Conceptual Model for Software as a Service (SaaS) Enterprise Resource Planning (ERP) Systems Adoption in Small and Medium Sized Enterprises (SMEs) Using the Technology-Organization-Environment (T-O-E) Framework. Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing, 918, pp. 143–152. doi:https://doi.org/10.1007/978-3-030-11890-7_15 DOI: https://doi.org/10.1007/978-3-030-11890-7_15
  89. Van Looy, A. (2017). A Quantitative Study of the Link Between Business Process Management and Digital Innovation. In J. Carmona, G. Engels, & A. Kumar (Eds.), Business Process Management Forum. BPM 2017. Lecture Notes in Business Information Processing, (pp. 177-192). Springer, Cham. doi:10.1007/978-3-319-65015-9_11 DOI: https://doi.org/10.1007/978-3-319-65015-9_11
  90. Veiga, J., Floyd, S., & Dechant, K. (2001). Towards Modelling the Effects of National Culture on it Implementation and Acceptance. Journal of Information Technology, 16(3), pp. 145–158. doi:10.1080/02683960110063654 DOI: https://doi.org/10.1080/02683960110063654
  91. Venkatesh, V., & Davis, F. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), pp. 186–204. DOI: https://doi.org/10.1287/mnsc.46.2.186.11926
  92. Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), pp. 157–178. DOI: https://doi.org/10.2307/41410412
  93. Venkatraman, N. (1994). IT-Enabled Business Transformation: From Automation to Business Scope Redefinition. Sloan Management Review, 35, pp. 73-87.
  94. Verma, S., & Bhattacharyya, S. (2017). Perceived strategic value-based adoption of big data analytics in emerging economy: A qualitative approach for Indian firms. Journal of Enterprise Information Management, 30(3), pp. 354–382. doi:10.1108/JEIM-10-2015-0099 DOI: https://doi.org/10.1108/JEIM-10-2015-0099
  95. Węziak-Białowolska, D. (2011). Operacjonalizacja i skalowanie w ilościowych badaniach społecznych. Zeszyty naukowe SGH, 16, pp. 1-49.
  96. Willcocks, L., & Lacity, M. (2016). Automation: Robots and the Future of Work. Stratford-upon-Avon, UK: Steve Brookes Publishing.
  97. Willcocks, L., Hindle, J., & Lacity, M. (2020). Becoming Strategic with Robotic Process Automation. Stratford-upon-Avon, UK: Steve Brookes Publishing.
  98. Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. Joreskog, & H. Wold (Eds.), Systems under indirect observations: Part II (pp. 1-54). Amsterdam, Netherlands: North-Holland.
  99. Wong, K.-K. (2019). Mastering partial least squares Structural Equation Modeling (PLS-SEM). Bloomington, USA: iUniverse.
  100. Yu, C.-S., & Tao, Y.-H. (2009). Understanding business-level innovation technology adoption. Technovation, 29(2), pp. 92-109. doi:10.1016/J.TECHNOVATION.2008.07.007 DOI: https://doi.org/10.1016/j.technovation.2008.07.007
  101. Zailani, S., Iranmanesh, M., Nikbin, D., & Beng, J. (2015). Determinants of RFID Adoption in Malaysia’s Healthcare Industry: Occupational Level as a Moderator. Journal of Medical Systems, 39(172). doi:10.1007/s10916-014-0172-4 DOI: https://doi.org/10.1007/s10916-015-0263-x
  102. Zhu, K., Kraemer, K. L., & Xu, S. (2006). The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business. Management Science, 52(10), pp. 1557-1576. doi:10.1287/mnsc.1050.0487 DOI: https://doi.org/10.1287/mnsc.1050.0487
  103. Zuboff, S. (1988). In the age of the smart machine: The future of work and power. New York, USA: Basic Books.

How to Cite

Sobczak, A. (2022). Analysis of the Conditions Influencing the Assimilation of the Robotic Process Automation by Enterprises. Human Technology, 18(2), 143–190. https://doi.org/10.14254/1795-6889.2022.18-2.4