ICT Technology Implementation and the Level of Process Maturity in an Organization

Dobrowolska, Emilia; Sliż, Piotr

doi:10.7172/1644-9584.96.2

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Artykuł - szczegóły

Czasopismo

Problemy Zarządzania

2022 | 20 | nr 2 (96) | 31-47

Tytuł artykułu

ICT Technology Implementation and the Level of Process Maturity in an Organization

Autorzy

Emilia Dobrowolska , Piotr Sliż

Warianty tytułu

Wdrożenie technologii ICT a poziom dojrzałości procesowej organizacji

Języki publikacji

Abstrakty

Cel: identyfikacja wybranych technologii ICT wspierających wyższe poziomy dojrzałości procesowej organizacji. Metodologia: badanie zostało zrealizowane z wykorzystaniem metod, takich jak analiza bibliometryczna, przegląd literatury, metody statyczne oraz sondażowe badanie opinii na próbie 48 dużych organizacji funkcjonujących w Polsce. Wyniki: zrealizowane postępowanie dostarczyło dowodów na to, że w badanej grupie jednostek istnieje statystycznie istotna zależność między implementacją technologii artificial intelligence (AI) oraz cloud computing (CC) i robotic process automation (RPA) a odpowiednio trzecim i czwartym poziomem dojrzałości procesowej według przyjętego modelu MMPM. Ograniczenia/implikacje badawcze: uzyskane wyniki badania obciążone są przede wszystkim wybraną nieprobabilistyczną techniką doboru próby, co powoduje ograniczenie uzyskanych wniosków do badanej grupy organizacji. Oryginalność/wartość: oryginalność tego artykułu wypełnia lukę poznawczą, polegającą na niedostatku publikacji przedstawiających relacje między stopniem implementacji technologii ICT a poziomem dojrzałości procesowej. Przedstawiony artykuł wypełnia tę lukę, wskazując statystyczne zależności między wdrożeniem artificial intelligence (AI), robotic process automation (RPA) i cloud computing (CC) a poziomem dojrzałości procesowej organizacji. (abstrakt oryginalny)

Purpose: The main objective of the article is to identify selected ICT technologies supporting higher levels of organizational process maturity. Design/methodology/approach: The research was conducted with the use of the following methods: bibliometric analysis, literature review and statistic methods. The empirical procedure was carried out on a non-random sample of 48 large organizations operating in Poland, using the CAWI technique. Findings: The empirical research carried out proved the existence, in the group of the organizations examined, of a statistically significant relationship between the implementation of artificial intelligence (AI), cloud computing (CC) and robotic process automation (RPA) technologies and, respectively, the third and fourth levels of process maturity, in accordance with the adopted multicriteria model of process maturity assessment (MMPM). Research limitations/implications: The burden of the presented empirical investigation results primarily arises from the applied technique of non-probabilistic research sample selection. This makes the obtained results limited to the examined sample of organizations. Originality/value: The originality of this article fills the cognitive gap consisting in the shortage of publications that present the relationship between the degree of implementation of ICT technology and the level of process maturity. The presented article addresses this gap by indicating a statistical relationship between the implementation of artificial intelligence (AI), robotic process automation (RPA), cloud computing (CC) technology and the level of process maturity of the organization. (original abstract)

Słowa kluczowe

Technologie informacyjne i telekomunikacyjne Zarządzanie procesami biznesowymi Analiza bibliometryczna Zrobotyzowana Automatyzacja Procesów

Information and Communication Technology (ICT) Business Process Management (BPM) Bibliometric analysis Robotic Process Automation (RPA)

Czasopismo

Problemy Zarządzania

Rocznik

2022

Tom

Numer

nr 2 (96)

Strony

31-47

Opis fizyczny

Twórcy

autor

Emilia Dobrowolska

University of Gdańsk, Poland

autor

Piotr Sliż

University of Gdańsk, Poland

Bibliografia

1. Abramovici, M. (2007). Future trends in product lifecycle management (PLM). In F. L. Krause (Ed.), The future of product development (pp. 665-674). Springer. https:// doi.org/10.1007/978-3-540-69820-3_64.
2. Agee, P., Gao, X., Paige, F., McCoy, A., & Kleiner, B. (2021). A human-centred approach to smart housing. Building Research & Information, 49(1), 84-99. https://doi.org/10. 1080/09613218.2020.1808946.
3. Aguirre, S., & Rodriguez, A. (2017, September). Automation of a business process using robotic process automation (RPA): A case study. In J. C. Figueroa-García, E. R. López-Santana, J. L. Villa-Ramírez, & R. Ferro-Escobar (Eds.), Applied computer sciences in engineering (pp. 65-71). Springer International Publishing. https:// doi.org/10.1007/978-3-319-66963-2_7.
4. Almeida, F., Santos, J. D., & Monteiro, J. A. (2020). The challenges and opportunities in the digitalization of companies in a post-COVID-19 world. IEEE Engineering Management Review, 48(3), 97-103. https://doi.org/10.1109/EMR.2020.3013206.
5. Anderson, I. A., Gisby, T. A., McKay, T. G., O'Brien, B. M., & Calius, E. P. (2012). Multi-functional dielectric elastomer artificial muscles for soft and smart machines. Journal of Applied Physics, 112(4), 041101. https://doi.org/10.1063/1.4740023.
6. Aysolmaz, B., İren, D., & Demirörs, O. (2013). An effort prediction model based on BPM measures for process automation. In S. Nurcan, P. Soffer, R. Schmidt, & I. Bider (Eds.), Enterprise, business-process and information systems modeling (Vol. 147, pp. 154-167). Springer. https://doi.org/10.1007/978-3-642-38484-4_12.
7. Babiceanu, R. F., & Seker, R. (2016). Big data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Computers in Industry, 81, 128-137. https://doi.org/10.1016/j.compind.2016.02.004.
8. Bitkowska, A., Sliż, P., Tenbrink, C., & Piasecka, A. (2020). Application of process mining on the example of an authorized passenger car service station in Poland. Foundations of Management, 12(1), 125-136. https://doi.org/10.2478/fman-2020-0010.
9. Bresciani, S., Ferraris, A., & Del Giudice, M. (2018). The management of organizational ambidexterity through alliances in a new context of analysis: Internet of Things (IoT) smart city projects. Technological Forecasting and Social Change, 136, 331-338. https:// doi.org/10.1016/j.techfore.2017.03.002.
10. Cherrier, S., & Deshpande, V. (2017). From BPM to IoT. Paper presented at the 15th International Conference on Business Process Management, Barcelona, Spain. https:// doi.org/10.1007/978-3-319-74030-0_23.
11. Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383-394. https://doi.org/10.1016/j.ijpe.2018. 08.019.
12. Dansana, D., Sahoo, S., & Mishra, B. K. (2022). Efficiency and reliability of IoT in smart agriculture. In P. Kumar Pattnaik, R. Kumar, & S. Pal (Eds.), Internet of Things and analytics for agriculture, (Vol. 3, pp. 301-327). Springer. https://doi. org/10.1007/978-981-16-6210-2_15.
13. Dezi, L., Santoro, G., Gabteni, H., & Pellicelli, A. C. (2018). The role of big data in shaping ambidextrous business process management: Case studies from the service industry. Business Process Management Journal, 24, 1163-1175. https://doi.org/10.1108/ BPMJ-07-2017-0215.
14. Dördüncü, H. (2022). Logistics, supply chains and smart factories. In İ. İyigün & Ö. F. Görçün (Eds.), Logistics 4.0 and future of supply chains (pp. 137-152). Springer. https://doi. org/10.1007/978-981-16-5644-6_9.
15. Fraga-Lamas, P., Fernández-Caramés, T. M., Blanco-Novoa, O., & Vilar-Montesinos, M. A. (2018). A review on industrial augmented reality systems for the industry 4.0 shipyard. IEEE Access, 6, 13358-13375. https://doi.org/10.1109/ACCESS.2018.2808326.
16. Gerrish, S. (2018). How smart machines think. MIT Press. https://doi.org/10.7551/ mitpress/11440.001.0001.
17. Hair, J. F., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
18. Hao, J., & Tao, Y. (2022). Adversarial attacks on deep learning models in smart grids. Energy Reports, 8, 123-129. https://doi.org/10.1016/j.egyr.2021.11.026.
19. Hekkala, R., Väyrynen, K., & Wiander, T. (2012). Information security challenges of social media for companies. Proceedings of the 2012 European Conference on Information Systems (ECIS), 56.
20. Holzmüller-Laue, S., Schubert, P., Göde, B., & Thurow, K. (2013). Visual simulation for the BPM-based process automation. In A. Kobyliński & A. Sobczak (Eds.), Perspectives in business informatics research. BIR 2013. Lecture notes in business information processing (Vol. 158, pp. 48-62). Springer. https://doi.org/10.1007/978- 3-642-40823-6_5.
21. Hussein, D. M. E. D. M., Hamed, M., & Eldeen, N. (2018). A blockchain technology evolution between business process management (BPM) and Internet of Things (IoT). International Journal of Advanced Computer Science and Applications, 9(8), 442-450. https://doi.org/10.14569/IJACSA.2018.090856.
22. Igwe, P. A., Rugara, D. G., & Rahman, M. (2022). A triad of Uppsala internationalization of emerging markets firms and challenges: A Systematic Review. Administrative Sciences, 12(1), 3. https://doi.org/10.3390/admsci12010003.
23. Janiesch, C., Koschmider, A., Mecella, M., Weber, B., Burattin, A., Di Ciccio, C., ... Zhang, L. (2020). The Internet of Things meets business process management: A manifesto. IEEE Systems, Man, and Cybernetics Magazine, 6(4), 34-44. https:// doi.org/10.1109/MSMC.2020.3003135.
24. Karami, K., & Akbarabadi, S. (2016). Developing a smart structure using integrated subspace-based damage detection and semi-active control. Computer-Aided Civil and Infrastructure Engineering, 31(11), 887-903. https://doi.org/10.1111/mice.12231.
25. Kealey, M. (2022). Smart village - The Canadian experience. In V. I. Lakshmanan, A. Chockalingam, V. Kumar Murty, & S. Kalyanasundaram (Eds.), Smart villages. Bridging the global - urban rural divide (pp. 231-246). Springer Publishing. https:// doi.org/10.1007/978-3-030-68458-7_17.
26. Levina, A., Novikov, A., & Borremans, A. (2018). BPM as a service based on cloud computing. In V. Murgul & M. Pasetti (Eds.), International Scientific Conference Energy Management of Municipal Facilities and Sustainable Energy Technologies EMMFT 2018 (Vol. 2, pp. 210-215). Springer Nature. https://doi.org/10.1007/978-3-030-19868-8_21.
27. Levonevskiy, D., Vatamaniuk, I., & Saveliev, A. (2018). Processing models for conflicting user requests in ubiquitous corporate smart spaces. Paper presented at the 13th International Scientific-Technical Conference on Electromechanics and Robotics "Zavalishin's Readings". In MATEC web of conferences (Vol. 161, p. 03006). EDP Sciences. https://doi.org/10.1051/matecconf/201816103006.
28. Liu, H., Ning, H., Mu, Q., Zheng, Y., Zeng, J., Yang, L. T., ... & Ma, J. (2019). A review of the smart world. Future Generation Computer Systems, 96, 678-691. https://doi. org/10.1016/j.future.2017.09.010.
29. Ma, J., Yang, L. T., Apduhan, B. O., Huang, R., Barolli, L., & Takizawa, M. (2005). Towards a smart world and ubiquitous intelligence: A walkthrough from smart things to smart hyperspaces and UbicKids. International Journal of Pervasive Computing and Communications, 1(1), 53-68. https://doi.org/10.1108/17427370580000113.
30. Ma, J., Zheng, Y., Ning, H., Yang, L. T., Huang, R., Liu, H., ... & Yau, S. S. (2015). Top challenges for smart worlds: A report on the Top10Cs forum. IEEE Access, 3, 2475-2480. https://doi.org/10.1109/ACCESS.2015.2504123.
31. Malathi, V., & Kavitha, V. (2022). Innovative services using cloud computing in smart health care. In A. K. Tyagi, A. Abraham, & A. Kaklauskas (Eds.), Intelligent interactive multimedia systems for e-healthcare applications (pp. 59-80). Springer. https://doi. org/10.1007/978-981-16-6542-4_5.
32. Mendling, J., Decker, G., Hull, R., Reijers, H. A., & Weber, I. (2018). How do machine learning, robotic process automation, and blockchains affect the human factor in business process management? Communications of the Association for Information Systems, 43(1), 297-320. https://doi.org/10.17705/1CAIS.04319.
33. Mora, H. L., & Sánchez, P. P. (2020, June). Digital transformation in higher education institutions with business process management: Robotic process automation mediation model. In Proceedings of CISTI'2020 - 15th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-6). IEEE. https://doi.org/10.23919/ CISTI49556.2020.9140851.
34. Mu, R., Haershan, M., & Wu, P. (2022). What organizational conditions, in combination, drive technology enactment in government-led smart city projects?. Technological Forecasting and Social Change, 174, 121220. https://doi.org/10.1016/j. techfore.2021.121220.
35. Ning, H., Liu, H., Ma, J., Yang, L. T., Wan, Y., Ye, X., & Huang, R. (2015). From internet to smart world. IEEE Access, 3, 1994-1999. https://doi.org/10.1109/ ACCESS.2015.2493890.
36. Pandey, D., Singh, N., Singh, V., & Khan, M. W. (2022). Paradigms of smart education with IoT approach. In S. N. Mohanty, J. M. Chatterjee, & S. Satpathy (Eds.), Internet of Things and its applications (pp. 223-233). Springer International Publishing. https:// doi.org/10.1007/978-3-030-77528-5_11.
37. Paschek, D., Luminosu, C. T., & Draghici, A. (2017). Automated business process management - In times of digital transformation using machine learning or artificial intelligence. In MATEC web of conferences (Vol. 121, p. 04007). EDP Sciences. https:// doi.org/10.1051/matecconf/201712104007.
38. Perez, B., Mazzaro, G., Pierson, T. J., & Kotz, D. (2022). Detecting the presence of electronic devices in smart homes using harmonic radar technology. Remote Sensing, 14(2), 327. https://doi.org/10.3390/rs14020327.
39. Rialti, R., Marzi, G., Silic, M., & Ciappei, C. (2018). Ambidextrous organization and agility in big data era: The role of business process management systems. Business Process Management Journal, 24(5). https://doi.org/10.1108/BPMJ-07-2017-0210.
40. Rico-Bautista, D., Guerrero, C. D., Collazos, C. A., Maestre-Gongora, G., Sánchez- -Velásquez, M. C., Medina-Cárdenas, Y., & Swaminathan, J. (2022). Smart university: Key factors for a cloud computing adoption model. In A. K. Nagar, D. S. Jat, G. Marín -Raventós, & D. Kumar Mishra (Eds.), Intelligent sustainable systems - Selected papers of WorldS4 (pp. 85-93). Springer. https://doi.org/10.1007/978-981-16- 6369-7_8.
41. Riekki, J., & Mämmelä, A. (2021). Research and education towards smart and sustainable world. IEEE Access, 9, 53156-53177. https://doi.org/10.1109/ACCESS.2021.3069902.
42. Saxena, V., Kumar, N., & Nangia, U. (2021). Smart grid: A sustainable smart approach. Journal of Physics: Conference Series, 2007(1), 012042. https://doi.org/10.1088/1742- 6596/2007/1/012042.
43. Sharifi, A., Allam, Z., Feizizadeh, B., & Ghamari, H. (2021). Three decades of research on smart cities: Mapping knowledge structure and trends. Sustainability, 13(13), 7140. https://doi.org/10.3390/su13137140.
44. Shen, Y. (2019). Intelligent infrastructure, ubiquitous mobility, and smart libraries - Innovate for the future. Data Science Journal, 18(1). https://doi.org/10.5334/dsj- 2019-011.
45. Sidorova, A., & Rafiee, D. (2019, January). AI agency risks and their mitigation through business process management: A conceptual framework. In Proceedings of the 52nd Hawaii International Conference on System Sciences. https://doi.org/10.24251/ HICSS.2019.704.
46. Skowrońska, A., & Zakrzewski R. (Eds.). (2020). Raport o stanie sektora małych i średnich przedsiębiorstw w Polsce. Polska Agencja Rozwoju Przedsiębiorczości. Sliż, P. (2018). Concept of the organization process maturity assessment
47. Sliż, P. (2018). Concept of the organization process maturity assessment. Journal of Economics & Management, 33, 80-95. https://doi.org/10.22367/jem.2018.33.05.
48. Sliż, P. (2019). Robotization of business processes and the future of the labor market in Poland - Preliminary research. Organizacja i Kierowanie, 185(2), 67-79. https:// doi.org/10.1007/978-3-030-30429-4_13.
49. Sliż, P. (2021). Organizacja procesowo-projektowa: Istota, modelowanie, pomiar dojrzałości. Difin.
50. Sung, M. S., Shih, S. G., & Perng, Y. H. (2022). Multi-criteria evaluation of site selection for smart community demonstration projects. Smart Cities, 5(1), 22-33. https://doi. org/10.3390/smartcities5010002.
51. Tallman, S., Luo, Y., & Buckley, P. J. (2018). Business models in global competition. Global Strategy Journal, 8(4), 517-535. https://doi.org/10.1002/gsj.1165.
52. Tapsoba, L., & Xiao, Z. (2017). Analysis of AI contribution to improving BPM of e-commerce in China: Examining the case of Taobao. Paper presented at the 2017 International Conference on Financial Management, Education and Social Science (FMESS 2017), Quingdao, China.
53. Tognetti, R., Smith, M., & Panzacchi, P. (Eds.). (2022). Climate-smart forestry in mountain regions. Springer. https://doi.org/10.1007/978-3-030-80767-2.
54. Verma, K., Chandnani, N., Bhatt, G., & Sinha, A. (2022). Internet of Things and smart farming. In S. N. Mohanty, J. M. Chatterjee, & S. Satpathy (Eds.), Internet of Things and its applications (pp. 283-303). Springer International Publishing. https://doi. org/10.1007/978-3-030-77528-5_15.
55. Weiser, M. (1991, September). The computer for the twenty-first century. Scientific American, 94-100. https://doi.org/10.1038/scientificamerican0991-94.
56. Ying, Y. H., Chang, K., & Lee, C. H. (2014). The impact of globalization on economic growth. Romanian Journal of Economic Forecasting, 17(2), 25-34.

Typ dokumentu

Bibliografia

Identyfikatory

DOI

10.7172/1644-9584.96.2

Identyfikator YADDA

bwmeta1.element.ekon-element-000171655142