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Do the innovation and digital transformation strategies induce SME performances in new normal era? Structural & confirmatory analysis models

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EN
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EN
One of major challenge in a sustainable growth, which organizations face is a slow adoption of the digital transformation. This research work presents the reasons that lead to the slow digitization process in medical device SMEs in southern Germany. In addition, by developing the conceptual model, this work highlights the effect of these improper implementations on SME's business performances and financial situation. The researchers applied correlational research design methodology, with simple random sampling techniques along with empirical and statistical study with primary data collection. The main study variables are SME’s financial situation, SMEs organizational performance, and medical digitization rules. The study demonstrated the negative impact of delayed digital mechanisms in terms of businesses and financial performances.The extra transparency restrictions that add burdens for SMEs, and the lack of training for the employees, which in overall add more difficulties for adopting innovation and digital transformation are other factors negatively affecting the studied process.
Twórcy
  • York Saint John University, York Business School, York YO31 7EX, United Kingdom
  • City University of Ajman, General Education Department, Al Talla 2, Ajman, UAE
  • American University of Middle East, College of Business Administration, 250 EQAILA, Kuwait
  • York Saint John University, York Business School, York YO31 7EX, United Kingdom
  • Polytechnic University of Philippines, Postgraduate college, Manila 1016, Philippines
Bibliografia
  • [1] S. Lakshminarayanan, Organizational behavior and work: a critical introduction , Oxford University Press, 2011. https://doi.org/10.1057/omj.2011.20.
  • [2] F.F. Padró, J.H. Green, Education administrators in wonderland: Figuring out policy-making and regulatory compliance when making decisions, in: Palgrave Handb. Educ. Law Sch., Springer International Publishing, Cham, 2018: pp. 141–166. https://doi.org/10.1007/978-3-319-77751-1_7.
  • [3] G. Secundo, P. Rippa, R. Cerchione, Digital Academic Entrepreneurship: A structured literature review and avenue for a research agenda, Technol. Forecast. Soc. Change. 157 (2020) 120118. https://doi.org/10.1016/j.techfore.2020.120118.
  • [4] A. Urbinati, D. Chiaroni, V. Chiesa, F. Frattini, The role of digital technologies in open innovation processes: an exploratory multiple case study analysis, R D Manag. 50 (2020) 136–160. https://doi.org/10.1111/radm.12313.
  • [5] S. Nambisan, K. Lyytinen, A. Majchrzak, M. Song, Digital Innovation Management: Reinventing Innovation Management Research in a Digital World, MIS Q. 41 (2017) 223–238. https://doi.org/10.25300/misq/2017/41:1.03.
  • [6] H. Aldrich, “The Democratization of Entrepreneurship? Hackers, Makerspaces, and Crowdfunding,” Acad. Manag. Proc. 2014 (2014) 10622. https://doi.org/10.5465/ambpp.2014.10622symposium.
  • [7] J. Huang, O. Henfridsson, M.J. Liu, S. Newell, Growing on steroids: Rapidly scaling the user base of digital ventures through digital innovaton, MIS Q. Manag. Inf. Syst. 41 (2017) 301–314. https://doi.org/10.25300/MISQ/2017/41.1.16.
  • [8] D.M. Steininger, Linking information systems and entrepreneurship: A review and agenda for IT-associated and digital entrepreneurship research, Inf. Syst. J. 29 (2019) 363–407. https://doi.org/10.1111/isj.12206.
  • [9] S. Hertling, D. Hertling, F. Loos, D. Martin, I. Graul, Digitization in gynecology and obstetrics in times of COVID-19: Results of a national survey, Internet Interv. 26 (2021) 100478. https://doi.org/10.1016/j.invent.2021.100478.
  • [10] S. Hertling, H. Paulheim, Order Matters: Matching Multiple Knowledge Graphs, in: K-CAP 2021 - Proc. 11th Knowl. Capture Conf., 2021: pp. 113–120. https://doi.org/10.1145/3460210.3493556.
  • [11] F. Giones, A. Brem, Digital Technology Entrepreneurship: A Definition and Research Agenda, Technol. Innov. Manag. Rev. 7 (2017) 44–51. https://doi.org/10.22215/timreview1076.
  • [12] S. Kraus, P. Moog, S. Schlepphorst, M. Raich, Crisis and turnaround management in smes: A qualitative-empirical investigation of 30 companies, Int. J. Entrep. Ventur. 5 (2013) 406–430. https://doi.org/10.1504/IJEV.2013.058169.
  • [13] G. Schiuma, Arts catalyst of creative organisations for the fourth industrial revolution, J. Open Innov. Technol. Mark. Complex. 3 (2017) 20. https://doi.org/10.1186/s40852-017-0072-1.
  • [14] J. Bergsland, O.J. Elle, E. Fosse, Barriers to medical device innovation, Med. Devices Evid. Res. 7 (2014) 205–209. https://doi.org/10.2147/MDER.S43369.
  • [15] V. Leiter, S.K. White, Enmeshed in controversy: claims about the risks of vaginal mesh devices, Heal. Risk Soc. 17 (2015) 64–80. https://doi.org/10.1080/13698575.2014.1000835.
  • [16] S. White, M. Pharoah, Oral Radiology-E-Book: Principles and Interpretation, Elsevier Health Sciences, 2009.
  • [17] B.A. De Mol, Regulation of risk management of medical devices and the role of litigation, J. Risk Res. 17 (2014) 735–748. https://doi.org/10.1080/13669877.2014.889201.
  • [18] M.E. Porter, J.E. Heppelmann, How smart, connected products are transforming competition, Harv. Bus. Rev. 92 (2014) 64–88.
  • [19] J.W. Dearing, Diffusion of Innovations, in: Oxford Handb. Organ. Chang. Innov., Routledge, 2021: pp. 611–638. https://doi.org/10.1093/oxfordhb/9780198845973.013.23.
  • [20] I.M. Sebastian, K.G. Moloney, J.W. Ross, N.O. Fonstad, C. Beath, M. Mocker, How big old companies navigate digital transformation, in: MIS Q. Exec., Routledge, 2017: pp. 197–213. https://doi.org/10.4324/9780429286797-6.
  • [21] C. Giebe, The Chief Digital Officer – Savior for the Digitalization in German Banks?, J. Econ. Dev. Environ. People. 8 (2019) 6. https://doi.org/10.26458/jedep.v8i3.633.
  • [22] W. Maass, J. Parsons, S. Purao, V.C. Storey, C. Woo, Data-driven meets theory-driven research in the era of big data: Opportunities and challenges for information systems research, J. Assoc. Inf. Syst. 19 (2018) 1253–1273. https://doi.org/10.17705/1jais.00526.
  • [23] R.T.S. Wisła, E. Balcerowska, M. Kozłowska, D. Szlompek, K. Gabryel, M. Gołacki, O. Intan Hamdan Livramento, J. Raffo, G. Valacchi, M. Zehtabchi, Innovation in the pharmaceutical and medical technologies industries of Poland, Econ. Res. Work. Pap. 45 (2018).
  • [24] H. Yamaue, Innovation of diagnosis and treatment for pancreatic cancer, Springer Singapore, 2017. https://doi.org/10.1007/978-981-10-2486-3.
  • [25] A. Arnould, R. Hendricusdottir, J. Bergmann, The Complexity of Medical Device Regulations Has Increased, as Assessed through Data-Driven Techniques, Prosthesis. 3 (2021) 314–330. https://doi.org/10.3390/prosthesis3040029.
  • [26] N.E.J. West, W.F. Cheong, E. Boone, N.E. Moat, Impact of the COVID-19 pandemic: A perspective from industry, Eur. Hear. Journal, Suppl. 22 (2020) P56–P59. https://doi.org/10.1093/EURHEARTJ/SUAA187.
  • [27] E. Thunborg, E. Osterberg, The Medical Device Regulation What Impact Will the New Regulation Have on the Medical Device Industry and How Will Companies Use Standards to Meet the New Requirements, 2021.
  • [28] MedTechEurope, The European Medical Technology Industry in figures, 2021. https://www.medtecheurope.org/wp-content/uploads/2021/06/medtech-europe-facts-and-figures-2021.pdf.
  • [29] H.F. Lin, Examining the factors influencing knowledge management system adoption and continuance intention, Knowl. Manag. Res. Pract. 11 (2013) 389–394. https://doi.org/10.1057/kmrp.2012.24.
  • [30] M.K. Okour, C.W. Chong, F.A.M. Abdel Fattah, Knowledge management systems usage: application of diffusion of innovation theory, Glob. Knowledge, Mem. Commun. 70 (2021) 756–776. https://doi.org/10.1108/GKMC-08-2020-0117.
  • [31] A. Balaid, M.Z.A. Rozan, S.N. Abdullah, Conceptual model for examining knowledge maps adoption in software development organizations, Asian Soc. Sci. 10 (2014) 118–132. https://doi.org/10.5539/ass.v10n15p118.
  • [32] S.M. Faisal, S. Idris, Innovation Factors Influencing the Supply Chain Technology (Sct) Adoption: Diffusion of Innovation Theory, Int. J. Soc. Sci. Res. 2 (2020) 128–145. http://myjms.moe.gov.my/index.php/ijssrhttp://myjms.moe.gov.my/index.php/ijssrhttp://myjms.moe.gov.my/index.php/ijssr.
  • [33] J.P. Wisdom, K.H.B. Chor, K.E. Hoagwood, S.M. Horwitz, Innovation adoption: A review of theories and constructs, Adm. Policy Ment. Heal. Ment. Heal. Serv. Res. 41 (2014) 480–502. https://doi.org/10.1007/s10488-013-0486-4.
  • [34] Y.M. Wang, Y.C. Wang, Determinants of firms’ knowledge management system implementation: An empirical study, Comput. Human Behav. 64 (2016) 829–842. https://doi.org/10.1016/j.chb.2016.07.055.
  • [35] M. Raynard, Understanding Academic E-books Through the Diffusion of Innovations Theory as a Basis for Developing Effective Marketing and Educational Strategies, J. Acad. Librariansh. 43 (2017) 82–86. https://doi.org/10.1016/j.acalib.2016.08.011.
  • [36] K. Bley, C. Leyh, T. Schäffer, Digitization of German enterprises in the production sector - Do they know how “digitized” they are?, in: AMCIS 2016 Surfing IT Innov. Wave - 22nd Am. Conf. Inf. Syst., 2016.
  • [37] C. Leyh, T. Schäffer, S. Forstenhäusler, SIMMI 4.0-Vorschlag eines Reifegradmodells zur Klassifikation der unternehmensweiten Anwendungssystemlandschaft mit Fokus Industrie 4.0, in: Multikonferenz Wirtschaftsinformatik, MKWI 2016, 2016: pp. 981–992.
  • [38] S. Tumbas, N. Berente, J. vom Brocke, Digital innovation and institutional entrepreneurship: Chief DigitalOfficer perspectives of their emerging role, J. Inf. Technol. 33 (2018) 188–202. https://doi.org/10.1057/s41265-018-0055-0.
  • [39] D.G. Collings, K. Mellahi, W.F. Cascio, Global Talent Management and Performance in Multinational Enterprises: A Multilevel Perspective, J. Manage. 45 (2019) 540–566. https://doi.org/10.1177/0149206318757018.
  • [40] H.A. Elsaman, R.P. Sergio, the Psychographics of Green Marketing Strategy Vis-a-Vis Corporate Social Responsibility: Implications To Organisational Growth, Int. J. Entrep. 25 (2021) 1–11.
  • [41] P.C. Verhoef, T. Broekhuizen, Y. Bart, A. Bhattacharya, J. Qi Dong, N. Fabian, M. Haenlein, Digital transformation: A multidisciplinary reflection and research agenda, J. Bus. Res. 122 (2021) 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022.
  • [42] N.J. Foss, T. Saebi, Fifteen Years of Research on Business Model Innovation: How Far Have We Come, and Where Should We Go?, J. Manage. 43 (2017) 200–227. https://doi.org/10.1177/0149206316675927.
  • [43] J. Wilson, Essentials of business research: A guide to doing your research project., Sage Publications Ltd., 2014.
  • [44] M. Elfil, A. Negida, Sampling methods in clinical research; an educational review, Arch. Acad. Emerg. Med. 7 (2019).
  • [45] J.W. Creswell, Research designs: Qualitative, quantitative, and mixed methods approach., Sage Publications Ltd., 2009.
  • [46] H. Collins, Creative Research, Bloomsbury Publishing Plc, 2018. https://doi.org/10.5040/9781474247115.
  • [47] W.O. Netemeyer, R. G., Bearden, S. Sharma, Scaling procedures: Issues and applications, SAGE Publications, 2003.
  • [48] M. Brunner, H.M. Süß, Analyzing the reliability of multidimensional measures: An example from intelligence research, Educ. Psychol. Meas. 65 (2005) 227–240. https://doi.org/10.1177/0013164404268669.
  • [49] B.K. Nkansah, On the Kaiser-meier-Olkin’s measure of sampling adequacy., Math. Theory Model. 8 (2011) 52–76.
  • [50] N. Shrestha, Factor Analysis as a Tool for Survey Analysis, Am. J. Appl. Math. Stat. 9 (2021) 4–11. https://doi.org/10.12691/ajams-9-1-2.
  • [51] A. Maćkiewicz, W. Ratajczak, Principal components analysis (PCA), Comput. Geosci. 19 (1993) 303–342. https://doi.org/10.1016/0098-3004(93)90090-R.
  • [52] H. Abdi, L.J. Williams, Principal component analysis, Wiley Interdiscip. Rev. Comput. Stat. 2 (2010) 433–459. https://doi.org/10.1002/wics.101.
  • [53] H.-Y. Kim, Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis, Restor. Dent. Endod. 38 (2013) 52. https://doi.org/10.5395/rde.2013.38.1.52.
  • [54] L. t. Hu, P.M. Bentler, Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives, Struct. Equ. Model. A Multidiscip. J. 6 (1999) 1–55. https://doi.org/10.1080/10705519909540118.
  • [55] E.C. Alexopoulos, Introduction to multivariate regression analysis, Hippokratia. 14 (2010) 23–28.
  • [56] C. Hagquist, M. Stenbeck, Goodness of fit in regression analysis - R2 and G2 reconsidered, Qual. Quant. 32 (1998) 229–245. https://doi.org/10.1023/A:1004328601205.
  • [57] V.F. Misangyi, J.A. LePine, J. Algina, F. Goeddeke, The adequacy of repeated-measures regression for multilevel research: Comparisons with repeated-measures ANOVA, multivariate repeated-measures ANOVA, and multilevel modeling across various multilevel research designs, Organ. Res. Methods. 9 (2006) 5–28. https://doi.org/10.1177/1094428105283190.
  • [58] V. Ponnusami, V. Gunasekar, S.N. Srivastava, Kinetics of methylene blue removal from aqueous solution using gulmohar (Delonix regia) plant leaf powder: Multivariate regression analysis, J. Hazard. Mater. 169 (2009) 119–127. https://doi.org/10.1016/j.jhazmat.2009.03.066.
  • [59] J.J. Binder, On the Use of the Multivariate Regression Model in Event Studies, J. Account. Res. 23 (1985) 370. https://doi.org/10.2307/2490925.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-75f2184f-376c-450d-a7fd-85f7b9afad2e
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