Artificial intelligence and organizational agilityan analysis of scientific production and future trends

  1. María Atienza-Barba 1
  2. María de la Cruz del Río-Rama 2
  3. Ángel Meseguer-Martínez 3
  4. Virginia Barba-Sánchez 3
  1. 1 Universidad de Extremadura
    info

    Universidad de Extremadura

    Badajoz, España

    ROR https://ror.org/0174shg90

  2. 2 Universidade de Vigo
    info

    Universidade de Vigo

    Vigo, España

    ROR https://ror.org/05rdf8595

  3. 3 Universidad de Castilla-La Mancha
    info

    Universidad de Castilla-La Mancha

    Ciudad Real, España

    ROR https://ror.org/05r78ng12

Mostra les afiliacions +
Revista:
European Research on Management and Business Economics

ISSN: 2444-8834

Any de publicació: 2024

Volum: 30

Número: 2

Pàgines: 133-145

Tipus: Article

DOI: 10.1016/J.IEDEEN.2024.100253 DIALNET GOOGLE SCHOLAR

Altres publicacions en: European Research on Management and Business Economics

Resum

The advancement of Artificial Intelligence (AI) is progressing rapidly, compelling companies to integrate it within their operational frameworks to sustain competitiveness, primarily driven by its impact on organizational agility (OA). Nevertheless, the absence of a robust theoretical framework underscores the limited understanding of the relationship between AI and OA. Within this context, the research aims to establish foundational knowledge, delineate the evolutionary trajectory of the topic, and identify prospective avenues for inquiry. To achieve this objective, bibliometric analysis is employed to gain comprehensive insights into the interplay between these variables and discern trends within this research domain. The utilization of the Web of Science (WoS) and Scopus databases up to January 2024 facilitates data collection, while Bibliometrix and Visme are instrumental in crafting a scientific production map. The analysis corroborates the novelty and growth potential of the subject matter, underscoring heightened author interest, particularly evident in 2023, against a backdrop of sparse and temporally dispersed publications until 2017. Notably, the prevalence of conference papers on this topic stands significantly high at 26.98 % in comparison to the total contributions, indicative of the research community's engagement. Furthermore, the findings underscore a robust association between the keywords AI and OA, delineating a burgeoning research domain that converges with the digital transformation of enterprises and the Theory of Standardization Process. The effective integration of AI into corporate operational frameworks marks the zenith of this transformative process, ushering in the genesis and overhaul of organizational routines. This study represents a pioneering endeavour within the literature, as it constitutes the inaugural bibliometric exploration of this subject matter. Moreover, it serves to underpin the establishment of theoretical underpinnings for future research endeavours as it outlines current trends and emerging future research trajectories, concerning the role of AI in OA.

€ Mostra el finançament

Informació de finançament

Finançadors

Referències bibliogràfiques

  • Agrawal, K. P. (2023). Organizational sustainability of generative AI-Driven optimization intelligence. Journal of Computer Information Systems. https://doi.org/10.1080/ 08874417.2023.2286540
  • Akter, S., Hossain, M. A., Sajib, S., Sultana, S., Rahman, M., Vrontis, D., & McCarthy, G. (2023). A framework for AI-powered service innovation capability: Review and agenda for future research. Technovation, 125. https://doi.org/10.1016/j. technovation.2023.102768 ´
  • Alvarez-García, J., Durán-Sánchez, A., del Río-Rama, M., de la, C., & Simonetti, B. (2023). Big data and tourism research: measuring research impact. Quality & Quantity, 57(3), 271–292. https://doi.org/10.1007/s11135-020-01044-z
  • Amankwah-Amoah, J., Khan, Z., Wood, G., & Knight, G. (2021). COVID-19 and digitalization: The great acceleration. Journal of Business Research, 136, 602–611. https://doi.org/10.1016/j.jbusres.2021.08.011
  • Arias-Pérez, J., Chac´on-Henao, J., & L´opez-Zapata, E. (2023). Unlocking agility: Trapped in the antagonism between co-innovation in digital platforms, business analytics capability and external pressure for AI adoption? Business Process Management Journal, 29(6), 1791–1809. https://doi.org/10.1108/BPMJ-10-2022-0484
  • Arias-Pérez, J., & V´ elez-Jaramillo, J. (2022). Ignoring the three-way interaction of digital orientation, Not-invented-here syndrome and employee’s artificial intelligence awareness in digital innovation performance: A recipe for failure. Technological Forecasting in Social Change, 174(1), Article 121305. https://doi.org/10.1016/j. techfore.2021.121305
  • Autio, E., Mudambi, R., & Yoo, Y. (2021). Digitalization and globalization in a turbulent world: Centrifugal and centripetal forces. Global Strategy Journal, 11(1), 3–16. https://doi.org/10.1002/gsj.1396
  • Barba-Sánchez, V., Arias-Antúnez, E., & Orozco-Barbosa, L. (2019). Smart cities as a source for entrepreneurial opportunities: Evidence for Spain. Technol Forecast Soc Change, 148. https://doi.org/10.1016/j.techfore.2019.119713
  • Barba-Sánchez, V., Gouveia-Rodrigues, R., & Meseguer-Martínez, ´ A. (2022). Information and communication technology (ICT) skills and job satisfaction of primary education teachers in the context of Covid-19. Theoretical model. El Profesional de la Informacion, 31(6). https://doi.org/10.3145/epi.2022.nov.17
  • Barba-Sánchez, V., Orozco-Barbosa, L., & Arias-Antúnez, E. (2021). On the impact of information technologies secondary-school capacity in business development: evidence from smart cities around the world. Frontiers in Psychology, 12. https://doi. org/10.3389/fpsyg.2021.731443
  • Briones-Bitar, J., Carri´on-Mero, P., Montalv´an-Burbano, N., & Morante-Carballo, F. (2020). Rockfall research: A bibliometric analysis and future trends. Geosciences (Basel), 10(10), 1–25. https://doi.org/10.3390/geosciences10100403
  • Calderón-Monge, E., & Ribeiro-Soriano, D. E. (2023). The role of digitalization in business and management: A systematic literature review. Review of Managerial Science, 1–43. https://doi.org/10.1007/s11846-023-00647-8
  • Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics, 22(1), 155–205. https://doi.org/10.1007/ BF02019280
  • Cándido, L. F., Lazaro, J. C., Freitas e Silva, A. O. D., & Barros Neto, J. D. P (2023). Sustainability transitions in the construction sector: A bibliometric review. Sustainability, 15(17). https://doi.org/10.3390/su151712814
  • Carrión-Mero, P., Montalv´ an-Burbano, N., Herrera-Franco, G., Domínguez-Granda, L., Bravo-Montero, L., & Morante-Carballo, F. (2022). Research trends in groundwater and stable isotopes. Water (Basel), 14(19). https://doi.org/10.3390/w14193173
  • Carroll, N., Conboy, K., & Wang, X. (2023). From transformation to normalisation: An exploratory study of a large-scale agile transformation. Journal of Information Technology, 38(3), 267–303. https://doi.org/10.1177/02683962231164428
  • Chen, X., & Siau, K. (2020). Business analytics/business intelligence and IT infrastructure: Impact on organizational agility. Journal of Organizational and End User Computing, 32(4), 138–161. https://doi.org/10.4018/JOEUC.2020100107
  • Cheng, C., & Wang, L. (2022). How companies configure digital innovation attributes for business model innovation? A configurational view. Technovation, 112, Article 102398. https://doi.org/10.1016/j.technovation.2021.102398
  • Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609–1630. https://doi.org/10.1002/ asi.22688
  • Davenport, T. H. (2018). The AI advantage: How to put the artificial intelligence revolution to work. The MIT Press. https://doi.org/10.7551/mitpress/11781.001.0001
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: an overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
  • Durán-Sánchez, A., de la, Cruz, del Río-Rama, M., ´ Alvarez-García, J., & Oliveira, C. (2022). Analysis of worldwide research on craft beer. Sage Open, 12(2). https://doi. org/10.1177/21582440221108154
  • Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10–11), 1105–1121. https://doi.org/10.1002/1097-0266 (200010/11)21. :10/11<1105::AID-SMJ133>3.0.CO;2-E.
  • Escudero Guirado, C., Martínez de Ibarreta Zorita, C., & Goytre Castro, C. (2018). Beyond signed T-shirts: A socio-technological model of equity crowdfunding adoption. In Journal of innovation economics & management, 26. https://doi.org/10.3917/jie. pr1.0024. n ◦ .
  • Fang, M., Nie, H., & Shen, X. (2023). Can enterprise digitization improve ESG performance? Econ Model, 118, Article 106101. https://doi.org/10.1016/j. econmod.2022.106101
  • Feldman, M. S., & Pentland, B. T. (2003). Reconceptualizing organizational routines as a source of flexibility and change. Adm Sci Q, 48(1), 94–118. https://doi.org/10.2307/ 3556620
  • Galán Hernández, J. J., Marín Díaz, G., & Gald´ on Salvador, J. L (2024). Artificial intelligence applied to human resources management: A bibliometric analysis. Lecture Notes in Networks and Systems, 932 LNNS, 269–277. https://doi.org/10.1007/ 978-3-031-54235-0_25
  • Gonçalves, D., Bergquist, M., Al¨ange, S., & Bunk, R. (2022). How digital tools align with organizational agility and strengthen digital innovation in automotive startups. Procedia Comput Sci, 196, 107–116. https://doi.org/10.1016/j.procs.2021.11.079
  • Gong, C., & Ribiere, V. (2021). Developing a unified definition of digital transformation. Technovation, 102, Article 102217. https://doi.org/10.1016/j. technovation.2020.102217
  • Grewal, D., Guha, A., Satornino, C. B., & Schweiger, E. B. (2021). Artificial intelligence: The light and the darkness. J Bus Res, 136, 229–236. https://doi.org/10.1016/j. jbusres.2021.07.043
  • Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. Calif Manage Rev, 61(4), 5–14. https:// doi.org/10.1177/0008125619864925
  • Han, R., Lam, H. K. S., Zhan, Y., Wang, Y., Dwivedi, Y. K., & Tan, K. H. (2021). Artificial intelligence in business-to-business marketing: A bibliometric analysis of current research status, development and future directions. Industrial Management & Data Systems, 121(12), 2467–2497. https://doi.org/10.1108/IMDS-05-2021-0300
  • He, X., & Liu, Y. (2024). Knowledge evolutionary process of artificial intelligence in e- commerce: Main path analysis and science mapping analysis. Expert Syst Appl, 238. https://doi.org/10.1016/j.eswa.2023.121801
  • Huang, C.-Y., Ceroni, J. A., & Nof, S. Y. (2000). Agility of networked enterprises - parallelism, error recovery and conflict resolution. Computers in Industry, 42(2), 275–287. https://doi.org/10.1016/S0166-3615(99)00076-7
  • Islam, A., & Naseem, A. (2023). Role of industry 4.0 tools in organizational performance of the IT sector. Kybernetes. https://doi.org/10.1108/K-09-2023-1697
  • Kent Baker, H., Pandey, N., Kumar, S., & Haldar, A. (2020). A bibliometric analysis of board diversity: Current status, development, and future research directions. J Bus Res, 108, 232–246. https://doi.org/10.1016/j.jbusres.2019.11.025
  • Khin, S., & Ho, T. C. F. (2019). Digital technology, digital capability and organizational performance: A mediating role of digital innovation. International Journal of Innovation Science, 11(2), 177–195. https://doi.org/10.1108/IJIS-08-2018-0083/ FULL/XML
  • Klos, C., Spieth, P., Clauss, T., & Klusmann, C. (2023). Digital transformation of incumbent firms: A business model innovation perspective. IEEE Transactions on Engineering Management, 70(6), 2017–2033. https://doi.org/10.1109/ TEM.2021.3075502
  • Kumar, S., Lim, W. M., Sivarajah, U., & Kaur, J. (2023). Artificial intelligence and blockchain integration in business: Trends from a bibliometric-content analysis. Information Systems Frontiers, 25(2), 871–896. https://doi.org/10.1007/s10796-022- 10279-0
  • Lee, J. E. S. (2023). The era of omni-learning: frameworks and practices of the expanded human resource development. Organ Dyn, (1), 52. https://doi.org/10.1016/j. orgdyn.2022.100916
  • Leone, D., Schiavone, F., Appio, F. P., & Chiao, B. (2021). How does artificial intelligence enable and enhance value co-creation in industrial markets? An exploratory case study in the healthcare ecosystem. J Bus Res, 129, 849–859. https://doi.org/ 10.1016/j.jbusres.2020.11.008
  • Li, H., & Yoo, S. (2022). From information systems resources to effective use: Moderating effect of network embeddedness. The Journal of Strategic Information Systems, 31(3), Article 101735. https://doi.org/10.1016/J.JSIS.2022.101735
  • Lin, Y. K., Lin, M., & Chen, H. (2019). Do electronic health records affect quality of care? Evidence from the HITECH act. Information Systems Research, 30(1), 306–318. https://doi.org/10.1287/isre.2018.0813
  • Lobschat, L., Mueller, B., Eggers, F., Brandimarte, L., Diefenbach, S., Kroschke, M., & Wirtz, J. (2021). Corporate digital responsibility. Journal of Business Research, 122, 875–888. https://doi.org/10.1016/j.jbusres.2019.10.006
  • Lu, Y., & Ramamurthy, K. (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS Quarterly: Management Information Systems, 35(4), 931–954. https://doi.org/ 10.2307/41409967
  • Marchiori, D. M., Rodrigues, R. G., Popadiuk, S., & Mainardes, E. W. (2022). The relationship between human capital, information technology capability, innovativeness and organizational performance: An integrated approach. Technological Forecasting of Social Change, 177, Article 121526. https://doi.org/ 10.1016/j.techfore.2022.121526
  • May, C., & Finch, T. (2009). Implementing, embedding, and integrating practices: An outline of normalization process theory. Sociology, 43(3), 535–554. https://doi.org/ 10.1177/0038038509103208
  • Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Antes, G., Atkins, D., Barbour, V., Barrowman, N., Berlin, J. A., Clark, J., Clarke, M., Cook, D., D’Amico, R., Deeks, J. J., Devereaux, P. J., Dickersin, K., Egger, M., Ernst, E., Gøtzsche, P. C., & Tugwell, P. (2009). Preferred reporting items for systematic reviews and meta- analyses: The PRISMA statement. PLoS Med., 6(7). https://doi.org/10.1371/journal. pmed.1000097
  • Montalván-Burbano, N., Velastegui-Montoya, A., Gurumendi-Noriega, M., Morante- Carballo, F., & Adami, M. (2021). Worldwide research on land use and land cover in the amazon region. Sustainability, 13(11). https://doi.org/10.3390/su13116039
  • Moral-Munoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: an up-to-date review. Profesional de La Información, 29(1). https://doi.org/10.3145/epi.2020.ene.03 Morley, J., Widdicks, K., & Hazas, M. (2018). Digitalisation, energy and data demand: The impact of Internet traffic on overall and peak electricity consumption. Energy Research & Social Science, 38, 128–137. https://doi.org/10.1016/j.erss.2018.01.018
  • Nasir, A., Shaukat, K., Hameed, I. A., Luo, S., Alam, T. M., & Iqbal, F. (2020). A bibliometric analysis of corona pandemic in social sciences: A review of influential aspects and conceptual structure. IEEE Access, 8, 133377–133402. https://doi.org/ 10.1109/ACCESS.2020.3008733
  • Nucci, F., Puccioni, C., & Ricchi, O. (2023). Digital technologies and productivity: A firm- level investigation. Economics Model, 128, Article 106524. https://doi.org/10.1016/ j.econmod.2023.106524
  • Pantea, S., Sabadash, A., & Biagi, F. (2017). Are ICT displacing workers in the short run? Evidence from seven European countries. Information Economics and Policy, 39, 36–44. https://doi.org/10.1016/j.infoecopol.2017.03.002
  • Paule-Vianez, J., Gómez-Martínez, R., & Prado-Román, C. (2020). A bibliometric analysis of behavioural finance with mapping analysis tools. European Research on Management and Business Economics, 26(2), 71–77. https://doi.org/10.1016/j. iedeen.2020.01.001
  • Pérez-Romero, M. E., ´ Alvarez-García, J., Flores-Romero, M. B., & Jim´ enez-Islas, D. (2023). UNESCO global geoparks 22 years after their creation: Analysis of scientific production. Land (Basel), 12(3). https://doi.org/10.3390/land12030671
  • Ramadan, M., Bou Zakhem, N., Baydoun, H., Daouk, A., Youssef, S., El Fawal, A., Elia, J., & Ashaal, A. (2023). Toward digital transformation and business model innovation: The nexus between leadership, organizational agility, and knowledge transfer. Adm Science, 13(8). https://doi.org/10.3390/admsci13080185
  • Reis, J., & Mel˜ ao, N. (2023). Digital transformation: A meta-review and guidelines for future research. Heliyon, 9(1). https://doi.org/10.1016/j.heliyon.2023.e12834
  • Rodríguez-Insuasti, H., Montalv´an-Burbano, N., Su´ arez-Rodríguez, O., Yonf´a- Medranda, M., & Parrales-Guerrero, K. (2022). Creative Economy: A worldwide research in business, management and accounting. In Sustainability, 14. https://doi. org/10.3390/su142316010
  • Sahid, A., Maleh, Y., Asemanjerdi, S. A., & Martín-Cervantes, P. A. (2023). A bibliometric analysis of the FinTech agility literature: evolution and review. International Journal of Financial Studies, 11(4). https://doi.org/10.3390/ijfs11040123
  • Sambamurthy, V., Bharadwaj, A., & Grover, V. (2003). Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary f irms. MIS Quarterly, 27(2), 237–263. https://doi.org/10.2307/30036530
  • Shafiabady, N., Hadjinicolaou, N., Din, F. U., Bhandari, B., Wu, R. M. X., & Vakilian, J. (2023). Using artificial intelligence (AI) to predict organizational agility. PLoS ONE, (5), 18. https://doi.org/10.1371/journal.pone.0283066
  • Shah, S. J. H. (2023). Chatbots for business and customer support. Trends, Applications, and Challenges of Chatbot Technology (pp. 212–221). IGI Global. https://doi.org/ 10.4018/978-1-6684-6234-8.ch009
  • Soto-Acosta, P. (2020). COVID-19 pandemic: shifting digital transformation to a high- speed gear. Information Systems Management, 37(4), 260–266. https://doi.org/ 10.1080/10580530.2020.1814461
  • Sreenivasan, A., Ma, S. R., Rehman, A. U., & Muthuswamy, S. (2023). Assessment of factors influencing agility. In Start-Ups Industry 4.0, 15. Sustainability. https://doi. org/10.3390/su15097564 Terzopoulos, G., & Satratzemi, M. (2019). Voice assistants and artificial intelligence in education. In Proceedings of the 9th Balkan Conference on Informatics (pp. 1–6). https://doi.org/10.1145/3351556.3351588
  • Tran, H., & Murphy, P. J. (2023). Editorial: Generative artificial intelligence and entrepreneurial performance. Journal of Small Business and Enterprise Development, 30 (5), 853–856. https://doi.org/10.1108/JSBED-09-2023-508
  • Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375
  • Truong, B. Q., Nguyen-Duc, A., & Van, N. T. (2023). A quantitative review of the research on business process management in digital transformation: A bibliometric approach. In Software, 2 pp. 377–399). https://doi.org/10.3390/software2030018
  • van Ark, B. (2016). The productivity paradox of the new digital economy. International Productivity Monitor, 31, 3–18. https://api.semanticscholar.org/CorpusID: 168697226.
  • Velastegui-Montoya, A., Montalván-Burbano, N., Pe˜ na-Villacreses, G., de Lima, A., & Herrera-Franco, G. (2022). Land use and land cover in tropical forest: global research. Forests, 13(10). https://doi.org/10.3390/f13101709
  • Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/ 10.1016/j.jbusres.2019.09.022
  • Wamba, S. F. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67. https://doi.org/10.1016/j. ijinfomgt.2022.102544
  • Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2020). The performance effects of big data analytics and supply chain ambidexterity: the moderating effect of environmental dynamism. International Journal of Production Economics, 222. https:// doi.org/10.1016/j.ijpe.2019.09.019
  • Wang, X. Q., Lin, X. L., & Shao, B. (2022). How does artificial intelligence create business agility? Evidence from chatbots. International Journal of Information Management, 66. https://doi.org/10.1016/j.ijinfomgt.2022.102535
  • Zhang, H., Ding, H., & Xiao, J. (2023). How organizational agility promotes digital transformation: An empirical study. Sustainability, 15(14). https://doi.org/10.3390/ su151411304
  • Zheng, Y., Venters, W., & Cornford, T. (2011). Collective agility, paradox and organizational improvisation: The development of a particle physics grid. Information Systems Journal, 21(4), 303–333. https://doi.org/10.1111/j.1365- 2575.2010.00360.x
  • Zhu, X., & Li, Y. (2023). The use of data-driven insight in ambidextrous digital transformation: How do resource orchestration, organizational strategic decision- making, and organizational agility matter? Technological Forecasting of Social Change, 196. https://doi.org/10.1016/j.techfore.2023.122851
  • Zupic, I., & ˇ Cater, T. (2015). Bibliometric methods in management and organization. Organ Research Methods, 18(3), 429–472. https://doi.org/10.1177/ 1094428114562629 13
Fuente de los datos: Dialnet