Skip to main content
SpringerLink
Log in

A moderated model of artificial intelligence adoption in firms and its effects on their performance

  • Published:
Information Technology and Management Aims and scope Submit manuscript

Abstract

Leveraging two prominent theories of technology adoption in firms, this study examines the organizational determinants of the adoption intensity of artificial intelligence (AI) and its effects on firms’ performance, under the moderating effects of technological turbulence. To conduct this study, a unique dataset was compiled via a survey of US-based managers involved with technology and AI adoption in high-tech goods and services, leading to 226 usable responses. Structural Equation Modeling was then applied to test the proposed model. The findings uncover the influence of technological, organizational, and environmental factors on the firms’ AI adoption intensity. Additionally, a positive correlation is observed between AI adoption intensity and firms' performance. Lastly, technological turbulence emerges as a crucial environmental factor moderating the effects of antecedents on AI. Given the feeble adoption of AI in firms despite its documented role in firms’ success, the current study can offer a road map to successfully implementing AI in firms and, thus, improving their performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author, [ST], upon reasonable request.

References

  1. Russell S, Norvig P, Canny J, Malik J, Edwards D (2003) Artificial intelligence: A modern approach. Prentice Hall, London

    Google Scholar 

  2. Sultana S, Akter S, Kyriazis E (2022) Theorising data-driven innovation capabilities to survive and thrive in the digital economy. J Strateg Mark 1:1–27. https://doi.org/10.1080/0965254X.2021.2013934

    Article  Google Scholar 

  3. Schmidt R, Zimmermann A, Möhring M, Keller B (2020) Value creation in connectionist artificial intelligence–a research agenda. In: Proceedings of AMCIS pp 1–10

  4. Sestino A, De Mauro A (2022) Leveraging artificial intelligence in business: implications, applications and methods. Technol Anal Strateg Manage 34:16–29. https://doi.org/10.1080/09537325.2021.1883583

    Article  Google Scholar 

  5. Enholm IM, Papagiannidis E, Mikalef P, Krogstie J (2022) Artificial intelligence and business value: a literature review. Inf Syst Front 24:1709–1734

    Article  Google Scholar 

  6. Seidlova R, Poživil J, Seidl J (2019) Marketing and business intelligence with help of ant colony algorithm. J Strateg Mark 27:451–463. https://doi.org/10.1080/0965254X.2018.1430058

    Article  Google Scholar 

  7. McClelland C (2017) How AI is pushing man and machine closer together. https://www.pwc.in/assets/pdfs/consulting/digital-enablement-advisory1/pwc-botme-boo/. Accessed 3 March 2024

  8. Ransbotham S, Kiron D, Gerbert P, Reeves M (2017) Reshaping business with artificial intelligence: Closing the gap between ambition and action. Sloan Manage Rev 59:1

    Google Scholar 

  9. Choi JJ, Ozkan B (2019) Disruptive innovation in business and finance in the digital world. Emerald Group Publishing

  10. Rogić S, Kašćelan L, Kašćelan V, Đurišić V (2022) Automatic customer targeting: a data mining solution to the problem of asymmetric profitability distribution. Inf Technol Manage 23:315–333. https://doi.org/10.1007/s10799-021-00353-5

    Article  Google Scholar 

  11. Passavanti R, Pantano E, Priporas CV, Verteramo S (2020) The use of new technologies for corporate marketing communication in luxury retailing: Preliminary findings. Qual Mark Res: An Int J 23:503–521. https://doi.org/10.1108/QMR-11-2017-0144

    Article  Google Scholar 

  12. Toorajipour R, Sohrabpour V, Nazarpour A, Oghazi P, Fischl M (2021) Artificial intelligence in supply chain management: a systematic literature review. J Bus Res 122:502–517. https://doi.org/10.1016/j.jbusres.2020.09.009

    Article  Google Scholar 

  13. Falcone P, Borrelli F, Asgari J, Tseng HE, Hrovat D (2007) A model predictive control approach for combined braking and steering in autonomous vehicles. In: Proceedings of mediterranean conference on control & automation. IEEE. https://doi.org/10.1109/MED.2007.4433694

  14. Rubin VL, Chen Y, Thorimbert LM (2010) Artificially intelligent conversational agents in libraries. Library Hi Tech 28:496–522. https://doi.org/10.1108/07378831011096196

    Article  Google Scholar 

  15. Borusiak B, Pierański B (2017) Augmented reality in retailing. Augmented Reality for Food Marketers and Consumers. Wageningen Academic Publishers.https://doi.org/10.3920/978-90-8686-842-1

  16. Kohn MS, Sun J, Knoop S, Shabo A, Carmeli B, Sow D, Syed-Mahmood T, Rapp W (2014) IBM’s health analytics and clinical decision support. Yearb Med Inform 23:154–162. https://doi.org/10.15265/IY-2014-0002

    Article  Google Scholar 

  17. Huang M-H, Rust RT (2021) A strategic framework for artificial intelligence in marketing. J Acad Mark Sci 49:30–50. https://doi.org/10.1007/s11747-020-00749-9

    Article  Google Scholar 

  18. Sanaei MR, Sobhani FM (2018) Information technology and e-business marketing strategy. Inf Technol Manag 19:185–196. https://doi.org/10.1007/s10799-018-0289-0

    Article  Google Scholar 

  19. Stone M, Aravopoulou E, Ekinci Y, Evans G, Hobbs M, Labib A, Laughlin P, Machtynger J, Machtynger L (2020) Artificial intelligence (AI) in strategic marketing decision-making: a research agenda. Bottom Line 33:183–200. https://doi.org/10.1108/BL-03-2020-0022

    Article  Google Scholar 

  20. Carpanzano E, Knüttel D (2022) Advances in artificial intelligence methods applications in industrial control systems: towards cognitive self-optimizing manufacturing systems. App Sci 12:10962. https://doi.org/10.3390/app122110962

    Article  Google Scholar 

  21. Lu S, Burton SL (2017) Man vs robots? Future challenges and opportunities within Artificial Intelligence (AI) health care education model. In: Proceedings of the RAIS conferece I 6:7

  22. Volkmar G, Fischer PM, Reinecke S (2022) Artificial Intelligence and machine learning: exploring drivers, barriers, and future developments in marketing management. J Bus Res 149:599–614. https://doi.org/10.1016/j.jbusres.2022.04.007

    Article  Google Scholar 

  23. Link M, Dukino C, Ganz W, Hamann K, Schnalzer K (2020) The use of AI-based assistance systems in the service sector: opportunities, challenges and applications. In: Proceedings of the AHFE 2020 virtual conference on human factors and systems interaction. Springer International Publishing. https://doi.org/10.1007/978-3-030-51369-6_2

  24. Hair Jr JF, Hult GTM, Ringle CM, Sarstedt M (2021) A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications

  25. Chen J, Zhou W (2021) Drivers of salespeople’s AI acceptance: What do managers think? J Pers Selling Sale Manage 42:1–14. https://doi.org/10.1080/08853134.2021.2016058

    Article  Google Scholar 

  26. Chatterjee S, Chaudhuri R, Vrontis D, Thrassou A, Ghosh SK (2021) Adoption of artificial intelligence-integrated CRM systems in agile organizations in India. Technol Forecast Soc Change 168:120783. https://doi.org/10.1016/j.techfore.2021.120783

    Article  Google Scholar 

  27. Rampersad G (2020) Robot will take your job: innovation for an era of artificial intelligence. J Bus Res 116:68–74. https://doi.org/10.1016/j.jbusres.2020.05.019

    Article  Google Scholar 

  28. Haenlein M, Kaplan A (2019) A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif Manage Rev 61:5–14. https://doi.org/10.1177/0008125619864925

    Article  Google Scholar 

  29. Paschen J, Kietzmann J, Kietzmann TC (2019) Artificial intelligence (AI) and its implications for market knowledge in B2B marketing. J Bus Ind Mark Manage 34:1410–1419. https://doi.org/10.1108/JBIM-10-2018-0295

    Article  Google Scholar 

  30. Columbus L (2017) McKinsey's State of machine learning and AI. https://www.forbes.com/sites/louiscolumbus/2017/07/09/mckinseys-state-of-machine-learning-and-ai-2017/?sh=285a878f75b6 Accessed 3 March 2024.

  31. Anayat S, Rasool G (2024) Artificial intelligence marketing (AIM): connecting-the-dots using bibliometrics. J Mark Theory and Pract 32:114–135. https://doi.org/10.1080/10696679.2022.2103435

    Article  Google Scholar 

  32. Bezuidenhout C, Heffernan T, Abbas R, Mehmet M (2022) The impact of artificial intelligence on the marketing practices of professional services firms. J Mark Theory and Pract 31:516–537. https://doi.org/10.1080/10696679.2022.2090005

    Article  Google Scholar 

  33. Mustak M, Salminen J, Plé L, Wirtz J (2021) Artificial intelligence in marketing: topic modeling, scientometric analysis, and research agenda. J Bus Res 124:389–404. https://doi.org/10.1016/j.jbusres.2020.10.044

    Article  Google Scholar 

  34. Guha A, Grewal D (2022) How robots will affect the future of retailing. AMS Rev 1:1–8. https://doi.org/10.1007/s13162-022-00241-3

    Article  Google Scholar 

  35. Vlačić B, Corbo L, e Silva SC, Dabić M (2021) The evolving role of artificial intelligence in marketing: a review and research agenda. J Bus Res 128:187–203. https://doi.org/10.1016/j.jbusres.2021.01.055

    Article  Google Scholar 

  36. Stewart DW (2009) Marketing accountability: linking marketing actions to financial results. J Bus Res 62:636–643. https://doi.org/10.1016/j.jbusres.2008.02.005

    Article  Google Scholar 

  37. Ruiz-Real JL, Uribe-Toril J, Torres JA, De Pablo J (2021) Artificial intelligence in business and economics research: trends and future. J Bus Econ Manage Decis 22:98–117. https://doi.org/10.3846/jbem.2020.13641

    Article  Google Scholar 

  38. Cook WA, Talluri VS (2004) How the pursuit of ROMI is changing marketing management. J Advert Res 44:244–254. https://doi.org/10.1017/S0021849904040322

    Article  Google Scholar 

  39. Hanssens DM, Pauwels KH (2016) Demonstrating the value of marketing. J Mark 80:173–190. https://doi.org/10.1509/jm.15.0417

    Article  Google Scholar 

  40. Rogers EM (1983) Diffusion of innovations, 2nd edn. The Free Press, NY

    Google Scholar 

  41. Tornatzky LG, Fleischer M (1990) The process of technology innovation. Lexington Books, Lexington

    Google Scholar 

  42. Oliveira T, Martins MF (2011) Literature review of information technology adoption models at firm level. Electron J Inf Sys Eval 14:1

    Google Scholar 

  43. Rogers EM (1995) Diffusion of innovations, 4th edn. The Free Press, NY

    Google Scholar 

  44. Weerakkody V, Dwivedi YK, Irani Z (2009) The diffusion and use of institutional theory: a cross-disciplinary longitudinal literature survey. J Inf Technol 24:354–368. https://doi.org/10.1057/jit.2009.16

    Article  Google Scholar 

  45. Aboelmaged MG (2014) Predicting e-readiness at firm-level: An analysis of technological, organizational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms. Int J Inf Manage 34:639–651. https://doi.org/10.1016/j.ijinfomgt.2014.05.002

    Article  Google Scholar 

  46. Yang Z, Sun J, Zhang Y, Wang Y (2015) Understanding SaaS adoption from the perspective of organizational users: a tripod readiness model. Comput Human Behav 45:254–264. https://doi.org/10.1016/j.chb.2014.12.022

    Article  Google Scholar 

  47. Ifinedo P (2008) Impacts of business vision, top management support, and external expertise on ERP success. Bus Process Manage J 14:551–568. https://doi.org/10.1108/14637150810888073

    Article  Google Scholar 

  48. Hung W-H, Chang L-M, Lin C-P, Hsiao C-H (2014) E-readiness of website acceptance and implementation in SMEs. Comput Human Behav 40:44–55. https://doi.org/10.1016/j.chb.2014.07.046

    Article  Google Scholar 

  49. Quinton S, Canhoto A, Molinillo S, Pera R, Budhathoki T (2018) Conceptualising a digital orientation: antecedents of supporting SME performance in the digital economy. J Strateg Mark 26:427–439. https://doi.org/10.1080/0965254X.2016.1258004

    Article  Google Scholar 

  50. Andrews W (2017) Applying artificial intelligence to drive business transformation: a gartner trend insight report. https://www.gartner.com/en/documents/3792874. Accessed 3 March 2024

  51. Srinivasan R, Lilien GL, Rangaswamy A (2002) Technological opportunism and radical technology adoption: an application to e-business. J Market Res 66:47–60. https://doi.org/10.1509/jmkg.66.3.47.18508

    Article  Google Scholar 

  52. Chen C-W, Lien N-H (2013) Technological opportunism and firm performance: moderating contexts. J Bus Res 66:2218–2225. https://doi.org/10.1016/j.jbusres.2012.02.001

    Article  Google Scholar 

  53. Voola R, Casimir G, Carlson J, Anushree Agnihotri M (2012) The effects of market orientation, technological opportunism, and e-business adoption on performance: a moderated mediation analysis. Australas Mark J 20:136–146. https://doi.org/10.1016/j.ausmj.2011.10.001

    Article  Google Scholar 

  54. Dutton JE, Duncan RB (1987) The influence of the strategic planning process on strategic change. Strateg Manage J 8:103–116. https://doi.org/10.1002/smj.4250080202

    Article  Google Scholar 

  55. Butschan J, Heidenreich S, Weber B, Kraemer T (2019) Tackling hurdles to digital transformation—the role of competencies for successful industrial internet of things (IIoT) implementation. Int J Innov Manage 23:1950036. https://doi.org/10.1142/S1363919619500361

    Article  Google Scholar 

  56. Agostini L, Nosella A (2019) The adoption of Industry 4.0 technologies in SMEs: results of an international study. Manage Decis 58:625–643. https://doi.org/10.1108/MD-09-2018-0973

    Article  Google Scholar 

  57. Yang C-C, Lin CY-Y (2009) Does intellectual capital mediate the relationship between HRM and organizational performance? Perspective of a healthcare industry in Taiwan. Int J Hum Resour Manage 20:1965–1984. https://doi.org/10.1080/09585190903142415

    Article  Google Scholar 

  58. Rialti R, Marzi G, Caputo A, Mayah KA (2020) Achieving strategic flexibility in the era of big data: the importance of knowledge management and ambidexterity. Manage Decis 58:1585–1600. https://doi.org/10.1108/MD-09-2019-1237

    Article  Google Scholar 

  59. Sleep S, Hulland J (2019) Is big data driving cooperation in the c-suite? The evolving relationship between the chief marketing officer and chief information officer. J Strateg Mark 27:666–678. https://doi.org/10.1080/0965254X.2018.1464496

    Article  Google Scholar 

  60. Shao Z, Feng Y, Hu Q (2016) Effectiveness of top management support in enterprise systems success: a contingency perspective of fit between leadership style and system life-cycle. Eur J Inf Syst 25:131–153. https://doi.org/10.1057/ejis.2015.6

    Article  Google Scholar 

  61. Anabila P, Achiriga V, Okpattah B, Asare S (2023) Market orientation and behavioural intentions of tourists in Ghana’s tourist destinations: the mediation role of tourist satisfaction. J Strateg Mark 31:99–115. https://doi.org/10.1080/0965254X.2021.1874489

    Article  Google Scholar 

  62. Narver JC, Slater SF (1990) The effect of a market orientation on business profitability. J Mark Res 54:20–35. https://doi.org/10.1177/002224299005400403

    Article  Google Scholar 

  63. Raymond L, Uwizeyemungu S, Fabi B, St-Pierre J (2018) IT capabilities for product innovation in SMEs: a configurational approach. Inf Technol Manage 19:75–87. https://doi.org/10.1007/s10799-017-0276-x

    Article  Google Scholar 

  64. Stratopoulos TC, Wang VX (2016) A framework for predicting emerging technology adoption. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2695858. Accessed 3 March 2024.

  65. Da Silveira GJ (2014) An empirical analysis of manufacturing competitive factors and offshoring. Int J Product Econ 150:163–173. https://doi.org/10.1016/j.ijpe.2013.12.031

    Article  Google Scholar 

  66. Pooya A, Faezirad M (2017) A taxonomy of manufacturing strategies and production systems using self-organizing map. J Ind Prod Eng 34:300–311. https://doi.org/10.1080/21681015.2017.1305996

    Article  Google Scholar 

  67. Kinkel S, Baumgartner M, Cherubini E (2022) Prerequisites for the adoption of AI technologies in manufacturing—evidence from a worldwide sample of manufacturing companies. Technovation 110:102375. https://doi.org/10.1016/j.technovation.2021.102375

    Article  Google Scholar 

  68. Zaltman G (2016) Marketing’s forthcoming age of imagination. AMS Rev 6:99–115. https://doi.org/10.1007/s13162-016-0082-3

    Article  Google Scholar 

  69. Moorman C, Day GS (2016) Organizing for marketing excellence. J Mark Res 80:6–35. https://doi.org/10.1509/jm.15.0423

    Article  Google Scholar 

  70. Aronson E, Wilson TD, Akert RM (2005) Social psychology: the heart and the mind, 5th edn. Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  71. Greenwood R, Oliver C, Lawrence TB, Meyer RE (2017) The Sage handbook of organizational institutionalism. Sage

    Book  Google Scholar 

  72. Lichtenthaler U (2009) Absorptive capacity, environmental turbulence, and the complementarity of organizational learning processes. Acad Manage J 52:822–846. https://doi.org/10.5465/amj.2009.43670902

    Article  Google Scholar 

  73. Jaworski BJ, Kohli AK (1993) Market orientation: antecedents and consequences. J Mark Res 57:53–70. https://doi.org/10.1177/002224299305700304

    Article  Google Scholar 

  74. Wilden R, Gudergan SP (2015) The impact of dynamic capabilities on operational marketing and technological capabilities: investigating the role of environmental turbulence. J Acad Mark Sci 43:181–199. https://doi.org/10.1007/s11747-014-0380-y

    Article  Google Scholar 

  75. Williams AM, Rodriguez Sanchez I, Škokić V (2021) Innovation, risk, and uncertainty: A study of tourism entrepreneurs. J Travel Res 60:293–311. https://doi.org/10.1177/0047287519896012

    Article  Google Scholar 

  76. Song M, Droge C, Hanvanich S, Calantone R (2005) Marketing and technology resource complementarity: an analysis of their interaction effect in two environmental contexts. Strateg Manag J 26:259–276. https://doi.org/10.1002/smj.450

    Article  Google Scholar 

  77. Grinstein A (2008) The effect of market orientation and its components on innovation consequences: a meta-analysis. J Acad Mark Sci 36:166–173. https://doi.org/10.1007/s11747-007-0053-1

    Article  Google Scholar 

  78. Qu R, Ennew CT (2003) An examination of the consequences of market orientation in China. J Strateg Mark 11:201–214. https://doi.org/10.1080/0965254032000133449

    Article  Google Scholar 

  79. Cetindamar D, Kitto K, Wu M, Zhang Y, Abedin B, Knight S (2022) Explicating AI literacy of employees at digital workplaces. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2021.3138503

    Article  Google Scholar 

  80. Andreou A, Silva M, Benevenuto F, Goga O, Loiseau P, Mislove A (2019) Measuring the Facebook advertising ecosystem. In: Proceedings of network and distributed system security symposium. https://doi.org/10.14722/ndss.2019.23280

  81. Li H (2019) Special section introduction: artificial intelligence and advertising. J Advert 48:333–337. https://doi.org/10.1080/00913367.2019.1654947

    Article  Google Scholar 

  82. Deng S, Tan C-W, Wang W, Pan Y (2019) Smart generation system of personalized advertising copy and its application to advertising practice and research. J Advert 48:356–365. https://doi.org/10.1080/00913367.2019.1652121

    Article  Google Scholar 

  83. Ehret M, Kashyap V, Wirtz J (2013) Business models: impact on business markets and opportunities for marketing research. Ind Mark Manage 42:649–655. https://doi.org/10.1016/j.indmarman.2013.06.003

    Article  Google Scholar 

  84. Hart S (1987) The use of the survey in industrial market research. J Mark Manage 3:25–38. https://doi.org/10.1080/0267257X.1987.9964025

    Article  Google Scholar 

  85. Baker HK, Mukherjee TK (2007) Survey research in finance: views from journal editors. Int J Manage Finance 3:11–25. https://doi.org/10.1108/17439130710721635

    Article  Google Scholar 

  86. Cannella J (2018) Artificial Intelligence in marketing. Arizona State University

  87. Criado JI, Sandoval-Almazan R, Valle-Cruz D, Ruvalcaba-Gómez EA (2021) Chief information officers’ perceptions about artificial intelligence: a comparative study of implications and challenges for the public sector. First Monday. https://doi.org/10.5210/fm.v26i1.10648

    Article  Google Scholar 

  88. Armstrong G, Adam S, Denize S, Kotler P (2014) Principles of marketing. Pearson

  89. Kline RB (2015) Principles and practice of structural equation modeling. Guilford publications, NY

    Google Scholar 

  90. Nunnally JC (1994) Psychometric Theory, 3rd ed. McGraw-hill

  91. Westland JC (2010) Lower bounds on sample size in structural equation modeling. Electron Commer Res Appl 9:476–487. https://doi.org/10.1016/j.elerap.2010.07.003

    Article  Google Scholar 

  92. Diamantopoulos A, Sarstedt M, Fuchs C, Wilczynski P, Kaiser S (2012) Guidelines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective. J Acad Mark Sci 40:434–449. https://doi.org/10.1007/s11747-011-0300-3

    Article  Google Scholar 

  93. Weijters B, Cabooter E, Schillewaert N (2010) The effect of rating scale format on response styles: the number of response categories and response category labels. Int J Res in Mark 27:236–247. https://doi.org/10.1016/j.ijresmar.2010.02.004

    Article  Google Scholar 

  94. Westland JC (2022) Information loss and bias in likert survey responses. PLoS ONE 17:e0271949. https://doi.org/10.1371/journal.pone.0271949

    Article  Google Scholar 

  95. Parasuraman A, Zeithaml VA, Malhotra A (2005) ES-QUAL: A multiple-item scale for assessing electronic service quality. J Serv Res 7:213–233. https://doi.org/10.1177/1094670504271156

    Article  Google Scholar 

  96. Wu F, Mahajan V, Balasubramanian S (2003) An analysis of e-business adoption and its impact on business performance. J Acad Mark Sci 31:425–447. https://doi.org/10.1177/0092070303255379

    Article  Google Scholar 

  97. Murray JY, Gao GY, Kotabe M (2011) Market orientation and performance of export ventures: the process through marketing capabilities and competitive advantages. J Acad Mark Sci 39:252–269. https://doi.org/10.1007/s11747-010-0195-4

    Article  Google Scholar 

  98. Sureshchandar G, Rajendran C, Anantharaman R (2002) Determinants of customer-perceived service quality: a confirmatory factor analysis approach. J Serv Mark 16:9–34. https://doi.org/10.1108/08876040210419398

    Article  Google Scholar 

  99. Papadas K-K, Avlonitis GJ, Carrigan M (2017) Green marketing orientation: conceptualization, scale development and validation. J Bus Res 80:236–246. https://doi.org/10.1016/j.jbusres.2017.05.024

    Article  Google Scholar 

  100. Crowne DP, Marlowe D (1960) A new scale of social desirability independent of psychopathology. J Consult Psychol 24:349. https://doi.org/10.1037/h0047358

    Article  Google Scholar 

  101. Podsakoff PM, MacKenzie SB, Podsakoff NP (2012) Sources of method bias in social science research and recommendations on how to control it. Annu Rev Psychol 63:539–569. https://doi.org/10.1146/annurev-psych-120710-100452

    Article  Google Scholar 

  102. Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88:879. https://doi.org/10.1037/0021-9010.88.5.879

    Article  Google Scholar 

  103. Stevens JP (2012) Applied multivariate statistics for the social sciences. Routledge, NY

    Book  Google Scholar 

  104. Westfall P, Henning K (2013) Texts in statistical science: Understanding advanced statistical methods. Taylor & Francis, Boca Raton, FL

    Google Scholar 

  105. Baek TH, Kim J, Yu JH (2010) The differential roles of brand credibility and brand prestige in consumer brand choice. Psychol Mark 27:662–678. https://doi.org/10.1002/mar.20350

    Article  Google Scholar 

  106. Hair JF, Ringle CM, Sarstedt M (2011) PLS-SEM: indeed a silver bullet. J Mark Theory Pract 19:139–152. https://doi.org/10.2753/MTP1069-6679190202

    Article  Google Scholar 

  107. Hair Jr JF, Sarstedt M, Ringle CM, Gudergan SP (2017) Advanced issues in partial least squares structural equation modeling. Sage publications

  108. Nunnally JC (1978) An overview of psychological measurement. Clin Diagn Ment Disord 1:97–146. https://doi.org/10.1007/978-1-4684-2490-4_4

    Article  Google Scholar 

  109. Hu LT, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Modeling 6:1–55. https://doi.org/10.1080/10705519909540118

    Article  Google Scholar 

  110. Fornell C, Larcker DF (1981) Structural equation models with unobservable variables and measurement error: algebra and statistics. Sage, Los Angeles, CA

    Google Scholar 

  111. Bagozzi RP (1983) A holistic methodology for modeling consumer response to innovation. Oper Res 31:128–176. https://doi.org/10.1287/opre.31.1.128

    Article  Google Scholar 

  112. Hwang H, Malhotra NK, Kim Y, Tomiuk MA, Hong S (2010) A comparative study on parameter recovery of three approaches to structural equation modeling. J Mark Res 47:699–712. https://doi.org/10.1509/jmkr.47.4.699

    Article  Google Scholar 

  113. Ryu E (2011) Effects of skewness and kurtosis on normal-theory based maximum likelihood test statistic in multilevel structural equation modeling. Behav Res Methods 43:1066–1074. https://doi.org/10.3758/s13428-011-0115-7

    Article  Google Scholar 

  114. Micceri T (1989) The unicorn, the normal curve, and other improbable creatures. Psychol Bull 105:156. https://doi.org/10.1037/0033-2909.105.1.156

    Article  Google Scholar 

  115. Lei M, Lomax RG (2005) The effect of varying degrees of nonnormality in structural equation modeling. Struct Equ Modeling 12:1–27. https://doi.org/10.1207/s15328007sem1201_1

    Article  Google Scholar 

  116. Duchessi P, O’Keefe R, O’Leary D (1993) A research perspective: Artificial intelligence, management and organizations. Intell Syst Account Financ Manag 2:151–159. https://doi.org/10.1002/j.1099-1174.1993.tb00039.x

    Article  Google Scholar 

  117. Hansen E, Nybakk E (2016) When innovativeness determines market orientation: the forest sector and the Great Recession. BioProd Bus 1:1

    Google Scholar 

  118. Davenport T, Guha A, Grewal D, Bressgott T (2020) How artificial intelligence will change the future of marketing. J Acad Mark Sci 48:24–42. https://doi.org/10.1007/s11747-019-00696-0

    Article  Google Scholar 

  119. van Esch P, Cui Y, Jain SP (2021) Self-efficacy and callousness in consumer judgments of AI-enabled checkouts. Psychol Mark 38:1081–1100. https://doi.org/10.1002/mar.21494

    Article  Google Scholar 

  120. DiMaggio PJ, Powell WW (1983) The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. Am Sociol Rev 1:147–160. https://doi.org/10.2307/2095101

    Article  Google Scholar 

  121. Lehmann DR, McAlister L, Staelin R (2011) Sophistication in research in marketing. J Mark 75:155–165. https://doi.org/10.1509/jmkg.75.4.155

    Article  Google Scholar 

  122. Lilien GL (2011) Bridging the academic–practitioner divide in marketing decision models. J Mark 75:196–210. https://doi.org/10.1509/jmkg.75.4.196

    Article  Google Scholar 

  123. Olan F, Arakpogun EO, Suklan J, Nakpodia F, Damij N, Jayawickrama U (2022) Artificial intelligence and knowledge sharing: contributing factors to organizational performance. J Bus Res 145:605–615. https://doi.org/10.1016/j.jbusres.2022.03.008

    Article  Google Scholar 

  124. Lucia-Palacios L, Bordonaba-Juste V, Polo-Redondo Y, Grünhagen M (2014) Technological opportunism effects on IT adoption, intra-firm diffusion and performance: evidence from the US and Spain. J Bus Res 67:1178–1188. https://doi.org/10.1016/j.jbusres.2013.05.004

    Article  Google Scholar 

  125. Eriksson T, Bigi A, Bonera M (2020) Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. TQM J 32:795–814. https://doi.org/10.1108/TQM-12-2019-0303

    Article  Google Scholar 

  126. Wamba-Taguimdje S-L, Fosso Wamba S, Kala Kamdjoug JR, Tchatchouang Wanko CE (2020) Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Bus Process Manage J 26:1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Nicolais School of Business, Wagner College, 1 Campus Rd, Staten Island, NY, 10301, USA

    Jing Chen

  2. School of Business, Indiana University Southeast, 4201 Grant Line Road, New Albany, IN, 47150, USA

    Saeed Tajdini

Authors
  1. Jing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saeed Tajdini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saeed Tajdini.

Ethics declarations

Conflict of interest

Data collection was funded by the Marcus Jonathan Hunt endowment, the Mike Loya in Business Administration endowment, and the Mike Loya Center for Innovation and Commerce at the University of Texas at El Paso.

Ethics approval and informed consent

The Institutional Review Board (IRB) of The University of Texas at El Paso reviewed and granted “exempt” status to this research. Also, the researchers obtained informed consent from all participants.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Tajdini, S. A moderated model of artificial intelligence adoption in firms and its effects on their performance. Inf Technol Manag (2024). https://doi.org/10.1007/s10799-024-00422-5

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10799-024-00422-5

Keywords

Search

Navigation