Management des données massives : une investigation empirique de l’effet des excès des bonnes choses

Auteurs

Mots-clés :

Too-Much-of-a-Good-Thing effect, inverted U-shaped curve, Big data, business value, medium and large firms

Résumé

Les entreprises adoptent des solutions pour gérer les données massives, mais un ensemble de preuves suggère que, dans certains cas, les données massives pourraient créer plus de problèmes que d’avantages. Nous avançons l’hypothèse que le problème n’est peut-être pas les données massives en elles-mêmes mais une trop grande quantité de données massives. Nous apportons une réponse à la question de recherche suivante : quand les données massives conduisent-elles à la destruction de valeur ? Ce type d’effets fait écho à l’effet des excès des bonnes choses («Too-Much-of-a-Good-Thing» - TMGT) décrit en gestion. Cette théorie semble également significative et applicable en systèmes d’information. Nous contribuons à l’évaluation de cet effet TMGT lié aux données massives. Nous avons collecté des données auprès d’un échantillon d’entreprises, et nous avons établi un ensemble de modèles de régression pour tester la relation entre les données massives et la création de valeur, en considérant la taille de l’entreprise comme un modérateur. Les données confirment l’existence d’une courbe en forme de U inversé et l’existence d’une modération liée à la taille de l’entreprise. Ces résultats élargissent l’applicabilité de la théorie de l’effet TMGT et peuvent être utiles aux entreprises qui envisagent d’investir dans les données massives.

Bibliographies de l'auteur

Claudio Vitari, Aix Marseille Univ, Université de Toulon, CERGAM, FEG, Aix-en-Provence, France

I am Full Professor at Aix-Marseille University (France). My research interests include Information Systems, Strategic Management, Ecological Economics. My publications encompass several articles in journals including Ecological Economics, Systèmes d'Information et Management, European Journal of Information Systems, Communications of the Association for Information Systems, International Journal Knowledge Management, Knowledge Management Research & Practice, Journal of Information Technologies: Cases and Applications. Many of my articles are published in the proceedings of different international conferences. I have over 15 years’ experience in teaching, research, management and consulting. I got my Ph.D. from Montpellier University (Montpellier, France) and the Carlo Cattaneo University (Castellanza, Italy). I received my French accreditation to supervise research (HDR) from Montpellier University (Montpellier, France).

Elisabetta Raguseo, Politecnico di Torino, Dipartimento di Ingegneria Gestionale e della Produzione

Elisabetta Raguseo (PhD) is Associate professor in Strategy and Economics at Politecnico di Torino (Italy) and Associate Editor of Information and Management journal and Journal of Travel Research. She is member of the Entrepreneurship and Innovation Centre at the Politecnico di Torino and of the European Industrial Engineering and Management Cluster. She was part of the Group of Experts for the Observatory on the Online Platform Economy of the European Commission (mandate 2018-2021) and a Marie Curie research fellow at the business school Grenoble Ecole de Management (France) in the years 2014-2016. Her research and teaching expertise is in strategic information systems, big data, artificial intelligence, tourism economics and digital transformation. Her research has been published in highly ranked, international journals including Journal of Travel Research, International Journal of Hospitality Management, International Journal of Production Research, Computers in Human Behavior, International Journal of Electronic Commerce, Information and Management, International Journal of Information Management and many more.

Federico Pigni, Grenoble Ecole de Management, Management of Technology and Strategy department

Federico Pigni is the Dean of the Faculty of Grenoble Ecole de Management, where he is Professor in Information Systems in the Management of Technology and Strategy department. He graduated cum laude in Business Administration and Management and holds a Ph.D. in Management Information Systems and Supply Chain Management. Since 1999, he has been working as a lecturer and research assistant at Carlo Cattaneo Unversity. In the following years, he started lecturing at the Catholic University in Milan and in 2007 at the Università Commerciale Luigi Bocconi in Milan (Italy). From 2007 to 2010 he was Senior Researcher at Carlo Cattaneo University's Lab#ID RFId laboratory. From 2000 to 2006 he has been owner of Lab4Consulting, a Web and software consulting company. Since then, his consulting activities have focused on IT-based innovation in the banking industry. In 2006 he also joined France Télécom R&D - Pole Service Sciences in Sophia Antipolis (France) for a post-doctorate, developing methodologies addressing the inter-organizational adoption of ICT. He teaches in the area of Information Systems and has a research interest in the strategic application of information systems in the interorganizational context and the use of innovative digital technologies to deliver customer service.

Références

Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and interpreting interactions. Sage.

Amankwah-Amoah, J. (2016). Emerging economies, emerging challenges: Mobilising and capturing value from big data. Technological Forecasting and Social Change, 110(Supplement C), 167–174. https://doi.org/10.1016/j.techfore.2015.10.022

Asay, M. (2017). 85% of big data projects fail, but your developers can help yours succeed. TechRepublic. https://www.techrepublic.com/article/85-of-big-data-projects-fail-but-your-developers-can-help-yours-succeed/

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327

Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing Construct Validity in Organizational Research. Administrative Science Quarterly, 36(3), 421–458. JSTOR. https://doi.org/10.2307/2393203

Bughin, J. (2016). Big data, Big bang? Journal of Big data, 3(1), 2. https://doi.org/10.1186/s40537-015-0014-3

Cadwalladr, C., & Graham-Harrison, E. (2018). The Cambridge analytica files. The Guardian, 21, 6–7.

Cappa, F., Oriani, R., Peruffo, E., & McCarthy, I. (2021). Big Data for Creating and Capturing Value in the Digitalized Environment: Unpacking the Effects of Volume, Variety, and Veracity on Firm Performance*. Journal of Product Innovation Management, 38(1), 49–67. https://doi.org/10.1111/jpim.12545

Cass, D. (1965). Optimum Growth in an Aggregative Model of Capital Accumulation. The Review of Economic Studies, 32(3), 233–240. JSTOR. https://doi.org/10.2307/2295827

Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63, 67-80.

Chen, H., Chiang, R., & Storey, V. (2012). Business Intelligence and Analytics: From Big data to Big Impact. Management Information Systems Quarterly, 36(4), 1165–1188.

Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73.

Clark, B. H., Abela, A. V., & Ambler, T. (2006). An Information Processing Model of Marketing Performance Measurement. Journal of Marketing Theory & Practice, 14(3), 191–208. https://doi.org/10.2753/MTP1069-6679140302

Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: Big data and the changing context of strategy. Journal of Information Technology, 30(1), 44-57.

Cox, M., & Ellsworth, D. (1997). Managing big data for scientific visualization. ACM Siggraph, 97, 21–38.

Davis, F., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003.

DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9–30.

Dong, J. Q., & Yang, C.-H. (2020). Business value of big data analytics: A systems-theoretic approach and empirical test. Information & Management, 57(1), 103124. https://doi.org/10.1016/j.im.2018.11.001

Ebner, K., Bühnen, T., & Urbach, N. (2014). Think Big with Big data: Identifying Suitable Big data Strategies in Corporate Environments. 2014 47th Hawaii International Conference on System Sciences, 3748–3757. https://doi.org/10.1109/HICSS.2014.466

Einhorn, H. J., & Hogarth, R. M. (1975). Unit Weighting Schemes for Decision Making. Organizational Behavior and Human Performance, 13(2), 171–192.

Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic Capabilities: What Are They? Strategic Management Journal, 21(10/11), 1105–1121.

Fleishman, E. A. (1998). Patterns of leadership behavior related to employee grievances and turnover: Some post hoc reflections. Personnel Psychology, 51(4), 825–834. https://doi.org/10.1111/j.1744-6570.1998.tb00740.x

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Gomes, J. F. S., de Weerd-Nederhof, P. C., Pearson, A. W., & Cunha, M. P. (2003). Is more always better? An exploration of the differential effects of functional integration on performance in new product development. Technovation, 23(3), 185–191. https://doi.org/10.1016/S0166-4972(01)00107-9

Goold, M. (1999). The Growth Imperative. Long Range Planning, 32(1), 127–129. https://doi.org/10.1016/S0024-6301(98)00133-2

Gregor, S., Martin, M., Fernandez, W., Stern, S., & Vitale, M. (2006). The transformational dimension in the realization of business value from information technology. The Journal of Strategic Information Systems, 15(3), 249–270. https://doi.org/10.1016/j.jsis.2006.04.001

Grover, V., Chiang, R. H. L., Ting-Peng Liang, & Dongsong Zhang. (2018). Creating Strategic Business Value from Big data Analytics: A Research Framework. Journal of Management Information Systems, 35(2), 388–423. https://doi.org/10.1080/07421222.2018.1451951

Haans, R. F. J., Pieters, C., & He, Z.-L. (2016). Thinking about U: Theorizing and testing U- and inverted U-shaped relationships in strategy research. Strategic Management Journal, 37(7), 1177–1195. https://doi.org/10.1002/smj.2399

Haffke, I., Kalgovas, B. J., & Benlian, A. (2017). Options for Transforming the IT Function Using Bimodal IT. MIS Q. Executive.

Hair, J. F., Black, W., Babin, B., Anderson, R. E., & Tatham, R. L. (2010). Multivariate Data Analysis (7th edition). Pearson.

Hastie, R., & Dawes, R. M. (2001). Rational Choice in an Uncertain World: The Psychology of Judgement and Decision Making. SAGE.

Illich, I. (1973). Tools for Conviviality. Harper & Row.

Kamioka, T., & Tapanainen, T. (2014). Organizational Use of Big data and Competitive Advantage-Exploration of Antecedents. PACIS, 2014, 18th.

Khallaf, A., Omran, M. A., & Zakaria, T. (2017). Explaining the inconsistent results of the impact of information technology investments on firm performance: A longitudinal analysis. Journal of Accounting & Organizational Change, 13(3), 359–380. https://doi.org/10.1108/JAOC-11-2015-0086

Lee, A. R., Son, S.-M., & Kim, K. K. (2016). Information and communication technology overload and social networking service fatigue: A stress perspective. Computers in Human Behavior, 55, 51–61. https://doi.org/10.1016/j.chb.2015.08.011

Lee, I. (2017). Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293–303. https://doi.org/10.1016/j.bushor.2017.01.004

Lehrer, C., Wieneke, A., Vom Brocke, J., Jung, R., & Seidel, S. (2018). How Big data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service. Journal of Management Information Systems, 35(2), 424–460. https://doi.org/10.1080/07421222.2018.1451953

Lin, W. T., & Shao, B. B. M. (2006). The business value of information technology and inputs substitution: The productivity paradox revisited. Decision Support Systems, 42(2), 493–507. https://doi.org/10.1016/j.dss.2005.10.011

Lind, J. T., & Mehlum, H. (2010). With or Without U? The Appropriate Test for a U-Shaped Relationship*. Oxford Bulletin of Economics and Statistics, 72(1), 109–118. https://doi.org/10.1111/j.1468-0084.2009.00569.x

Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24(3), 149–157. https://doi.org/10.1016/j.jsis.2015.08.002

Love, P. E. D., Irani, Z., Standing, C., Lin, C., & Burn, J. M. (2005). The enigma of evaluation: Benefits, costs and risks of IT in Australian small–medium-sized enterprises. Information & Management, 42(7), 947–964. https://doi.org/10.1016/j.im.2004.10.004

Lynch, C. (2008). Big data: How do your data grow? Nature, 455(7209), 28–29. https://doi.org/10.1038/455028a

Mackenzie, J. S. (1899). The Idea of Progress. International Journal of Ethics, 9(2), 195–213.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, H. B. (2011). Big data: The next frontier for innovation, competition, and productivity (p. 156). McKinsey & Company.

McAfee, A., & Brynjolfsson, E. (2012). Big data: The Management Revolution. Harvard Business Review, October. https://hbr.org/2012/10/big-data-the-management-revolution

Medhora, S. (2019, February 18). Over 2000 people died after receiving Centrelink robo-debt notice, figures reveal. In Triple j. https://www.abc.net.au/triplej/programs/hack/2030-people-have-died-after-receiving-centrelink-robodebt-notice/10821272

Melville, N., Kraemer, K., & Gurbaxani, V. (2004). REVIEW: INFORMATION TECHNOLOGY AND ORGANIZATIONAL PERFORMANCE: AN INTEGRATIVE MODEL OF IT BUSINESS VALUE. MIS Quarterly, 28(2), 283–322.

Mikalef, P., Pappas, I. O., Krogstie, J., & Pavlou, P. A. (2019). Big data and business analytics: A research agenda for realizing business value. Information & Management, 103237. https://doi.org/10.1016/j.im.2019.103237

Mikalef, P., Pappas, I. O., Krogstie, J., & Pavlou, P. A. (2020). Big data and business analytics: A research agenda for realizing business value. Information & Management, 57(1), 103237. https://doi.org/10.1016/j.im.2019.103237

Mithas, S., Tafti, A., Bardhan, I., & Goh, J. M. (2012). Information Technology and Firm Profitability: Mechanisms and Empirical Evidence. MIS Quarterly, 36(1), 205–224.

Mitra, S. (2005). Information Technology as an Enabler of Growth in Firms: An Empirical Assessment. Journal of Management Information Systems, 22(2), 279–300. JSTOR.

Müller, O., Fay, M., & Vom Brocke, J. (2018). The Effect of Big data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics. Journal of Management Information Systems, 35(2), 488–509. https://doi.org/10.1080/07421222.2018.1451955

Navanath, R. (2020, March 20). Big data Market 2020 Growth Analysis, Opportunities, Trends, Developments and Forecast to 2026. Skyline Gazette. https://skyline-gazette.com/2020/03/20/big-data-market-2020-growth-analysis-opportunities-trends-developments-and-forecast-to-2026/

NewVantage Partners. (2019). Big data and AI Executive Survey 2019 (p. 16). NewVantage Partners.

Nisbet, R. (1994). History of the Idea of Progress (2nd edition). Routledge.

Nonninger, L. (2018, June 2). Here’s why China is concerned about Tencent and Alibaba’s credit scoring efforts. Business Insider. https://www.businessinsider.com/china-tencent-and-alibabas-new-credit-scoring-solution-2018-2

Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic Journal of Information Systems Evaluation, 14(1), 110.

Pettey, C. (2016). Busting Bimodal Myths. //www.gartner.com/smarterwithgartner/busting-bimodal-myths/

Pierce, J. R., & Aguinis, H. (2013). The Too-Much-of-a-Good-Thing Effect in Management. Journal of Management, 39(2), 313–338. https://doi.org/10.1177/0149206311410060

Podsakoff, P. M., & Organ, D. W. (1986). Self-Reports in Organizational Research: Problems and Prospects. Journal of Management, 12(4), 531–544. https://doi.org/10.1177/014920638601200408

Qian, G., & Li, L. (2003). Profitability of Small- and Medium-Sized Enterprises in High-Tech Industries: The Case of the Biotechnology Industry. Strategic Management Journal, 24(9), 881–887. JSTOR.

Raguseo, E. (2018). Big data technologies: An empirical investigation on their adoption, benefits and risks for companies. International Journal of Information Management, 38(1), 187–195. https://doi.org/10.1016/j.ijinfomgt.2017.07.008

Raguseo, E., Pigni, F., & Piccoli, G. (2018). Conceptualization, Operationalization, and Validation of the Digital Data Stream Readiness Index. Journal of Global Information Management (JGIM), 26(4), 92–112. https://doi.org/10.4018/JGIM.2018100106

Raguseo, E., Vitari, C., & Pigni, F. (2020). Profiting from big data analytics: The moderating roles of industry concentration and firm size. International Journal of Production Economics, 229, 107758. https://doi.org/10.1016/j.ijpe.2020.107758

Ren, S. J., Wamba, S. F., Akter, S., Dubey, R., & Childe, S. J. (2017). Modelling quality dynamics, business value and firm performance in a big data analytics environment. International Journal of Production Research, 55, 5011–5026. https://doi.org/10.1080/00207543.2016.1154209

Rich, J. T. (2013). THE GROWTH IMPERATIVE. Journal of Business Strategy. https://doi.org/10.1108/eb039992

Richardson, S., Petter, S., & Carter, M. (2021). Five Ethical Issues in the Big Data Analytics Age. Communications of the Association for Information Systems, 49(1). https://doi.org/10.17705/1CAIS.04918

Rieley, J., & Clarkson, I. (2001). The impact of change on performance. Journal of Change Management, 2(2), 160–172. https://doi.org/10.1080/714042499

Schroeder, R. G., & Benbasat, I. (1975). An Experimental Evaluation of the Relationship of Uncertainty in the Environment to Information Used by Decision Makers*. Decision Sciences, 6(3), 556–567. https://doi.org/10.1111/j.1540-5915.1975.tb01043.x

Steelman, Z. R., Havakhor, T., Sabherwal, R., & Sabherwal, S. (2019). Performance Consequences of Information Technology Investments: Implications of Emphasizing New or Current Information Technologies. Information Systems Research, 30(1), 204–218. https://doi.org/10.1287/isre.2018.0798

Street, C. T., & Meister, D. B. (2004). Small Business Growth and Internal Transparency: The Role of Information Systems. MIS Quarterly, 28(3), 473–506. JSTOR. https://doi.org/10.2307/25148647

Taylor, F. W. (2012). The Principles of Scientific Management.

Tiwana, A., Wang, J., Keil, M., & Ahluwalia, P. (2007). The Bounded Rationality Bias in Managerial Valuation of Real Options: Theory and Evidence from IT Projects*. Decision Sciences, 38(1), 157–181. https://doi.org/10.1111/j.1540-5915.2007.00152.x

Tsai, K.-H., Liao, Y.-C., & Hsu, T. T. (2015). Does the use of knowledge integration mechanisms enhance product innovativeness? Industrial Marketing Management, 46, 214–223. https://doi.org/10.1016/j.indmarman.2015.02.030

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance Of Information Technology: Toward A Unified View. MIS Quarterly, 27(3), 425–478.

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, 107498. https://doi.org/10.1016/j.ijpe.2019.09.019

Wolff, J. (2016). Perverse Effects in Defense of Computer Systems: When More Is Less. Journal of Management Information Systems, 33(2), 597–620. https://doi.org/10.1080/07421222.2016.1205934

Zhang, S., Zhao, L., Lu, Y., & Yang, J. (2016). Do you get tired of socializing? An empirical explanation of discontinuous usage behaviour in social network services. Information & Management, 53(7), 904–914. https://doi.org/10.1016/j.im.2016.03.006

Publiée

2022-07-01

Comment citer

Vitari, C., Raguseo, E., & Pigni, F. (2022). Management des données massives : une investigation empirique de l’effet des excès des bonnes choses. Systèmes d’Information Et Management (French Journal of Management Information Systems), 27(3), 87–122. Consulté à l’adresse https://revuesim.org/index.php/sim/article/view/1187

Numéro

Vol. 27 No 3 (2022)

Rubrique

Article de recherche

Licence

L'auteur a la responsabilité de vérifier si le matériel soumis est soumis au droit d'auteur ou aux droits de propriété (par exemple, figures, tableaux, photographies, illustrations, documentation commerciale et données). L'auteur devra obtenir l'autorisation de reproduire ces éléments et inclure ces autorisations dans leur soumission finale.

Nous avons pour politique de demander à tous les auteurs de transférer gratuitement à la revue les droits d'auteur. Il y a deux grandes raisons à cela :

  • la propriété du droit d'auteur par le propriétaire du journal facilite la protection internationale contre la violation du droit d'auteur, la diffamation ou le plagiat;
  • il garantit également que les demandes de réimpression ou de reproduction d'une contribution, en totalité ou en partie, sous forme imprimée ou électronique, soient traitées efficacement conformément à notre politique générale qui encourage la diffusion des connaissances dans le cadre du droit d'auteur.

Conformément à la loi française sur la propriété intellectuelle, l'auteur d'un article conserve les droits moraux :

  • Le droit de paternité qui permet à l'auteur de voir son nom associé à chacune des exploitations de son œuvre.
  • Le droit au respect de l’œuvre qui peut être revendiqué par l'auteur s'il constate que lors d'une exploitation, son œuvre a été dénaturée (coupe, remontage…).