Utilização de sistemas de inteligência de negócios em empresas de serviços
DOI :
https://doi.org/10.32459/2447-8717e239Mots-clés :
Inteligência empresarial, Sistemas de informação gerencial, Serviços, Estratégia organizacionalRésumé
O trabalho teve como objetivo entender os fatores que facilitam a utilização de sistemas de Business Intelligence em empresas de serviços. Para isso foi utilizado um modelo conhecido como Expectation-Confirmation Theory (ECT) adaptado para avaliar a aceitação do sistema. Além dos constructos do modelo original proposto por Bhattacherjee (2001) que são: Confirmação, Satisfação, Utilidade Percebida, Intenção Comportamental de Uso e Continuidade de Uso; foram incluídos três constructos no modelo adaptado: Hábito, Qualidade da Informação e Estratégia Direcionada pela Tecnologia. O questionário obteve 81 respondentes usuários de sistemas de BI. As conclusões apontam que as hipóteses foram confirmadas, exceto o efeito moderador do Hábito na Continuidade de Uso e a influência da Estratégia Direcionada pela Tecnologia. Como contribuições, destacam-se: a proposta de uma adaptação do modelo ECT, por meio de uma pesquisa aplicada no mercado brasileiro. A conclusão obtida foi de que a estratégia da empresa ser ou não direcionada pela tecnologia não afeta a utilidade percebida do sistema de BI.
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AHN, T.; RYU, S.; HAN, I. The impact of Web quality and playfulness on user acceptance of online retailing. Information and Management, v. 44, n. 3, p. 263–275, 2007.
AUDEMARD, J. Objectifying Contextual Effects. The Use of Snowball Sampling in Political Sociology. BMS Bulletin of Sociological Methodology/ Bulletin de Methodologie Sociologique, v. 145, n. 1, p. 30–60, 2020.
BACH, M. P.; ZOROJA, J.; ČELJO, A. An extension of the technology acceptance model for business intelligence systems: project management maturity perspective. International Journal of Information Systems and Project Management, v. 5, n. 2, p. 5–21, 2017. Disponível em: <http://www.sciencesphere.org/ijispm/archive/ijispm-050201.pdf>. Acesso em: 5 nov. 2017.
BAG, S. et al. Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, v. 153, n. October 2019, p. 104559, 2020. Disponível em: <https://doi.org/10.1016/j.resconrec.2019.104559>.
BHATTACHERJEE, A. Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, v. 25, n. 3, p. 351–370, 2001.
CHUTTUR, M. Overview of the Technology Acceptance Model: Origins , Developments and Future Directions. Sprouts: Working Papers on Information Systems, v. 9, n. 2009, p. 1–23, 2009. Disponível em: <http://sprouts.aisnet.org/9-37>.
COHEN, J. Statistical Power Analysis for the Behavioral Sciences. 2nd. ed. New York: Psychology Press, 1988.
DAI, H. M. et al. Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Computers and Education, v. 150, n. February, p. 103850, 2020. Disponível em: <https://doi.org/10.1016/j.compedu.2020.103850>.
DAMACENA, C.; PETROLL, M. DE LA M.; MELO, B. S. DE. Revista eletrônica de administração : Read. Revista Eletrônica de Administração, v. 22, n. 3, p. 413–434, 16 fev. 2017. Disponível em: <https://seer.ufrgs.br/read/article/view/58251/41572>. Acesso em: 15 out. 2019.
DAVIS, F.; BAGOZZI, R.; WARSHAW, P. User acceptance of computer technology: a comparison of two theoretical models. Management science, v. 35, n. 8, p. 982–1003, 1989. Disponível em: <http://pubsonline.informs.org/doi/abs/10.1287/mnsc.35.8.982>.
DAVIS, F. D. Perceived Usefulness , Perceived Ease Of Use , And User Acceptance. MIS Quarterly, v. 13, n. 3, p. 319–339, 1989.
DIAS, G. A.; VIEIRA, A. A. N. Big data: questões éticas e legais emergentes. Ciência da Informação. [S.l: s.n.]. Disponível em: <http://revista.ibict.br/index.php/ciinf/article/view/2284>. Acesso em: 15 out. 2015. , 6 ago. 2013
DUTTA, D. K. Hypercompetitive Environments , Coopetition Strategy , and the Role of Complementary Assets in Building Competitive Advantage : Insights From the Resource-Based View. Strategic Management Review, v. 9, n. 1, p. 1–11, 2015.
ESPOSITO VINZI, V. .; TRINCHERA, L. .; AMATO, S. PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement. In: HEIDELBER (Org.). . Handbook of partial least squares. 1st. ed. Berlin: Springer, 2010. .
FAUL, F. et al. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, v. 41, n. 4, p. 1149–1160, 2009. Disponível em: <http://www.springerlink.com/index/10.3758/BRM.41.4.1149>.
FITZSIMMONS, J. A.; FITZSIMMONS, M. J. Administração de Serviços - Operações, Estratégia e Tecnologia da Informação. 7. ed. Porto Alegre: Bookman, 2014.
FLINT, D. J.; BLOCKER, C. P.; BOUTIN, P. J. Customer value anticipation, customer satisfaction and loyalty: An empirical examination. Industrial Marketing Management, v. 40, n. 2, p. 219–230, fev. 2011. Disponível em: <http://www.sciencedirect.com/science/article/pii/S0019850110001173>. Acesso em: 6 abr. 2015.
FORNELL, C.; LARCKER, D. F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, v. 18, n. 1, p. 39, 1981. Disponível em: <http://www.jstor.org/stable/3151312?origin=crossref>.
GATIGNON, H.; XUEREB, J. Strategic Orientation of the Firm and New Product Performance. Journal of Marketing Research, v. 34, n. 1, p. 77–90, 1997.
GOETTEMS, L. C. M.; MAÇADA, A. C. G.; MALLMANN, G. L. Impacto do Sistema de Gestão de Transporte no Desempenho das Operações e na Relação com Fornecedores de Serviço de Transporte: uma percepção dos usuários. Revista de Ciências da Administração, p. 72–92, 2019.
GOODMAN, L. Snowball Sampling. The Annals of Mathematical Statistics, v. 32, n. 1, p. 148–170, 1961.
GRUBLJESIC, T.; JAKLIC, J. Conceptualization of the Business Intelligence Extended Use Model. JOURNAL OF COMPUTER INFORMATION SYSTEMS, v. 55, n. 3, p. 72–82, 2015.
HAIR, J. et al. A primer on partial least squares structural equations modeling (PLS-SEM). [S.l.]: SAGE, 2014.
HAIR, J. F. et al. Multivariate Data Analysis. 7th. ed. New York City: [s.n.], 2010.
HAIR, J.; RINGLE, C.; STARSTEDT, M. PLS-SEM: Indeed a Silver Bullet. Faculty Publications. [S.l: s.n.]. Disponível em: <http://digitalcommons.kennesaw.edu/facpubs/2387>. Acesso em: 10 fev. 2015. , 2011
HAN, Y.-M.; SHEN, C.-S.; FARN, C.-K. Determinants of continued usage of pervasive business intelligence systems. Information Development, v. 32, n. 3, p. 424–439, 2016.
HENSELER, J.; RINGLE, C. M.; SINKOVICS, R. The use of partial least squares path modeling in international marketing. Advances in International Marketing, v. 20, p. 277–319, 2009.
HOU, C. Exploring the user acceptance of business intelligence systems in Taiwan’s electronics industry: applying the UTAUT model. International Journal of Internet and Enterprise Management, v. 8, n. 3, p. 195, 1 jan. 2014a. Disponível em: <http://www.deepdyve.com/lp/inderscience-publishers/exploring-the-user-acceptance-of-business-intelligence-systems-in-5Qg0jUIoUG>. Acesso em: 8 mar. 2016.
HOU, C. Understanding business intelligence system continuance intention : An empirical study of Taiwan ’ s electronics industry. Information Developmen, v. 32, n. 5, p. 1359–1371, 2016.
HOU, C. User Acceptance of Business Intelligence Systems in Taiwan’s Electronics Industry. Social Behavior and Personality, v. 42, n. 949, p. 583–596, 2014b.
IŞIK, Ö.; JONES, M. C.; SIDOROVA, A. Business intelligence success: The roles of BI capabilities and decision environments. Information and Management, v. 50, n. 1, p. 13–23, 2013.
KUO, R.; LEE, G. KMS adoption: the effects of information quality. Management Decision, v. 47, n. 10, p. 1633–1651, 2009. Disponível em: <http://www.emeraldinsight.com/doi/10.1108/00251740911004727>.
LIMAYEM, M.; HIRT, S. G.; CHEUNG, C. M. K. How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance. MIS Quarterly, v. 31, n. 4, p. 705–737, 2007.
LOVEMAN, G. W. Hay diamantes en la mina de datos. Harvard Business Review. [S.l.]: Impact Media Comercial S.A. , 2004
MIKALEF, P. et al. Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, v. 98, n. February, p. 261–276, 2019. Disponível em: <https://doi.org/10.1016/j.jbusres.2019.01.044>.
MILAN, G. S. et al. ANTECEDENTES DA INTENÇÃO DE RECOMPRA DE UMA MARCA DE SMARTPHONES. REAd. Revista Eletrônica de Administração (Porto Alegre), v. 23, n. 3, p. 147–172, 20 nov. 2017. Disponível em: <https://seer.ufrgs.br/read/article/view/61873/44691>. Acesso em: 15 out. 2019.
OEI, M. H. H. Finding factors that determine acceptance of Operational Business Intelligence in organisations. . Delft: [s.n.], 2014.
PEJIĆ BACH, M.; ČELJO, A.; ZOROJA, J. Technology Acceptance Model for Business Intelligence Systems : Preliminary Research. Procedia - Procedia Computer Science, v. 100, p. 995–1001, 2016. Disponível em: <http://dx.doi.org/10.1016/j.procs.2016.09.270>.
PEREIRA, F. A. DE M. et al. Satisfaction and continuous use intention of e-learning service in Brazilian public organizations. Computers in Human Behavior, v. 46, p. 139–148, 2015. Disponível em: <http://dx.doi.org/10.1016/j.chb.2015.01.016>.
POPOVIČ, A. et al. Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, v. 54, n. 1, p. 729–739, 2012.
QURESHI, J. A. et al. Exploring Service Brand Associations: A Consumers’ Perspective in Rising Service Economy. Studies of Applied Economics, v. 39, n. 1, 24 set. 2021. Disponível em: <http://ojs.ual.es/ojs/index.php/eea/article/view/3799>. Acesso em: 4 dez. 2020.
RICHARDS, G. et al. Business Intelligence Effectiveness and Corporate Performance Management: An Empirical Analysis. Journal of Computer Information Systems, v. 59, n. 2, p. 188–196, 4 mar. 2019. Disponível em: <https://www.tandfonline.com/doi/full/10.1080/08874417.2017.1334244>. Acesso em: 15 out. 2019.
RINGLE, C. M.; DA SILVA, D.; BIDO, D. D. S. Structural Equation Modeling with the Smartpls. Revista Brasileira de Marketing, v. 13, n. 02, p. 56–73, 2014. Disponível em: <http://www.revistabrasileiramarketing.org/ojs-2.2.4/index.php/remark/article/view/2717>.
SALGANIK, M. J.; HECKATHORN, D. D. Sampling and estimation in hidden populations using respondent-driven sampling. Sociological methodology, v. 34, n. 1, p. 193–240, 2004.
SARSTEDT, M. et al. Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, v. 5, n. 1, p. 105–115, 2014. Disponível em: <http://dx.doi.org/10.1016/j.jfbs.2014.01.002>.
SCANFONE, L.; TORRES JÚNIOR, N. Compreendendo as Inovações Empreendidas nos Sistemas de Operações de Serviços por meio do Método PCN Analysis: um estudo de múltiplos casos em serviços de atendimento ao cidadão. Revista de Ciências da Administração, p. 75–93, 2017.
SHARMA, S. Applied Multivariate Techniques. Toronto: John Wiley & Sons, 1996.
SHIAU, W. L.; LUO, M. M. Continuance intention of blog users: The impact of perceived enjoyment, habit, user involvement and blogging time. Behaviour and Information Technology, v. 32, n. 6, p. 570–583, 2013.
TENENHAUS, M. et al. PLS path modeling. Computational Statistics and Data Analysis, v. 48, n. 1, p. 159–205, 2005.
TEO, T. Modelling technology acceptance in education: A study of pre-service teachers. Computers and Education, v. 52, n. 2, p. 302–312, 2009. Disponível em: <http://dx.doi.org/10.1016/j.compedu.2008.08.006>.
TURBAN, E. et al. Business Intelligence - Um Enfoque Gerencial Para a Inteligência do Negócio. 1. ed. Porto Alegre: [s.n.], 2009.
VENKATESH, V. et al. User acceptance of information technology: Toward a unified view. MIS Quarterly, v. 27, n. 3, p. 425–478, 2003. Disponível em: <http://www.jstor.org/stable/30036540>.
VENKATESH, V.; MORRIS, M. G. Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly Vol., v. 24, n. 1, p. 115–139, 2000.
VENKATESH, V.; THONG, J.; XU, X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS quarterly, v. 36, n. 1, p. 157–178, 2012.
WETZELS, M.; ODEKERKEN-SCHRÖDER, G.; OPPEN, C. VAN. Using PLS Path Modeling for Assesing Hierarchical Construct Models : Guidelines and Empirical Illustration. MIS Quarterly, v. 33, n. 1, p. 177–195, 2009.
WIXOM, B. H.; TODD, P. A. A theoretical integration of user satisfaction and technology acceptance. Information System Research, v. 16, n. 1, p. 85–102, 2005.
WONG, C. T.; TAN, C. L.; MAHMUD, I. Business Intelligence System Adoption: a Systematic Literature Review of Two Decades. International Journal of Industrial Management, v. 6, p. 1–8, 2020.
WU, X. et al. Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, v. 26, n. 1, p. 97–107, jan. 2014. Disponível em: <http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6547630>. Acesso em: 23 jul. 2014.
YOON, T. E.; GHOSH, B.; JEONG, B. User Acceptance of Business Intelligence ( BI ) Application : Technology , Individual Difference , Social Influence , and Situational Constraints. 2014, Hawaii: [s.n.], 2014.
YANG, Tiantian; YANG Feng; MEN, Jinji. Understanding consumers’ continuance intention toward recommendation vlogs: An exploration based on the dual-congruity theory and expectation-confirmation theory. Eletronic Commerce Research Applications, v.59, 2023. Disponível em: < https://doi.org/10.1016/j.elerap.2023.101270>
YUEN, K. F. et al. Factors influencing autonomous vehicle adoption: an application of the technology acceptance model and innovation diffusion theory. Technology Analysis and Strategic Management, v. 0, n. 0, p. 1–15, 2020. Disponível em: <https://doi.org/10.1080/09537325.2020.1826423>.
ZHIGANG, W.; LEI, Z.; XINTAO, L. Consumer Response to Corporate Hypocrisy From the Perspective of Expectation Confirmation Theory. Frontiers in Psychology, v. 11, n. November, 2020.
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