Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3568231.3568245acmotherconferencesArticle/Chapter ViewAbstractPublication PagessietConference Proceedingsconference-collections
research-article

Business Analytic and Business Value: A Review and Bibliometric Analysis of a Decade of Research

Published: 13 January 2023 Publication History

Abstract

In the last ten years, research related to business analytics (BA), from previous business intelligence (BI) to big data (BD), has increasingly attracted the attention of researchers. This phenomenon is inseparable from the unprecedented growth of data in volume, variety, and velocity and the effort to derive business value from these emerging opportunities. Several studies have been conducted to make literature studies and bibliometric analyses to review knowledge trends and describe future research directions. Seeing the growing interest in the BA topic and the emergence of new challenges and knowledge still fragmented, we consider that further research is needed to conduct literature and bibliometric analysis related to business analytics and business value. We used the VOS Viewer tool to perform a bibliometric analysis of the SCOPUS database between 2012-2021 on 748 sample articles through publication distribution analysis, citation analysis, keyword co-occurrence analysis to see the evolution of research in which topics were established, emerged, or declined. Based on the bibliometric analysis and content analysis, we identified four themes and one conceptual Framework as the research's theoretical foundation: (1) business analytic asset development, (2) business analytic capability development, (3) business analytic impact and organizational capability, (4) firm performance and moderating factors. We also identified several topics that represent hotspots in business analytics that align with the potential for further research that is still wide open.

References

[1]
A. Ashrafi, A. Zare Ravasan, P. Trkman, and S. Afshari, "The role of business analytics capabilities in bolstering firms’ agility and performance," (in English), Int J Inf Manage, Article vol. 47, pp. 1-15, 2019.
[2]
R. Dubey, "Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour," (in English), J. Clean. Prod., Article vol. 196, pp. 1508-1521, 2018.
[3]
T. H. Davenport, "From analytics to artificial intelligence," (in English), J. Bus. Anal., Article vol. 1, no. 2, pp. 73-80, 2018.
[4]
Y. Zhang, M. Zhang, J. Li, G. Liu, M. M. Yang, and S. Liu, "A bibliometric review of a decade of research: Big data in business research – Setting a research agenda," J. Bus. Res., vol. 131, no. April 2019, pp. 374-390, 2021.
[5]
K. Mashingaidze and J. Backhouse, "The relationships between definitions of big data, business intelligence and business analytics: A literature review," Int. J. Bus. Inf. Syst., vol. 26, no. 4, pp. 488-505, 2017.
[6]
M. W. Barbosa, A. d. l. C. Vicente, M. B. Ladeira, and M. P. V. de Oliveira, "Managing supply chain resources with Big Data Analytics: a systematic review," International Journal of Logistics Research and Applications, vol. 21, no. 3, pp. 177-200, 2018.
[7]
K. Božič and V. Dimovski, "Business intelligence and analytics use, innovation ambidexterity, and firm performance: A dynamic capabilities perspective," (in English), J Strategic Inform Syst, Article vol. 28, no. 4, 2019, Art no. 101578.
[8]
D. Arunachalam, N. Kumar, and J. P. Kawalek, "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," (in English), Transp. Res. Part E Logist. Transp. Rev., Article vol. 114, pp. 416-436, 2018.
[9]
C. Soh and M. L. Markus, "How IT Creates Business Value: A Process Theory Synthesis," ICIS 1995 Proceedings., pp. Paper 4-Paper 4, 1995. [Online].
[10]
N. P. Melville, K. Kraemer, and V. Gurbaxani, "Information Technology Organizational Performance: Integrative Model of IT Business Value," MIS Quarterly, vol. 28, no. 2, pp. 282-322, 2004.
[11]
P. Mikalef, I. O. Pappas, J. Krogstie, and P. A. Pavlou, "Big data and business analytics: A research agenda for realizing business value," Inf Manage, vol. 57, no. 1, 2020.
[12]
V. Grover, R. H. L. Chiang, T.-P. Liang, and D. Zhang, "Creating Strategic Business Value from Big Data Analytics: A Research Framework," Journal ofManagement Information Systems, vol. 35, no. 2, pp. 388-423, 2018. [Online]. Available:
[13]
R. Kahli and V. Grover, "Business value of IT: An essay on expanding research directions to keep up with the times," Journal of the Association for Information Systems, vol. 9, no. 1, pp. 23-39, 2008.
[14]
B. Fahimnia, J. Sarkis, and H. Davarzani, Green supply chain management: A review and bibliometric analysis. Elsevier, 2015, pp. 101-114.
[15]
H. Chen, R. H. L. Chiang, and V. C. Storey, "Business Intelligence and Analytics:From Big Data To Big Impact," MIS Quarterly, vol. 36, no. 4, pp. 1165-1188, 2012. [Online]. Available:
[16]
D. Ivanov and A. Dolgui, "A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0," (in English), Prod Plann Control, Article 2021.
[17]
S. Bag, J. Ham, C. Pretorius, S. Gupta, and Y. K. Dwivedi, "Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities," Technological Forecasting & Social Change, vol. 163, no. November 2020, pp. 120420-120420, 2021.
[18]
H. Fatorachian and H. Kazemi, "Impact of Industry 4.0 on supply chain performance," Prod Plann Control, vol. 32, no. 1, pp. 63-81, 2021.
[19]
R. Dubey, A. Gunasekaran, S. J. Childe, S. Fosso Wamba, D. Roubaud, and C. Foropon, "Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience," (in English), Int J Prod Res, Article vol. 59, no. 1, pp. 110-128, 2021.
[20]
F. Ciampi, S. Demi, A. Magrini, G. Marzi, and A. Papa, "Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation," J. Bus. Res., vol. 123, no. June 2020, pp. 1-13, 2021.
[21]
S. Bag and M. S. Rahman, "The role of capabilities in shaping sustainable supply chain flexibility and enhancing circular economy-target performance: an empirical study," (in English), Supply Chain Manage., Article 2021.
[22]
R. D. Raut, S. K. Mangla, V. S. Narwane, M. Dora, and M. Liu, "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," (in English), Transp. Res. Part E Logist. Transp. Rev., Article vol. 145, 2021, Art no. 102170.
[23]
V. Chistov, N. Aramburu, and J. Carrillo-Hermosilla, "Open eco-innovation: A bibliometric review of emerging research," J. Clean. Prod., vol. 311, no. May, pp. 127627-127627, 2021.
[24]
N. Hajiheydari, M. Talafidaryani, S. H. Khabiri, and M. Salehi, "Business model analytics: technically review business model research domain," Foresight, vol. 21, no. 6, pp. 654-679, 2019.
[25]
Y. Zhang, Y. Huang, A. L. Porter, G. Zhang, and J. Lu, "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technol. Forecast. Soc. Change, vol. 146, no. June 2018, pp. 795-807, 2019.
[26]
J. Z. Zhang, P. R. Srivastava, D. Sharma, and P. Eachempati, "Big data analytics and machine learning: A retrospective overview and bibliometric analysis," Expert Systems with Applications, vol. 184, no. May, pp. 115561-115561, 2021.
[27]
V. Grover, R. H. L. Chiang, T. P. Liang, and D. Zhang, "Creating Strategic Business Value from Big Data Analytics: A Research Framework," (in English), J Manage Inf Syst, Article vol. 35, no. 2, pp. 388-423, 2018.
[28]
V. H. Trieu, "Getting value from Business Intelligence systems: A review and research agenda," (in English), Decis Support Syst, Article vol. 93, pp. 111-124, 2017.
[29]
A. Gunasekaran, "Big data and predictive analytics for supply chain and organizational performance," (in English), J. Bus. Res., Article vol. 70, pp. 308-317, 2017.
[30]
O. Kwon, N. Lee, and B. Shin, "Data quality management, data usage experience and acquisition intention of big data analytics," Int J Inf Manage, vol. 34, no. 3, pp. 387-394, 2014.
[31]
M. M. Queiroz, S. C. F. Pereira, R. Telles, and M. C. Machado, "Industry 4.0 and digital supply chain capabilities: A framework for understanding digitalisation challenges and opportunities," Benchmarking, 2019.
[32]
R. Dubey, "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," (in English), Int J Prod Econ, Article vol. 226, 2020, Art no. 107599.
[33]
M. Haseeb, H. I. Hussain, B. Ślusarczyk, and K. Jermsittiparsert, "Industry 4.0: A solution towards technology challenges of sustainable business performance," (in English), Soc. Sci., Article vol. 8, no. 5, 2019, Art no. 154.
[34]
S. F. Wamba, A. Gunasekaran, S. Akter, S. J. F. Ren, R. Dubey, and S. J. Childe, "Big data analytics and firm performance: Effects of dynamic capabilities," (in English), J. Bus. Res., Article vol. 70, pp. 356-365, 2017.
[35]
D. J. Teece, "Explicating Dynamic Capabilities: The Nature And Microfoundations Of (Sustainable) Enterprise Performance," Business, vol. 920, no. October, pp. 1-43, 2007.
[36]
B. Roßmann, A. Canzaniello, H. von der Gracht, and E. Hartmann, "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," (in English), Technol. Forecast. Soc. Change, Article vol. 130, pp. 135-149, 2018.
[37]
W. El Hilali, A. El Manouar, and M. A. Janati Idrissi, "Reaching sustainability during a digital transformation: a PLS approach," (in English), Int. J. Innov. Sci., Article 2020.
[38]
S. Bresciani, F. Ciampi, F. Meli, and A. Ferraris, "Using big data for co-innovation processes: Mapping the field of data-driven innovation, proposing theoretical developments and providing a research agenda," Int J Inf Manage, no. February, pp. 102347-102347, 2021.
[39]
R. Rialti, G. Marzi, M. Silic, and C. Ciappei, "Ambidextrous organization and agility in big data era: The role of business process management systems," Bus. Process Manage. J., vol. 24, no. 5, pp. 1091-1109, 2018.
[40]
K. Božič and V. Dimovski, "Business intelligence and analytics for value creation: The role of absorptive capacity," (in English), Int J Inf Manage, Article vol. 46, pp. 93-103, 2019.
[41]
P. Mikalef, J. Krogstie, I. O. Pappas, and P. Pavlou, "Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities," (in English), Inf Manage, Article vol. 57, no. 2, 2020, Art no. 103169.
[42]
S. Bag, J. H. C. Pretorius, S. Gupta, and Y. K. Dwivedi, "Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities," (in English), Technol. Forecast. Soc. Change, Article vol. 163, 2021, Art no. 120420.
[43]
R. Rialti, L. Zollo, A. Ferraris, and I. Alon, "Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model," (in English), Technol. Forecast. Soc. Change, Article vol. 149, 2019, Art no. 119781.
[44]
S. Bag, S. Gupta, T. Choi, and A. Kumar, "Roles of Innovation Leadership on Using Big Data Analytics to Establish Resilient Healthcare Supply Chains to Combat the COVID-19 Pandemic: A Multimethodological Study," (in English), IEEE Trans Eng Manage, Article 2021.
[45]
W. Yu, C. Y. Wong, R. Chavez, and M. A. Jacobs, "Integrating big data analytics into supply chain finance: The roles of information processing and data-driven culture," (in English), Int J Prod Econ, Article vol. 236, 2021, Art no. 108135.

Cited By

View all
  • (2024)Twenty-four years of empirical research on trust in AI: a bibliometric review of trends, overlooked issues, and future directionsAI & SOCIETY10.1007/s00146-024-02059-yOnline publication date: 2-Oct-2024

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
SIET '22: Proceedings of the 7th International Conference on Sustainable Information Engineering and Technology
November 2022
398 pages
ISBN:9781450397117
DOI:10.1145/3568231
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 January 2023

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

SIET '22

Acceptance Rates

Overall Acceptance Rate 45 of 57 submissions, 79%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)19
  • Downloads (Last 6 weeks)1
Reflects downloads up to 08 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Twenty-four years of empirical research on trust in AI: a bibliometric review of trends, overlooked issues, and future directionsAI & SOCIETY10.1007/s00146-024-02059-yOnline publication date: 2-Oct-2024

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media