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How can organizations leverage big data to innovate their business models? A systematic literature review

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  • Acciarini, Chiara
  • Cappa, Francesco
  • Boccardelli, Paolo
  • Oriani, Raffaele

Abstract

The use of big data has garnered increasing importance in academic research and managerial practice thanks to the benefits it can produce in terms of innovation. However, big data also has drawbacks that have been overlooked so far. Therefore, to ensure the benefits outweigh the costs of big data, and to unlock the full potential of big data in terms of business model innovation, we argue that companies need to have a clear map of all its possible uses. With this aim in mind, we have summarized the current state of scholarship, outlined the uses of big data across different business areas in private and public organizations, and the types of methodologies adopted, and we have suggested future research avenues, building upon our systematic literature review of 311 articles indexed in the Scopus database. In this manner, we contribute to increasing our scientific understanding of the big data phenomenon, and we provide theoretical and practical advice on the possible uses of big data that may allow companies to innovate their business models.

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  • Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    • Handle: RePEc:eee:techno:v:123:y:2023:i:c:s016649722300024x
    DOI: 10.1016/j.technovation.2023.102713
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    as
    1. Ujjal Kumar Mukherjee & Kingshuk K. Sinha, 2018. "Product Recall Decisions in Medical Device Supply Chains: A Big Data Analytic Approach to Evaluating Judgment Bias," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1816-1833, October.
    2. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    3. Yong-Hong Kuo & Andrew Kusiak, 2019. "From data to big data in production research: the past and future trends," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4828-4853, August.
    4. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    5. Kim, Jin-Young & Lee, Min-Jae, 2016. "Living with casinos: The triple-helix approach, innovative solutions, and big data," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 33-41.
    6. Maxime C. Cohen, 2018. "Big Data and Service Operations," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1709-1723, September.
    7. Keren Naa Abeka Arthur & Richard Owen, 2019. "A Micro-ethnographic Study of Big Data-Based Innovation in the Financial Services Sector: Governance, Ethics and Organisational Practices," Journal of Business Ethics, Springer, vol. 160(2), pages 363-375, December.
    8. Xu, Zhenning & Frankwick, Gary L. & Ramirez, Edward, 2016. "Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective," Journal of Business Research, Elsevier, vol. 69(5), pages 1562-1566.
    9. Mazzei, Matthew J. & Noble, David, 2017. "Big data dreams: A framework for corporate strategy," Business Horizons, Elsevier, vol. 60(3), pages 405-414.
    10. Juliane Begenau & Maryam Farboodi & Laura Veldkamp, 2018. "Big Data in Finance and the Growth of Large Firms," Working Papers 18-08, New York University, Leonard N. Stern School of Business, Department of Economics.
    11. Juliane Begenau & Maryam Farboodi & Laura Veldkamp, 2018. "Big Data in Finance and the Growth of Large Firms," NBER Working Papers 24550, National Bureau of Economic Research, Inc.
    12. Sachin S. Kamble & Angappa Gunasekaran, 2020. "Big data-driven supply chain performance measurement system: a review and framework for implementation," International Journal of Production Research, Taylor & Francis Journals, vol. 58(1), pages 65-86, January.
    13. Roberto Moro Visconti & Donato Morea, 2019. "Big Data for the Sustainability of Healthcare Project Financing," Sustainability, MDPI, vol. 11(13), pages 1-17, July.
    14. Shahriar Akter & Samuel Fosso Wamba, 2016. "Big data analytics in E-commerce: a systematic review and agenda for future research," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 173-194, May.
    15. Lamba, Kuldeep & Singh, Surya Prakash, 2019. "Dynamic supplier selection and lot-sizing problem considering carbon emissions in a big data environment," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 573-584.
    16. Lee, In, 2017. "Big data: Dimensions, evolution, impacts, and challenges," Business Horizons, Elsevier, vol. 60(3), pages 293-303.
    17. Sestino, Andrea & Prete, Maria Irene & Piper, Luigi & Guido, Gianluigi, 2020. "Internet of Things and Big Data as enablers for business digitalization strategies," Technovation, Elsevier, vol. 98(C).
    18. Charles J. Corbett, 2018. "How Sustainable Is Big Data?," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1685-1695, September.
    19. Hau L. Lee, 2018. "Big Data and the Innovation Cycle," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1642-1646, September.
    20. Hu, Baoliang, 2014. "Linking business models with technological innovation performance through organizational learning," European Management Journal, Elsevier, vol. 32(4), pages 587-595.
    21. Lynn Wu & Lorin Hitt & Bowen Lou, 2020. "Data Analytics, Innovation, and Firm Productivity," Management Science, INFORMS, vol. 66(5), pages 2017-2039, May.
    22. Cheng-Kui Huang & Tawei Wang & Tzu-Yen Huang, 2020. "Initial Evidence on the Impact of Big Data Implementation on Firm Performance," Information Systems Frontiers, Springer, vol. 22(2), pages 475-487, April.
    23. Daniel Nunan & Marialaura Di Domenico, 2017. "Big Data: A Normal Accident Waiting to Happen?," Journal of Business Ethics, Springer, vol. 145(3), pages 481-491, October.
    24. Juliane Begenau & Laura Veldkamp & Maryam Farboodi, 2018. "Big Data in Finance and the Growth of Large Firms," 2018 Meeting Papers 155, Society for Economic Dynamics.
    25. Akter, Shahriar & Motamarri, Saradhi & Hani, Umme & Shams, Riad & Fernando, Mario & Mohiuddin Babu, Mujahid & Ning Shen, Kathy, 2020. "Building dynamic service analytics capabilities for the digital marketplace," Journal of Business Research, Elsevier, vol. 118(C), pages 177-188.
    26. Christoph Zott & Raphael Amit, 2008. "The fit between product market strategy and business model: implications for firm performance," Strategic Management Journal, Wiley Blackwell, vol. 29(1), pages 1-26, January.
    27. Frank, Alejandro Germán & Dalenogare, Lucas Santos & Ayala, Néstor Fabián, 2019. "Industry 4.0 technologies: Implementation patterns in manufacturing companies," International Journal of Production Economics, Elsevier, vol. 210(C), pages 15-26.
    28. Yu, Lean & Zhao, Yaqing & Tang, Ling & Yang, Zebin, 2019. "Online big data-driven oil consumption forecasting with Google trends," International Journal of Forecasting, Elsevier, vol. 35(1), pages 213-223.
    29. Zhong, Ray Y. & Huang, George Q. & Lan, Shulin & Dai, Q.Y. & Chen, Xu & Zhang, T., 2015. "A big data approach for logistics trajectory discovery from RFID-enabled production data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 260-272.
    30. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
    31. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Roubaud, David & Fosso Wamba, Samuel & Giannakis, Mihalis & Foropon, Cyril, 2019. "Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain," International Journal of Production Economics, Elsevier, vol. 210(C), pages 120-136.
    32. Lena Steinhoff & Denni Arli & Scott Weaven & Irina V. Kozlenkova, 2019. "Online relationship marketing," Journal of the Academy of Marketing Science, Springer, vol. 47(3), pages 369-393, May.
    33. Chang Liu & Yongfu Feng & Dongtao Lin & Liang Wu & Min Guo, 2020. "Iot based laundry services: an application of big data analytics, intelligent logistics management, and machine learning techniques," International Journal of Production Research, Taylor & Francis Journals, vol. 58(17), pages 5113-5131, September.
    34. Jun, Seung-Pyo & Park, Do-Hyung & Yeom, Jaeho, 2014. "The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preference," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 237-253.
    35. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    36. Francesco Cappa & Stefano Franco & Federica Rosso, 2022. "Citizens and cities: Leveraging citizen science and big data for sustainable urban development," Business Strategy and the Environment, Wiley Blackwell, vol. 31(2), pages 648-667, February.
    37. El-Kassar, Abdul-Nasser & Singh, Sanjay Kumar, 2019. "Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 483-498.
    38. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    39. Vinicius Luiz Ferraz Minatogawa & Matheus Munhoz Vieira Franco & Izabela Simon Rampasso & Rosley Anholon & Ruy Quadros & Orlando Durán & Antonio Batocchio, 2019. "Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations," Sustainability, MDPI, vol. 12(1), pages 1-29, December.
    40. Jayashankar M. Swaminathan, 2018. "Big Data Analytics for Rapid, Impactful, Sustained, and Efficient (RISE) Humanitarian Operations," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1696-1700, September.
    41. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    42. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    43. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    44. Jabbour, Charbel Jose Chiappetta & Jabbour, Ana Beatriz Lopes de Sousa & Sarkis, Joseph & Filho, Moacir Godinho, 2019. "Unlocking the circular economy through new business models based on large-scale data: An integrative framework and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 546-552.
    45. George Baryannis & Sahar Validi & Samir Dani & Grigoris Antoniou, 2019. "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2179-2202, April.
    46. Raymond Yiu Keung Lau & Wenping Zhang & Wei Xu, 2018. "Parallel Aspect‐Oriented Sentiment Analysis for Sales Forecasting with Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1775-1794, October.
    47. Chaoyang Zhang & Zhengxu Wang & Kai Ding & Felix T.S. Chan & Weixi Ji, 2020. "An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7059-7077, December.
    48. Elisabetta Raguseo & Claudio Vitari, 2018. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," International Journal of Production Research, Taylor & Francis Journals, vol. 56(15), pages 5206-5221, August.
    49. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    50. Xiao Liu & Param Vir Singh & Kannan Srinivasan, 2016. "A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing," Marketing Science, INFORMS, vol. 35(3), pages 363-388, May.
    51. Lorenzo Ardito & Roberto Cerchione & Pasquale Del Vecchio & Elisabetta Raguseo, 2019. "Big data in smart tourism: challenges, issues and opportunities," Current Issues in Tourism, Taylor & Francis Journals, vol. 22(15), pages 1805-1809, September.
    52. Chen-Fu Chien & Chiao-Wen Liu & Shih-Chung Chuang, 2017. "Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5095-5107, September.
    53. Janssen, Marijn & van der Voort, Haiko & Wahyudi, Agung, 2017. "Factors influencing big data decision-making quality," Journal of Business Research, Elsevier, vol. 70(C), pages 338-345.
    54. Weiguang Fang & Yu Guo & Wenhe Liao & Karthik Ramani & Shaohua Huang, 2020. "Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2751-2766, May.
    55. Ashish Kumar Jha & Maher Agi & Eric W.T. Ngai, 2020. "A note on big data analytics capability development in supply chain," Post-Print hal-03164004, HAL.
    56. Marshall Fisher & Ananth Raman, 2018. "Using Data and Big Data in Retailing," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1665-1669, September.
    57. Seitz, Alexander & Grunow, Martin & Akkerman, Renzo, 2020. "Data driven supply allocation to individual customers considering forecast bias," International Journal of Production Economics, Elsevier, vol. 227(C).
    58. Jian Jin & Ying Liu & Ping Ji & Hongguang Liu, 2016. "Understanding big consumer opinion data for market-driven product design," International Journal of Production Research, Taylor & Francis Journals, vol. 54(10), pages 3019-3041, May.
    59. Claudio Vitari & Elisabetta Raguseo, 2020. "Big data analytics business value and firm performance: linking with environmental context," International Journal of Production Research, Taylor & Francis Journals, vol. 58(18), pages 5456-5476, September.
    60. Huang, Ying & Porter, Alan L. & Cunningham, Scott W. & Robinson, Douglas K.R. & Liu, Jianhua & Zhu, Donghua, 2018. "A technology delivery system for characterizing the supply side of technology emergence: Illustrated for Big Data & Analytics," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 165-176.
    61. Ghasemaghaei, Maryam & Calic, Goran, 2020. "Assessing the impact of big data on firm innovation performance: Big data is not always better data," Journal of Business Research, Elsevier, vol. 108(C), pages 147-162.
    62. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov, 2019. "The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(3), pages 829-846, February.
    63. Julsrud, Dr. Tom Erik & Krogstad, Dr. Julie Runde, 2020. "Is there enough trust for the smart city? exploring acceptance for use of mobile phone data in oslo and tallinn," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    64. Dong Li & Xiaojun Wang, 2017. "Dynamic supply chain decisions based on networked sensor data: an application in the chilled food retail chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5127-5141, September.
    65. Angappa Gunasekaran & Yahaya Y. Yusuf & Ezekiel O. Adeleye & Thanos Papadopoulos, 2018. "Agile manufacturing practices: the role of big data and business analytics with multiple case studies," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 385-397, January.
    66. Shradha A. Gawankar & Angappa Gunasekaran & Sachin Kamble, 2020. "A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context," International Journal of Production Research, Taylor & Francis Journals, vol. 58(5), pages 1574-1593, March.
    67. André Hanelt & René Bohnsack & David Marz & Cláudia Antunes Marante, 2021. "A Systematic Review of the Literature on Digital Transformation: Insights and Implications for Strategy and Organizational Change," Journal of Management Studies, Wiley Blackwell, vol. 58(5), pages 1159-1197, July.
    68. Samayita Guha & Subodha Kumar, 2018. "Emergence of Big Data Research in Operations Management, Information Systems, and Healthcare: Past Contributions and Future Roadmap," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1724-1735, September.
    69. Ghasemaghaei, Maryam & Calic, Goran, 2019. "Does big data enhance firm innovation competency? The mediating role of data-driven insights," Journal of Business Research, Elsevier, vol. 104(C), pages 69-84.
    70. Krista Foster & Pooja Penninti & Jennifer Shang & Sunder Kekre & Gajanan G. Hegde & Arvind Venkat, 2018. "Leveraging Big Data to Balance New Key Performance Indicators in Emergency Physician Management Networks," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1795-1815, October.
    71. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    72. Michael Trusov & Liye Ma & Zainab Jamal, 2016. "Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting," Marketing Science, INFORMS, vol. 35(3), pages 405-426, May.
    73. Mahmood, Tarique & Mubarik, Muhammad Shujaat, 2020. "Balancing innovation and exploitation in the fourth industrial revolution: Role of intellectual capital and technology absorptive capacity," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    74. Ashfaq Farooqui & Kristofer Bengtsson & Petter Falkman & Martin Fabian, 2020. "Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations," International Journal of Production Research, Taylor & Francis Journals, vol. 58(16), pages 4947-4963, July.
    75. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    76. Tingliang Huang & Jan A. Van Mieghem, 2014. "Clickstream Data and Inventory Management: Model and Empirical Analysis," Production and Operations Management, Production and Operations Management Society, vol. 23(3), pages 333-347, March.
    77. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    78. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    79. Amado, Alexandra & Cortez, Paulo & Rita, Paulo & Moro, Sérgio, 2018. "Research Trends On Big Data In Marketing: A Text Mining And Topic Modeling Based Literature Analysis," European Research on Management and Business Economics (ERMBE), Academia Europea de Dirección y Economía de la Empresa (AEDEM), vol. 24(1), pages 1-7.
    80. Alain Yee Loong Chong & Eugene Ch’ng & Martin J. Liu & Boying Li, 2017. "Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5142-5156, September.
    81. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.
    82. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    83. Gupta, Shivam & Chen, Haozhe & Hazen, Benjamin T. & Kaur, Sarabjot & Santibañez Gonzalez, Ernesto D.R., 2019. "Circular economy and big data analytics: A stakeholder perspective," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 466-474.
    84. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    85. Mukesh Kumar & Gary Graham & Patrick Hennelly & Jagjit Srai, 2016. "How will smart city production systems transform supply chain design: a product-level investigation," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7181-7192, December.
    86. Shen, Bin & Choi, Tsan-Ming & Chan, Hau-Ling, 2019. "Selling green first or not? A Bayesian analysis with service levels and environmental impact considerations in the Big Data Era," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 412-420.
    87. Ray Y. Zhong & Chen Xu & Chao Chen & George Q. Huang, 2017. "Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2610-2621, May.
    88. Shan, Wei & Qiao, Tong & Zhang, Mingli, 2020. "Getting more resources for better performance: The effect of user-owned resources on the value of user-generated content," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    89. Qi Feng & J. George Shanthikumar, 2018. "How Research in Production and Operations Management May Evolve in the Era of Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1670-1684, September.
    90. Roberts, John H. & Kayande, Ujwal & Stremersch, Stefan, 2014. "From academic research to marketing practice: Some further thoughts," International Journal of Research in Marketing, Elsevier, vol. 31(2), pages 144-146.
    91. Jun, Chae Nam & Chung, Chung Joo, 2016. "Big data analysis of local government 3.0: Focusing on Gyeongsangbuk-do in Korea," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 3-12.
    92. Wallace J. Hopp & Jun Li & Guihua Wang, 2018. "Big Data and the Precision Medicine Revolution," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1647-1664, September.
    93. Shivam Gupta & Sachin Modgil & Angappa Gunasekaran, 2020. "Big data in lean six sigma: a review and further research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 58(3), pages 947-969, February.
    94. Erik Hofmann, 2017. "Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5108-5126, September.
    95. Pratima (Tima) Bansal & Jury Gualandris & Nahyun Kim, 2020. "Theorizing Supply Chains with Qualitative Big Data and Topic Modeling," Journal of Supply Chain Management, Institute for Supply Management, vol. 56(2), pages 7-18, April.
    96. Opresnik, David & Taisch, Marco, 2015. "The value of Big Data in servitization," International Journal of Production Economics, Elsevier, vol. 165(C), pages 174-184.
    97. Chen, Peng-Ting, 2018. "Medical big data applications: Intertwined effects and effective resource allocation strategies identified through IRA-NRM analysis," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 150-164.
    98. Nguyen Dang Tuan, Minh & Nguyen Thanh, Nhan & Le Tuan, Loc, 2019. "Applying a mindfulness-based reliability strategy to the Internet of Things in healthcare – A business model in the Vietnamese market," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 54-68.
    99. Ghasemaghaei, Maryam, 2020. "The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage," International Journal of Information Management, Elsevier, vol. 50(C), pages 395-404.
    100. Kamble, Sachin S. & Gunasekaran, Angappa & Gawankar, Shradha A., 2020. "Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications," International Journal of Production Economics, Elsevier, vol. 219(C), pages 179-194.
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    1. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    2. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    3. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    4. Benzidia, Smail & Makaoui, Naouel & Bentahar, Omar, 2021. "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    5. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    6. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    7. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    8. Sundarakani, Balan & Ajaykumar, Aneesh & Gunasekaran, Angappa, 2021. "Big data driven supply chain design and applications for blockchain: An action research using case study approach," Omega, Elsevier, vol. 102(C).
    9. Oduro, Stephen & De Nisco, Alessandro & Mainolfi, Giada, 2023. "Do digital technologies pay off? A meta-analytic review of the digital technologies/firm performance nexus," Technovation, Elsevier, vol. 128(C).
    10. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Shore, Adam & Ram, Pratibha, 2023. "Examining the role of virtue ethics and big data in enhancing viable, sustainable, and digital supply chain performance," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    11. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    12. Xu, Jinou & Pero, Margherita & Fabbri, Margherita, 2023. "Unfolding the link between big data analytics and supply chain planning," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    13. Ghasemaghaei, Maryam & Calic, Goran, 2020. "Assessing the impact of big data on firm innovation performance: Big data is not always better data," Journal of Business Research, Elsevier, vol. 108(C), pages 147-162.
    14. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    15. Patrucco, Andrea S. & Marzi, Giacomo & Trabucchi, Daniel, 2023. "The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions," Technovation, Elsevier, vol. 126(C).
    16. Biman Darshana Hettiarachchi & Stefan Seuring & Marcus Brandenburg, 2022. "Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis," Operations Management Research, Springer, vol. 15(3), pages 858-878, December.
    17. Juan J. Lull & Roberto Cervelló-Royo & José Luis Galdón, 2024. "Crossroads between Big Data and entrepreneurship: current key trends," International Entrepreneurship and Management Journal, Springer, vol. 20(4), pages 2763-2790, December.
    18. Candice WALLS & Brian BARNARD, 2020. "Success Factors of Big Data to Achieve Organisational Performance: Theoretical Perspectives," Expert Journal of Business and Management, Sprint Investify, vol. 8(1), pages 1-16.
    19. Bag, Surajit & Pretorius, Jan Ham Christiaan & Gupta, Shivam & Dwivedi, Yogesh K., 2021. "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 and Social Change, Elsevier, vol. 163(C).
    20. Korayim, Diana & Chotia, Varun & Jain, Girish & Hassan, Sharfa & Paolone, Francesco, 2024. "How big data analytics can create competitive advantage in high-stake decision forecasting? The mediating role of organizational innovation," Technological Forecasting and Social Change, Elsevier, vol. 199(C).

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