From intuitive to data-driven decision-making in digital transformation: A framework of prevalent managerial archetypes

被引:17
|
作者
Korherr, Philipp [1 ]
Kanbach, Dominik K. [1 ]
Kraus, Sascha [2 ,3 ]
Mikalef, Patrick [4 ,5 ]
机构
[1] HHL Leipzig Grad Sch Management, Jahnallee 59, D-04109 Leipzig, Germany
[2] Free Univ Bozen Bolzano, Piazza Univ 1, I-39100 Bolzano, Italy
[3] Univ Johannesburg, Dept Business Management, Johannesburg, South Africa
[4] Norwegian Univ Sci & Technol, Sem Saelandsvei 9, N-7491 Trondheim, Norway
[5] SINTEF Digital, Dept Technol Management, S P Andersens vei 3, N-7031 Trondheim, Norway
来源
DIGITAL BUSINESS | 2022年 / 2卷 / 02期
关键词
Analytics; Decision-making; Top management; Digital Transformation; Management research; BIG DATA ANALYTICS; PREDICTIVE ANALYTICS; DYNAMIC-CAPABILITIES; FIRM PERFORMANCE; SUPPLY CHAIN; IMPACT; INNOVATION; EVOLUTION;
D O I
10.1016/j.digbus.2022.100045
中图分类号
F [经济];
学科分类号
02 ;
摘要
The use of analytics in corporate decision-making processes demands a paradigm shift within companies, and particularly among their top executives. Corporate leaders represent a major lever for this change. Therefore, a deeper understanding of their managerial capabilities, characteristics and contribution in this context is required. Aiming to provide actionable guidance on how to manage the shift to data-driven decision making, this study helps to develop a more profound understanding of this emerging managerial role by examining managerial success factors following a semi-structured interview approach. With insights from interviews with 32 top executives from Germany across different industries, this paper research proposes four managerial archetypes that are relevant to mastering the digital transformation towards analytics-based decision-making processes. Furthermore, it sheds light on the characteristics, capabilities, and contributions of the four archetypes-Analytical Thinker, Coach, Guide, and Strategist. Although the archetypes have differentiated attributes and qualities, all four seem of importance in manifesting analytics in organizations. Our findings provide guidelines to assess the top management's abilities to manage digital transformation projects. Furthermore, the results serve as basis for future empirical research on the human aspect of analytical capabilities regarding leadership.
引用
收藏
页数:11
相关论文
共 50 条
  • [11] Next-generation data center energy management: a data-driven decision-making framework
    Milic, Vlatko
    FRONTIERS IN ENERGY RESEARCH, 2024, 12
  • [12] Heart teams in the Netherlands: From teamwork to data-driven decision-making
    Wierda, E.
    van Veghel, D.
    Hirsch, A.
    de Mol, B. A. J. M.
    NETHERLANDS HEART JOURNAL, 2020, 28 (SUPPL 1) : 73 - 77
  • [13] Elementary teachers' perceptions of data-driven decision-making
    Schelling, Natalie
    Rubenstein, Lisa DaVia
    EDUCATIONAL ASSESSMENT EVALUATION AND ACCOUNTABILITY, 2021, 33 (02) : 317 - 344
  • [14] EMERGE - A DATA-DRIVEN MEDICAL DECISION-MAKING AID
    HUDSON, DL
    ESTRIN, T
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1984, 6 (01) : 87 - 91
  • [15] Data-driven multiobjective decision-making in cash management
    Salas-Molina, Francisco
    Rodriguez-Aguilar, Juan A.
    EURO JOURNAL ON DECISION PROCESSES, 2018, 6 (1-2) : 77 - 91
  • [16] Exploring Data-Driven Decision-Making for Enhanced Sustainability
    Chavez, Zuhara
    Gopalakrishnan, Maheshwaran
    Nilsson, Viktor
    Westbroek, Arvid
    SPS 2022, 2022, 21 : 392 - 403
  • [17] Data-driven decision-making for wastewater treatment process
    Han, Hong-Gui
    Zhang, Hui-Juan
    Liu, Zheng
    Qiao, Jun-Fei
    CONTROL ENGINEERING PRACTICE, 2020, 96
  • [18] A Data-Driven Simulator for Assessing Decision-Making in Soccer
    Mendes-Neves, Tiago
    Mendes-Moreira, Joao
    Rossetti, Rosaldo J. F.
    PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2021), 2021, 12981 : 687 - 698
  • [19] Data-driven decision-making in emergency remote teaching
    Maya Botvin
    Arnon Hershkovitz
    Alona Forkosh-Baruch
    Education and Information Technologies, 2023, 28 : 489 - 506
  • [20] Elementary teachers’ perceptions of data-driven decision-making
    Natalie Schelling
    Lisa DaVia Rubenstein
    Educational Assessment, Evaluation and Accountability, 2021, 33 : 317 - 344