pith. sign in

arxiv: 2604.09623 · v1 · submitted 2026-03-18 · 💻 cs.CY · econ.GN· q-fin.EC

The Hourglass Revolution: A Theoretical Framework of AI's Impact on Organizational Structures in Developed and Emerging Markets

Krishna Kumar Balaraman , Venkat Ram Reddy Ganuthula This is my paper

Pith reviewed 2026-05-15 09:00 UTC · model grok-4.3

classification 💻 cs.CY econ.GNq-fin.EC
keywords AI organizational impacthourglass configurationmiddle management automationalgorithmic coordinationstructural fluidityhybrid agencyinstitutional theorydigital transformation
0
0 comments X

The pith

AI takes over middle management to produce hourglass-shaped organizations that connect top leadership directly to operations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a theoretical framework showing that artificial intelligence replaces traditional middle management layers, resulting in an hourglass configuration where top executives and frontline workers connect through AI systems. This shift rests on three mechanisms: algorithmic coordination that integrates activities without human intermediaries, structural fluidity that lets organizations stay stable yet change form, and hybrid agency that combines human and machine decision-making. The framework draws on institutional theory to argue that these mechanisms produce different structural patterns in developed markets versus emerging markets. Successful adoption, the authors state, will still require attention to local technological capabilities and cultural contexts even though the overall direction appears global.

Core claim

AI assumes traditional middle management functions and thereby creates an hourglass organizational configuration. Algorithmic coordination, structural fluidity, and hybrid agency enable forms of organizing that cross traditional structural boundaries. These mechanisms operate differently across developed and emerging markets to yield distinct patterns of transformation, while still requiring attention to local technological and cultural conditions for effective use.

What carries the argument

The hourglass configuration, produced when AI performs middle-management roles and connects senior leadership to operational levels through algorithmic coordination, structural fluidity, and hybrid agency.

If this is right

  • Organizations achieve both stability and rapid adaptation at once through structural fluidity.
  • Hybrid agency supplies organizational capabilities that exceed purely human-centered forms.
  • Algorithmic coordination functions as a distinct integration mechanism replacing some human oversight.
  • Global AI strategies still require local adjustments for technological capacity and cultural fit.
  • Distinct transformation patterns appear in developed markets compared with emerging markets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training programs for managers may shift from supervising people to supervising AI coordination systems.
  • Emerging-market firms could adopt flatter structures faster by skipping layers that developed markets still retain.
  • Performance data on coordination speed and decision quality could test whether hourglass forms outperform traditional hierarchies.

Load-bearing premise

The three mechanisms generate distinct structural changes in developed versus emerging markets due to institutional differences, without specified empirical tests or counterexamples.

What would settle it

A side-by-side comparison of matched organizations in developed and emerging markets that shows identical reductions in middle-management layers and identical fluidity patterns after comparable AI adoption would falsify the claim of market-specific transformation.

Figures

Figures reproduced from arXiv: 2604.09623 by Krishna Kumar Balaraman, Venkat Ram Reddy Ganuthula.

Figure 1
Figure 1. Figure 1: Evolution of Organizational Forms in the AI Era [PITH_FULL_IMAGE:figures/full_fig_p025_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Mechanisms of AI-Enabled Organizational Transformation [PITH_FULL_IMAGE:figures/full_fig_p026_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The Hourglass Structure Components [PITH_FULL_IMAGE:figures/full_fig_p027_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Market Context Implementation Patterns [PITH_FULL_IMAGE:figures/full_fig_p028_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Theoretical Framework of AI-Enabled Organizational Transformation [PITH_FULL_IMAGE:figures/full_fig_p029_5.png] view at source ↗
read the original abstract

This paper presents a theoretical framework examining how artificial intelligence (AI) transforms organizational structures, introducing an "hourglass" configuration that emerges as AI assumes traditional middle management functions. The analysis identifies three key mechanisms algorithmic coordination, structural fluidity, and hybrid agency that demonstrate how AI enables organizational forms transcending traditional structural boundaries. These mechanisms illustrate how AI enables new modes of organizing to go beyond existing structural boundaries. Drawing on institutional theory and digital transformation research, we examine how these mechanisms operate differently in developed and emerging markets, producing distinct patterns of structural transformation. Our framework offers three important theoretical contributions: (1) conceptualizing algorithmic coordination as a unique form of organizational integration, (2) explaining how structural fluidity allows organizations to achieve stability and adaptability at the same time, and (3) the theoretical argument that hybrid agency surpasses traditional, human centric forms of organizational capabilities. Our analysis shows that while the move to AI enabled strategies overall seems quite global, successful application will need to pay sufficient attention to the technological capabilities, cultural dimensions, and contexts of the market.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper presents a theoretical framework for AI-driven changes in organizational structures, centered on an emerging 'hourglass' configuration in which AI assumes middle-management functions. It identifies three mechanisms—algorithmic coordination, structural fluidity, and hybrid agency—that purportedly enable forms transcending traditional boundaries, and claims these mechanisms produce distinct transformation patterns in developed versus emerging markets via institutional theory. The framework is said to yield three contributions: conceptualizing algorithmic coordination as organizational integration, explaining structural fluidity's dual stability-adaptability, and arguing hybrid agency exceeds human-centric capabilities.

Significance. If the mechanisms were derived from explicit logical steps or literature mappings and the market-differentiation claim were grounded with causal pathways or boundary conditions, the framework could offer a useful conceptual lens for integrating AI into organizational theory and highlighting contextual differences. As presented, however, the absence of derivations, evidence, or counterexamples limits its contribution to a set of untested assertions.

major comments (3)
  1. [Abstract] Abstract: The three mechanisms are introduced as explanatory devices for the hourglass configuration and then immediately invoked to justify the framework's three contributions, creating a circular structure with no independent derivation or external benchmark provided.
  2. [Abstract] Abstract: The claim that the mechanisms 'operate differently in developed and emerging markets, producing distinct patterns of structural transformation' draws on institutional theory and digital transformation research yet supplies no explicit mapping of how cultural, technological, or regulatory differences alter algorithmic coordination, structural fluidity, or hybrid agency, nor any counterexamples or boundary conditions.
  3. [Abstract] Abstract: The three listed contributions restate the mechanisms (e.g., 'conceptualizing algorithmic coordination as a unique form of organizational integration') without demonstrating how they follow from prior analysis or generate falsifiable predictions.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'three key mechanisms algorithmic coordination' is missing a dash or colon for readability.
  2. The manuscript would benefit from explicit definitions of the novel terms 'hourglass configuration,' 'structural fluidity,' and 'hybrid agency' at first use.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We appreciate the identification of potential circularity and lack of explicit mappings in the abstract. Our response addresses each point, and we commit to revisions where appropriate to enhance the framework's clarity and rigor.

read point-by-point responses

  1. Referee: [Abstract] The three mechanisms are introduced as explanatory devices for the hourglass configuration and then immediately invoked to justify the framework's three contributions, creating a circular structure with no independent derivation or external benchmark provided.

    Authors: We agree that the abstract's structure may suggest circularity. The mechanisms are derived from a synthesis of AI literature and organizational theory in the body of the paper. To address this, we will revise the abstract to first present the logical derivation steps from prior research before linking to the contributions, thereby clarifying the independent basis for each. revision: yes

  2. Referee: [Abstract] The claim that the mechanisms 'operate differently in developed and emerging markets, producing distinct patterns of structural transformation' draws on institutional theory and digital transformation research yet supplies no explicit mapping of how cultural, technological, or regulatory differences alter algorithmic coordination, structural fluidity, or hybrid agency, nor any counterexamples or boundary conditions.

    Authors: The full manuscript references institutional differences, but the abstract does not detail the mappings. We will add explicit examples, such as how stricter data regulations in developed markets constrain algorithmic coordination compared to emerging markets' focus on hybrid agency for rapid scaling. Boundary conditions like digital infrastructure levels will be specified to make the claims more precise. revision: yes

  3. Referee: [Abstract] The three listed contributions restate the mechanisms (e.g., 'conceptualizing algorithmic coordination as a unique form of organizational integration') without demonstrating how they follow from prior analysis or generate falsifiable predictions.

    Authors: As a theoretical contribution, the framework aims to extend concepts rather than test them empirically. However, we recognize the need for clearer linkage. We will revise to show how each contribution builds on the mechanisms and include example falsifiable predictions, such as 'Organizations with high structural fluidity will exhibit lower middle-management layers in AI-adopting firms.' revision: yes

Circularity Check

1 steps flagged

Contributions reduce to restating the three introduced mechanisms

specific steps
  1. self definitional [Abstract]
    "The analysis identifies three key mechanisms algorithmic coordination, structural fluidity, and hybrid agency that demonstrate how AI enables organizational forms transcending traditional structural boundaries. ... Our framework offers three important theoretical contributions: (1) conceptualizing algorithmic coordination as a unique form of organizational integration, (2) explaining how structural fluidity allows organizations to achieve stability and adaptability at the same time, and (3) the theoretical argument that hybrid agency surpasses traditional, human centric forms of organizational"

    The mechanisms are introduced to explain the hourglass structure and AI's impact; the listed contributions consist exactly of conceptualizing/explaining those mechanisms, making the contributions equivalent to the framework's own definitional steps by construction rather than an independent derivation.

full rationale

The paper's derivation introduces the hourglass configuration and three mechanisms (algorithmic coordination, structural fluidity, hybrid agency) as the core analysis. It then lists the framework's theoretical contributions as precisely the acts of conceptualizing and explaining those same mechanisms. This creates a self-contained loop where the claimed novelty is equivalent to the definitional premises. The assertion of distinct patterns in developed vs. emerging markets is drawn from institutional theory citations but supplies no explicit mappings, equations, or derivations, leaving the differentiation as an added premise. No fitted parameters, self-citations of prior author theorems, or ansatzes are present, so circularity is limited to the conceptual self-definition rather than a full reduction to inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 4 invented entities

The framework rests on domain assumptions from institutional theory and digital transformation literature, and introduces several new conceptual entities (hourglass configuration and the three mechanisms) that lack independent empirical evidence or formal derivation within the paper.

axioms (2)
  • domain assumption Institutional theory applies to explain differences in AI-driven organizational changes between developed and emerging markets
    Invoked to account for distinct patterns based on technological capabilities, cultural dimensions, and market contexts.
  • domain assumption AI assumes traditional middle management functions leading to structural transformation
    Central premise for the emergence of the hourglass configuration.
invented entities (4)
  • Hourglass configuration no independent evidence
    purpose: Describes the new AI-enabled organizational structure with reduced middle layer
    Introduced as the emergent form when AI takes over middle management functions.
  • Algorithmic coordination no independent evidence
    purpose: Unique form of organizational integration via AI
    Conceptualized as one of the three key mechanisms enabling boundary-transcending forms.
  • Structural fluidity no independent evidence
    purpose: Mechanism allowing simultaneous stability and adaptability
    One of the three mechanisms in the framework.
  • Hybrid agency no independent evidence
    purpose: New form of organizational capability surpassing human-centric approaches
    Theoretical argument presented as a contribution.

pith-pipeline@v0.9.0 · 5497 in / 1754 out tokens · 89366 ms · 2026-05-15T09:00:16.213563+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

69 extracted references · 69 canonical work pages

  1. [1]

    hierarchical suppression

    The Hourglass Structure: A New Organizational Paradigm The increasing adoption of AI in traditional middle management functions drives a fundamental transformation from pyramid to 'hourglass' structures. This shift represents not merely incremental change but a foundational reconceptualization of organizational design in an AI-augmented world. This is mor...

    work page 2018

  2. [2]

    Cross-Market Implementation and Institutional Contexts The manifestation of AI-enabled organizational transformation varies systematically across different market contexts, reflecting deep institutional and structural differences between developed and emerging markets. This variation extends beyond simple differences in adoption rates or implementation ca...

    work page 2021

  3. [3]

    Discussion and Implications The organizational integration of AI presents implications that extend far beyond conventional technology adoption concerns. For example, in the operation of it, embedding AI greatly hastens the pace while at the same time maximizes the efficacy of decision-making (Krakowski et al., 2022), allowing organizations to process larg...

    work page 2022

  4. [4]

    Aghina, W., Ahlback, K., De Smet, A., Lackey, G., Lurie, M., Murarka, M., & Handscomb, C. (2018). The five trademarks of agile organizations. McKinsey Quarterly, 33(1), 1-11

    work page 2018

  5. [5]

    Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Press

    work page 2018

  6. [6]

    A., Mehrpour, M., & Nikooravesh, A

    Ahmady, G. A., Mehrpour, M., & Nikooravesh, A. (2016). Organizational structure. Procedia - Social and Behavioral Sciences, 230, 455-462

    work page 2016

  7. [7]

    E., & Mousavi, R

    Alavi, M., Leidner, D. E., & Mousavi, R. (2024). A Knowledge Management Perspective of Generative Artificial Intelligence. Journal of the Association for Information Systems, 25(1), 1- 12

    work page 2024

  8. [8]

    Belloc, F., Drago, F., & Ippoliti, R. (2022). Gender and AI in European labor markets. Labour Economics, 73, 102119

    work page 2022

  9. [9]

    H., & Pachidi, S

    Benbya, H., Davenport, T. H., & Pachidi, S. (2020). Artificial intelligence in organizations: Current state and future opportunities. MIS Quarterly Executive, 19(4), 201-220

    work page 2020

  10. [10]

    Bernstein, E., Bunch, J., Canner, N., & Lee, M. (2016). Beyond the holacracy hype. Harvard Business Review, 94(7/8), 38-46

    work page 2016

  11. [11]

    Boos, A. K. (2024). Conceptualizing Automated Decision-Making in Organizational Contexts. Philosophy & Technology, 37(3), 92. 10. Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 7, 3-11. 11. Burton, R. M., Obel, B., & Håkonsson, D. D. (2020). Organizational design: A step-by-step approach (4th ed.)....

    work page 2024

  12. [12]

    Caplan, R., & Boyd, D. (2018). Isomorphism through algorithms: Institutional dependencies in the case of Facebook. Big Data & Society, 5(1), 1-12

    work page 2018

  13. [13]

    Cappelli, P., Singh, H., Singh, J., & Useem, M. (2010). The India way: Lessons for the U.S. Academy of Management Perspectives, 24(2), 6-24

    work page 2010

  14. [14]

    F., & Montealegre, R

    Cascio, W. F., & Montealegre, R. (2016). How technology is changing work and organizations. Annual Review of Organizational Psychology and Organizational Behavior, 3, 349-375. 15. Chamorro-Premuzic, T., Wade, M., & Jordan, J. (2018). As AI makes more decisions, the nature of leadership will change. Harvard Business Review Digital Articles, 2-6. 16. Chatte...

    work page 2016

  15. [15]

    A., & Steinberger, T

    Csaszar, F. A., & Steinberger, T. (2021). Organizations as artificial intelligences: The use of artificial intelligence analogies in organization theory. Organization Science, 32(6), 1695-1715

    work page 2021

  16. [16]

    Davenport, T. H. (2018). The AI advantage: How to put the artificial intelligence revolution to work. MIT Press

    work page 2018

  17. [17]

    H., & Kirby, J

    Davenport, T. H., & Kirby, J. (2016). Only humans need apply: Winners and losers in the age of smart machines. Harper Business

    work page 2016

  18. [18]

    J., & Powell, W

    DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147-160

    work page 1983

  19. [19]

    Domini, G., Grazzi, M., Moschella, D., & Treibich, T. (2022). Threats and opportunities in the digital era: Automation spikes and employment dynamics. Research Policy, 51(7), 104544

    work page 2022

  20. [20]

    R., Bell, J

    Doshi, A. R., Bell, J. J., Mirzayev, E., & Vanneste, B. S. (2024). Generative artificial intelligence and evaluating strategic decisions. Strategic Management Journal

    work page 2024

  21. [21]

    & Vayena, E

    Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—an ethical framework for a good AI society. Minds and Machines, 28(4), 689-707

    work page 2018

  22. [22]

    Fountaine, F., McCarthy, B., & Saleh, T. (2019). Building the AI-powered organization. Harvard Business Review, 97(4), 62-73

    work page 2019

  23. [23]

    B., & Osborne, M

    Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280

    work page 2017

  24. [24]

    Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28-36. 27. George, G., Haas, M. R., & Pentland, A. (2014). Big data and management: From the editors. Academy of Management Journal, 57(2), 321-326

    work page 1974

  25. [25]

    Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660

    work page 2020

  26. [26]

    Graef, I., & Prüfer, J. (2021). Governance of data sharing: A European perspective. IEEE Security & Privacy, 19(4), 53-62

    work page 2021

  27. [27]

    Graef, I., & Prüfer, J. (2021). Governance of data sharing: A law & economics proposal. Research Policy, 50(9), 104330. 31. Grønsund, T., & Aanestad, M. (2020). Augmenting the algorithm: Emerging human-in-the-loop work configurations. The Journal of Strategic Information Systems, 29(2), 101614. 32. Hamel, G. (2011). First, let's fire all the managers. Har...

    work page 2021

  28. [28]

    Hasan, R., & Ojala, A. (2024). Managing artificial intelligence in international business:Toward a research agenda on sustainable production and consumption. Thunderbird International Business Review, 66(2), 151-170. 34. IBM. (2018). The quest for AI leadership: A study of AI management systems [White paper]. IBM Institute for Business Value

    work page 2024

  29. [29]

    IMF Staff. (2024). Employment, technology, and growth in the 21st century (Working Paper No. WP/24/15). International Monetary Fund

    work page 2024

  30. [30]

    J., & Varma, A

    Jaiswal, A., Arun, C. J., & Varma, A. (2021). Rethinking managerial capabilities in the age of AI: Evidence from Indian firms. Journal of Business Research, 128, 704-718

    work page 2021

  31. [31]

    Janssen, M., Weerakkody, V., Ismagilova, E., Sivarajah, U., & Irani, Z. (2020). A framework for analysing blockchain technology adoption: Integrating institutional, market and technical factors. International journal of Information Management, 50, 302-309

    work page 2020

  32. [32]

    Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586

    work page 2018

  33. [33]

    Johansen, R. (2017). The new leadership literacies: Thriving in a future of extreme disruption and distributed everything. Berrett-Koehler Publishers

    work page 2017

  34. [34]

    D., Kessler, T

    Kaplan, A. D., Kessler, T. T., Brill, J. C., & Hancock, P. A. (2023). Trust in artificial intelligence: Meta-analytic findings. Human factors, 65(2), 337-359

    work page 2023

  35. [35]

    Khanna, T., & Palepu, K. G. (2010). Winning in emerging markets: A road map for strategy and execution. Harvard Business Press

    work page 2010

  36. [36]

    Kolbjørnsrud, V. (2024). Designing the Intelligent Organization: six principles for Human-AI collaboration. California Management Review, 66(2), 44-64. 43. Kolbjørnsrud, V., Amico, R., & Thomas, R. J. (2016). How artificial intelligence will redefine management. Harvard Business Review Digital Articles, 2-11

    work page 2024

  37. [37]

    Korinek, A., & Stiglitz, J. E. (2017). Artificial intelligence and its implications for income distribution and unemployment. NBER Working Paper No. 24174

    work page 2017

  38. [38]

    Krakowski, S., Luger, J., & Raisch, S. (2022). Artificial intelligence and human-machine competitive advantage. Academy of Management Review, 47(3), 412-439

    work page 2022

  39. [39]

    Kumar, N., & Puranam, P. (2012). India inside: The emerging innovation challenge to the West. Harvard Business Press

    work page 2012

  40. [40]

    Luo, Y. (2021). Digital resilience in international business: A conceptual framework and future research directions. Journal of International Business Studies, 52(9), 1616-1633

    work page 2021

  41. [41]

    & Sanghvi, S

    Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., ... & Sanghvi, S. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute

    work page 2017

  42. [42]

    March, J. G. (1999). The pursuit of organizational intelligence. Blackwell Publishers

    work page 1999

  43. [43]

    McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60-68

    work page 2012

  44. [44]

    Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434

    work page 2021

  45. [45]

    Mintzberg, H. (1979). The structuring of organizations. Prentice-Hall

    work page 1979

  46. [46]

    Mirbabaie, M., Stieglitz, S., & Frick, N. R. (2021). Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. Health and Technology, 11(4), 693-731. 54. Ocasio, W., Rhee, L., & Boynton, D. (2020). Attention, knowledge, and organizational learning. Organization Science, ...

    work page 2021

  47. [47]

    (2020-2024)

    Oxford Insights. (2020-2024). Government AI readiness index [Annual reports 2020-2024]. Oxford Insights

    work page 2020

  48. [48]

    Puranam, P. (2018). The microstructure of organizations. Oxford University Press

    work page 2018

  49. [49]

    Puranam, P., Alexy, O., & Reitzig, M. (2014). What's "new" about new forms of organizing? Academy of Management Review, 39(2), 162-180

    work page 2014

  50. [50]

    G., & Wulf, J

    Rajan, R. G., & Wulf, J. (2006). The flattening firm: Evidence from panel data on the changing nature of corporate hierarchies. The Review of Economics and Statistics, 88(4), 759-773

    work page 2006

  51. [51]

    Ranson, S., Hinings, B., & Greenwood, R. (1980). The structuring of organizational structures. Administrative Science Quarterly, 25(1), 1-17

    work page 1980

  52. [52]

    Rao, H., & Sutton, R. (2014). Bad to great: The path to scaling up excellence. McKinsey Quarterly, 51(1), 1-12

    work page 2014

  53. [53]

    Reeves, M., Levin, S., & Ueda, D. (2015). The biology of corporate survival. Harvard Business Review, 93(1-2), 46-55

    work page 2015

  54. [54]

    Reeves, M., Levin, S., & Ueda, D. (2018). The biology of corporate survival: Learning from nature's rules. Harvard Business Review, 96(1), 46-55

    work page 2018

  55. [55]

    Robertson, B. J. (2015). Holacracy: The revolutionary management system that abolishes hierarchy. Henry Holt and Company

    work page 2015

  56. [56]

    A., & Øvrelid, E

    Sanner, T. A., & Øvrelid, E. (2020). Informating hospital workflow coordination. Computer Supported Cooperative Work, 29(4), 387-417

    work page 2020

  57. [57]

    Sharma, G., & Good, D. (2013). The work of middle managers: Sensemaking and sensegiving for creating positive social change. Journal of Applied Behavioral Science, 49(1), 95-122

    work page 2013

  58. [58]

    R., Ben-Menahem, S

    Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66-83

    work page 2019

  59. [59]

    Singh, M., & Anantatmula, V. S. (2013). Impact of technology implementation on organizational performance. International Journal of Information Technology Project Management, 4(1), 27-48

    work page 2013

  60. [60]

    (2021, March 15)

    Soper, T. (2021, March 15). Inside Amazon's AI-powered human resources: System tracks workers' every move, can automatically fire them. GeekWire

    work page 2021

  61. [61]

    Subramaniam, M., Iyer, B., & Venkatraman, V. (2015). Competing in digital ecosystems. Business Horizons, 58(6), 599-608

    work page 2015

  62. [62]

    Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15-42

    work page 2019

  63. [63]

    M., & Ross, J

    Tarafdar, M., Beath, C. M., & Ross, J. W. (2019). Using AI to enhance business operations. MIT Sloan Management Review, 60(4), 37-44

    work page 2019

  64. [64]

    T., & Almirall, E

    Tschang, F. T., & Almirall, E. (2021). Artificial intelligence as augmenting automation: Implications for employment. Academy of Management Perspectives, 35(4), 642-659

    work page 2021

  65. [65]

    Vincent, V. U. (2021). Integrating intuition and artificial intelligence in organizational decision-making. Business Horizons, 64(4), 425-438. 74. Wadhwa, A., Phelps, C., & Kotha, S. (2008). Corporate venture capital, informational leveraging, and resource dependence. Journal of Management Studies, 45(1), 107-131

    work page 2021

  66. [66]

    J., & Daugherty, P

    Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114-123

    work page 2018

  67. [67]

    World Economic Forum. (2020). The future of jobs report 2020. World Economic Forum

    work page 2020

  68. [68]

    K., & Pearce, J

    Xin, K. K., & Pearce, J. L. (1996). Guanxi: Connections as substitutes for formal institutional support. Academy of Management Journal, 39(6), 1641-1658

    work page 1996

  69. [69]

    Xue, L., & Pang, Z. (2022). Ethical governance of artificial intelligence: An integrated analytical framework. Journal of Digital Economy, 1(1), 44-52. Figure 1: Evolution of Organizational Forms in the AI Era Figure 1 illustrates the evolution from traditional hierarchical structures to the AI-enabled hourglass form, highlighting the key structural trans...

    work page 2022