Submit a Manuscript to the Journal

Asia Pacific Business Review

For a Special Issue on

Algorithmic Management in the Digital Era in the Asia Pacific: Employee Impacts and Responses

Abstract deadline
31 October 2023

Manuscript deadline
30 January 2024

Cover image - Asia Pacific Business Review

Special Issue Editor(s)

Ying Zhu, Australian Centre for Asian Business, University of South Australia, Australia
[email protected]

Zhi-Xing Xu, Business School, Beijing Normal University, Beijing, China
[email protected]

Chris Rowley, Kellogg College, Oxford University & Bayes Business School, City, University of London, UK

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Algorithmic Management in the Digital Era in the Asia Pacific: Employee Impacts and Responses

Algorithmic management (AM), which defined as the use of programmed algorithms by an organization to partially or completely execute workforce management function, such as monitoring the work; assigning tasks, targets or schedules; rating productivity and performance, has significantly reshaped the business environment. So far, it has become a vital aspect of organizational management across industries at the digital era (Gagné, Parent-Rocheleau, et al., 2022). Either in the gig economy, or in traditional work settings, the adoption of AM is increasing and many management functions that are executed by using algorithmic systems. Given the complexity in the variation of AM being used within and between organizations, it has profound impact on the wellbeing of both employees and organizations.

However, organizational research about algorithmic management and its impact is still at an infancy stage. Interestingly, some studies suggest it generates more negative rather than positive outcomes for employees. For example, some scholars found that algorithmic management reduces motivational job characteristics, such as job autonomy, task significant, and job complexity, largely because its close monitoring employees’ performance with strict requirements and serious punishment for making mistake (Parent-Rocheleau & Parker, 2022). As for work motivation, other scholars also found that algorithmic management reduces employees’ intrinsic motivation with fewer social contacts and implied tasks threat (Gagné et al., 2022). Algorithmic management has also been found to link with lower initiative, due to employees’ comparatively powerless under algorithms management. Lack of transparency, reliance on the resources provided by algorithms, and pressure to meet requirements generate high uncertainty and dissatisfaction among employees (Wood et al., 2019). In addition, algorithmic management alters employees’ attention, preferences, and decision priority, making employees more like tools attached to algorithms rather than independent human being (Yeung, 2017). To cope with, or say resist, the control imposed by algorithms, employees were found to, individually or collectively, engage into various kinds of behaviors, from ealgoactivism to game playing (Cameron, 2022; Cameron & Rahman, 2022; Kellogg et al., 2020).

While the existing studies mainly tell a story about “control and escape”, it is no doubt that algorithmic management is an unstoppable trend which will shape the nature of work. Currently, more organizations are adopting AM at their workplace, which seems to suggest the coexistence of high efficiency for work outcome but unfavorable psychological states for employees. There are other aspects of employees’ work lives that have not been touched in existing studies, such as the role, relationship and identity of employees under algorithmic management, and the unique advantages of human employees. Ethics, particularly, responsible innovation, privacy, hacking, and surveillance related to AM are also important and need to be addressed. In addition, stakeholders, such as managers, family, community and the government, have also not been considered. Obviously, there are many questions that need to be examined and answered. The time has therefore come for researchers to contribute to better understanding of the impact of algorithmic management on employees and the response from employees towards it.

We welcome submissions on topics including, but not limited to, the following:

We, therefore, propose this Special Issue on:

  1. The role, relationship and identity of organization members under Algorithmic Management;
  2. What is the unique advantages of human employees under algorithmic management;
  3. Employees’ psychological characteristics and behavioral patterns under the control of algorithm;
  4. How Algorithmic Management absorbs and empowers human intelligence;
  5. Employees’ work motivation and incentive mechanism under the supervision of algorithm management;
  6. The theory of human-machine coexistence and coevolution within the new organization;
  7. Human capital development and creation within algorithm management;
  8. Ethics, particularly, responsible innovation, privacy, hacking, and surveillance, at digital era;
  9. Effective and efficient leadership under algorithmic management at digital era;
  10. Knowledge management and employee knowledge behavior at digital era;
  11. Government regulations related issues, such as labour regulations and enforcement on adopting AM and its impact on workers.

Submission Instructions

For questions in the first instance contact: Zhixing Xu at [email protected] and Professor Zhu Ying at [email protected].

Submissions should be submitted online and follow the APBR guidelines in full, including:

  1. Country(s) covered in the title.
  2. Clear on methods and dates collected, etc.
  3. Substantive and separate sections of implications for both: 1) theory and theory development; 2) business and management practice.


31 Oct 2023: Deadline for submission abstracts (max. 500 words) + short bios

11 Nov 2023: Presentation in the special Meeting in Beijing, China

30 Jan 2024: Submission of full papers

31 Feb 2024: Results of the first review

30 March 2024: Submission of revised drafts

31 April 2024: Final decision of the submitted articles

May 2024: Publication of accepted papers online

Instructions for AuthorsSubmit an Article