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App-Work and Algorithmic Management in the Gig Economy: A Working Relationship and HRM Perspective

App-Work and Algorithmic Management in the Gig Economy: A Working Relationship and HRM Perspective

 
James Duggan, Ultan Sherman, Ronan Carbery & Anthony McDonnell
Cork University Business School, University College Cork, Ireland
Division: Business & Economics
 

Research Overview:

Defined as an economic system that uses online platforms to digitally connect workers with consumers, discourse around the gig economy traverses from the positive, with emphasis on workers’ autonomy and flexibility, to those viewing it as a means by which businesses lower costs and erode employment standards (Stewart & Stanford, 2017). App-work, the gig work variant upon which this paper is focused, refers to service-providing intermediary digital platform organisations (or ‘apps’) that deploy workers to perform tasks (e.g. transportation with Uber, food-delivery with Deliveroo) for customers, with the organisation intervening in setting quality standards and managing workers (De Stefano, 2016).
Although app-work impacts our understanding of what constitutes work, little is known about its implications for the employment relationship and human resource management (HRM). While HRM is traditionally conceptualised as the managerial activities for maintaining employment relationships, the gig economy establishes a different type of working relationship, as workers are not legally employed by organisations. Despite this, gig organisations nevertheless implement a variety of HRM activities to manage their working relationship with app-workers. This paper’s objective is to examine HRM processes in app-work, with a particular focus on the criticality and impact of algorithmic management, a novel means of utilising technology to manage workers. Specifically, this paper seeks to examine the nature of recruitment, work assignment, and performance management for app-workers. Empirically, this paper forms part of a larger qualitative study with the parties involved in app-working arrangements. Preliminary findings will be presented at the conference.

Theoretical Foundations:

Algorithms embedded within platforms typically fulfil a rudimentary HR function for app-workers, as they are used to undertake several HR processes, albeit without the need for face-to-face interaction (Gandini, 2018). Accordingly, we identify three specific areas of HRM that are likely to be especially formative in gaining further insight into app-working relationships in terms of how they are created and managed.
Recruitment & Selection:
App-work organisations rely on the speedy availability of their workforce. This results in an often-ruthless approach in ensuring a readily accessible source of labour (Kuhn & Maleki, 2017). Likewise, app-working relationships are established remarkably quickly, in that workers are typically hired promptly, without regard for past employment and with no promise for future employment (De Stefano, 2016). A traditional HR approach to workforce planning focuses on getting the right number of people, with the right skills, employed at the right time. However, the success of app-work relies on deliberate oversupply of labour, with algorithms often incentivising workers to ‘log-in’ during peak times (e.g. Uber’s surge-pricing) (Rosenblat, 2018). The competitive nature of recruitment and the need for shortlisting is therefore seemingly redundant in the gig economy, as provided individuals fulfil the cited criteria, they are ‘hired’ with little face-to-face interaction.
Work Assignment:
The supposed non-financial reward of app-work is that workers are afforded a high degree of flexibility and autonomy by choosing when and where to work (Healy et al., 2017). However, for most app-workers, the reality of the autonomy advertised is often limited (Wood et al., 2018). In reality, workers must work long hours and at peak times in order to garner high earnings and maintain good ratings and are often left with little choice but to accept whichever tasks are offered (Prassl, 2018). Thus, for most platforms, rather than simply ‘matching’ workers and customers, they act as digital work intermediaries that use algorithms to tightly manage a large, invisible workforce (Meijerink & Keegan, 2019).
Performance Management:
Platforms typically manage workers’ performance extensively in various ways. Many platforms issue various kinds of performance metrics to app-workers, including comparisons to other workers and overall rankings in comparison to average performance levels, as a means of ensuring high productivity (Van Doorn, 2017). Additionally, most platforms utilise anonymous customer ratings of workers as a means of performance evaluation (Gramano, 2018). However, there appears to be limited transparency over how exactly algorithms are altered, by whom, and under whose instruction, which is especially relevant in this scenario and worthy of future investigation given the people management implications. As the platform has the opportunity to exercise penetrating control over all aspects of the work, the app-worker can be described as being in a situation comparable to a permanent probationary period (Gramano, 2018).

Research Implications:

Through eliminating the more interpersonal and empathetic aspects of people management, algorithmic management may have a negative impact on app-workers’ sense of well-being and commitment, causing workers to lose trust and confidence in the absence of an organisational partner (Gilbert et al., 2011). Given the novelty and lack of HR research across gig economy scholarship, this paper seeks to build understanding on the potentially disruptive nature of algorithmic management as a rudimentary HRM tool within app-working. While undoubtedly innovative, the hyper-flexibility of work in the gig economy and the crucial role of technology potentially often leaves workers isolated and insecure, thereby challenging our understandings of work and the HR function (Taylor et al., 2017). Likewise, there are further unanswered questions around gig work, broadly encompassing the experiences of workers in roles and optimal human resource practices. This research offers the opportunity to craft and broaden existing scholarship to encompass the power relations that exist between gig organisations and workers in order to maintain relevance in a rapidly changing world of work, and subsequently, to explore the associated implications for HRM research, policy and practice.

References:

De Stefano, V. (2016) ‘The Rise of the ‘Just-In-Time’ Workforce: On-Demand Work, Crowdwork and Labour Protection in the Gig Economy’, International Labour Office: Conditions of Work and Employment Series, 71.
Gandini, A. (2018) ‘Labour Process Theory and the Gig Economy’, Human Relations, 1-18.
Gilbert, C., De Winne, S. & Sels, L. (2011) ‘The Influence of Line Managers and HR Department on Employees’ Affective Commitment’, International Journal of Human Resource Management, 22(8), 1618-37.
Gramano, E. (2018) ‘Digitalisation and Work: Challenges from the Platform Economy’, Social Science Research Network.
Healy, J., Nicholson, D. & Pekarek, A. (2017) ‘Should We Take The Gig Economy Seriously?’, Labour and Industry: A Journal of the Social and Economic Relations of Work, 27(3), 232-248.
Kuhn, K.M. & Maleki, A. (2017) ‘Micro-Entrepreneurs, Dependent Contractors, and Instaserfs: Understanding Online Labour Platform Workforces’, Academy of Management Perspectives, 31(3), 183-200.
Meijerink,. J. & Keegan, A. (2019) ‘Conceptualizing human resource management in the gig economy: Toward a platform ecosystem perspective’, Journal of Managerial Psychology, forthcoming.
Prassl, J. (2018) Humans as a Service: The Promise and Perils of Work in the Gig Economy. Oxford: OUP.
Rosenblat, A. (2018) Uberland: How Algorithms are Rewriting the Rules of Work. Oakland, California: University of California Press.
Stewart, A. & Stanford, J. (2017) ‘Regulating Work in the Gig Economy: What are the Options?’, The Economic and Labour Relations Review, 28(3), 420-437.
Taylor, M., Marsh, G., Nicol, D. & Broadbent, P. (2017) Good Work: The Taylor Review of Modern Working Practices. London: Department of Business, Energy & Industrial.
Van Doorn, N. (2017) ‘Platform Labour: On the Gendered and Racialised Exploitation of Low-Income Service Work in the ‘On-Demand’ Economy’, Information, Communication & Society, 20(6), 898-914.
Wood, A.J., Graham, M., Lehdonvirta, V. & Hjorth, I. (2018) ‘Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy’, Work, Employment and Society, 1-20.