Algorithmic control and gig workers: a legitimacy perspective of Uber drivers

Figures & data

Figure 1. Human-based control vs. algorithmic control (based on Cram & Wiener, 2020, p. 74).

Table 1. Basic forms of algorithmic control (AC).

Form	Definition	Illustrative examples
Gatekeeping AC	Use of AC to screen/vet and monitor workers in order to control who is allowed to commence, or to continue, working for a given company (e.g., Griesbach et al., 2019; Kellogg et al., 2020)	Uber uses its app-based AC system to evaluate new drivers and their car against a list of formal requirements; as well, Uber conducts background screenings and uses algorithm-based technologies to continuously monitor each driver’s identity, along with their criminal and driving records.
Guiding AC	Use of AC to oversee and guide worker behaviour, as well as to provide feedback on performance, in order to control how workers conduct their daily work (e.g., Chan & Humphreys, 2018; Kellogg et al., 2020; Rosenblat & Stark, 2016)	Via a varied set of algorithms and a driver app, Uber provides drivers with personalised feedback and performance metrics (e.g., average customer ratings, ride acceptance and completion rates) and offers suggestions on driver behaviours that have been found to increase customer satisfaction (e.g., opening doors, playing jazz music, etc.).

Table 2. Dimensions of micro-level legitimacy judgements in relation to algorithmic control (AC).

Dimension	Short description	Illustrative gig worker example
Autonomy	Workers judge AC practices as empowering, while allowing them to act independently.	An Uber driver values the flexibility of working when and where he or she chooses.
Fairness	Workers judge AC practices as fair, just, and reasonable.	An Uber driver views the passenger ratings to be an effective, fair, and reasonable feedback mechanism.
Privacy	Workers judge AC practices as appropriately respecting their personal information during data collection, storage, and use.	An Uber driver feels that the location and app-usage data collected by the company are relevant to her/his work and will contribute to better work outcomes.

Figure 2. Research model and hypotheses.

Table 3. Hypothesis test results.

Analysis step	Step 1: Control variables			Step 2: Main effects
Dep. variable	Micro-level legitimacy	Continuance intention	Workaround use	Micro-level legitimacy	Continuance intention	Workaround use
Indep. variable	ß (t-value)	ß (t-value)	ß (t-value)	ß (t-value)	ß (t-value)	ß (t-value)
Age	−0.046 (1.161)	0.022 (0.509)	−0.135 (3.614)***	−0.019 (0.612)	0.003 (0.091)	−0.121 (3.779)***
Gender: Female (dummy)	0.014 (0.358)	0.034 (0.879)	−0.076 (1.836)	−0.005 (0.187)	0.017 (0.527)	−0.060 (1.783)
Household income	0.127 (3.023)**	0.077 (1.993)*	0.091 (2.168)*	0.022 (0.680)	0.069 (2.149)*	0.101 (2.763)**
Multi-homing (dummy)	−0.009 (0.230)	−0.026 (0.700)	0.069 (1.679)	−0.045 (1.622)	0.024 (0.798)	0.018 (0.527)
Main income (dummy)	0.007 (0.157)	−0.016 (0.355)	−0.030 (0.628)	0.001 (0.021)	−0.024 (0.698)	−0.025 (0.648)
Full-time driver (dummy)	0.024 (0.472)	0.052 (1.232)	0.013 (0.216)	−0.017 (0.566)	0.055 (1.653)	0.004 (0.083)
Ride-hailing income	0.073 (1.501)	0.066 (1.529)	0.072 (1.370)	−0.034 (0.980)	0.061 (1.691)	0.073 (1.697)
Ride-hailing experience	−0.042 (1.112)	0.028 (0.843)	−0.093 (2.231)*	−0.049 (1.604)	0.052 (1.942)	−0.114 (3.235)**
Overall job satisfaction	0.413 (8.540)***	0.407 (9.698)***	−0.109 (2.401)*	0.211 (5.356)***	0.183 (4.875)***	0.099 (2.590)*
Gatekeeping AC				H1: -0.388 (7.268)***
Guiding AC				H2: 0.482 (10.207)***
Micro-level legitimacy					H3: 0.582 (15.739)***	H4: -0.554 (10.976)***
R^2 (%)/R^2 adjusted (%)	22.7%/21.5%	20.2%/19.0%	5.4%/4.1%	54.5%/53.6%	48.8%/48.0%	31.5%/30.4%

N = 621 Uber drivers. Significant effects in bold. * p < 0.05, ** p < 0.01, *** p < 0.001; two-tailed test.

Table 4. Analysis of mediation effects.

	Direct effects		Indirect effects (via micro-level legitimacy judgements)
Path	ß (t-value)	CI ^a	ß (t-value)	CI ^a	Analysis results
GaAC → CI	0.003 (0.074)	[−0.062; 0.072]	−0.175 (6.051)***	[−0.236; −0.124]	Full mediation
GaAC → WU	0.488 (10.778)***	[0.398; 0.575]	0.135 (3.884)***	[0.071; 0.208]	Partial mediation
GuAC → CI	0.235 (4.486)***	[0.134; 0.336]	0.221 (5.970)***	[0.152; 0.296]	Partial mediation
GuAC → WU	0.082 (2.116)*	[0.007; 0.158]	−0.170 (6.469)***	[−0.227; −0.124]	Partial mediation

Notes: N = 621 Uber drivers. Significant effects in bold. * p < 0.05, ** p < 0.01, *** p < 0.001; two-tailed test.

^a = Bias-corrected 95% confidence interval (5,000 bootstrap samples).

GaAC = Gatekeeping AC, GuAC = Guiding AC, CI = Continuance intention, WU = Workaround use.

Figure 3. Summary of study results.

Table 5. Overview of promising opportunities for future research.

Topic	Research opportunity
AC perspectives	Adopting a multi-actor perspective that goes beyond AC system ‘users’ (i.e., workers) and their behaviours to also include “the actions of the technology and its designers, management and their implementation decisions” (Huysman, 2020, p. 308). Using experience-sampling research designs (e.g., Benlian, 2020; Fisher & To, 2012) to capture workers’ momentary experiences with, and reactions to, AC practices during their everyday lives, and/or longitudinal designs (e.g., multi-wave surveys) that allow for stronger causal inferences. Examining workers’ perceptions/judgements of, and their reactions to, AC through alternative theoretical lenses, such as an affective ‘mood-swing’ lens (e.g., D’Arcy & Lowry, 2019; Ormond et al., 2019) or a habits lens (e.g., Polites & Karahanna, 2013).
AC contexts	Conducting replication studies to examine the extent to which our study results generalise to other … Cultural contexts, such as high power-distance cultures in Asian countries where a premium is placed on compliance (Hofstede, 1980; Sweetman, 2012); National contexts, and in particular the European context, where strict laws and regulations on data protection and privacy (e.g., the EU’s General Data Protection Regulation, GDPR) restrict the scope of AC applications and some countries impose general restrictions on platform-based gig work (Uzunca et al., 2018); Platform contexts/types, such as so-called “Gardener” platforms that are characterised by loose control and low rivalry (e.g., Couchsurfing) (Constantiou et al., 2017), as well as gig platforms that focus on the mediation of high-skilled work (e.g., Upwork) (De Groen et al., 2016); Work contexts in general, including traditional organisations (e.g., retail and service companies), where the use of AC is becoming increasingly common (Mateescu & Nguyen, 2019) and where AC systems complement existing human-based control systems and are targeted at regular full-time employees (as opposed to independent freelance workers).
AC typology & measurement	Developing more fine-grained typologies of AC practices; for example, by drawing on Kellogg et al.’s (2020) “6 Rs” framework to provide a more detailed view on, and propose sub-forms of, gatekeeping and guiding AC. Engaging in systematic efforts to develop measurement scales that (better) account for the particularities of AC practices (Croitor & Benlian, 2019). Exploring the nature and enactment of informal clan and self-controls in platform-based settings and how such controls interact with AC practices in shaping gig workers’ legitimacy judgements, and eventually in generating different intensities of AC compliance and resistance (Gill, 2019).

Table A1. Operationalisation of focal study constructs.

Construct	Items	Reference(s)
Gatekeeping AC (GaAC)	(1) In my opinion, it is hard to get approved to drive for Uber. (2) Overall, Uber sets strict formal criteria for driver approval. ^D (3) Driving for Uber is subject to stringent screening processes. ^D (4) It is difficult for me to comply with all the Uber driver requirements.	Croitor and Benlian (2019)
Guiding AC (GuAC)	(1) Uber informs me about the activities I am expected to perform. (2) Uber monitors my driving behaviour. (3) Uber informs me on whether I meet their expectations on driver behaviours. (4) If Uber wants me to adjust my driving behaviour, I’m asked to do so (e.g., via the driver app). (5) Uber evaluates my driving activities. (6) Specific performance metrics are established for my job as an Uber driver. (7) The extent to which I attain predefined performance goals is monitored by Uber. (8) Uber ensures that I, as a driver, am aware of the extent to which I achieve predefined performance goals. (9) As an Uber driver, I am dependent on my performance in relation to specific metrics defined by Uber. ^D	Challagalla and Shervani (1997); Weibel et al. (2016)
Autonomy judgment (AJ)	(1) During my work as an Uber driver, I am autonomous to decide on my own course of action. (2) I feel independent from Uber during my work as a ride-hailing driver. (3) During my ride-hailing work, I feel controlled by Uber. ^R, D	Goldbach et al. (2018)
Fairness judgment (FJ)	(1) Uber applies its policies and guidelines fairly across all drivers. (2) Uber’s driver policies and guidelines are applied consistently. (3) The driver policies and guidelines of Uber are fair (e.g., based on accurate information). (4) Uber’s driver policies and guidelines uphold ethical and moral standards.	Wu (2013)
Privacy judgment (PJ)	(1) I feel that Uber’s policies and practices are an invasion of privacy. ^R (2) I feel uncomfortable about the types of personal information that Uber collects. ^R (3) The way that Uber monitors my behaviours makes me feel uneasy. ^R (4) I feel that the methods used by Uber to collect information are an invasion of my personal privacy. ^R (5) As an Uber driver, I have little reason to be concerned about my privacy. ^D	Alge et al. (2006); see also Lowry et al. (2015)
Continuance intention (CI)	(1) I plan to work as an Uber driver in the future. (2) I intend to continue driving for Uber in the future. (3) I expect my work as an Uber driver to continue in the future.	Goldbach et al. (2018)
Workaround use (WU)	(1) I look for ways to avoid the instructions and guidelines provided by the Uber driver app. (2) Instead of following the work instructions delivered through the Uber driver app, I often use alternatives. (3) During my work as an Uber driver, I use alternative sources of information (e.g., Waze) instead of the driver app. (4) I avoid relying on the Uber driver app whenever I can.	Laumer et al. (2017)

^RReverse-coded item; ^D Deleted based on item-validation process.

Table B1. Descriptive statistics.

Variable	Characteristics (# of drivers)
Personal information
Age	< 29 years (150)		30–39 years (250)		40–49 years (131)		50–59 years (54)		60+ years (36)
Gender	Female (199)			Male (420)				Other (2)
Household income	< $40k (108)		$40-60k (151)		$60-80k (143)		$80-100k (99)		$100k+ (120)
Education level	No degree (6)		High school (187)		College (380)		Doctoral (22)		Other degree (26)
Work information
Ride-hailing firm	Single-homing: Uber only (391)					Multi-homing: Uber & Lyft (230)
Main motive	Main income (177)			Supplementary income (407)				Fun/socialise, etc. (37)
Hours per week	Part-time driver: Less than 20 (197)			Part-time driver: 20–40 (342)				Full-time driver: More than 40 (82)
Ride-hailing income (per month)	< $500 (105)	$500-1k (162)		$1-2k (159)		$2-3k (108)		$3-4k (62)		$4k+ (25)
Tenure	< 6 months (104)		6–12 months (209)		1–2 years (213)		2–4 years (84)		4+ years (11)
Total # of trips	Average: 625 trips
Overall job satisfaction	Very satisfied (274)	Satisfied (232)		Somewhat satisfied (89)		Somewhat dissatisfied (14)		Dissatisfied (8)		Very dissatisfied (4)

N = 621 Uber drivers.

Table C1. Construct correlations, composite reliability (CR), and Average variance extracted (AVE).

	GaAC	GuAC	AJ	FJ	PJ	CI	WU
GaAC	0.849
GuAC	−0.120	0.769
AJ	0.183	0.249	0.804
FJ	−0.206	0.700	0.313	0.831
PJ	−0.650	0.148	−0.185	0.320	0.832
CI	−0.231	0.574	0.259	0.679	0.305	0.903
WU	0.652	−0.162	0.219	−0.256	−0.691	−0.255	0.805
CR	0.836	0.920	0.783	0.900	0.900	0.930	0.880
AVE	0.720	0.591	0.647	0.691	0.692	0.816	0.648
Mean	3.271	5.494	4.795	5.515	4.433	5.700	3.442
Std. dev.	1.846	1.224	1.540	1.243	1.910	1.291	1.906
Min.	1	1	1	1	1	1	1
Max.	7	7	7	7	7	7	7

GaAC = Gatekeeping AC, GuAC = Guiding AC, AJ = Autonomy judgment, FJ = Fairness judgment, PJ = Privacy judgment, CI = Continuance intention, WU = Workaround use. Diagonal cells (bold) show square root of AVE.

Table C2. Cross loadings between items and constructs.

	GaAC	GuAC	AJ	FJ	PJ	CI	WU
GaAC_1	0.766	0.007	0.199	−0.064	−0.477	−0.093	0.498
GaAC_4	0.924	−0.169	0.135	−0.246	−0.613	−0.264	0.604
GuAC_1	−0.132	0.790	0.193	0.542	0.126	0.460	−0.125
GuAC_2	−0.065	0.740	0.111	0.457	0.064	0.428	−0.128
GuAC_3	−0.134	0.809	0.197	0.611	0.200	0.476	−0.178
GuAC_4	0.035	0.723	0.236	0.485	0.008	0.347	−0.017
GuAC_5	−0.152	0.806	0.183	0.572	0.140	0.469	−0.157
GuAC_6	−0.097	0.762	0.197	0.529	0.100	0.448	−0.091
GuAC_7	−0.037	0.742	0.188	0.483	0.076	0.424	−0.079
GuAC_8	−0.102	0.773	0.219	0.585	0.140	0.456	−0.176
AJ_1	0.176	0.225	0.694	0.223	−0.171	0.190	0.194
AJ_2	0.136	0.193	0.902	0.279	−0.141	0.227	0.172
FJ_1	−0.204	0.592	0.248	0.858	0.318	0.594	−0.248
FJ_2	−0.135	0.591	0.293	0.817	0.205	0.552	−0.195
FJ_3	−0.185	0.561	0.250	0.827	0.306	0.546	−0.227
FJ_4	−0.156	0.585	0.253	0.823	0.225	0.566	−0.179
PJ_1	−0.530	0.096	−0.223	0.233	0.859	0.225	−0.589
PJ_2	−0.533	0.162	−0.055	0.283	0.758	0.283	−0.503
PJ_3	−0.571	0.102	−0.141	0.242	0.835	0.233	−0.620
PJ_4	−0.531	0.133	−0.192	0.301	0.871	0.273	−0.587
CI_1	−0.207	0.496	0.242	0.607	0.270	0.901	−0.232
CI_2	−0.252	0.519	0.227	0.600	0.286	0.919	−0.242
CI_3	−0.168	0.538	0.231	0.632	0.270	0.889	−0.217
WU_1	0.564	−0.148	0.180	−0.230	−0.585	−0.207	0.828
WU_2	0.538	−0.125	0.220	−0.156	−0.563	−0.197	0.813
WU_3	0.474	−0.055	0.203	−0.158	−0.537	−0.135	0.783
WU_4	0.519	−0.183	0.112	−0.268	−0.539	−0.271	0.795

For item details, see Table A1 in Appendix A; for a list of abbreviations, refer to Table C1 above.

Table C3. Heterotrait-monotrait (HTMT) ratio of construct correlations.

	GaAC	GuAC	AJ	FJ	PJ	CI	WU
GaAC	–
GuAC	0.156	–
AJ	0.372	0.391	–
FJ	0.245	0.792	0.487	–
PJ	0.870	0.163	0.300	0.372	–
CI	0.279	0.637	0.395	0.781	0.351	–
WU	0.893	0.182	0.366	0.300	0.828	0.295	–

Refer to Table C1 above for a list of abbreviations.

Table D1. Assessment of CMB using partial correlation procedure (Social desirability).

Model	Baseline model			“Social desirability” model
Dep. variable	Micro-level legitimacy	Continuance intention	Workaround use	Micro-level legitimacy	Continuance intention	Workaround use
Indep. variable	ß (t-value)	ß (t-value)	ß (t-value)	ß (t-value)	ß (t-value)	ß (t-value)
Age	−0.019 (0.612)	0.003 (0.091)	−0.121 (3.779)***	−0.024 (0.755)	0.004 (0.109)	−0.107 (3.452)**
Gender: Female (dummy)	−0.005 (0.187)	0.017 (0.527)	−0.060 (1.783)	−0.007 (0.239)	0.018 (0.542)	−0.058 (1.732)
Household income	0.022 (0.680)	0.069 (2.149)*	0.101 (2.763)**	0.023 (0.709)	0.071 (2.216)*	0.088 (2.522)*
Multi-homing (dummy)	−0.045 (1.622)	0.024 (0.798)	0.018 (0.527)	−0.044 (1.612)	0.023 (0.773)	0.015 (0.463)
Main income (dummy)	0.001 (0.021)	−0.024 (0.698)	−0.025 (0.648)	−0.003 (0.105)	−0.023 (0.654)	−0.017 (0.461)
Full-time driver (dummy)	−0.017 (0.566)	0.055 (1.653)	0.004 (0.083)	−0.012 (0.396)	0.053 (1.567)	−0.003 (0.086)
Ride-hailing income	−0.034 (0.980)	0.061 (1.691)	0.073 (1.697)	−0.03 (0.866)	0.062 (1.715)	0.062 (1.514)
Ride-hailing experience	−0.049 (1.604)	0.052 (1.942)	−0.114 (3.235)**	−0.046 (1.556)	0.049 (1.817)	−0.112 (3.226)**
Overall job satisfaction	0.211 (5.356)***	0.183 (4.875)***	0.099 (2.590)*	0.202 (5.376)***	0.192 (5.061)***	0.101 (2.719)**
Gatekeeping AC	−0.388 (7.268)***			−0.39 (7.674)***
Guiding AC	0.482 (10.207)***			0.447 (9.112)***
Micro-level legitimacy		0.582 (15.739)***	−0.554 (10.976)***		0.581 (14.703)***	−0.536 (10.986)***
R^2 (%) /R^2 adjusted (%)	54.5% / 53.6%	48.8% / 48.0%	31.5% / 30.4%	55.1% / 54.2%	48.0% / 47.0%	35.1% / 34.0%

N = 621 Uber drivers. Significant effects in bold. * p < 0.05, ** p < 0.01, *** p < 0.001; two-tailed test.

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