“好压力, 坏压力?” 算法规范压力对服务绩效的双刃剑效应

doi:10.3724/SP.J.1041.2025.1813

摘要/Abstract

摘要：

算法驱动的平台工作模式使零工工作者普遍面临算法规范压力。算法规范压力作为一种新型工作压力, 会对零工工作者的心理和行为产生复杂的双重影响。本文基于工作要求−资源模型, 阐释了算法规范压力的双元混融特性, 并构建了其对零工工作者服务绩效的双刃剑效应模型。通过在线情景实验(研究1)和三阶段、多来源的实地问卷调研(研究 2), 研究发现, 算法规范压力通过激发零工工作者的趋近式工作重塑正向影响服务绩效, 同时通过激发回避式工作重塑负向影响服务绩效。此外, 算法透明度和在线社群支持在这一过程中起到了重要的调节作用。具体而言, 在高算法透明度和高在线社群支持的情况下, 算法规范压力通过趋近式工作重塑对服务绩效的间接正向效应更强, 而通过回避式工作重塑对服务绩效的间接负向效应更弱。本文全面揭示了算法规范压力的作用机制, 为平台优化算法管理实践提供了理论依据和实践启示。

关键词: 算法规范压力, 工作重塑, 服务绩效, 算法透明度, 在线社群支持

Abstract:

With the continuous increase in the number of gig workers, work pressure has become a significant public concern. Gig workers experience algorithmic regulatory pressure from platforms through automatic task allocation, real-time guidance, and tracking evaluation, which permeate the entire work process. Coping strategies adopted under such pressure directly influence workers’ physical and mental health, as well as their work outcomes. However, prior research has only explored the conceptual nature of algorithmic regulatory pressure and its potential impact on individual well-being and work behavior. The mechanism through which this pressure affects service performance remains unclear. To address this gap, the present study applies the job demand-resource (JD-R) theory to investigate the impact of algorithmic regulatory pressure on gig workers' service performance. JD-R theory identifies two broad categories of working conditions—job demands and job resources. Job resources trigger motivational processes, whereas job demands lead to health-impairment processes. Moreover, job resources can buffer the adverse effects of job demands. Based on this framework, the study hypothesizes that algorithmic regulatory pressure exerts a double-edged sword effect on service performance by inducing distinct job crafting behaviors, particularly when two key resources—algorithmic transparency and online community support—are present.

Two studies were conducted to test the hypotheses. Study 1 employed a scenario-based experiment to examine the causal relationship between algorithmic regulatory pressure and job crafting behaviors. A total of 377 take-away riders were recruited via an online survey platform (Credamo) and randomly assigned to either a high- or low-pressure scenario. Participants provided demographic data, viewed an experimental video, and responded to manipulation checks and job crafting measures. The final valid sample comprised 358 riders. Study 2 involved a three-wave, multi-source survey to test the proposed model, incorporating objective service performance metrics. Each wave was spaced four weeks apart. A total of 450 ride-hailing drivers were recruited through Credamo. At Time 1, participants reported demographics, algorithmic regulatory pressure, time pressure, alienation pressure, physical and mental pressure, proactive personality, algorithmic transparency, and online community support. At Time 2, they reported their approach and avoidance job crafting behaviors. At Time 3, drivers’ service performance was obtained from platform-generated metrics. The final sample comprised 350 drivers.

Across both studies, SPSS and Mplus were used to conduct ANOVA, linear regression, confirmatory factor analysis, and Bayesian estimation. To test moderating effects, the Johnson-Neyman technique was applied in R, with plots illustrating the moderation. Results confirmed the double-edged sword effect of algorithmic regulatory pressure. Specifically, algorithmic regulatory pressure positively affected service performance through approach job crafting and negatively via avoidance job crafting. These effects were amplified by algorithmic transparency and online community support.

The study offers several theoretical contributions. First, it advances understanding of the dual nature and impact of algorithmic regulatory pressure in the gig economy. This pressure embodies both hindering and challenging job demands, thereby exerting a dual influence on service performance. This perspective enriches existing frameworks on job stress. Second, using the JD-R model, the study examines the underlying mechanisms of this dual effect, revealing the mediating role of job crafting and challenging prevailing assumptions about autonomy loss in digital labor. Third, by examining the moderating effects of algorithmic transparency and online community support, the study identifies key contextual resources that regulate whether the outcomes of pressure are constructive or detrimental. Finally, the findings extend the JD-R theory’s applicability to digital labor, demonstrating its relevance in emerging work arrangements and contributing to the evolution of this theoretical model.

Key words: algorithmic regulatory pressure, job crafting, service performance, algorithmic transparency, online community support

中图分类号:

B849: C93

高雪原, 张志朋, 谢宝国, 龙立荣, 尹奎. (2025). “好压力, 坏压力?” 算法规范压力对服务绩效的双刃剑效应. 心理学报, 57(10), 1813-1831.

GAO Xueyuan, ZHANG Zhipeng, XIE Baoguo, LONG Lirong, YIN Kui. (2025). “Good pressure, bad pressure?” The double-edged sword effect of algorithmic regulatory pressure on service performance. Acta Psychologica Sinica, 57(10), 1813-1831.

图/表 10

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步骤与变量	趋近式工作重塑(模型1)		回避式工作重塑(模型2)		服务绩效(模型3)
步骤与变量	β	SE	β	SE	β	SE
控制变量
性别	−0.08	0.08	0.07	0.16	−0.06	0.10
年龄	−0.01	0.01	0.01	0.02	0.01	0.01
教育程度	−0.11	0.07	−0.11	0.13	0.04	0.08
职业类型	0.03	0.09	−0.04	0.16	0.13	0.10
职业经验	0.04	0.04	−0.13	0.07	0.04	0.04
主动性人格	0.21^***	0.04	−0.04	0.08	0.41^***	0.05
社会赞许性	−0.02	0.03	0.28^***	0.06	−0.19^***	0.04
自变量
算法规范压力(操纵)	0.29^***	0.07	0.58^***	0.14	0.03	0.10
中介变量
趋近式工作重塑					0.55^***	0.07
回避式工作重塑					−0.17^***	0.04
R²	0.14^***	0.69	0.14^***	1.33	0.29^***	0.93

步骤与变量	趋近式工作重塑(模型1)		回避式工作重塑(模型2)		服务绩效(模型3)
步骤与变量	β	SE	β	SE	β	SE
控制变量
性别	−0.08	0.08	0.07	0.16	−0.06	0.10
年龄	−0.01	0.01	0.01	0.02	0.01	0.01
教育程度	−0.11	0.07	−0.11	0.13	0.04	0.08
职业类型	0.03	0.09	−0.04	0.16	0.13	0.10
职业经验	0.04	0.04	−0.13	0.07	0.04	0.04
主动性人格	0.21^***	0.04	−0.04	0.08	0.41^***	0.05
社会赞许性	−0.02	0.03	0.28^***	0.06	−0.19^***	0.04
自变量
算法规范压力(操纵)	0.29^***	0.07	0.58^***	0.14	0.03	0.10
中介变量
趋近式工作重塑					0.55^***	0.07
回避式工作重塑					−0.17^***	0.04
R²	0.14^***	0.69	0.14^***	1.33	0.29^***	0.93

中介效应	Indirect effect	Posterior S.D.	95% CI
H2a: 算法规范压力→趋近式工作重塑→服务绩效	0.16	0.05	[0.077, 0.256]
H2b: 算法规范压力→回避式工作重塑→服务绩效	−0.10	0.03	[−0.164, −0.045]
整体中介: 算法规范压力→趋近式/回避式工作重塑→服务绩效	0.06	0.06	[−0.050, 0.179]

中介效应	Indirect effect	Posterior S.D.	95% CI
H2a: 算法规范压力→趋近式工作重塑→服务绩效	0.16	0.05	[0.077, 0.256]
H2b: 算法规范压力→回避式工作重塑→服务绩效	−0.10	0.03	[−0.164, −0.045]
整体中介: 算法规范压力→趋近式/回避式工作重塑→服务绩效	0.06	0.06	[−0.050, 0.179]

变量	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
1.性别	——
2.年龄	0.03	——
3.教育程度	0.14^**	−0.03	——
4.职业类型	0.16^***	−0.21^***	0.18^***	——
5.职业经验	−0.03	0.48^***	−0.04	−0.35^**	——
6.主动性人格	0.05	−0.03	0.13^*	0.03	−0.02	(0.95)
7.算法规范压力	−0.02	0.10	0.04	0.03	0.06	0.29^***	(0.92)
8.时间压力	−0.02	0.03	0.00	0.02	−0.08	0.07	0.19^***	(0.90)
9.疏离压力	−0.06	−0.04	0.02	0.08	−0.08	0.11	0.21^***	0.50^***	(0.88)
10.身心压力	0.08	−0.04	0.07	0.05	−0.04	0.11^*	0.15^**	0.45^***	0.40^***	(0.84)
11.算法透明度	−0.04	0.05	−0.08	−0.08	0.01	0.07	0.13^*	0.05	0.09	−0.02	(0.87)
12.在线社群支持	−0.06	0.04	−0.03	0.02	−0.04	0.19^***	0.23^***	0.09	0.13^*	0.06	0.16^***	(0.94)
13.趋近式工作重塑	−0.04	−0.01	0.22^***	0.06	−0.05	0.11^*	0.30^***	0.20^***	0.14^**	0.14^*	−0.05	0.11^*	(0.94)
14.回避式工作重塑	−0.07	0.04	0.02	−0.05	0.01	0.13^*	0.29^***	0.26^***	0.12^*	0.17^***	0.15^**	0.16^***	0.07	(0.91)
15.服务绩效	0.01	−0.02	0.06	0.10	−0.06	0.13^*	0.06	0.00	0.08	0.03	−0.08	0.31^***	0.27^***	−0.14^**	——
平均值	0.26	30.67	2.69	0.27	2.86	5.65	3.29	3.76	2.67	4.03	5.58	5.75	5.76	5.37	245.59
标准差	0.44	7.64	0.59	0.45	2.02	0.94	1.61	1.90	1.45	1.58	1.21	1.08	0.79	1.16	35.18