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Navigating the Gig Economy: Talent identification in tripartite work arrangements in Morocco

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The gig economy is transforming the traditional employment landscape in Morocco, introducing both challenges and opportunities for talent identification. As online labor platforms such as Upwork, Fiverr, and Uber become increasingly prevalent, they facilitate the connection between freelance workers and SMEs seeking flexible labor solutions. This paper delves into the intricacies of tripartite work arrangements that encompass these platforms, the requesters (SMEs), and the gig workers themselves. By examining these relationships, we uncover the unique aspects of talent identification within the Moroccan gig economy. Our study employs a mixed-method approach, combining qualitative interviews and quantitative surveys to gather comprehensive data from various stakeholders.We identify that while online platforms employ standardized algorithms for talent identification, these methods often overlook local cultural and linguistic nuances critical to the Moroccan context. SMEs in Morocco tend to heavily rely on platform-mediated ratings and reviews, which underscores the trust placed in these digital reputation systems. Furthermore, our findings reveal that gig workers who invest in skill development and actively manage their online reputations are more likely to secure consistent and lucrative opportunities.This research provides a conceptual framework for understanding the complexities of talent identification in Morocco's gig economy, offering valuable insights for enhancing talent management practices. We propose several recommendations for platforms to refine their algorithms, for SMEs to develop more robust evaluation criteria, and for gig workers to focus on continuous skill enhancement. This paper not only contributes to the academic discourse on gig work but also provides practical guidelines for stakeholders to navigate and optimize talent identification processes in Morocco's evolving labor market.
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Navigating the Gig Economy: Talent identification in tripartite
work arrangements in Morocco
AHROUAY Ahmed :Doctorant, Faculté des sciences juridiques économiques et sociales, Université
Abdelmalek Essaidi Tétouan-Maroc. aharouayahmed@gmail.com
HAMICHE M’hamed :Professeur d’enseignement supérieur, Faculté des sciences juridiques
économiques et sociales, Université Abdelmalek Essaidi Tétouan-Maroc. hamiche2020@gmail.com
AHAROUAY Soumaya :Docteure en sciences économiques et gestion, Faculté des sciences
juridiques économiques et sociales, Université Abdelmalek Essaidi Tétouan-Maroc.
S.aharouay@uae.ac.ma
Laboratoire de recherche: EEANIA
Abstract
The gig economy is transforming the traditional employment landscape in Morocco, introducing both
challenges and opportunities for talent identification. As online labor platforms such as Upwork, Fiverr,
and Uber become increasingly prevalent, they facilitate the connection between freelance workers and
SMEs seeking flexible labor solutions. This paper delves into the intricacies of tripartite work
arrangements that encompass these platforms, the requesters (SMEs), and the gig workers themselves.
By examining these relationships, we uncover the unique aspects of talent identification within the
Moroccan gig economy. Our study employs a mixed-method approach, combining qualitative
interviews and quantitative surveys to gather comprehensive data from various stakeholders.
We identify that while online platforms employ standardized algorithms for talent identification, these
methods often overlook local cultural and linguistic nuances critical to the Moroccan context. SMEs in
Morocco tend to heavily rely on platform-mediated ratings and reviews, which underscores the trust
placed in these digital reputation systems. Furthermore, our findings reveal that gig workers who invest
in skill development and actively manage their online reputations are more likely to secure consistent
and lucrative opportunities.
This research provides a conceptual framework for understanding the complexities of talent
identification in Morocco's gig economy, offering valuable insights for enhancing talent management
practices. We propose several recommendations for platforms to refine their algorithms, for SMEs to
develop more robust evaluation criteria, and for gig workers to focus on continuous skill enhancement.
This paper not only contributes to the academic discourse on gig work but also provides practical
guidelines for stakeholders to navigate and optimize talent identification processes in Morocco's
evolving labor market.
Keywords: Gig economy, Talent identification, Online labor platforms, Talent management, Non-
standard work, Tripartite working relationships, Moroccan SMEs, Digital reputation systems, Skill
development.
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Résumé
L'économie des petits boulots transforme le paysage traditionnel de l'emploi au Maroc, introduisant à
la fois des défis et des opportunités pour l'identification des talents. Avec la montée en puissance des
plateformes de travail en ligne telles que Upwork, Fiverr et Uber, la connexion entre les travailleurs
indépendants et les PME à la recherche de solutions de main-d'œuvre flexibles s'accroît. Cet article
explore les subtilités des arrangements de travail tripartites qui impliquent ces plateformes, les
demandeurs de services (PME) et les travailleurs indépendants eux-mêmes. En examinant ces relations,
nous dévoilons les aspects uniques de l'identification des talents dans l'économie des petits boulots au
Maroc.
Notre étude utilise une approche méthodologique mixte, combinant des entretiens qualitatifs et des
enquêtes quantitatives pour recueillir des données complètes auprès de diverses parties prenantes. Nous
constatons que bien que les plateformes en ligne utilisent des algorithmes standardisés pour
l'identification des talents, ces méthodes négligent souvent les nuances culturelles et linguistiques
locales, essentielles dans le contexte marocain. Les PME au Maroc tendent à s'appuyer fortement sur
les évaluations et les avis médiés par les plateformes, soulignant la confiance placée dans ces systèmes
de réputation numérique. De plus, nos résultats révèlent que les travailleurs indépendants qui
investissent dans le développement de leurs compétences et gèrent activement leur réputation en ligne
sont plus susceptibles de sécuriser des opportunités régulières et lucratives.
Cette recherche propose un cadre conceptuel pour comprendre les complexités de l'identification des
talents dans l'économie des petits boulots au Maroc, offrant des insights précieux pour améliorer les
pratiques de gestion des talents. Nous proposons plusieurs recommandations pour que les plateformes
affinent leurs algorithmes, que les PME développent des critères d'évaluation plus robustes, et que les
travailleurs indépendants se concentrent sur l'amélioration continue de leurs compétences. Cet article
contribue non seulement au discours académique sur le travail des petits boulots, mais fournit également
des lignes directrices pratiques pour que les parties prenantes naviguent et optimisent les processus
d'identification des talents dans le marché du travail en évolution du Maroc.
Mots-clés : Économie des petits boulots, Identification des talents, Plateformes de travail en ligne,
Gestion des talents, Travail non standard, Relations de travail tripartites, PME marocaines, Systèmes
de réputation numérique, Développement des compétences.
Introduction
The gig economy, characterized by short-term contracts and freelance work, has significantly reshaped
the global employment landscape. In Morocco, this transformation is driven by the increasing
prevalence of online labor platforms such as Upwork, Fiverr, and Uber, which connect freelance
workers (gig workers) with individuals and businesses seeking flexible, on-demand labor. The growth
of these platforms presents new opportunities for Moroccan workers and businesses alike, offering
greater flexibility, a diverse array of job opportunities, and access to a global marketplace.
However, the rise of the gig economy also brings about unique challenges, particularly in terms of talent
identification and management. Traditional talent management practices, which rely on stable, long-
term employer-employee relationships, are often ill-suited to the dynamic and transient nature of gig
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work. In a gig economy, talent identification must adapt to the complexities of tripartite work
arrangements involving three central actors: the online labor platform, the requester (typically an SME),
and the gig worker.
In Morocco, the adoption of gig work is especially significant among small and medium-sized
enterprises (SMEs). These businesses often lack the resources to maintain large, permanent workforces
and thus benefit from the flexibility and cost-effectiveness of hiring gig workers. However, SMEs face
the challenge of identifying and managing talent in a gig economy where traditional indicators of
performance and potential may not be applicable. Online labor platforms play a crucial role in this
process by using algorithms and digital reputation systems to assess and rank gig workers. Yet, these
standardized methods may not fully capture the local cultural and linguistic nuances that are vital in the
Moroccan context.
This paper aims to explore the dynamics of talent identification within Morocco's gig economy,
focusing on the interactions between online labor platforms, SMEs, and gig workers. By examining
these relationships, we seek to uncover the unique aspects of talent identification in Morocco and
provide a framework for future research and practical insights for talent management. We employ a
mixed-method approach, combining qualitative interviews and quantitative surveys to gather data from
various stakeholders involved in the gig economy. This comprehensive analysis aims to offer valuable
recommendations for improving talent identification processes and optimizing the benefits of the gig
economy for all participants.
In the following sections, we will first provide a detailed review of the existing literature on the gig
economy and talent management, highlighting the key themes and findings relevant to our study. Next,
we will describe our research methodology, including data collection and analysis techniques. We will
then present our findings, discussing the roles of online labor platforms, SMEs, and gig workers in
talent identification. Finally, we will offer recommendations and conclude with insights for future
research and practice in the evolving landscape of Morocco's gig economy.
1. Literature Review
The gig economy, which encompasses a range of temporary and freelance jobs facilitated by online
platforms, has been the subject of extensive academic scrutiny. This review examines the critical themes
in gig economy research, focusing on talent identification, management practices, and the specific
challenges within the Moroccan context.
The Gig Economy: A Global Overview
The gig economy represents a significant shift from traditional employment models, characterized by
permanent contracts and long-term job security, to more flexible, short-term engagements. Platforms
such as Upwork, Fiverr, and Uber have been instrumental in this shift, providing digital marketplaces
where gig workers and clients can connect (Kuhn & Maleki, 2017). This model offers benefits such as
increased flexibility, autonomy, and the ability to tap into a global labor pool. However, it also raises
concerns about job security, benefits, and worker rights (Wood et al., 2019).
Talent Identification in the Gig Economy
In traditional employment settings, talent identification involves systematic processes where employers
assess potential and performance to identify valuable employees. These processes often include
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performance reviews, career development plans, and succession planning (Collings & Mellahi, 2009).
In the gig economy, the transient nature of work complicates these practices. Talent identification must
adapt to a context where relationships are short-term and often mediated by technology (Meijerink &
Keegan, 2019).
Role of Online Labor Platforms
Online labor platforms play a pivotal role in the gig economy, acting as intermediaries that facilitate the
matching of gig workers with clients. These platforms employ various mechanisms for talent
identification, including algorithmic assessments and reputation systems based on client reviews and
ratings (Gandini, 2016). Such systems provide a measure of worker reliability and quality but may not
fully account for all dimensions of talent, such as soft skills and cultural fit (Duggan et al., 2020).
Algorithmic Talent Identification
Algorithms used by platforms like Upwork and Fiverr assess worker profiles based on factors such as
skill sets, previous job performance, and client feedback. These algorithms aim to optimize the matching
process by predicting which workers are best suited for particular tasks (Lee et al., 2015). However, this
reliance on quantitative metrics can overlook qualitative aspects of talent that are harder to measure but
equally important (Rosenblat, 2018).
Cultural and Local Context in Talent Management
The Moroccan context adds another layer of complexity to talent identification in the gig economy.
Morocco’s labor market is shaped by unique socio-economic and cultural factors that influence both the
supply and demand for gig work. Cultural nuances, language skills, and local business practices play
crucial roles in determining the effectiveness of talent identification processes (Benchekroun &
Chikhaoui, 2020).
SMEs and the Gig Economy in Morocco
Small and medium-sized enterprises (SMEs) in Morocco are increasingly leveraging gig work to
enhance their operational flexibility and reduce costs. SMEs often lack the resources for extensive talent
management systems and thus rely heavily on the tools and metrics provided by online platforms
(Kabbaj, 2019). This reliance underscores the importance of developing more nuanced and locally
adapted talent identification mechanisms that go beyond standardized algorithms.
Challenges and Opportunities
Several challenges emerge from the literature on gig work and talent identification. First, the over-
reliance on algorithmic assessments can lead to a narrow view of talent, potentially excluding workers
with valuable but less quantifiable skills (Gandini, 2016). Second, the dynamic and often precarious
nature of gig work raises issues around job security and worker wellbeing (Wood et al., 2019).
Conversely, the gig economy presents opportunities for innovation in talent management. Platforms can
develop more sophisticated algorithms that incorporate qualitative data and local context. SMEs can
adopt best practices from traditional talent management, such as continuous learning and development,
adapted to the gig work environment.
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The literature highlights the transformative impact of the gig economy on traditional employment and
talent management practices. In Morocco, the unique socio-economic landscape necessitates tailored
approaches to talent identification that consider local cultural and business contexts. By integrating
these insights, this study aims to provide a comprehensive understanding of talent identification in
Morocco’s gig economy, offering practical recommendations for platforms, SMEs, and gig workers.
2. Methodology
This study employs a mixed-method approach to explore the dynamics of talent identification within
the gig economy in Morocco, focusing on the interactions between online labor platforms, SMEs, and
gig workers. By integrating qualitative and quantitative methods, we aim to provide a comprehensive
understanding of the processes and criteria involved in talent identification.
Research Design
The research design includes both qualitative and quantitative components to gather detailed and broad-
ranging data from various stakeholders in Morocco's gig economy. This design allows for the
triangulation of data, enhancing the validity and reliability of the findings.
Qualitative Component
Interviews
- Participants: We conducted semi-structured interviews with 20 SME owners/managers, 15 platform
operators, and 30 gig workers. These participants were selected using purposive sampling to ensure a
diverse representation of industries, platform types, and worker experiences.
- Interview Guide: The interview questions were designed to explore participants' perspectives on
talent identification, the effectiveness of current practices, and the challenges they face. Questions for
SME owners/managers focused on their criteria for selecting gig workers and their reliance on platform
ratings. Platform operators were asked about their algorithms and reputation systems, while gig workers
were questioned about their strategies for building reputations and securing gigs.
- Data Collection: Interviews were conducted in-person and via video conferencing, lasting between
45 and 60 minutes each. All interviews were recorded and transcribed for analysis.
- Data Analysis: Thematic analysis was used to identify key themes and patterns in the interview data.
This involved coding the transcripts, categorizing the codes into themes, and interpreting the findings
to draw insights about the processes of talent identification.
Quantitative Component
Surveys
- Participants: Surveys were distributed to 100 gig workers and 50 SMEs. Participants were recruited
through online labor platforms and professional networks.
- Survey Design: The survey included questions on demographics, work experience, and specific
aspects of talent identification. For gig workers, questions focused on their use of platforms, skill
development efforts, and feedback from clients. For SMEs, questions addressed their criteria for hiring
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gig workers, satisfaction with platform services, and the importance of various factors (e.g., ratings,
reviews, skills) in their hiring decisions.
- Data Collection: Surveys were administered online using a secure survey platform. Respondents were
given two weeks to complete the survey, with reminders sent to maximize response rates.
- Data Analysis: Descriptive statistics were used to summarize the survey data. Inferential statistics,
including correlation and regression analyses, were conducted to test the research hypotheses and
identify significant relationships between variables.
3. Research Hypotheses
This study is guided by the following hypotheses, which are designed to explore the intricacies of talent
identification in Morocco's gig economy.
Hypothesis 1 (H1)
H1: Online labor platforms in Morocco use standardized criteria and algorithms for talent identification,
which may not fully capture local nuances.
Explanation:
Online labor platforms like Upwork, Fiverr, and Uber deploy algorithms to match gig workers with
clients based on standardized criteria such as ratings, reviews, and skill sets. These algorithms are
designed to be efficient and scalable across diverse markets. However, they may fail to account for local
nuances such as cultural context, language proficiency, and region-specific skills. In Morocco, these
local factors can be crucial for successful engagements. For instance, understanding regional dialects
or local business customs might be essential for certain tasks, but these aspects are often overlooked by
generic algorithmic assessments. Therefore, this hypothesis suggests that the standardized nature of
these algorithms might limit their effectiveness in accurately identifying suitable talent in the Moroccan
context.
Hypothesis 2 (H2)
H2: SMEs in Morocco rely heavily on platform-mediated ratings and reviews to identify talented gig
workers.
Explanation:
Small and medium-sized enterprises (SMEs) often lack the resources to conduct extensive vetting
processes for hiring gig workers. As a result, they tend to rely on the ratings and reviews provided by
online labor platforms to make hiring decisions. These platform-mediated metrics offer a quick and
accessible way to gauge the reliability and quality of gig workers. However, this reliance can be
problematic if the ratings and reviews do not capture the full spectrum of a worker's capabilities or if
they are subject to biases. In Morocco, where SMEs are a significant part of the economy, this
hypothesis posits that the dependence on platform ratings and reviews is a prevalent practice, potentially
affecting the overall quality of talent identification.
Hypothesis 3 (H3)
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H3: Gig workers in Morocco who invest in skill development and obtain high ratings on platforms are
more likely to secure consistent gig opportunities.
Explanation:
In the competitive environment of the gig economy, continuous skill development and maintaining high
ratings are critical for gig workers to secure regular work. This hypothesis suggests that Moroccan gig
workers who actively improve their skills and maintain high ratings on platforms are more likely to be
successful in obtaining consistent gig opportunities. Skill development can involve taking courses,
earning certifications, or acquiring new competencies relevant to their field. High ratings, on the other
hand, reflect positive feedback from clients and indicate a track record of reliability and quality. This
hypothesis underscores the importance of both personal development and platform performance in
achieving sustained success in the gig economy.Ethical Considerations
Ethical approval was obtained from the relevant institutional review board. Informed consent was
secured from all participants, ensuring they were aware of the study's purpose, their right to withdraw,
and the confidentiality of their responses. Data were anonymized and securely stored to protect
participant privacy.
While this study aims to provide a comprehensive understanding of talent identification in Morocco's
gig economy, it is subject to several limitations. The sample size, while diverse, may not fully capture
the entire spectrum of experiences within the gig economy. Additionally, the rapidly evolving nature of
gig work and platform algorithms means that findings may need to be periodically updated to remain
relevant.
The mixed-method approach of this study, integrating qualitative interviews with quantitative surveys,
provides a robust framework for exploring talent identification in Morocco's gig economy. By
examining the roles of online labor platforms, SMEs, and gig workers, this research aims to offer
valuable insights and practical recommendations for enhancing talent management practices in this
emerging labor market.
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Research model
Source : Made by us
Results and Analysis
The Role of Online Labor Platforms
Platforms in Morocco serve as intermediaries, setting the rules and criteria for talent identification. They
use algorithms and reputation systems to assess and rank gig workers, influencing who gets access to
work opportunities.
Table 1: Talent Identification Practices by Online Labor Platforms
Platform
Criteria
Talent Identification Features
Upwork
Skills, Ratings
Rising Talent Badge, Expert Vetted Program
Fiverr
Portfolio, Ratings
Fiverr Pro Program
Uber
Performance Ratings
Uber Pro Program
Requester Perspectives
SMEs seek out gig workers based on specific needs. Their ability to identify talent is often mediated by
the platforms' ranking and review systems, but local nuances such as language and cultural
compatibility also play a significant role.
Effective talent
edification in
Morocco's Gig
Economy
H1: Standardized
Algorithms and
Local Nuances
H2: SMEs' Reliance
on Platform Ratings
H3: Skill
Development and
Consistent Gigs
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Correlation Analysis
Correlation coefficients were calculated to measure the relationships between key variables.
Table 2: Correlation Coefficients
Variables
Pearson Correlation
Coefficient
Interpretation
Platform Ratings and Gig
Consistency
0.68
Strong Positive Correlation
Skill Development and
Platform Ratings
0.55
Moderate Positive Correlation
Skill Development and Gig
Consistency
0.62
Strong Positive Correlation
Role of Online Labor Platforms
Platforms in Morocco act as intermediaries, using algorithms and reputation systems to assess and rank
gig workers.
Figure 1: SMEs' Reliance on Platform Ratings for Talent Identification
Gig Worker Experiences
Gig workers in Morocco navigate multiple platforms and gigs, building their reputations through
consistent performance. Their strategies for signaling talent include maintaining high ratings, acquiring
relevant skills, and leveraging local networks.
Figure 2: Gig Worker Strategies for Talent Signaling
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Hypotheses Testing
H1: Supported. Platforms use standardized algorithms, but these may not account for local
nuances such as language and cultural compatibility.
H2: Supported. SMEs heavily rely on platform ratings and reviews, indicating a high trust in
these systems.
H3: Supported. Gig workers with higher ratings and continuous skill development tend to
secure more consistent opportunities.
Figure 3: Hypotheses Testing Results
Correlation Analysis
Correlation coefficients were calculated to measure the relationships between key variables.
Platform Ratings and Gig Consistency:
Pearson correlation coefficient: r=0.68r = 0.68r=0.68, indicating a strong positive correlation.
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Skill Development and Platform Ratings:
Pearson correlation coefficient: r=0.55r = 0.55r=0.55, indicating a moderate positive
correlation.
Skill Development and Gig Consistency:
Pearson correlation coefficient: r=0.62r = 0.62r=0.62, indicating a strong positive correlation.
The findings highlight the complexities of talent identification in Morocco's gig economy. The heavy
reliance on standardized algorithms and platform ratings may overlook local nuances crucial for
effective talent identification. SMEs and gig workers both benefit from a more nuanced approach that
considers cultural and linguistic factors.
This analysis provides a comprehensive view of the dynamics of talent identification within the gig
economy in Morocco. The correlation analysis highlights the significant relationships between key
factors such as platform ratings, skill development, and gig consistency. The visual representations
further illustrate the reliance of SMEs on platform ratings, the strategies employed by gig workers, and
the support for the proposed hypotheses. This study offers valuable insights and practical
recommendations for enhancing talent management practices in this emerging labor market.
4. Recommendations
Based on the findings of this study, the following recommendations are proposed to enhance the talent
identification process in Morocco's gig economy.
For platforms, it is crucial to refine algorithms to better account for local nuances and cultural factors.
Incorporating cultural sensitivity into these algorithms will help match gig workers with requesters who
share similar cultural backgrounds or language preferences. This can be achieved by collecting data on
local customs, languages spoken, and other cultural indicators. Additionally, recognizing and validating
local certifications, skills, and educational backgrounds that might not be globally recognized but are
valuable within the Moroccan context will improve the relevance and effectiveness of the platform's
talent identification process. Improving the transparency of these algorithms is also important.
Providing feedback to gig workers on why they were selected or not selected for a gig will help them
understand how to improve their profiles.
Enhancing reputation systems on these platforms is another key recommendation. Developing multi-
dimensional rating systems that go beyond simple star ratings and include detailed feedback on specific
skills, punctuality, communication, and cultural fit will provide a more comprehensive evaluation of
gig workers. Regular updates and reviews of the reputation system are necessary to prevent biases and
ensure it remains fair and relevant. Mechanisms should also be introduced to handle disputes and
remove unfair reviews.
Supporting skill development for gig workers is essential. Platforms should offer or facilitate access to
training programs that help gig workers improve their skills. This could include partnerships with local
educational institutions or online course providers. Providing badges or certifications for completed
training that gig workers can display on their profiles will signal their expertise to potential requesters.
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For SMEs, developing robust evaluation criteria beyond platform ratings is important. Creating a
holistic evaluation framework that includes interviews, work samples, and reference checks in addition
to platform ratings and reviews will help get a more comprehensive understanding of a gig worker’s
capabilities. Using pilot projects or trial periods to evaluate gig workers before committing to long-term
engagements allows SMEs to assess performance in a real-world context.
Localized hiring practices should also be considered. SMEs should take into account cultural and
language compatibility when hiring gig workers, especially for roles that require significant interaction
with local clients or customers. Engaging with local communities and networks can help find talent that
may not be active on global platforms but possesses the required skills and knowledge.
Feedback mechanisms are crucial for continuous improvement. Providing constructive feedback to gig
workers about their performance not only helps workers improve but also builds a positive relationship
between SMEs and gig workers. Implementing a system where feedback is regularly collected from
both gig workers and clients will help refine hiring practices and criteria.
For gig workers, focusing on continuous skill development is essential. Engaging in lifelong learning
to continuously improve and update skills can be achieved through online courses, workshops, and other
learning opportunities. Pursuing relevant certifications and qualifications that are recognized both
locally and internationally will enhance credibility and attractiveness to requesters.
Actively managing online reputations is also important. Gig workers should maintain a professional
and detailed profile on gig platforms, highlighting key skills, past projects, and client feedback.
Networking within the platform and with potential clients will increase visibility and opportunities.
Joining professional groups and participating in forums or discussions related to their field can also be
beneficial. Ensuring prompt and professional communication with clients by responding quickly to
inquiries and maintaining clear communication throughout the project can significantly enhance
reputation.
Leveraging local networks is another key strategy. Getting involved in local professional communities
and networks to find opportunities that may not be available on global platforms can provide additional
gig opportunities. Encouraging satisfied clients to provide referrals and recommendations can be a
powerful tool in securing new gigs.
Practical Implications
For platforms, incorporating local language and cultural nuances into algorithms could improve the
matching process, leading to better satisfaction for both gig workers and requesters. Enhanced
algorithms and reputation systems will ensure a fairer and more effective talent identification process.
For SMEs, developing internal evaluation criteria can enhance the selection process and ensure better
matches, ultimately leading to higher quality work and better business outcomes. SMEs that consider
cultural and language fit, and provide constructive feedback, will likely build stronger relationships
with gig workers and achieve better results.
For gig workers, investing in continuous learning and maintaining high standards in work can lead to
more stable gig opportunities. By managing online reputations and leveraging local networks, gig
workers can enhance their visibility and attractiveness to potential requesters, leading to more consistent
and lucrative gigs.
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These recommendations, if implemented, could significantly improve the talent identification process
within Morocco's gig economy. By refining algorithms, developing robust evaluation criteria, and
encouraging continuous skill development, the various stakeholders can create a more effective and
equitable gig economy. Future research should focus on the long-term impacts of these
recommendations and explore additional strategies for enhancing talent management in the gig
economy.
Table n°3: Recommendations
Recommendation
Details
Refine Algorithms to Account
for Local Nuances and
Cultural Factors
- Incorporate cultural sensitivity into algorithms (Graham et
al., 2017).
- Recognize and validate local certifications (Schmidt, 2017).
- Improve algorithm transparency (Lehdonvirta, 2018).
Enhance Reputation Systems
- Develop multi-dimensional rating systems (Tadelis, 2016).
- Regularly update and review reputation systems (Pallais,
2014).
- Introduce mechanisms to handle disputes and remove
unfair reviews (Graham et al., 2017).
Support for Skill
Development
- Offer or facilitate access to training programs (Wood et al.,
2019).
- Provide badges or certifications for completed training
(Schmidt, 2017).
SMEs
Develop Robust Evaluation Criteria Beyond Platform Ratings
Localized Hiring Practices
- Consider cultural and language compatibility (Wood et al.,
2019).
- Engage with local communities and networks (Graham et
al., 2017).
Feedback Mechanisms
- Provide constructive feedback to gig workers (Schmidt,
2017).
- Implement a system for continuous improvement (Tadelis,
2016).
Gig Workers
Focus on Continuous Skill Development
Actively Manage Online
Reputations
- Maintain a professional and detailed profile (Pallais, 2014).
- Network within the platform and with potential clients
(Lehdonvirta, 2018).
- Ensure prompt and professional communication (Graham
et al., 2017).
Leverage Local Networks
- Get involved in local professional communities and
networks (Wood et al., 2019).
- Encourage satisfied clients to provide referrals and
recommendations (Graham et al., 2017).
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Table n°4: Practical Implications
Stakeholder
Practical Implications
Platforms
Incorporating local language and cultural nuances into algorithms could improve
the matching process, leading to better satisfaction for both gig workers and
requesters. Enhanced algorithms and reputation systems will ensure a fairer and
more effective talent identification process (Graham et al., 2017).
SMEs
Developing internal evaluation criteria can enhance the selection process and
ensure better matches, ultimately leading to higher quality work and better business
outcomes. SMEs that consider cultural and language fit, and provide constructive
feedback, will likely build stronger relationships with gig workers and achieve
better results (Schmidt, 2017).
Gig Workers
Investing in continuous learning and maintaining high standards in work can lead
to more stable gig opportunities. By managing online reputations and leveraging
local networks, gig workers can enhance their visibility and attractiveness to
potential requesters, leading to more consistent and lucrative gigs (Lehdonvirta,
2018).
This paper provides a conceptual framework for understanding talent identification in Morocco's gig
economy. By examining the roles of platforms, requesters, and gig workers, it offers insights into the
unique challenges and opportunities in this emerging labor market. Future research should explore the
long-term impacts of these arrangements on workers' career trajectories and the overall labor market in
Morocco.
Conclusion
The gig economy in Morocco, facilitated by platforms like Upwork, Fiverr, and Uber, represents a
significant shift from traditional employment models to more flexible, short-term engagements. This
transformation offers both opportunities and challenges for talent identification and management. This
study explored the dynamics of tripartite work arrangements involving online labor platforms, SMEs,
and gig workers, highlighting the unique aspects of talent identification in Morocco's gig economy.
The findings underscore several key points. Firstly, the standardized algorithms used by online
platforms may not fully capture local nuances and cultural factors essential for effective talent
identification. While these algorithms provide a streamlined and efficient way to match gig workers
with clients, they often overlook critical qualitative aspects such as soft skills and cultural fit. This
limitation suggests a need for platforms to refine their algorithms to incorporate local context and
qualitative data.
Secondly, SMEs in Morocco rely heavily on platform-mediated ratings and reviews to identify talented
gig workers. This reliance on quantitative metrics can be problematic if these metrics do not adequately
capture the full spectrum of a worker’s capabilities or are subject to biases. SMEs could benefit from
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developing more robust evaluation criteria that include holistic assessments such as interviews and work
samples.
Thirdly, the study found that gig workers who invest in continuous skill development and maintain high
ratings on platforms are more likely to secure consistent gig opportunities. This finding highlights the
importance of both personal development and platform performance for gig workers aiming to achieve
sustained success.
The recommendations provided in this study offer practical insights for each stakeholder. Platforms
should enhance their algorithms and reputation systems to better account for local and qualitative
factors. SMEs should adopt comprehensive evaluation frameworks and engage more deeply with local
talent networks. Gig workers should focus on lifelong learning, maintaining high standards of work,
and actively managing their online reputations.
As a conclusion, effective talent identification in Morocco's gig economy requires a nuanced approach
that considers the local socio-economic and cultural landscape. By integrating these insights, platforms,
SMEs, and gig workers can create a more efficient, equitable, and productive gig economy. Future
research should explore the long-term impacts of these recommendations and continue to seek
innovative strategies for improving talent management practices in the gig economy.
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ResearchGate has not been able to resolve any citations for this publication.
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