Motivation in Sharing Economy-Based Service Triads: Operations of a Ride-Sharing Company


With the rapid rise of the digital revolution, indirect service offerings to the customers through third parties, now referred to as the sharing economy, have become the norm for many operations. This has some implications both on the general service provisions and key operational aspects such as how to motivate the service suppliers and who is responsible for that. This study evaluated the operations of one ride-sharing company and sought to understand the motivation of the service suppliers from a triadic perspective of employer (firm), service supplier (driver), and customer rather than the traditional employer-employee perspective. A cross-sectional descriptive design was used. The drivers were generally contented with the remuneration and reward system as well as the training and development opportunities. The findings showed that the firm has not focused as much on motivating its drivers from the customer-related side.

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Wainaina, G. and Mutogoh, H. (2022) Motivation in Sharing Economy-Based Service Triads: Operations of a Ride-Sharing Company. Journal of Service Science and Management, 15, 164-181. doi: 10.4236/jssm.2022.153011.

1. Introduction

New trends in the economy have rendered business unusual in modern operations. Sharing economy (SE), for instance, has disrupted the definition of employees and restructured supply chains all over the world. The duality of production, the triadic relationship structure, and the gig economy factor in the sharing economy has revolutionized how the employees are treated, assigned tasks, and controlled by operations. It is becoming increasingly clear that to optimally manage modern operations, existing management techniques on aspects such as motivation need to be revisited in light of these emergent behavioural business environments (Molobi et al., 2020; Čambalíková, 2021). Aware that organizational survival and sustainability hinge on a motivated human resource, operations have realized that employees are a crucial asset. The biggest challenge though is that motivation is a fluid concept, which evolves continuously subject to the trends in the economy. As such, operations oftentimes grapple with keeping up with these new trends that require a total paradigm shift in how they handle their prized assets, the employees (Aleksić et al., 2019). Ride-sharing companies acclaimed as the most successful innovations in the sharing economy, provide a good example of modern operations that are struggling with employee motivation. Firms such as Uber have on several occasions been marred by the drivers’ disgruntlement despite the positive reviews by customers. This is evidenced by the several court cases regarding the drivers’ treatment, complains of physical wellness, concerns about the remuneration structure, the dissatisfaction with the feedback system among many other issues (CEOC, 2015). The challenge for ride-sharing operations is that due to the triadic nature of the service provision model, some aspects of employee motivation, such as the feedback system, are to a large extent dependent on the customers. The question therefore arises, will these firms be more dexterous in handling drivers if they perceive and model their motivation as a function of both the operation and customers? This study seeks to answer this question by trying to understand the motivation of employees or service providers in a Sharing Economy-based Service Triad (SEST).

2. Review on Underpinning Theories

This study is underpinned by two business management theories: the Unified Service Theory (UST), addressing duality of production and differentiates modern operations such as SEs and SESTs from traditional operations, and Herzberg’s two-factor theory of motivation that highlights both intrinsic and extrinsic motivation factors which are important to operations management.

Unified Service Theory

Conjectured by Sampson (2001), the Unified Service Theory (UST) states that customer input is not only necessary but also a sufficient condition in defining and differentiating service production from non-service production processes. In service processes, customers generally provide significant input into the transformation processes while in non-service processes, a group of customers contribute ideas to the design of the product but the individual customer’s role is merely reduced to selecting and consuming the output in question. UST highlights the co-production between the customers and the operation, emphasizes the customers’ inputs during the production process and champions for some win-win strategic trade-offs for both parties. The theory unifies other models and theories pertaining to quality, strategy, capacity management, supply management, demand management amongst others in service industries (Sampson & Froehle, 2006). The major foundation for UST is that customers are involved in the production processes as suppliers of inputs such as ideas, assets, or information and as consumers of the eventual output. As such, the role of customers in service industries has been expanded to accommodate their dual functions, as service suppliers and service receivers resulting in a bidirectional service supply chain structure (Fitzsimmons & Fitzsimmons, 2001). To a large extent, issues of quality, capacity and demand management in service industries rely on the customer. These variables are often hard to manage because customers’ inputs and expectations are usually subjective depending on their behaviour, mood, training, experiences, communication skills, and financial capability amongst others (Sampson & Froehle, 2006). This therefore complicates how operations can motivate this group of service suppliers. Related to the extant study is the reality that a ride-sharing supplier/driver’s service transformation process and hence motivation is influenced by demands from both the ride-sharing company and the customer requesting for a ride.

Herzberg’s two-factor Theory

It was developed by Herzberg et al. (1959) after conducting research to find out the relationship between job satisfaction and productivity. He postulated that there are two groups of factors which can either promote or hinder job satisfaction. First, there are motivator factors which are intrinsic to the job and if present, lead to job satisfaction. These are factors which are related to the job itself such as the content of the work, levels of responsibility, job advancement, achievements, recognition, and rewards and so on. Secondly, there are hygiene factors which are extrinsic to the job, that is, they relate to the environment within which the job is performed. According to Herzberg et al. (1959), if present, these factors are not necessarily a source of job satisfaction but if absent, they could lead to dissatisfaction. Examples include work relationships, remuneration, supervision styles, fringe benefits, physical working conditions, status within the organization and the organization’s policies and administration. Hygiene factors are basic requirements for employees to perform their jobs while motivator factors push the employees to perform better, achieve high targets and become committed to the organization. As such, the two factors are interdependent for optimal job satisfaction, motivation, and performance. Elding (2005) postulated that hygiene factors are similar to the physiological, safety and social needs in Maslow’s hierarchy of needs theory while motivator factors are similar to the self-esteem and self-actualization needs. Evaluating the intrinsic and extrinsic job characteristics and to what extent they contribute to the job satisfaction or dissatisfaction of the employees is very crucial for any organization, whether traditional or in the sharing economy.

3. Factors of Motivation in Sharing Economy-Based Service Triads

Since to a large extent SESTs are produced virtually, m otivation should be understood differently from motivation in traditional operations settings. The digital connectivity in SESTs operations has rendered normal office work interaction minimal or unnecessary (Kauffman & Naldi, 2020). Additionally, the co-duality of production and the triadic structure that exists in SESTs requires that the employees be motivated from two dimensions: as service providers and service receivers (Kauffman & Naldi, 2020; Arvidsson, 2018). This means that motivation in SESTs is two dimensional, covering the employee and service supplier from both the organization and customer sides.

From the organization side, motivation is determined by factors such as the remuneration and reward system, the training and development opportunities, and the working environment. From the customer end, motivation is spurred by aspects such as the feedback system, fair treatment, trust, amongst others (Sijabat, 2019; Ekabu, 2018).

3.1. Motivation from the Organizational Side of the Triad

Many studies have linked motivation of employees to the organization’s productivity, customer satisfaction and employee loyalty. Operations have increasingly realized that human resources are a key asset and central to their performance and have, as a result, purposed to increase their levels of motivation (Adi, 2000; Rothberg, 2005). The same is true for every operation, including those in SEs and SESTs as they also depend on employees for sustainability. The factors that promote motivation from the operation-side are the remuneration and reward system, training and development opportunities, and the working environment.

Remuneration is the total income that one earns for work done. It can be a one-off payment or a series of payments which is determined by the agreed upon rules (Ojeleye, 2017). It is a parameter that is used to measure if the time and effort that one puts into a particular task is worth it and has the potential of binding an employee to a particular organization or speeding up their intention to leave (Bhatti, 2007; Bergiel et al., 2009). For most employees, the remuneration structure is the single most important factor that determines whether one will take the job and stay with the company or leave (Ramlall, 2013). The reward system refers to the prizes, monetary and non-monetary, given to the employees based on their productivity and performance evaluation (Dessler, 2007). The employees would be awarded for reaching a particular level of production or for providing exemplary service within or surpassing the target. The remuneration and reward system are used by operations to attract and retain the best workforce (Babić & Lukić, 2008). In traditional operations, the remuneration and reward package included a whole range of options from monetary to non-monetary. Direct monetary options include the basic salary and bonuses while indirect non-monetary options include pension, health insurance, transport allowance, housing allowance, paid-for education, paid holidays, paid leave days amongst others (Satka, 2019). Rational potential employees always assess both the monetary and non-monetary benefits before deciding on whether the job is a good fit or not (Goldhaber et al., 2007). In contemporary operations such as SEs and SESTs, the service provision structure is digitalized and provided ‘on-demand’, also known as the ‘gig economy’. The remuneration for these contemporary operations is done per hourly basis or per completed task. A large percentage of the remuneration is direct and monetary and other indirect benefits such as medical insurance, pension and house insurance are non-existent (Mishel, 2018). One can argue that other benefits such as flexibility and work autonomy compensate for the non-existing indirect benefits, but studies have shown that gig employees want other contemporary benefits such as the tailored financial wellness benefit (Manji, 2020).

Training and development is very important for the productivity of any operation (Elnaga & Imran, 2013). Professional training and development involve impacting the employees with necessary skills, expertise, and knowledge to carry out their tasks effectively. It involves both working on the employees’ weaknesses and building up their strengths through formal training or coaching. Training aims at filling the short-term gaps while development aims at building the long-term capacity of the operation in terms of skill set, attitude, and productivity of the employees (Luchak, 2003). Traditional operations are increasingly spending more on training and developing their employees using different styles, which can be on-the-job or off-the-job. On-the-job training examples include mentoring, coaching, apprenticeship, job rotation, understudy and so on. Off-the-job training includes lectures, seminars, simulations amongst others (Walters & Rodriguez, 2017). Contemporary operations such as SEs and SESTs look at training and development differently. The ‘employees’, being contracted service suppliers as well as customers of the platform, are expected to show up well equipped for the job at hand. In fact, operations such as Uber took a step further to charge the ‘service suppliers’ as much as $65 for training courses on city navigation and providing professional services (Forbes, 2014). The question is whether this is suitable and sustainable and whether it hinders or promotes the productivity of its service suppliers.

The working environment entails the settings within which a job is performed. The conditions can be different depending on the job type and can range from very comfortable to extremely difficult. Difficult circumstances can be brought about; first, by external conditions such as weather conditions, pollution, the cleanliness of the work environment, and other interferences; secondly, by individual factors such as age, gender, health status, sitting posture and so on; and thirdly, organizational related conditions such as the schedule, working hours, the work pace, shifts, and physical strain amongst others (Bakotic & Babic, 2013). Comfortable working conditions enhance productivity amongst employees while difficult conditions are likely to hinder optimal performance unless properly monitored and mitigated. Other work conditions include safety against personal injury by work equipment as well as harassment and violent attacks to the employees. Employees need to feel safe to perform their duties. Optimal social interactions with the management and other colleagues are also key in promoting good working environments for employees (European Foundation and International Labor Organization, 2019). In traditional operations, the organizations are expected to provide an optimal working environment for their employees. In Kenya for instance, the Work Injury Benefits Act (WIBA) requires the workstations to be inspected before the company can operate in the premises; this is in addition to fire inspections by the county government to ensure the security of the workers. The Employment Act specifies the maximum number of hours that an employee is expected to work in a day, in addition to minimum leave days per year assigned to the employees (Work Injury Benefits Act, 2007; Employment Act, 2007). Regulation regarding the work environment for contemporary operations such as SEs and SESTs has been long due. There have been numerous debates on whether drivers in ride-sharing environments should be employees or contractors. According to most agreements, drivers are considered independent contractors and therein lies the problem. For instance, the current Kenyan laws are insufficient to determine whether they are employees or contractors (Wambaa, 2018) and thus it is a challenge to protect them and improve their working environment using the existing laws.

3.2. Motivation from the Customer Side of the Triad

Due to the structure of SESTs, some of the functions that were traditionally subject to the management of the organization have been shifted to the customer such as providing feedback and task assignment up to some level. Therefore, knowingly, or unknowingly, some aspects of motivating the service suppliers fall on the customers (Obstfeld, 2005; Pathak et al., 2014). The study considers the following customer motivating factors; feedback system, fair treatment, and trust.

Feedback is the provision of information to an individual regarding their performance mostly to improve their productivity. It informs the individuals where they are at in terms of their own targets, the team’s goals, and the organization’s objectives. It also provides them insights on how their colleagues, customers and the management view them (Kluger & DeNisi, 1996; Ashford & Cummings, 1983). In traditional operations, feedback usually comes from management, where the manager and employee have a sit-down to discuss achieved verses set targets, strengths, weakness, plus some constructive criticisms (Blanchard & Johnson, 2015). The type of feedback provided could be outcome or process feedback. Outcome feedback pertains to the results of performance or level of productivity while process feedback pertains to how one does their job. Depending on the operation’s feedback system, the feedback source could be single-source (from management, subordinates, or horizontal colleagues) or multisource, from two or more sources (Geister et al., 2006). In SESTs, since most interactions are between the service supplier and the customer, a lot of feedback on performance often originates from the customer. Ride-sharing firms such as Uber for instance have rating systems where customer rates the driver based on their experience during the trip. When one gives lower ratings, the app prompts the customer to provide additional feedback to back it up. Similarly, higher ratings have options of compliments which can be chosen to accompany the rating. The ratings are averaged for the previous 500 rides and the average rating of the driver can be viewed by the customers when they request a ride (Uber, 2021). Though anonymous and sort of somehow standardized, the ratings are still quite subjective and dependent on the customers’ moods, feelings, and individual perceptions at the time of the ride. Uber introduced the binary system to counter this, whereby the drivers can also rate the customers based on the trip experience (The Guardian, 2019). The question is whether this has been successful in promoting objective feedback provision.

Treatment is the way one deals with or behaves toward someone or something. For employees, this involves how they are treated by the management, their colleagues, and the customers in general. Unfair treatment is a major contributor to lackluster performance and employee turnover in most operations across the world (Hassan, 2013). Most labor laws around the world recognize the operations’ prerogative to hire, fire, demote, lay-off, lay-down and set the terms of service for their employees; but they also protect the employees’ human rights. In Kenya, the Employment Act provides regulations on fair treatment of employees such as protection against discrimination (against race, tribe, color, disability amongst others), sexual harassment, child, and youth labor, forced wages, amongst others. Fair employee treatment is very crucial in enhancing their trust in the operation and ensuring their longevity in the job (Choi, 2011; Kim & Rubianty, 2011; Rubin, 2011).

Respect in the workplace is paramount as it is a human law. The humanity principle demands that operations do not treat employees as a means to an end but an end in themselves (Dillon, 2018). Caring for employees involves show of empathy to their well-being and helping them to be better versions of themselves (Smith, 2020). In traditional operations, fair treatment would be the sole responsibility of the management (Noddings, 1984, 2002) but in contemporary organizations some aspects of fair treatment lie with the customers because of the nature of interactions. Uber, for instance, has provided guidelines on fair treatment such as regulations on physical contact, sexual assault, and misconduct, threatening and rude behavior, unwanted contact, discrimination amongst others (Uber, 2021). The problem arises in the enforcement of these rules since Uber management does not enforce the rules but expect the customers and the local governments to enforce them. The question then arises on whether the drivers (service suppliers) are getting fair treatment in the course of their work.

Trust is one’s perception about others and the decision to act, behave and engage the other party dependent on the formed perception. Trust is crucial for an operation’s success as it influences the interaction amongst employees, interactions with customers as well as employees’ turnover rates (Annamalai et al., 2010). Trust enables transactions between businesses, operations, employees and even customers with the belief that the other party will act accordingly and ethically (Owoyele, 2017). Covey and Merrill (2006) postulates that operations that have high trust earn their shareholders three-times more than operations with low trust. In traditional operations, trust was viewed from the employee and management perspective. That is, the management would foster trust by ensuring that their employees had job security, certainty, autonomy, flexibility, and purpose to carry out their individual and team tasks effectively. This would in turn bolster cooperation amongst colleagues and management thus promoting intrinsic motivation. Studies have shown that trust between employees and their customers foster positive engagement and build the employee-customer bond (Gilson et al., 2005; Okello & Gilson, 2015). In contemporary operations such as SEs and SESTs, despite the tight control by the management, there is minimal interaction between the management and the service suppliers thus shifting the importance of trust between service suppliers and customers. These operations require the sharing of one’s property or time with a stranger that opens one to unprecedented risk (Tussyadiah, 2015; Botsman & Rogers, 2011). According to Olson (2013), trust is the most cited hindrance to participating in SEs and SESTs because of mistrust amongst the strangers in the transactions in addition to concerns on privacy. Trust is positively correlated to perceived benefits from and the willingness to participate in the platform but is negatively correlated to the perceived risks. A 2019 Uber report estimated that over 6,000 people were assaulted in 2017 and 2018, about 45% of these were Uber riders. Incidences like these erode trust amongst the Uber drivers and their customers and this can hinder the drivers’ motivation (North, 2019).

3.3. Conceptual Framework

While Sharing Economy is a new concept which has been established in the past decade, the Sharing Economy-based service triads is an even newer concept which was first introduced by Li et al. (2019). As such, there is no existing research work on motivation in the sharing economy-based service triads but similar studies on motivation in the sharing economy or motivation in collaborative services have been done presenting varied conclusions (see for instance Lin & Lo, 2016; Benoit et al., 2017; Buda et al., 2019; Mayasari & Haryanto, 2018; Berger et al., 2019; Lee et al., 2018; Pettica-Harris et al., 2018; Matar & Aoun, 2019; Sijabat, 2019; Norlander et al., 2021). This study sought to analyze motivation in sharing economy-based service triads, specifically looking at ride-sharing drivers. The aim was to evaluate the links between motivation of service suppliers in SESTs and organizational factors (such as the remuneration and reward systems, training and development opportunities, the working environment) and also with customers related factors (such as feedback systems, fair treatment, and trust) as outlined in Figure 1.

4. Methodology

To evaluate the aspect of motivation in Sharing Economy-based Service Triads, a

Figure 1. Hypothesized model.

Table 1. Participant demographics.

descriptive research design was adopted. The population of the study entailed drivers of a ride-sharing firm in Nairobi, Kenya. According to Strauss and Corbin (1998), a sample size should be based on theoretical saturation—the point where additional data collected is unproductive. However, it is not easy to determine sample size a priori (Sim et al., 2018). Since this study embraces positivist epistemology, a large sample (Sim et al., 2018) of 50 drivers was favored. Additionally, a sample of 50 is manageable and provides the minimally required number of cases for conducting a binary logistic regression analysis (Gill et al., 2010; Hair et al., 2010; Burns & Burns, 2008; Kleinbaum & Klein, 2010; Taherdoost, 2016). Respondents were identified in a pre-determined concentration site. Table 1 presents a summary of the demographic information of the respondents—age, level of education and gender.

A majority of the respondents, 90%, were male while the rest were female. Those with either primary level or secondary level education were 64% while the rest possessed college certificates or university degrees. The women drivers were relatively more educated that their male counterparts, all of them had at least a college certificate. The mean age of the respondents was 31.4 years. A majority of them were aged between 28 and 34 years of age.

The study relied on primary data obtained via structured questionnaires (Bryman & Bell, 2018). The questions were digitized on the Open Data Kit platform and a link shared with interviewee drivers. Descriptive statistics and bar graphs were used in initial data analysis. For comparison and relational measures, binary logistic regression and normal linear regression analyses were applied on the data.

5. Findings

In terms of organizational factors that motivate service suppliers in a SEST, findings of the study showed that the ride-sharing firm was doing relatively well in its remuneration and reward system as well as providing training and development opportunities. However, it fell short in providing a good working environment for its drivers. With the exception of university graduates, most respondents reported that the remuneration and reward system prompted them to work harder, and they earned more than their peers in the same business. The drivers seemed contented with the training and development opportunities availed by the firm but those with lower ratings felt that there were skills which they lacked that if trained on, would help them perform better. On the other hand, driving is a very physically straining job and barely allowed the drivers time for other social activities. The drivers were also unlikely to get help or support when work issues arose, but they countered this by relating very well and supporting each other when necessary.

In establishing the customer-related factors that motivate service suppliers in a SEST, findings showed that the customer-related factors were a huge impediment to the drivers’ motivation (Figure 2). The feedback system was deemed subjective, unfair treatment was common and there was serious lack of trust between the drivers and the customers. A majority of the drivers reported to being victims of subjective rating on occasion and would prefer another feedback system.

A good number of them had experienced sexual harassment, physical or verbal abuse, disrespect or discrimination from their customers. The unfair treatment was especially more rampant amongst the female drivers than their male counterparts. As for trust, at least half of the respondent drivers felt that they

Figure 2. Responses on the feedback system.

could not trust their customers. Despite the drivers reporting that they were comfortable driving their customers to any location at any given time, the lack of trust could explain their very small presence in the ‘unsafe’ areas or neighborhoods in Nairobi.

The findings on the relationship between motivation and each individual organizational or customer-related factor showed that despite most of the factors being important, the drivers did not perceive the training and development opportunities, the working environment and fair treatment as significant contributors to their motivation. To illustrate, Table 2 presents a summary of data relating motivation to fair treatment. The p-value is far higher than 5% (0.803 > 0.05), indicating that fair treatment is not statistically significant factor or determinant of ride-sharing driver’s motivation.

The most significant factors to a driver’s motivation were the remuneration system, the feedback system and trust. Indeed, a summary of data relating motivation to trust is captured in Table 3. The p-value is far lower than 5% (0.016 < 0.05), indicating that trust is a statistically significant factor or determinant of motivation of a drive.

It is important to note that these three significant factors directly affected their earnings and safety. The findings showed high positive correlation between motivation and the reward/remuneration system implying that a fair, competitive system that allowed the service suppliers to earn equal or a bit more than their counterparts and enabled them to afford their basic needs was a huge motivator. In fact, results show that the drivers were not opposed to working extra hours as long their efforts matched the remuneration. Furthermore, though bothered by the unfair treatment especially by their customers, this did not impede their

Table 2. Correlation between motivation and fair treatment.

Table 3. Relationship between motivation and trust.

decision to work as long as the income was good. There was also a positive correlation between feedback and motivation, the more the drivers perceived the feedback system to be fair and objective, the more motivated they were in their tasks. The customer feedback is important as it determines how many rides a driver gets and therefore the final income. Additionally, there was a significant positive correlation between trust and motivation. The more the drivers felt like they could trust and be trusted by their customers, the more likely they were to be motivated.

Other demographic variables such as gender, education, rating, type of vehicle, additional income sources and age did not significantly affect the motivation of the drivers; but the number of years worked for the firm almost always turned out to be significant in the correlation equations. The more years the driver worked, the more they were motivated. It can be argued that more years worked for the firm alluded to more experience which sharpened their skill set, helped them navigate the working environment and learnt how to engage with their customers and diffuse some challenging situations, hence its significance.

6. Conclusion

It is clear that the ride-sharing company has focused on the motivation of its service suppliers from the traditional perspective of the employer-employee relationship. It addresses the motivation of its service suppliers relatively well from the organizational side on issues such as their remuneration and their training and development. However, there is the minimum focus on equally strengthening the motivation of the service suppliers from the customer perspective. A lot of the concern-raising issues such as sexual harassment, verbal and physical abuse, discrimination, subjective feedback system, and lack of trust between the customers and the drivers are all customer-facing factors. Considering that the service suppliers, or in this case the drivers, interact more with the customers than the organization on a day-to-day basis, these customer-related concerns can easily trigger work dissatisfaction, high turnover rates, and lack of interest by potential female drivers who are often the victims.

Firms in SESTs need to rethink how to motivate their drivers on issues that are customer-facing. For instance, they can improve the feedback and driver rating system by incorporating some automatic factual entries that would add weight to the drivers’ overall rating. Examples include: did the driver arrive to pick up the customer within the suggested time by the app? Did the driver take the best route possible for the customer? Did the driver reach out to the customer first when the ride was requested? How many cancelled rides does the driver have in a given period? All this information can be easily and automatically collected from the app before, during, and after the ride, and then the weighted average of these entries together with the customer’s feedback can then be used to determine the driver’s rating. Increasing trust between the drivers and customers can be achieved using technology. For instance, SESTs should consider asking the drivers and customers to provide additional personal information that can then be used to determine and guarantee the trustworthiness of both the customers and drivers. Then using smart or intelligent devices, the information does not necessarily have to be shared with the other party; one just has to ascertain their identity using facial recognition, thump print, voice recognition, or One Time Passcodes (OTPs) which then prompts the system to verify their identity and trustworthiness before the trip commences. To encourage more customers and drivers to participate and provide their information, the firms can create premium accounts for the customers for preferential treatment and offer the drivers premium rates for providing trusted services. Fair treatment can be improved by encouraging prompt reporting of any verbal, physical, or sexual abuse, as well as discrimination. Reports should be comprehensively investigated by the internal team with support from the government and policing unit. The guilty parties, drivers or customers, should be immediately denied access to the app and blacklisted. The information should also be shared with other criminal investigation departments so as to warn the general public against such persons.

Just like it is with any study, there were a couple of limitations to this study. First, it is possible that a study including competitor ride-sharing firms or even other contexts of sharing economies could yield richer insights. Second, some respondents were not able to use the open data kit tool on their own. As such, the help of a research assistant was necessitated to help the drivers to translate, interpret and enter the data on the online questionnaire. It could be possible that during the translation, interpretation, and data entry, some responses were misunderstood or misrepresented. However, this was minimal. But importantly, this being one of the pioneer studies on the motivation of service suppliers from a triadic perspective, further studies can be done on the same or a similar topic using different methodologies, larger sample sizes, and alternative analytical tools so as to beef up literature on the topic. Additionally, longitudinal studies focused on ride-sharing firms in other cities globally can be conducted to ascertain whether the findings are consistent and provide generalizable recommendations for SESTs operations.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.


[1] Adi, D. Y. (2000). Motivation as a Means of Effective Staff Productivity in the Public Sector: A Case Study of Nigerian Immigration Service, Borno State of Nigeria. Unpublished MPA Thesis, School University of Maiduguri Nigeria.
[2] Aleksić, M., Jerotijević, G., & Jerotijević, Z. (2019). Modern Approaches to Employee Motivation. Ekonomika, 65, 121-133.
[3] Annamalai, T., Abdullah, A. G. K., & Alasidiyeen, N. J. (2010). The Mediating Effects of Perceived Organizational Support on the Relationships between Organizational Justice, Trust and Performance Appraisal in Malaysian Secondary Schools. European Journal of Social Sciences, 13, 623-632.
[4] Arvidsson, A. (2018). Value and Virtue in the Sharing Economy. The Sociological Review, 66, 289-301.
[5] Ashford, S. J., & Cummings, L. L. (1983). Feedback as an Individual Resource: Personal Strategies of Creating Information. Organizational Behavior and Human Performance, 32, 370-398.
[6] Babić, M., & Lukić, Z. (2008). Management—Theory, Functions, Institutional Aspects and Corporate Governance. Ekonomski Fakultet.
[7] Bakotic, D., & Babic, T. (2013). Relationship between Working Conditions and Job Satisfaction: The Case of Croatian Ship Building Company. International Journal of Business and Social Science, 4, 206-213.
[8] Benoit, S., Baker, T. L., Bolton, R. N., Gruber, T., & Kandampully, J. (2017). A Triadic Framework for Collaborative Consumption (CC): Motives, Activities and Resources & Capabilities of Actors. Journal of Business Research, 79, 219-227.
[9] Berger, T., Frey, B. C., Levin, G., & Danda, R. S. (2019). Uber Happy? Work and Well-Being in the ‘Gig Economy’. Economic Policy, 34, 429-477.
[10] Bergiel, B. E., Nguyen, V. Q., Clenney, B. F., & Taylor, G. S. (2009). Human Resource Practice (11th ed.). Kogan Page Limited.
[11] Bhatti, S. (2007). Human Resource Management. A Competitive Advantage. Indian Journal of Industrial Relations, 42, 746-751.
[12] Blanchard, K., & Johnson, S. (2015). The New One-Minute Manager (1st ed.). William Morrow & Company.
[13] Botsman, R., & Rogers R. (2011). What’s Mine Is Yours: The Rise of Collaborative Consumption. Harper Business.
[14] Bryman, A., & Bell, E. (2018). Business Research Methods (5th ed.). Oxford University Press.
[15] Buda, G., Pethes, B., & Lehota, J. (2019). Dominant Consumer Attitudes in the Sharing Economy—A Representative Study in Hungary. Resources, 9, Article No. 1.
[16] Burns, R. B., & Burns, R. A. (2008). Business Research Methods and Statistics Using SPSS. Sage Publishing Ltd.
[17] Čambalíková, A. (2021). Modern Trends in Business Management in the Light of Globalization. SHS Web of Conferences, 92, Article ID: 02009.
[18] CEOC (2015). Uber Technologies Inc.: Managing Opportunities and Challenges. Daniels Fund Ethics Initiative.
[19] Choi, S. (2011). Organizational Justice and Employee Work Attitudes: The Federal Case. American Review of Public Administration, 41, 185-204.
[20] Covey, S. M., & Merrill, R. R. (2006). The SPEED of Trust: The One Thing That Changes Everything. Simon and Schuster.
[21] Dessler, G. (2007). Basics of Human Resource Management. Data Status.
[22] Dillon, R. S. (2018). Respect. Stanford Encyclopedia of Philosophy.
[23] Dillon, R. S. (2018). Respect. Stanford Encyclopedia of Philosophy.
[24] Ekabu, P. K. (2018). Motivation Factors and Turnover Intentions for Teachers in Public Secondary Schools in Meru County, Kenya. The University of Nairobi.
[25] Elding, D. J. (2005). Modelling Employee Motivation and Performance. Doctoral Dissertation, University of Birmingham.
[26] Elnaga, A., & Imran, A. (2013). The Effect of Training on Employee Performance. European Journal of Business and Management, 5, 137-147.
[27] Employment Act (2007).
[28] European Foundation and International Labor Organization (2019). Working Conditions in a Global Perspective. Publications Office of the European Union.
[29] Fitzsimmons, J. A., & Fitzsimmons, M. J. (2001). Service Management: Operations, Strategy, and Information Technology. McGraw-Hill.
[30] Forbes (2014). Uber Skimps on Driver Training, Then Charges Drivers $65 for Basic Driver Skills Course.
[31] Geister, S., Konradt, U., & Hertel, G. (2006). Effects of Process Feedback on Motivation, Satisfaction, and Performance in Virtual Teams. Small Group Research, 37, 459-489.
[32] Gill, J., Johnson, P., & Clark, M. (2010). Research Methods for Managers. SAGE Publications.
[33] Gilson, L., Palmer, N., & Schneider, H. (2005). Trust and Health Worker Performance: Exploring a Conceptual Framework Using South African Evidence. Social Science & Medicine, 61, 1418-1429.
[34] Goldhaber, D., Gross, B., & Player, A. (2007). Are Public Schools Really Losing Their Best? Assessing the Career Transitions of Teachers and Their Implications for the Quality of Teacher Workforce. CALDER Working Paper 12, the Urban Institute.
[35] Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate Data Analysis (7th ed.). Prentice Hall.
[36] Hassan, S. (2013). Does Fair Treatment in the Workplace Matter? An Assessment of Organizational Fairness and Employee Outcomes in Government. The American Review of Public Administration, 43, 539-557.
[37] Herzberg, F., Mausner, B., & Snyderman, B. B. (1959). The Motivation to Work (2nd ed.). John Wiley & Sons.
[38] Kauffman, R. J., & Naldi, M. (2020). Research Directions for Sharing Economy Issues. Electronic Commerce Research and Applications, 43, Article ID: 100973.
[39] Kim, S. E., & Rubianty, D. (2011). Perceived Fairness of Performance Appraisals in Federal Government: Does It Matter? Review of Public Personnel Administration, 31, 329-348.
[40] Kleinbaum, D., & Klein M. (2010). Logistic Regression: A Self-Learning Text (3rd ed.). Springer.
[41] Kluger, N. A., & DeNisi, A. (1996). The Effects of Feedback Interventions on Performance: A Historical Review, a Meta-Analysis, and a Preliminary Feedback Intervention Theory. Psychological Bulletin, 119, 254-284.
[42] Lee, Z. W., Chan, T. K., Balaji, M. S., & Chong, A. Y. L. (2018). Why People Participate in the Sharing Economy: An Empirical Investigation of Uber. Internet Research, 28, 829-850.
[43] Li, D., Liu, G., Jia, F., & Sun, H. (2019). Sharing Economy-Based Service Triads: Towards an Integrated Framework and a Research Agenda. Journal of Cleaner Production, 218, 1031-1044.
[44] Lin, S. W., & Lo, L. Y. S. (2016, June). Motivation for Using the Social Commerce Website in the Sharing Economy: A Two-Factor Theory Perspective [Paper Presentation]. Pacific Asia Conference on Information Systems (PACIS).
[45] Luchak, A. A. (2003). What Kind of Voice Do Loyal Employees Use? British Journal of Industrial Relations, 41, 115-134.
[46] Manji, L. (2020). Changing Employee Benefits in a Modern Gig Economy. HRD Online.
[47] Matar, L., & Aoun, G. (2019). Motivation of the Sharing Economy Users in the Middle East: The Case of Lebanon. Journal of Internet e-Business Studies, 2019, Article ID: 539198.
[48] Mayasari, I., & Haryanto, H. C. (2018). Motivational Factors of the Collaborative Consumption in the Era of Sharing Economy. Gadjah Mada International Journal of Business, 20, 331-353.
[49] Mishel, L. (2018). Uber and the Labor Market: Uber Drivers’ Compensation, Wages, and the Scale of Uber and the Gig Economy. Economic Policy Institute.
[50] Molobi, L., Kabiraj, S., & Siddik, N. A. (2020). Behavioral Intention Factors Influencing Sharing Economy Innovations: An Exploratory Research of Uber in South Africa. Indian Institute of Management.
[51] Noddings, N. (1984). Caring, a Feminine Approach to Ethics & Moral Education. University of California Press.
[52] Noddings, N. (2002). Starting at Home: Caring and Social Policy. University of California Press.
[53] Norlander, P., Jukic, N., Varma, A., & Nestorov, S. (2021). The Effects of Technological Supervision on Gig Workers: Organizational Control and Motivation of Uber, Taxi, and Limousine Drivers. The International Journal of Human Resource Management, 32, 4053-4077.
[54] North, A. (2019). It’s Not Just Passengers Being Assaulted in Ubers. Drivers Are at Risk, Too. Vox Media.
[55] Obstfeld, D. (2005). Social Networks, the Tertius Iungens Orientation, and Involvement in Innovation. Administrative Science Quarterly, 50, 100-130.
[56] Ojeleye, Y. (2017). The Impact of Remuneration on Employees’ Performance: A Study of Abdul Gusau Polytechnic, Talata-Mafara and State College of Education Maru, Zamfara State. Arabian Journal of Business and Management Review (Nigerian Chapter), 4, 34-43.
[57] Okello, D. R., & Gilson, L. (2015). Exploring the Influence of Trust Relationships on Motivation in the Health Sector: A Systematic Review. Human Resources for Health, 13, Article No. 16.
[58] Olson, K. (2013). National Study Quantifies Reality of the “Sharing Economy” Movement. Campbell Mithun.
[59] Owoyele, S. (2017). Factors Influencing Employee Motivation and Its Impact on Employee Performance. Unpublished Master’s Thesis, Centria University of Applied Sciences.
[60] Pathak, S. D., Wu, Z., & Johnston, D. (2014). Toward a Structural View of Co-Opetition in Supply Networks. Journal of Operational Management, 32, 254-267.
[61] Pettica-Harris, A., deGama, N., & Ravishankar, M. N. (2018). Postcapitalist Precarious Work and Those in the ‘Drivers’ Seat: Exploring the Motivations and Lived Experiences of Uber Drivers in Canada. Organization, 27, 36-59.
[62] Ramlall, S. (2003). Organizational Application. Managing Employee Retention as a Strategy for Increasing Organizational Competitiveness. Applied H.R.M. Research, 8, 63-72.
[63] Rothberg, G. (2005). Fourth Quarter. The Role of Idea in the Managers Workplace: Theory and Practice. Pakistan Management Review, 42, 48-73.
[64] Rubin, E. V. (2011). Exploring the Links between Procedural Fairness and Union Membership in Federal Government. Review of Public Personnel Administration, 31, 128-142.
[65] Sampson, S. E. (2001). Understanding Service Businesses: Applying Principles of the Unified Service Theory. John Wiley & Sons.
[66] Sampson, S. E., & Froehle, C. M. (2006). Foundations and Implications of a Proposed Unified Services Theory. Production and Operations Management, 15, 329-343.
[67] Satka, E. (2019). The Impact of Remuneration on Employee Performance in the Trading Company. Agricultural University of Tirana.
[68] Sijabat, R. (2019). Sharing Economy: A Study on the Factors Influencing Users’ Motivation to Use Ride Sharing Platforms. DeReMa Jurnal Manajemen, 14, 65-87.
[69] Sim, J., Saunders, B., Waterfield, J., & Kingstone, T. (2018). Can Sample Size in Qualitative Research Be Determined a Priori? International Journal of Social Research Methodology, 21, 619-634.
[70] Smith, M. K. (2020). Nel Noddings, the Ethics of Care and Education. The Encyclopedia of Pedagogy and Informal Education.
[71] Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research Techniques. SAGE Publications.
[72] Taherdoost, H. (2016). Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research. International Journal of Advance Research in Management, 5, 18-27.
[73] The Guardian (2019). Uber to Ban Riders with Low Ratings: Will You Pass the Test?
[74] Tussyadiah, I. P. (2015). An Exploratory Study on Drivers and Deterrents of Collaborative Consumption in Travel. In I. Tussyadiah, & A. Inversini (Eds.), Information & Communication Technologies in Tourism 2015 (pp. 817-883). Springer.
[75] Uber (2021). How Star Ratings Work.
[76] Walters, K., & Rodriguez, J. (2017). The Importance of Training and Development in Employee Performance and Evaluation. World Wide Journal of Multidisciplinary Research and Development, 3, 206-212.
[77] Wambaa, I. W. (2018). An Analysis of Uber Drivers’ Employment Status in Accordance with the Kenyan Labor Law. Strathmore University Law School.
[78] Work Injury Benefits Act (WIBA) (2007).

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