Sustainability of E-Hailing Services: The Roles of Job Preferences, Competency and Resilience

Malaysian government have provided several economic incentives since 2017 to promote jobs in e-hailing industry. Based on the current knowledge, it is apparent that there is lack of studies in determining the realistic attributes and overall specification of e-hailing jobs. This quantitative study explores the antecedents of e-hailing jobs’ sustainability. It describes the motivations of individual’s appeals in particular from B40 segments for e-hailing jobs in Malaysia. This study explores three perspectives of service sustainability in terms of job preferences, competency and resilience among 397 e-hailing drivers in major cities of Malaysia. Additionally, this study provides considerable evidence from the supply side of ehailing service provision especially with respect to the motives of becoming a e-hailing driver. The results identified job preferences and competency as significant predictors for e-hailing service sustainability, while job resilience is not significant. These findings may help e-hailing companies to better understand what drivers look for in their employment. Additionally, this study provides insight for individuals who are currently considering becoming e-hailing drivers in the promising and ever evolving e-hailing services.


Introduction
The advent of e-hailing services since 2012, has created new interest among the Malaysian community to take part in the changing pattern of labour market thus e-hailing driver becomes one of the most common jobs in Malaysia. As of 2019, there were about 160,000 ridesharing drivers employed for this job (Department of Statistics, 2020). Attributable to extra income potential and accessible employment opportunity, Malaysian government have provided several economic incentives since 2017 to promote jobs in e-hailing industry. Incentives include cash aid for car ownership for the purpose of becoming drivers for eligible citizens and the recent incentives in Pelan Jana Semula Ekonomi Negara (Penjana) 2021 package worth RM75 million, RM50 million for Gig Economy stages and RM25 million for Malaysia Digital Economy Corporation (MDEC) Global Online Workforce (GLOW) programs aim to improve the livelihood, social security and welfare of e-hailing drivers. These economic initiatives were expected to further motivate the uptake of e-hailing jobs and improve the socio-economic status of unemployed citizens and Malaysian B40 segment. In recent efforts, research related to e-hailing have mainly done in the areas of demand side such as user's preferences (Wang et al., 2020) , service quality (Azudin et al., 2018), matching algorithm (Peng et al., 2020), safety aspects of e-hailing use (Meshram et al., 2020), governance (Jais & Marzuki, 2020). Based on the current knowledge, it is apparent that there is lack of studies in determining the realistic attributes and overall specification of e-hailing jobs. This study contends that there is a need to explore the motives of becoming an e-hailing driver and further develop a specification model for this job. It also aims to determine the antecedents behind e-hailing job by analysing three variables namely job preferences, job competency and job resilience in a single framework in order to offer a comprehensive understanding of ehailing service sustainability.

Job Sustainability
Sustainability are widely discussed in career literature (Valcour, 2015;McDonald & Hite, 2018). Various definitions were found in the literature. Job sustainability is concerned with empowering young people to the concept of helping them to continue to be employed individual with a career (Knockaert & Maillefert, 2004). A sustainable job continuity over time can be encouged by combining the efforts of the working individuals themselves and all policy makers and stakeholders involving in career environment (Rapuano, 2020). An individual act as a main career player that has to be responsible for their own career and get involved in career self-management in order to build job sustainability. By focusing on a life-long career path and developing self-competencies, a person will be more attractive, valuable and marketable in any career path they entered (Rapuano, 2020). A person will then able to achieve own satisfaction through any particular job they engaged with.

Job Preferences
Job preference is a concept used to explain the relationship between an individual and their work. Job or work preferences is the results of peoples' desire from their involvement in work. Job preference can influence person's career decisions and it is crucial determinants in their job motivation (Konrad, Ritchie, Lieb, & Corrigall, 2000). According to research on e-hailing drivers' job preference, e-hailing drivers appear to be attracted to this profession largely because of the flexibility that the services offers and the earnings level per hour (Hall & Krueger, 2016& Chen & Sheldon, 2016. Apart from that, Shokoohyara (2018) performed analysis of ridesharing platforms from drivers' perspective and the study showed that ridesharing drivers consider job flexibility, work-life balance and availability to meet new people as the positive reasons of continuing to work in ridesharing platforms. Malin & Chandler (2016) found the same results from their interviewed with ridesharing drivers whereby flexibility and sociability are the major reasons for drivers to involve with e-hailing platforms.

Job Competencies
Janis & Ziaran (2017) defined job competency as a collection of knowledge, experiences, skills, abilities and other suitable characteristics that ensure effective performance at work. Competency measurement tend to be standardized but in reality, each and every position and job is unique and different thus, it presupposes different elements of assessment (Puteh, Kaliannan, & Alam, 2016). According to Francis-Smythe et al (2013) job competencies act as a tool that facilitates in certain job performance. In the context of conducting a ehailing services, it is important for drivers to be competence. Shove, Pantzar, & Watson (2012) proposed three-element of social practice framework which inlcudes competence as one of the element. According to Shove et al (2012) competence is defined as "multiple forms of understanding and knowledgeability". It encompasses an individuals' skills, know-how, educational knowledge and the provision of sufficient information. Cass & Faulconbridge (2016) agreed that the adoption of sufficient competencies, information, understanding and knowledgeability is crucial elements for persons in deciding to use a specific type of transportation. Research by Bauer (2017) portrays that drivers for e-hailing services have to first understand the whole purpose and process involved in their work. It is crucial for drivers to have the know-how regarding the service ability of the e-hailing platforms.

Job Resilience
Job resilience is the capability of a person to absorb an unpleasant environment pertaining to their job. Job resilience also describes a person's ability to fulfil a highly pressured situation while performing certain task at work. In addition, a resilient worker is often featured by their continuous learning attitude, resilience to disagreeable occurrence and self-management at work (Ahmad et al., 2019). Finally, job resilience can be defined as (Naswall, Kuntz, Hodliffe, & Malinen, 2015) an employee's capability to utilize resources at workplace even when facing challenging circumstances to support the organization's goals. Persons with great job resiliency proved to have a better focus on constant learning that enables them to compete in the global economy (Ahmad et al., 2019). A recent study performed by Mishra & McDonald (2017) revealed that the development of job competency over the transition of time leads to employee resilient behavior. There are several evidences that caused the improvement of job resilience. It is related with several types of skills and competencies for example interpersonal and communication skills (Mansfield et al., 2012). Ahmad et al (2019) & Mansfield et al (2012) found that job competency as a significant influence with job resilience. Research performed by Ahmad et al (2019) pertaining to found that the mediating roles of job resilience positively affected job competency.

Methodology
E-hailing drivers in few major cities in Malaysia have been selected for this study, and target respondents consist of both two and four wheelers' drivers. Purposive sampling was employed for this study as the sampling here was confined by specific types of people who can provide the desired information. The sample size calculation for this study was based on Tabachnick, Fidell, and Osterlind (2001), who recommended a five-to-one ratio for each item to be factor analysed. The questionnaires were distributed from August until November, 2020 through e-hailing associations in Malaysia focusing on the main cities, which are Klang valley, Penang, Johor Bahru, Pantai Timur (Kelantan, Terengganu, Pahang), and Borneo (Sabah and Sarawak). A total of 392 questionnaires collected but only 372 were usable for further analysis, where 20 questionnaires were removed due to straight lining. There were 80% male and 20% female respondents from both 2 and 4 wheelers, and 70% were graduates and 30% non-graduates. All items were answered by the respondents using a five-point Likert scale. Measurements were adapted from well-known works of literature, i.e. Poon, Briscoe, Abdul-Ghani & Jones (2015) for job sustainability (4 items), Zhang, Bufquin and Lu (2019) for six dimensions of job preferences, namely financial (4 items), social (4 items), freedom and flexibility (4 items), easiness to operate (4 items), availability of resources (4 items) and excitement about the future (4 items), Bharwani and Jauhari (2013) for job competencies (10 items), and (Ahmad, Latif, Bilal, & Hai, 2019) for job resilience (6 items).
Partial lease square (PLS), a variance-based approach to structural equation modeling (SEM) has been chosen to evaluate the research model. Decision to use PLS after assessing the multivariate skewness and kurtosis where the data we have collected was not multivariate normal and as suggested by to assess the multivariate skewness and kurtosis using the software at https://webpower.psychstat.org/models/kurtosis/results.php . The Mardia's multivariate skewness (β = 552.1683, ρ<0.01) and Mardia's multivariate kurtosis (β = 2582.0182, ρ<0.01), therefore we proceeded with SmartPLS, which is a non-parametric analysis software.

Findings and Discussion
Two types of validity were being examined to assess the measurement model. First is the convergent validity and second is the discriminant validity. Table 1 indicates the summary of the convergent validity measurements which ascertained the loadings, average variance extracted (AVE), and composite reliability (CR). The standardized values of loadings recommended are greater than 0.708 and AVE should be greater than 0.5 (Fornell & Larcker, David, 1981;. However, researchers still maintain items with loadings in the range of 0.6 to 0.7 as CR and AVE are all above the trash hold values of 0.7 and 0.5 respectively.

Discussion and Conclusion
Jobs in e-hailing industry have been heralded as solution to the current economic and social issues. This study determines the realistic attributes and overall specification of ehailing jobs. This study uncovered the motives of becoming an e-hailing driver. Therefore, this study has theoretical and practical significance in terms of providing considerable evidence regarding the motives of individual to take part in the changing pattern of sharing economy in particular e-hailing services. As mentioned above, exploration on the supply side of ehailing is rather limited, therefore this study contributes to the literature by determining the antecedents behind e-hailing service offering. Second this study analyses the three variables of job preferences, job competency and job resilience in a single framework in order to offer a comprehensive understanding of motives that drivers may have in the delivery of e-hailing services. Furthermore, the quantitative findings of this study have provided empirical evidences for the development of e-hailing jobs. Previous studies mainly investigated on the demand side of service provision focusing on the users, rather than exploring the phenomenon from the perspective of supply side in particular drivers' aspect. From the practical standpoint findings from this study may help e-hailing companies to better understand what drivers look for in their employment. Additionally, this study provides insight for individuals who are currently considering becoming e-hailing drivers in the promising and ever evolving e-hailing services. The present study revealed the job specification model for e-hailing jobs that individual seek from their participation in the services provision. Using quantitative research approach several antecedents emerged from this investigation. This study revealed many significant antecedents of e-hailing service sustainability, however this study is not free from limitation. This study serves as exploratory in nature and sought to better understand the supply side of e-hailing service provision with the inclusion of three main antecedents. More research is needed to determine additional antecedents, in the proposed structural model.