POLITECNICO DI MILANO Facoltà di Ingegneria Corso di Laurea in Management Engineering

INVESTIGATING SHARING ECONOMY SERVICES: HOW OPERATIONS ARE CONFIGURED IN THE MOBILITY INDUSTRY.

EXAMINING POSSIBLE LEAN PRINCIPLES APPLICATION AND OPERATIONAL REACTION TO COVID-19.

Relatore: Prof. Rossini Matteo

Tesi di Laurea di: Casazza Rebecca: 919952 Ratano Federica: 921021

Anno Accademico 2019 / 2020

Table of contents

ABSTRACT ...... 7 ESTRATTO ...... 8 EXECUTIVE SUMMARY ...... 9 The Reference Literature ...... 10 The Research Goals ...... 12 The Research Methodology ...... 13 The Results Achieved ...... 14 The Conclusions ...... 16

1. INTRODUCTION ...... 17 2. LITERATURE REVIEW...... 20 2.1. Introduction on Sharing Economy ...... 20 2.1.1. Definitions ...... 20 2.1.2. Sharing Economy Industries ...... 22 2.1.3. Sharing Platform Models ...... 22 2.1.4. Sharing Economy Classification ...... 24 2.1.5. Sharing Economy Characteristics ...... 25 2.2. Systematic Literature Review ...... 26 2.2.1. Operations in Sharing Economy Services ...... 26 2.2.2. Literature Review Methodology ...... 29 2.2.3. Comparison Matrices ...... 31 2.2.3.1. Axes for Classification ...... 31 2.2.3.2. Matrices Discussion ...... 35 2.3. Papers Summary ...... 43 2.3.1. Capacity Management...... 43 2.3.2. Demand Management ...... 44 2.3.3. Asset Management ...... 44 2.3.4. Resource Planning & Control ...... 45 2.3.5. Queue Management ...... 45 2.3.6. Logistics / Distribution Channels ...... 46 2.3.7. Sustainability ...... 47 2.3.8. Innovation Management ...... 48 2.3.9. Quality Management ...... 49 2.3.10. Measurement of Customer Satisfaction ...... 51 2.3.11. Design Management ...... 51

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2.3.12. Service Operations Strategy ...... 51 2.3.13. Pricing System ...... 51 2.3.14. Regulation System ...... 54 2.3.15. Game Theory ...... 54 2.3.16. Lean Management ...... 55 2.3.17. Operations Management...... 55 2.3.18. General Study ...... 56 2.4. Research Questions ...... 58 3. ANALYSIS ...... 59 3.1. Platform Classification and Characteristics ...... 59 3.2. Analysis of Key Performance Indicators and Stressed Operations Pillars ...... 60 3.2.1. P2P vs B2P KPIs Analysis ...... 61 3.2.2. P2P vs B2P Stressed Operations Pillars ...... 66 3.3. Real Case Studies ...... 68 3.3.1. Key Performance Indicators ...... 70 3.3.1.1. Analysis Within-Case Studies ...... 70 3.3.1.1.1. B2P – Literature vs Real Case Study ...... 70 3.3.1.1.2. B2P – Within-Case Analysis ...... 74 3.3.1.1.3. P2P – Literature vs Real-Case Study ...... 76 3.3.1.1.4. P2P – Within-Case Analysis ...... 81 3.3.1.2. Analysis cross-case studies ...... 83 3.3.2. Operations Management ...... 87 3.3.2.1. Analysis Within-Case studies ...... 87 3.3.2.1.1. B2P – Operations Management Description ...... 87 3.3.2.1.2. B2P - Business Practices Adopted ...... 95 3.3.2.1.3. B2P – New KPIs Development ...... 98 3.3.2.1.4. Insights on B2P Station-based Sharing-model ...... 100 3.3.2.1.5. P2P – Operations Management Description ...... 102 3.3.2.1.6. P2P – Business Practices Adopted ...... 109 3.3.2.1.7. P2P – Operations Structure ...... 109 3.3.2.1.8. P2P – New KPIs Development ...... 111 3.3.2.2. Analysis Cross-Case Studies ...... 113 3.3.2.2.1. B2P vs P2P – Operations Management Description ...... 113 3.3.2.2.2. B2P vs P2P – Business Practices Adopted ...... 116 4. LEAN MANAGEMENT APPLICATION ...... 118 4.1. Lean Management Philosophy ...... 118 4.1.2. Principles ...... 118

2 4.1.3. Value-Added vs Non-Value-Added Activities ...... 119 4.1.4. Muri, Mura, Muda ...... 119 4.2. Lean Management Concepts Applied in Sharing Economy ...... 120 4.2.1. Value-Added vs Non-Value-Added Operations ...... 120 4.2.2. Waste Occurring in SES Transport Sector ...... 122 4.3. Real-Cases Adopting Lean ...... 125 4.4. Exploiting Lean Tools ...... 127 4.4.1. Maintenance ...... 127 4.4.2. Rebalancing & Refuelling ...... 137 4.4.3. Drivers’ Behaviour Management and Trip Optimisation ...... 139 4.4.4. Establishing Continuous Improvement ...... 142 5. COVID vs SES ...... 144 5.1. Characteristics Collapse ...... 145 5.2. Sectorial Impact ...... 146 5.2.1. Accommodation ...... 146 5.2.2. Transportation ...... 146 5.2.3. Food and Good Delivery ...... 147 5.2.4. Space ...... 147 5.2.5. Crowdfunding ...... 148 5.3. Research Question...... 148 5.4. Analysis ...... 149 5.4.1. During Lockdown ...... 149 5.4.1.1. B2P ...... 150 5.4.1.2. P2P ...... 152 5.4.2. During the Recovery Phase ...... 154 5.4.2.1. Vehicle Disinfection ...... 155 5.4.2.2. PPE Distribution ...... 159 5.4.2.3. PPE Compliance Check ...... 160 5.4.3. African Situation ...... 161 6. CONCLUSIONS ...... 163 Appendix A ...... 169 Appendix B ...... 170 Appendix C ...... 173 Appendix D ...... 179 BIBLIOGRAPHY ...... 180 SITOGRAPHY ...... 194

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List of figures

Figure 1 - Sharing Platform Model ...... 23 Figure 2 - First Classification Matrix ...... 24 Figure 3 - Second Classification Matrix ...... 25 Figure 4 - Business Operations Models Concept ...... 27 Figure 5 - Twofold Strategic Flow ...... 27 Figure 6 - Sharing Platform Model Examples ...... 59 Figure 7 - Legend for KPIs Groups ...... 61 Figure 8 - Geographical Map of Case Studies ...... 69 Figure 9 - Business Growth Phases of P2P Platforms ...... 83 Figure 10 - Rebalancing Stage 1 ...... 88 Figure 11 - Rebalancing Stage 2 and 3 ...... 88 Figure 12 - Uber Organisational Structure ...... 110 Figure 13 - Cellular Manufacturing - B2P Case ...... 128 Figure 14 - Fixed Repair Position - B2P Case ...... 129 Figure 15 - Toolbox with shaped spaces ...... 136 Figure 16 - Filled toolbox ...... 136 Figure 17 - FMEA Format ...... 139 Figure 18 - FMEA Complete Format ...... 140 Figure 19 - Process Width and Market Tolerance Window Representation ...... 141 Figure 20 - Optimal Situation ...... 158 Figure 21 - Worst Situation ...... 158 Figure 22 - Realistic Situation (1) ...... 158 Figure 23 - Realistic Situation (2) ...... 158

4 List of tables

Table 1 - Key Words Used ...... 30 Table 2 - Articles & Conferences Distribution ...... 31 Table 3 - Matrix Cases ...... 34 Table 4 - Third Pair-wise Comparison Matrix ...... 42 Table 5 - Second Pair-wise Comparison Matrix ...... 42 Table 6 - First Pair-wise Comparison Matrix ...... 42 Table 7 - B2P KPIs Datasheet 1 ...... 71 Table 8 - B2P KPIs Datasheet 2 ...... 72 Table 9 - Literature and Real-cases Cost Items ...... 73 Table 10 - P2P KPIs Datasheet 1 ...... 76 Table 11 - P2P KPIs Datasheet 2 ...... 78 Table 12 - Common KPIs among P2P Platforms ...... 81 Table 13 - B2P Operations Drivers ...... 94 Table 14 - Business Practice Adopted ...... 95 Table 15 - Data Summary of P2P Platforms ...... 102 Table 16 - Operations Summary ...... 114 Table 17 - VA and NVA Activities Summary ...... 122 Table 18 - Seven Wastes Reflected in B2P and P2P Companies ...... 125 Table 19 - 5s Vocabulary ...... 131 Table 20 - Human Errors vs Causes of Defect Matrix ...... 133 Table 21 - Poka-yoke Six Principles ...... 133 Table 22 - Strategies Adopted During COVID-19 Lockdown ...... 150 Table 23 - Operations Managed During COVID-19 Lockdown - B2P case ...... 152 Table 24 - Operations Managed During COVID-19 Lockdown - P2P Case ...... 153 Table 25 - Operations Managed During Recovery Phase - B2P and P2P Case...... 161

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List of graphs

Graph 1 - P2P Industries Distribution ...... 35 Graph 2 - P2P Transport Services Distribution ...... 35 Graph 3 - B2P Industries Distribution ...... 36 Graph 4 - B2P Transport Services Distribution ...... 36 Graph 5 - P2P Operations Pillars Distribution ...... 38 Graph 6 - B2P Operations Pillars Distribution ...... 39 Graph 7 - P2P KPIs Distribution...... 62 Graph 8 - B2P KPIs Distribution ...... 63 Graph 9 - P2P Ride-hailing KPIs Distribution ...... 65 Graph 10 - P2P Ride-sharing KPIs Distribution ...... 65 Graph 11 - B2P Free floating Vehicle-sharing KPIs Distribution ...... 66 Graph 12 - Operations Pillars Distribution in Transport P2P Platforms ...... 67 Graph 13 - Operations Pillars Distribution in Transport B2P Platforms ...... 67 Graph 14 - Literature Internal B2P KPIs Distribution ...... 71 Graph 15 - Real-case Internal B2P KPIs Distribution ...... 71 Graph 16 - Literature Internal P2P KPIs Distribution ...... 76 Graph 17 - Real-case Internal P2P KPIs Distribution...... 76 Graph 18 - Surge Lever Effect on Demand ...... 80 Graph 19 - Cross-case KPIs Distribution Comparison ...... 86 Graph 20 - Rented Vehicles Daily Distribution ...... 98 Graph 21 - Trade-off Between Quality and Quantity for P2P Firms ...... 106 Graph 22 - Active Drivers Daily Distribution ...... 111

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ABSTRACT

Consumers’ habits have been experiencing a route change in the past decade. Four main megatrends are affecting society: (1) the spread of advanced digital platforms, (2) the effort to use resources more efficiently and with economic rationality, (3) the change in attitude with ownership, (4) the globalisation and urbanisation. The business response to these market changes is the development of Sharing Economy Services (SES), which enable peer-exchanges of idle products through online platforms. SES are in rapid growth and are spreading to many sectors. Among the leading ones, transportation industry offers to companies the possibility to furtherly improve. Therefore, understanding how sharing-mobility organisations are structured is the intent of this research. In particular, through multiple-case studies, within-case and cross-case analyses of peer-to- peer (P2P) and business-to-peer (B2P) models are carried out. The aim is to define the Operations they rely on, starting from the examination of the main Key Performance Indicators (KPIs). Furthermore, since these platforms do not deal with any productive process, it is compelling to verify if Lean Management practices could be applied to sharing-mobility context and to what extent. Finally, considering the unusual scenario that the whole population is facing due to global pandemic caused by COVID-19, the study has been expanded to investigate the consequences generated in operational terms.

Key words: Sharing Economy, Operations Management, Lean Management, Key Performance Indicator, KPI, Sharing-mobility, Transportation, P2P sharing-model, B2P sharing-model, Ride- hailing, Free-floating vehicle-sharing, Coronavirus, COVID-19.

7

ESTRATTO

Le abitudini dei consumatori hanno cambiato direzione negli ultimi dieci anni. Quattro principali megatrend, infatti, ancora oggi influenzano la società: (1) la diffusione di piattaforme digitali avanzate, (2) l’impegno nell’utilizzo più efficiente ed economicamente razionale delle risorse, (3) il cambiamento di mentalità nei confronti dell’avere possesso di un bene, (4) la globalizzazione e l’urbanizzazione. La risposta delle imprese a questi cambiamenti di mercato è stato lo sviluppo delle Sharing Economy Services (SES), le quali consentono lo scambio di prodotti inutilizzati tra pari attraverso l’utilizzo di internet. Le SES stanno affrontando oggi la fase di crescita, diffondendosi ed espandendosi rapidamente in molti settori, tra i quali l'industria dei trasporti offre alle aziende grandi possibilità di miglioramento. Di conseguenza, il fine ultimo di questa ricerca è comprendere la struttura delle compagnie che offrono il servizio di mobilità-condivisa. In particolare, attraverso molteplici casi studio, vengono sviluppate delle analisi sulle piattaforme peer-to-peer (P2P) e business-to-peer (B2P), volte in seguito a confrontare questi due modelli di condivisione. L'obiettivo è definire le Operations alla base, partendo dallo studio dei principali Indicatori Chiave di Performance (KPI). Inoltre, dal momento che queste aziende non si occupano di alcun processo produttivo, è interessante verificare la possibilità di applicare le pratiche di gestione snella (Lean Management) alla conduzione delle Operations presentate e in che misura. Infine, considerando lo scenario insolito che l'intera popolazione sta affrontando a causa della pandemia globale provocata dal COVID-19, lo studio è ampliato con l’indagine dell’impatto generatosi in termini operativi.

Parole chiave: Sharing Economy, Gestione delle Operations, Gestione Lean, Indicatori Chiave di Performance, KPI, Mobilità-condivisa, Trasporti, P2P sharing-model, B2P sharing-model, Ride- hailing, Free-floating vehicle-sharing, Coronavirus, COVID-19.

8 EXECUTIVE SUMMARY

EXECUTIVE SUMMARY

This research focuses on the Sharing Economy Services, the phenomenon that is changing the methods of providing services to people in the various sectors in which it operates, introducing “an economic system in which people can share possessions, services etc., usually by means of the internet” [Oxford Dictionary, 2015]. The paper tries to investigate which operations must be managed by companies to support this economic model and how they are handled. In particular, the research methodology presented in the Introduction will illustrate, in detail, the path followed in the realisation of the work. This Master thesis starts with a reference literature review trying to frame the scope of the analysis, indicating what is meant by Sharing Economy, what are the characterising elements of this economic system and which types of business models a SES enterprise can adopt. Secondly, an overview on operations management, its distinctive factors and the consequent managerial implications has been introduced. Thus, a systematic literature review has been structured in order to understand, in detail, their influence on Sharing Economy Services. This allowed to define some research questions for further investigation, representing the objectives of the study. The analysis has been conducted collecting data and information through videocall interviews which enabled to draw up some case studies for deepen the most relevant operations carried out by SES companies. Chapter 3, therefore, presents the results of the empirical analysis and the cost functions to quantify the expenditures sustained by these firms in managing their operations. In particular, it identifies the main KPIs categories monitored, which are used as a reading key of operations analysis. In Chapter 4, however, the focus shifts to the state of diffusion of the Lean Management in SES organisations, first at corporate level in general and then concentrating on the operations studied. The poor adoption of this production model, emerged during the literature review and the interviews, enables to propose tools to be implemented, divided according to the operational activities to which they can be applied to. In Chapter 5, before the explanations of the conclusions obtained through the work carried out, the impact of COVID-19 on operations is analysed, also considering the government policy issued.

9 EXECUTIVE SUMMARY

The Reference Literature

Considering the goals of the work, four research areas were defined to be explored thought the analysis of the literature: • the notion of Sharing Economy and the related pillars on which it is based; • the typologies of business models that can be adopted by enterprises and the related level of diffusion in the various sectors; • the previous existing knowledge of operations managed by SES firms; The research source was Scopus, an internet database containing only scientific articles. The first part of the analysis of the literature review enabled to define the Sharing Economy in an objective sense, despite the many ways in which authors name it. Indeed, the term defines an economic model in which a digital platform allows the customer to have access to an asset without owning it. Furthermore, its main synonyms were examined in order to discover which terms overlap with the general definition, like Collaborative Consumption and Access-based Consumption and those that have a peculiar meaning and refer to particular cases, such as Circular Economy and Collaborative Economy. The definition clearly sums up the four canonical characteristics of the Sharing Economy, which are [6]: • ICT-enabled, technology and web-based communications are the basis for platform transactions; • idling-capacity, which made the Sharing Economy spread rapidly; • access to goods and services rather than ownership, giving the possibility to choose among a greater number of goods and services that people otherwise could not afford; • variety of resources shared, which can be tangible and physical assets, but also intangible and human, like skills, talents or human time. Secondly, the exploration of the business sectors in which Sharing Economy allowed to define the boundaries of this research. Transportation is the most well-known industry, which includes ride- hailing, ride-sharing, and car-sharing platforms. By expanding this last category to vehicle-sharing, also bike- and boat-sharing can be considered as part of this area. Being the most widespread one, this thesis aims to discover the operations managed by companies operating in this sector, leaving the analysis of those handled in the others for future research. Relevant for the analysis was the Sharing Economy linkage with the two-sided market concept, generating four noteworthy business models which are different due to the diverse typologies of users that interact and after which they are named: peer-to-peer (P2P), business-to-peer (B2P), peer-to- business (P2B) and business-to-business (B2B). The first is the most common one and it is a network

10 EXECUTIVE SUMMARY that puts at the sides of the platform the peers, which are equally privileged, equipotent participants. The second most widespread typology is the B2P which represents the relationship between a business enterprise and its customers for sales and/or assistance activities. Being the most common business models adopted by SES, this research is focalised on them.

The second part of the literature review was carried out with the purpose of analysing the operations in the Sharing Economy scenario. Over the years, globalisation, customisation, speed of technological development and other cultural, economic and technological factors have influenced the way firms, their resources and Operations are organised [7]. In this view, Sharing Economy is for sure a phenomenon that led many changes and whose success depends mostly on how demand and supply are matched and how underused resources are efficiently distributed in the marketplace. These tasks are up to operations management which manipulates operational levers to provide the required capacity and to influence the demand, dealing with its uncertainty and variability. Especially for peer- to-peer and on-demand service platforms, capacity is made up of heterogenous workers that typically can self-schedule their workday. As a consequence, the managerial challenge for operations management is to find the equilibrium that satisfies market demand and achieves a high degree of service level, scheduling and dislocating the resources to guarantee the service availability [74A]. The papers of the systematic literature review, coming from further Scopus articles and a collection of scientific readings about this subject named “Sharing Economy - Making supply meet demand”, have been classified into three pairwise comparison matrices obtained combining three axes: (1) types of two-sided markets meaning P2P, B2P, P2B and B2P, (2) operations pillars which indicate the type of operations the various studies refer to and (3) industries studied inside the reviews. The first matrix brought out that the most widespread industry, according to P2P and B2P platforms, was the transportation one. However, the related service typology distribution is different. Indeed, concerning the first one, the most common service is ride-hailing, while for B2P enterprises it is vehicle-sharing represented by a lot of firms of car-sharing and bike-sharing. The other high-standing industries were accommodation and food and good delivery/sharing. The second matrix highlighted which were the most important operations depending on the business model types. Indeed, each sharing platform model is characterised by distinctive features, deriving by the type of actors involved and the way they are mediated, which are reflected in the operations pillars discussed in the various papers. Considering P2P and B2P cases, capacity management, demand management and pricing were themes discussed with approximately the same frequency. The first two stem from the fact that SES platforms typically have to deal with the matching problem, while pricing reflects the most effective tools through which it is possible to influence demand level,

11 EXECUTIVE SUMMARY thus maximising profit. In addition, papers on P2P platforms focused on quality and queue management. Indeed, gathering feedbacks through reputation systems and keeping track of agents’ behaviours is the way through which they can guarantee safety to customers and create a form of loyalty around the platform; whereas, the queuing theory is well-suited to represent the dynamics between customers and service-providers, by interpreting the latter as servers waiting to process the former. Moving towards the B2P model, a distinctive feature with respect to the previous one is the presence of inventory and employees that the firm can directly influence. As a consequence, asset management represented one of the main topics of B2P platforms together with resource planning & control and innovation management. This matrix allowed also to discover the lack of studies regarding lean management, justified later by the companies interviewed who stated their poor adoption of this production model. The last pairwise comparison considered industries and operations pillars. Concerning transport, the matrix confirmed that the operations pillars pertinent to this sector are capacity, demand and asset management along with pricing system, quality and queue management. The last part of the systematic literature review concerned the discussion of the papers studied, grouped by the operational pillar they referred to.

The Research Goals

Operations management is a crucial function for organisations to deliver a valuable service to clients. This aspect gains particularly importance in SES platforms, since Sharing Economy is purely hinged on customer experience rather than on products offer. However, the literature review pointed out that the structure of the operations managed by SES enterprises had not yet been studied. Hence, the first objectives of this research are: • to analyse how operations are organised and how their structure changes according to the sharing business model adopted by the platform; • to investigate operations using the monitored KPIs as reading key. In addition, the cost functions linked to the operations have been designed to analyse also their impact in monetary terms, highlighting the differences between P2P and B2P platforms. The second part of the analysis focuses on lean management, due to its widespread diffusion in Western enterprises in the recent years and its ability to reduce operational costs. Thus, the further goal pursued is to analyse which lean management tools can be applied to each activity with the detected adaptions, being the lean management typically used in the production system and not in the service one.

12 EXECUTIVE SUMMARY

During the course of this Master thesis, COVID-19 has spread in Italy as well as throughout the world, forcing companies to be comply with the anti-contagion policies issued by governments and to survive to the unusual market changes at the same time. Therefore, after studying the impact of the coronavirus pandemic on multiple industries, two additional objectives have been established: • to identify which additional operations were managed during the lockdown phase; • to define which new ones were established during the recovery phase together with the design of the related cost functions.

Considering that operations change according to the industry where the enterprise competes, the work is restricted to the transport sector, being the most representative of the Sharing Economy, leaving the analysis of other industries for future studies.

The Research Methodology

The research path followed consists of several sequential phases, the first of which was the analysis of the literature. At a later stage, the objectives of the work described above were defined and the most suitable methodologies for collecting relevant information on the areas under analysis were set. The data was collected using interviews organised with Sharing Economy firms in order to obtain information on which make assessments and identify the most interesting business cases. Since the Sharing Economy is widespread all over the world, the interviews were organised with companies from many countries. After collecting the information of interest: • the KPIs were analysed to verify if there was relation between the sharing model adopted by the platforms and the performances monitored by its operations; • the operations structure was analysed together with the construction of the cost functions; • lean management cases were taken as a starting point to suggest the implementation of other tools; • COVID-19 impact was detected in order to investigate firms’ behaviour during the lockdown and the recovery phase. The research was carried out in order to analyse firstly the two business models separately and then compare them performing a cross-case study. Considering B2P organisations, it is important to underline that only the free-floating service was analysed in order to be consistent with the interviews.

13 EXECUTIVE SUMMARY

The Results Achieved

In this study, the facets of the Sharing Economy were addressed with particular attention to the transport sector. According to ten case studies, two clusters were investigated: P2P ride-haling and B2P free-floating vehicle-sharing. Hence, through an accurate analysis of the KPIs, as reading key, the practical and economic interpretation of the operations has been obtained. Their examination revealed that there are two relevant aspects for B2P platforms mainly related to the physical and economic sides of fleet management. The first one includes (1) the service coverage and availability, meaning the monitoring of activities that guarantee the optimal fuel level and mechanical conditions for vehicles, and (2) the best possible disposition of means in town in order to reduce their idle time and increase the usage. The second aspect, instead, is linked to the costs incurred by managing these operations. As concern P2P case, the service availability is translated into the optimal matching. Indeed, one of the key aspects arising from its KPIs browsing is trip and match timeliness. Moreover, unlike B2P scenario, these platforms have to link strangers, hence reputational indicators are crucial in order to enable mutual trust. The analysis of B2P indicators confirmed the importance of the three support activities for the service availability, meaning rebalancing, refuelling and maintenance, which were studied in practical terms together with the convenience in adopting certain business practices, depending on the vehicle offered, 2-wheeled or 4-wheeled. In addition, also minor operations have been examined, like recovering, supervision and cleaning, in order to fully describe the operational scenario that B2P companies have to manage. Moving to the P2P case, the analysis of the KPIs was consistent with operations purely concerning the management of riders and variable drivers. Therefore, they mainly imply carmen recruitment, training and behaviour management, fleet inspection and demand-capacity balance, through operational levers. Furthermore, since the company is a platform, the collection of data and their processing was supposed as a crucial aspect to provide the perfect matching. Finally, thanks to the interviews, it was possible to understand how operations are organised according to the level of business growth, which increases together with the firm size and business volume. The whole operations analysis was conducted in parallel with an assessment of the incurred cost functions, thus giving a more engineering and quantitative footprint. This allowed to understand that the two business models have to manage the trade-off between variability and costs. Indeed, although P2P has to deal with a variable capacity, it has lower expenditures to sustain for IT management and investment. This also allows to have fewer sunk costs, unlike the B2P case.

14 EXECUTIVE SUMMARY

The second research question derives from the gap arisen in the systematic literature review, where among all the papers read, lean philosophy was applied only to a single case. This work demonstrated how this productive management can be shaped on the sharing mobility industry, especially on manual B2P tasks to which many techniques have been applied. For instance, for maintenance warehouse new layouts and traversal policies have been shaped, together with the application of SMED (Single-Minute digit Exchange of Die), OTED (One-Touch Exchange of Die), 5-whys and poka-yoke principles. Whereas, takt time has been re-interpreted for refuelling and rebalancing optimisation. For the last one, SMED has been adapted too. P2P organisations, on the other hand, can adopt small improvements tools to reduce the root causes generating failures during the service delivery, through FMEA (Failure Mode and Effect Analysis) definition; moreover, only after developing a good data collection and automatic analysis apparatus, it is possible to establish a new procedure to define optimal trips allocation and try to nullify waste arising during the trip. The contribution of this second section may inspire Sharing Economy platforms to introduce lean practices. Finally, due to the unusual period in which the development of this work took place, it was in its interest to include operational COVID-19 implications into the analysis. The research was performed in order to deeply analyse the companies’ operational response to the challenges of two distinct periods: during the lockdown and during the recovery phase. Considering the first one, both P2P and B2P companies have undertaken similar strategies to keep their business alive, which needed only few support operations to be managed, while the others were temporarily dismissed. Only P2P enterprises had to handle an additional activity related to the PPE distribution, in accordance with the policies issued by the government. This operation was extended to B2P organisations during the recovery phase, together with others necessary to provide the service in the safest possible way, with the aim of restoring customer trust in the shared mobility services. All the additional activities have been studied making explicit the related cost functions in order to analyse also their economic dimension. Aware of the consequences generated by this unusual pandemic, firms can learn how to gain flexibility to respond effectively to large exogenous market changes and prepare a plan for future events that may involve a second sudden drop in demand, avoiding bankruptcy.

15 EXECUTIVE SUMMARY

The Conclusions

The analyses carried out enabled to respond to the objectives that were identified in the early stages of the work. It was possible to identify the structure of the operations, understanding the impact of vehicle typology on their management and on the business practice adopted. Moreover, comparing B2P and P2P cases pointed out the most salient differences among them. The analysis of the KPIs highlighted the most important indicators reflecting the critical factors to manage operationally and those impacting on customer experience and service perception on which firms must perform better than competitors. The poor adoption of lean management by organisations enable to exploit many tools that can help them to reduce waste and costs. Considering maintenance performed in-house, it turned out to be a real small project in order to design the factory’s structure to be in compliance with the lean philosophy. Finally, the last objective was to assess the operational impact of COVID-19. Dividing the analysis into the two time periods characterising the pandemic, it was possible to understand the related operational and monetary impacts, following strategic decisions. Furthermore, it enabled to assess the issues companies had to face due to clients’ fear and how operations have helped overcome them.

16 INTRODUCTION

1. INTRODUCTION

For more than a decade, consumers’ habits have been experiencing a route change. Four main megatrends are affecting social norms, implying market transformation. According to a 2015-PWC research [1], users have always been inclined to access-driven economic activities which are facilitated by the recent huge diffusion of digital platforms and devices. Moreover, global resource scarcity, combined with personal objects idleness, prompted people to take interest in a more efficient sources usage and to opt for environmental-friendly consumption choices, too. It has been estimated, for example, that around 740 million cars over the 1 billion on world’s roads are used by only one person, or that the average household has 3,000 dollars asset value mainly idle. In this scenario, the opposite side is represented by the 43% of US consumers who agree that owning today is a burden and 57% who think that access is the new ownership [2]. Indeed, customers are more oriented to buy performances rather than products, [3] which relieves them from the efforts needed to purchase and maintain their goods. Furthermore, thanks to globalisation and urbanisation, the market offer is wider and easily available online but, at the same time, a community-based solution unlocks the possibility for occasional products rental by the neighbourhood. Combining these market changes together, the business response is represented by the development of the Sharing Economy, declined in various ways and applied to different economic and social sectors, where Sharing Economy Services (SES) enable mutual exchanges of idle products among peers through digital platforms. People are increasing their degree of personal interaction where they prefer, to a faceless company, an individual delivering products or services, moving from a transaction-based service to an experience-driven one [1]. SES have been developing since early 2010, when the five main sharing-sectors (crowdfunding, online work, media streaming, room- and car-sharing) counted for 15$ billion, against 240$ billion of the corresponding traditional operating models in 2013. Their expansion is increasing further, since it is estimated that by 2025 the balance will be equal, with 335$ billion up to both sides [1], and even more businesses will be covered by this phenomenon. Among the leading sectors, transportation industry is one of the most likely to further succeed and spread. Indeed, following the “access-ship” trend, the young generation prefers to find online solutions rather than to own something. Its potential is revealed by the possibility to apply sharing methods to the most disparate vehicles, adopting the proper platform models. In particular, it is possible to distinguish between the peer-to-peer model (P2P) and the business-to-peer one (B2P). The former provided the first version of sharing mobility, established by individuals who offered taxi- rides through their idle cars, using an intermediated platform to connect with the customers. Uber,

17 INTRODUCTION

BlaBlaCar, Didi Chuxing are well-known examples of this business. However, facing this rapid disruption, existing cars manufacturers needed to re-think their business and reacted by adding the sharing service to their purchase solution, adopting the B2P model. Indeed, according to it, vehicles dispatched in town are available to be rented by unlocking them through the app. In this way, alongside the P2P organisations, new solutions started to come up like Share Now, the joint venture of Daimler AG and BMW forming one of the largest car-sharing companies in the world, Enjoy, the ENI service in collaboration with Fiat, OFO, a Chinese platform offering bike-sharing service, Jump, proving electric bicycles etc. Sharing Economy firms gave birth to a phenomenon called sharing mobility which is more and more widespread in recent times. It gives the possibility of moving from one place to another through shared vehicles provided using services such as car-, scooter- and bike-sharing services, car-pooling etc. Furthermore, it contributes to the smart mobility, a tool for achieving sustainable development of cities. In addition, by extending the transportation industry, also local communalities opted for this solution. For instance, the ATM company in Milan offers a station-based bike-sharing service, where citizens can pick-up from the related stations the means and leave it at another dedicated point. Therefore, considering the wide sharing mobility service range, it turns interesting to evaluate its structure. Indeed, it is still unclear how the operational side of firms is organised, and which functions are needed in order to successfully run this kind of business. Moreover, the majority of these enterprises, like the giants Uber and Didi, are “simple” intermediators between demand and offer without providing any tangible output, thus it is of interest to understand their business model and further detail the operations which empower service delivery.

Hence, the paper starts addressing the Sharing Economy phenomenon by examining its features and the characteristics of the industries where it is expanding; consequently, the related four platform models are described and classified. Once the context has been set, a systematic literature review is conducted to understand the operational state of the art, strictly focusing on operations studies. Secondly, through managers’ interviews, data about the main sharing-models, B2P and P2P, are collected and analysed into multiple-case studies. Starting from key performance indicators browsing, the research is extended to structure the main operations managed, together with the definition of their main characteristics. Hence, according to the two sharing models, the within- and the cross-case analyses are carried out. Furthermore, since these platforms do not deal with any productive process, it is compelling to verify if lean practices, generally applied to the manufacturing plants, have been already implemented; otherwise, the benefits

18 INTRODUCTION they can bring and to what extent will be studied. Thus, in chapter 4, lean management concepts will be used to classify B2P and P2P operations and, sequentially, some tools will be introduced in the attempt to reduce waste and costs sustained by Sharing Economy companies. Finally, considering the unusual scenario that the whole population is facing due to the global pandemic, the study investigates the operational and economic implications of the strategies implemented by the various firms in order to survive in the short-medium term. At the end, the main results and the novelty of the thesis work are presented in the final chapter, along with limits and further future researches.

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2. LITERATURE REVIEW

2.1. Introduction on Sharing Economy

The Sharing Economy is a new economic system born after the economic crisis of 2008. The general idea behind this phenomenon is the transformation of unused or underused resources owned by the individual in productive assets. For instance, cars and houses represent significant investment, but are little exploited compared to their potential. There are multiple causes behind this phenomenon in addition to the economic situation, such as the increasingly importance of environmental, social and technological factors and the emergence of Open Innovation. Due to these aspects, different businesses were created that amplified the possibility of sharing and exchanging skills and resources; these services eliminate the intermediation of commercial, financial and institutional structures, thus proposing new commercial consumption models and an innovative way of managing people’s time and work. Although many articles trace the birth of the Sharing Economy to the 2008 economic crisis, one of the first enablers was eBay, founded in 1995. This marketplace provided a global online channel where anyone could purchase or sell just any kind of item. Actually, the first appearance of Sharing Economy term was in 1978, introduced by Marcus Felson and Joel Spaeth [1C].

2.1.1. Definitions The activities and organisations that are commonly referred to as “Sharing Economy” have also been labelled as “Collaborative Consumption”, “Access-based Consumption”, “Circular Economy” and “Collaborative Economy”. In this section various definitions are analysed in order to clarify the meaning of these terminologies and to understand the differences and similarities among them. Starting from the denotation provided by the Oxford Dictionary (2015) about Sharing Economy is defined as “an economic system in which people can share possessions, services etc., usually by means of the internet”. Meelen & Frenken1 (2015) defined it as “consumers (or firms) granting each other temporary access to their under-utilized physical assets (idle capacity), possibly for money”. Finally, according to PriceWaterhouseCoopers1 (2014) the “Sharing Economy uses digital platforms to allow customers to have access to, rather than ownership of, tangible and intangible assets”.

1 Cristiano Codagnone and Bertin Martens, 2016, “Scoping the Sharing Economy: Origins, Definitions, Impact and Regulatory Issues”, Institute for Prospective Technological Studies Digital Economy Working Paper 2016/01, J2C100369

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All these definitions, like others provided by disparate authors (Appendix A), have in common the temporary access, rather than ownership, to underused resources. This pattern characterises also Collaborative Consumption. Botsman and Rogers 2 (2010) interpreted Collaborative Consumption as the phenomenon of “consumers accessing underutilized resources (goods, services, information, skills, time and money) in creative and innovative ways, reinventing traditional market behaviors (renting, lending, swapping, sharing, bartering and gifting) and enabling access over ownership”. Moreover, Collaborative Consumption is typically associated to commercial transactions; for example, Belk1 (2014) defined it as “people coordinating the acquisition and distribution of a resource for a fee or other compensation”. The third concept based on accessibility is Access-based Consumption, as the name itself suggests. Bardhu & Eckhardt1 (2012) gave the following definition: “transactions that can be market mediated but where no transfer of ownership takes place and differ from both ownership and sharing”. The last two terms, Circular Economy and Collaborative Economy, have different meanings with respect to the previous ones. The European Union defines the Circular Economy as “an economy where the value of products, materials and resources is maintained in the economy for as long as possible, and the generation of waste minimised” (Eurostat, 2019). So, it refers to an environmentally sustainable economic system which typically adopts the 3-Rs: reuse, recycle and remanufacturing [22A]. In order to pursue circularity, the Sharing Economy is one of the most important business models which developed from the Collaborative Economy concept [72A] and allows to share resources and channels. The term Collaborative Economy is more difficult to define. According to C. Codagnone and B. Martens1, Sharing Economy and Collaborative Economy have been used in an interchangeable way. However, in some papers, it refers to platforms for co-housing community and co-owned products [5A], whereas in other ones this term is distinguished from Sharing Economy since the former mediates commercial transactions where a company is involved, while the latter refers to private, and often non-commercial, transactions [18A]. In conclusion, there are different concepts related to this phenomenon; some of them, like Collaborative Consumption and Access-based Consumption, overlap with the general definition of Sharing Economy, while others, such as Circular Economy and Collaborative Economy, have a peculiar meaning and refer to particular cases.

2 Rafael Laurenti, Jagdeep Singh, Joao Miguel Cotrim, Martina Toni and Rajib Sinha, 2019, “Characterizing the Sharing Economy State of the Research: A Systematic Map”

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2.1.2. Sharing Economy Industries From its inception to today, this new economic model has spread to many business sectors. Referring to L.S. Revenko and N.S. Revenkob, 2019 [4A] six main areas can be distinguished. The first one, and the most well-known, is transportation which includes ride-hailing, ride-sharing, and car-sharing platforms. By expanding this last category to vehicle-sharing, also bike- and boat- sharing can be considered as part of this area. Moreover, the authors consider goods delivery as belonging to this sector. The second area is real estate, where accommodation sharing, and leasing of office spaces are included. Household goods, furniture and equipment is the third sector. Typically, in this category there are platforms which aim at linking people who can share and rent products from their neighbours. The first three sectors provided services using idle tangible resources, while, moving towards remaining areas, they are related to pure services. Indeed, the fourth one is entertainment and communications, including sharing of photos, videos, software, and other digital contents. Investment is the fifth category which relates to financial resources gathered though crowdfunding to support the implementation of someone else’s expensive projects. The last one is services which includes job- related apps, that is people providing their skills and job capabilities. However, in accordance with the authors, this list is not exhaustive as it does not cover all the industries where sharing platforms are arising. To this concern, R. Laurenti, J. Singh, J.M. Cotrim, M. Toni and R. Sinha [4] integrate the missing business sectors. Indeed, the authors consider other intangible assets which for example includes crowd-work (i.e. Job Wizard) and energy sharing (i.e. Prosume), other sectors, among which entails digital manufacturing and healthcare (i.e. Cohealo), finance with crowdfunding platforms (i.e. GoFundMe) and food for meal-sharing (i.e. Food For Free), where clothes-sharing (i.e. Bag Borrow Or Steal) is included, too.

2.1.3. Sharing Platform Models The Sharing Economy is strongly linked to the two-sided market concept. Two-sided or multi-sided markets are characterised by the presence of two or more distinct groups of users, which influence the results of each other both with their participation in the network and their decision, typically through externalities3. In order to enable the interactions between the users that transact or interact, one or more platforms are built. Thus, the Sharing Economy can be seen as a marketplace, namely a platform, that uses technology in order to combine transactions between independent suppliers and customers, as shown in Figure 1.

3 “Network effects which directly or indirectly increase based on the number of users involved”

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Figure 1 - Sharing Platform Model

There are four noteworthy modes of sharing solutions based on two-sided market platforms which are different due to the diverse typologies of users that interact and from which they are named. The most common one is the peer-to-peer (P2P) platform, which is a network that poses at the sides of the platform the peers, which are equally privileged, equipotent participants. In a peer-to-peer network, the nodes share resources among each other without the use of a centralised administrative system. The label P2P is preferred to C2C because the two sides are not always only customers but customers and providers at the same time. The second most widespread typology is the business-to-customer (B2C) which represents the relationship between a business enterprise and its customers for sales and/or assistance activities. In this case, the two dictions business-to-customer (B2C) and business-to-peer (B2P) are overlapped since the peer can be considered only as a customer of the company. The third type of two-sided market is the business-to-business (B2B) which describes the commercial transaction between organisations. The relationship can be of two types: between a firm and its suppliers for procurement activities or between an enterprise and its clients which are both at business level. The last one named peer-to-business (P2B) is a platform that links to organisations, peers which put themselves at their service, levering on their capabilities and their time. To better clarify this unknown type of sharing platform, two examples are provided. The first one is Samsung which organises competitions aimed at collecting projects from users in order to innovate their products on the market in a more effective and economic way. The other one is Amazon Flex, a service where each individual can become a courier of the company by self-managing.

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2.1.4. Sharing Economy Classification According to the article “Platforms in the peer-to-peer Sharing Economy”4, there are three main differences taking as reference the P2P Sharing Economy platform business models: • Capacity-constrained vs unconstrained assets → the unconstrained resources can be consumed simultaneously by many people without implications in capacity management. On the contrary, sharing platform of capacity-constrained assets needs to be efficient in matching capacity and demand. • Access provision vs transfer of asset ownership → according to this distinction, traditional e- retailing are generally not considered Sharing Economy platforms since goods are sold instead of shared. • Peer-to-peer vs platform-provided assets → marketer-provided sharing platforms have their own inventory of assets while peer-to-peer ones are based on resources made available by the same peers. Based on these distinctions, the first classification is graphically presented in Figure 2:

Figure 2 – First Classification Matrix

The main axes on which different platforms can be classified are two: the first is inherent in the ownership or non-ownership of the resources used to provide the service, while the second refers to the type of transaction that takes place. Therefore, if the asset is purchased and the property passes from the seller to the buyer, or if the customer can only have access to the property, leaving the ownership in the hands of the seller, paying just for the service.

4 Jochen Wirtz, Kevin Kam Fung So, Makarand Amrish Mody, Stephanie Q. Liu and HaeEun Helen Chun (2019).

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A second classification can be done between P2P vs B2C platform, the most common two-sided market typologies, and not-for-profit (NFP) vs for-profit (FP), according to the final goal of the intermediary.

Figure 3 - Second Classification Matrix

According to C. Codagnone and B. Martens, 2016 [5], the true sharing, that was advocated at the beginning of the sharing movement, was characterised only by the desire to exchange services or objects without the purpose of making profit. Indeed, the P2P platforms originated from not-for- profit initiatives as Wikipedia, Couchsurfing and Freecycle. Today this group is far smaller in terms of number of users. From Figure 3, other three groups can be identified: • Commercial P2P→ it represents the bulk of the Sharing Economy since today a lot of companies are born with the aim to make profit by selling a service that is provided by users who make their means available. • Commercial B2C → the Sharing Economy is linked to the normal transactions of B2C channel through the use of Internet. • Empty set → it is an empty set because businesses are, by definition, for-profit.

2.1.5. Sharing Economy Characteristics So far, the main market models and their classifications have been discussed but, although they represent the different ways in which distinct actors interact, there are some common traits characterising them [6].

First of all, sharing companies are ICT-enabled; technology and web-based communications are the basis for platform transactions. Consequently, this facilitates mediation between unknown actors in an efficient way by providing much information which, in return, helps reducing transaction costs

25 LITERATURE REVIEW associated with sharing among strangers. Indeed, payment mode, reputation system through feedbacks and information about the owner and/or the renter are part of the data available on platforms and this, along with the easiness of interaction enabled by technology, makes the exchange and the matching of supply and demand more efficient.

The second common feature is the usage of idling capacity. This is crucial since it is one of the factors that made Sharing Economy spread so rapidly. On this topic, it has been estimated, for instance, that a car is parked 95% of its time (Barter, 2013; Morris, 2016)[4B] and, according to National Association of Home Builders (NAHB), there are around 7.5 million second homes in United States which result idle most of the time (Zhao, 2016)[4B], in addition to the hundreds empty rooms in primary homes. So, an empty seat in a car or an unlived room becomes an opportunity for profit.

The third characteristic stems from the previous ones and is the access to goods and services. This is another innovative element typical of Sharing Economy and it gives the possibility to choose among a greater number of goods and services and to own, for a while, things that people otherwise could not afford. For example, in an interview granted to the Italian newspaper “La Stampa”, Arun Sundarajan, professor at New York University, explained how also luxury cars, like Tesla, are shared.

However, by considering the classification of the two-sided market models introduced in paragraph 2.1.3, they are not all based on access provision, but also on transfer of ownership. These other platforms, which still rely on idle resources, facilitate the redistribution of goods, both by selling or giving them for free.

Finally, last factor characterising the Sharing Economy model is that sharing platforms do not simply enable the access to tangible and physical resources, but they are related also to intangible and human assets, like skills, talents or human time.

2.2. Systematic Literature Review

2.2.1. Operations in Sharing Economy Services Operations can be defined as the company function that is responsible for producing and delivering goods to customers. The operational process transforms inputs, which consider both the resources that will be transformed, such as materials, information and customers, and the people and the facilities that actually perform the activities, into outputs, which coincide with products or services delivered to clients. Indeed, the customer is the final objective of Operations and, for this reason, the Operations strategy has to adopt a double perspective, as shown in Figure 4: by assuming the

26 LITERATURE REVIEW

“customer’s lens”, it has to identify their needs and deliver distinctive brand value, providing value- adding activities, while, at the same time, it must be in line with the overall corporate strategy at a cost that makes the product or service price commercially viable [3]. To this purpose, production, product design, manufacturing, distribution, planning, quality are part of the Operations that devise a single coherent value proposition for customers in a profitable and sustainable way for the company.

Customer lens: the brand proposition

Business Operations Models Commercial Operations viability management

Figure 4 - Business Operations Models Concept

Source – A. Braithwaite & M. Christopher, 2015

In order to pursue all the operational goals, the Operations strategy matches business objectives with employees and managers’ experience. The former is the top-down approach that translates corporate objectives set by the Board of Directors into business objectives that, in turn, influence Operations strategy; the latter, is the operational experience deriving from daily activities that through a bottom- up flow helps spotting problems and identifying potential solutions. This twofold flow could create a long-lasting virtuous cycle between Business and Operating model that allows the enterprise to be flexible and react fast to market changes (Figure 5).

Figure 5 - Twofold Strategic Flow

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Indeed, nowadays, new factors are arising and influencing firms, their resources and the way Operations are organised; globalisation, customisation, speed of technological development and other cultural, economic and technological factors are among them. Sharing economy is for sure a phenomenon that brings many changes in this view. The trends that entail include the diversification of resources and property assets shared by actors, the growth of marketplace in volume, the acceleration and simplification of the transaction process, and the optimisation of the income and expenditure of end users [7]. However, all the new opportunities to create value, that these trends unlock, depend on how they are managed by the company and its Operations. Without loss of generality, the success of Sharing Economy Services depends mostly on how demand and supply are matched and how underused resources are efficiently distributed in the marketplace. These tasks are up to operations management which manipulates operational levers to provide the required capacity and to influence the demand, dealing with its uncertainty and variability. Despite, the dynamics of suppliers and those of renters in Sharing Economy system differ from traditional businesses and reveal many managerial challenges.

First of all, there are many small suppliers and service providers to be mediated by the platform to many small buyers and borrowers. Moreover, supply and demand sides are no longer separated because renters may decide to become owners and the other way around so that supply and demand influence each other; indeed, having more owners increases the service availability for renters and vice versa. Furthermore, one single service provider or one single resource unit can satisfy the consumption need of more customers, thanks to the exploitation of its idle capacity. The managerial challenges arising from these factors mainly imply lowering market frictions, transaction costs and moral hazard. Then, there are features on the capacity side that distinguish the sharing business model from the traditional one; the same is for the demand side. Especially for peer-to-peer and on-demand service platforms, capacity is made up of heterogenous workers that typically can self-schedule their workday. They have decisional power on where, when and how to work. As a consequence, the managerial challenges for operations management is to find the equilibrium that satisfies market demand and achieves a high degree of service level. Indeed, capacity affects demand and vice versa so that, for example, more available drivers means lower delays. The managerial instruments that mediators use are pricing and wages variation. Finally, also customers, like servers, are spatially and temporally distributed, without any restriction of request time for the service or even advanced notice of where and when they plan to be served. Again, operations management is responsible for scheduling and dislocating the resources to guarantee the service availability [74A].

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Dealing with all the managerial challenges presented above, operations management can generate operational efficiencies that are peculiar of Sharing Economy platforms; in particular, T. I. Tunca [4B], identified five related factors. As already mentioned, one of the core characteristics of Sharing Economy is the utilisation of idle resources; this is the main source on which Operations can leverage to create efficiency. Indeed, platforms that adopt the sharing business model can cover sunk and fixed costs, integrating them with the revenues deriving from their service. Whereas, platforms can gain a competitive cost advantage with respect to traditional commercial providers which pay taxes. This last advantage, that nowadays is a central debate of Sharing Economy, together with the support of global Internet infrastructure, enables to lower barriers to entry into markets. Then, sharing platforms utilize bit sized resources; in this way, many micro-loans charged onto a peer-to-peer lending app can be grouped to support a borrower without the need to go through banks, financing costs and overhead. Finally, Sharing Economy creates work possibilities to employ human idle time, and assigns new roles, hence those who could be interested in providing rides for fares can easily become drivers.

So far, an overview on operations management, its distinctive factors and the consequent managerial implications have been introduced. In order to understand in detail their influence on Sharing Economy Services, a systematic literature review has been structured following the methodology explained in the next section.

2.2.2. Literature Review Methodology After defining the main goal of the literature review, in order to study the operations in the Sharing Economy sectors and how they are managed, the current articles, including substantive findings such as theoretical and methodological contributions to particular topics, were searched using as reference database Scopus. To perform the analysis two classes of key words were defined: the first one formed by those terms that can be used in order to define the Sharing Economy, partly already discussed at the beginning of this academic research as synonymous of “Sharing Economy” and partly found through Scopus articles. Indeed, in the article written by R. Laurenti, J. Singh, J.M. Cotrim, M. Toni and R. Sinha [4], seven additional terms have been listed by Botsman and Rogers to define this phenomenon: “Collaborative Economy”, “Collaborative Consumption”, “Sharing Economy”, “Collaborative Production”, “Peer Economy”, “Collaborative Finance” and “Collaborative Education”. The last two terms were not used in our online research since the former leads only to 0.062% of the articles on Scopus related to Sharing Economy, while the latter refers to papers published between 2005 and 2008, therefore before the birth of this new economy. Moreover, in the C. Codagnone and M. Bertin’s paper [5], two further synonymous have been found: “Circular

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Economy” and “Access-based Consumption”. Lastly, “Platform Economy” was chosen among the key words listed by A. Braverman, J.G. Dai, X. Liu and L. Ying [8]. For what concern the class regarding the pillars of the operations management, the reference text was the methodological investigation entitled “Research Methodologies in Supply Chain Management” [9] supported by EurOMA (European Operations Management Association) in which the common topics studied in the operations managements field are presented regarding both service and production system. In Table 1, the key words used are summarised.

Table 1 - Key Words Used

Through the combinations of these key words, 118 articles & reviews and 24 conferences were found of which respectively 87 and 11 were downloadable, furthermore since the beginning of the Sharing Economy is set for 2008, all those articles previously published have consistently been discarded.

In order to complete the analysis and considering that 26.27% of the articles and 54.17% of the conferences have not been read, the book “Sharing Economy – Making Supply Meet Demand” [10] was added, which examines the challenges and opportunities arising from Sharing Economy adopted an operations management perspective. Inside the book there are 21 chapters that can be considered as individual articles.

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2.2.3. Comparison Matrices 2.2.3.1. Axes for Classification To have a visual representation of how papers are distributed among the various categories of operations, a matrix was created using the two macro-categories of key words as axes as shown in Table 2.

Table 2 - Articles & Conferences Distribution

Since a lot of articles fell inside the broad box “operations”, the distribution was unclear for the purpose of this work. Furthermore, adopting this method, it was not possible to classify also the chapters of the book since they were not found on Scopus and no keywords appear in their abstracts. Consequently, to represent the distribution of the various papers more thoroughly, three new axes have been defined to reclassify them based on their content: • type of two-sided market classifies articles according to the type of business model described that can be P2P, B2P, B2B, P2B which we already described in chapter 2.1.3. The line “unclassified” was created for those articles that do not refer to any kind of platform; • operations pillars indicate the type of operations to which the various papers refer to. The categories, added to those already used for the articles research, try to divide in a more detailed way the papers found and have been defined on the basis of the topics covered in the various reports. The additional columns are the following: ▪ asset management → it examines how the assets, owned by the company, are managed in order to make profit. For example, how batteries for electric vehicles are administrated; ▪ game theory → it studies the interactions between the players of the platform and wants to model them using the evolution game theory; ▪ innovation management → it explains how innovation is managed in the Sharing Economy and how it is possible to innovate existing sectors; ▪ logistics / distribution channel → it classifies the papers concerning the distribution of goods; ▪ pricing system → it studies price setting and tries to model pricing decisions;

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▪ regulation system → it embeds how Sharing Economy should be regulated due to its novelty; ▪ sustainability → it discusses the effects of Sharing Economy on sustainability and how it can still improve them; ▪ operations management → it includes all the articles that analyse platforms operations without entering deeply in any topics presented above; ▪ general study → it contains all studies about the Sharing Economy phenomenon in general such as classifications and definitions;

• industry regards the sector studied inside the reviews. The presented list takes into account the business sectors discussed in section 2.1.2. However, by going through the papers and in order to better define the areas of interest, some adjustments have been made and explained in the list below: ▪ accommodation → platforms providing empty rooms and houses are part of this sector; the most representative one is Airbnb. However, also not-for-profit communities belong to this industry, such as Stay4free; ▪ financing → this category entails both crowdfunding (i.e. GoFundMe) and for-profit platforms providing micro-loans (i.e. LendingClub); ▪ food and goods delivery → the transportation group can be divided into smaller ones, including food and good delivery, and from now on, all the sub-groups will be considered separately. Thus, in this one all the platforms for meals and products delivery, such as Just Eat or Amazon Flex, are included; ▪ food and goods sharing → there are apps which aim at furnishing meals for charity or extra-food that would be wasted and platforms which aim at exchanging tools or dresses for free or for a fee that belong to this category. For example, LeftoverSwap allows users to share unwanted food through the app while Peerby to rent what needed that is available in the neighbourhood; ▪ manufacturing → thanks to Internet of Things and the application of new information technologies, companies can share their resources inside and outside the facility; ▪ network crowdsourcing → it is a sourcing model in which individuals and organisations obtain goods, services, ideas, information, finances, opinions, micro-tasks, etc. from a large, relatively open, group of participants by means of the internet which is used in order to attract new users. Considering this, online advertising can be considered a type of network crowdsourcing. Another subcategory listed here is named crowd-mapping, which

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aggregates different inputs captured from communications and social media feeds and combines them with geographic data in order to create digital map. Examples of this are OpenStreetCam and Mapillary; ▪ online buying/renting → this category is formed by those online platforms that enable individuals, as well as companies, to sell or rent their goods and services. The difference between this online business and the ecommerce one is that the platform is just a virtual place in which to post goods. The ownership of the asset passes from the supplier to the customer without going through the intermediator. A known example is Groupon; ▪ ride-sharing → it belongs to the “transportation” group. Indeed, as previously explained, its three sub-categories are separated since they represent the majority of the considered papers. Consequently, for a most effective classification, four groups have been identified, according to the way the service is carried out. In ride-sharing platforms, the driver has already a destination and decides to share the ride with other passengers that need to go in the same direction. This can be done by simply splitting the costs or by asking a fee. BlaBlaCar is part of the ride-sharing group; ▪ ride-hailing → in the second sub-category, the agent acts as a “taxi-driver”. Indeed, he just brings passengers to the destinations they need. A typical example, symbol of Sharing Economy, is Uber; ▪ vehicle-sharing → the “vehicles” gathered in this last sub-group are cars, bikes, electric- vehicles (i.e. WEshare) and boats (i.e. GetMyBoat). These platforms can be station-based, like BikeMi where bicycles must be picked up and returned to company stations, or free- floating, like Ofo or Enjoy where vehicles are dislocated in town; ▪ skills → services and resources shared by individuals and organisations are part of this sector, such as TaskRabbit, which is a platform for freelancers, and 3D printing service provided by 3D-hub; ▪ spaces → in real estate industry, along with accommodation that has been already discussed, also co-working areas (i.e. LiquidSpace), gardens (i.e. Landshare) and parking- areas (ParkNow) are shared and belong to this cluster; ▪ utilities → in this category are grouped those articles that refer to the set of services provided by private organisations and consumed by the public as coal, electricity, natural gas, water, etc. In particular, the Sharing Economy can allow the exchange of these goods directly among individuals, based on the concept of unused capacity; ▪ unclassified → this line has been added where it is not possible to recognize any classification, as for the type of two-sided market.

33 LITERATURE REVIEW

Although some examples have been already provided, to clarify the disparate platforms competing in the industries presented, a more detailed matrix (Table 3) is reported with the cases encountered in all the papers of systematic literature review. It is built combining two classifications discussed in section 2.1.4: the vertical axis refers to peer-to-peer vs business-to-peer platforms, while the horizontal one distinguishes between transfer of ownership and access provision. Moreover, in order to also embed the distinction for-profit vs not-for-profit, platforms providing service for free are followed by the NFP acronym.

PEER TO PEER (P2P) TRANSFER OF OWNERSHIP ACCESS PROVISION E-COMMERCE: Etsy, eBay, Goodsm, Latest Free Stuff, Darudar ACCOMMODATION: Airbnb, Roomorama, Couchsourfing (NFP), vrbo (ex FINANCING: Kickstarter, Indiegogo, Gofundme, Rockethub (part of HomeAway), Globalfreeloaders (NFP), BeWelcome (NFP), Hospitality Club (NFP), Crowdfunder), AngelList, Zopa, CrowdRise, Razoo, Miloserdie, Life WWOOF (NFP), WarmShower, Pasporta Servo (NFP), Xiaozhu, Rentm, Line HomeExchange, Roost, Hapimag, Stay4free (NFP), LoveHomeSwap, TrustRoots FOOD SHARING: Leftoverswap, Food For Free, Food For the Poor (NFP), HomeForExchange, 9flats FASHION-SHARING: Poshmark, Crown&Caliber, The RealReal, FINANCING: Gumtree (NFP), Freecycle (NFP), Craigslist, Kiva, LendingClub, thredUP, Trandsey, Kidizen, 99dresses (no more existing) Prosper ONLINE GROUP BUYING: Groupon, LivingSocial, Mercata (no FOOD AND GOODS DELIVERY: Amazon Flex (P2B), Deliveroo, Deliv, more existing), Mobshop (no more existing), Letsbuying (no more Instacart, JoyRun, Zipments, DoorDash, Postmates, Dada, Fretbay, Grubhub (ex existing) Eat24), Sidecar (no more existing), Lieferando VIDEO AND MEDIA: Shareman, MediaGet, eMule, eDonkey2000, GOODS SHARING: Peerby, Sharehood, Zilok, Fat Llama, Rentmania, Gnutella, Shareaza NeightborGoods, Machinery Link, FriendsWithThings, Karzoo, 1000tools (no more SERVICE: ANGI Homeservices Inc (union of HomeAdvisor and Angie's existing), Style Lend, BagBorrowOrSteal, MsPairs, YCloset, PeerbyGo, Sharewood list) RIDE-SHARING: BlaBlaCar, Bolt, Klaxit (ex IDVroom), Liftshare, Kapten, UTILITIES: Yeloha Zimride, Carpoolworld RIDE-SOURCING: Uber, Lyft, Didi Chuxing, Curb, Bilpoolen, Grab, GoJek, Kabbee, Gett (Ex GetTaxi), Careem, FreeNow (ex MyTaxi), ReachNow SKILLS AND SERVICES: TaskRabbit, Wonolo, AirTasker, Wag, Fiverr, BestDoctors, Heal, Zocdoc, Vetpronto, Upwork, Avvo, Yourmechanic, tutor.com, Wag, Patreon, MTurk (Amazon Mechanical Turk), freelancer.com (also P2B), skillshare, Handy, Vizeat, Eatwith SPACES (gardens): Shared Earth, Landshare SPACES (parking spaces): Citizen space, JustPark, ParkNow, ParkMobile, ParkLine, RingGo VEHICLE SHARING (bike-sharing): Spinlister VEHICLE-SHARING (boat-sharing): Boatsetter, GetMyBoat, Boatbound VEHICLE SHARING (car-sharing): Turo, Darenta, Carma, Flightcar, GetAround (ex Drivy) VIDEO AND MEDIA: Youtube, Wikipedia, Pledgemusic, Spotify, The Pirate Bay

BUSINESS TO PEER (B2P) TRANSFER OF OWNERSHIP ACCESS PROVISION E-COMMERCE: Amazon, Alibaba, Warby Parker, Bobonos, Tmall FASHION SHARING: Eleven James, Rocksbox, Switch, Le Tote, Rent the FOOD SHARING: Reduce Go Runway GOODS DELIVERY: Google Shopping (ex Google Express) FOOD DELIVERY: Sprig (no more existing), BlueApron SERVICE (cloud-base): Mindsphere platform, General Electric predix RIDE-SHARING: Ola platform, CASICloud platform, Quirky SERVICE: 3D hubs, Arise Virtual Solutions, LiveOps SPACES (co-working areas): WeWork, Loffice, DeskSurfing, OpenDesk, LiquidSpace VEHICLE SHARING (bike-sharing): GyorBike, MOL BuBi, Donkey Republic, BlinkeeCity, Bicing, Scott e-bicycle, Ofo, Mobike, Hellobike, Xiaoming Bicycle, Citi Bike, Divvy, Ford GoBike, Bycykel VEHICLE-SHARING (car-sharing): Zipcar, Hertz, Witkar, Procotip, Avalon Carsharing, BeeRides, GreenGo, ShareNow, MOL Limo, Emov, Europcar, Autolib' BlueIndy, Avis, TOGO, WEshare (Volkswagen's EVs), Maven (GM's EVs), Audi-on- demand (Audi's EVs) Table 3 - Matrix Cases

34 LITERATURE REVIEW

2.2.3.2. Matrices Discussion Using the axes presented in the previous section, it is possible to build three pair-wising matrices. The first one was based on the type of two-sided market and the industries to which the articles refer to (Table 6), the second one highlights the dispersion between types of two-sided market and operations pillars among which the papers were classified into more detail boxes (Table 5)and the third matrix has as axes the industries and the operations pillars (Table 4).

1. Sharing platform models vs industries Based on the first matrix, it is clear that the most used business models in the Sharing Economy are P2P and B2P. For this reason, some more explanatory pie charts have been created to analyse the macro differences between them. Starting from the P2P market (Graph 1), the industry that has proven to be the most widespread and studied is the transport one (42.42%), of which the three main forms of Sharing Economy are presented in Graph 2.

Graph 1 - P2P Industries Distribution Graph 2 - P2P Transport Services Distribution

From Graph 1, it can be noticed that the most common service is ride-hailing (76.19%) followed by ride- (19.05%) and vehicle-sharing (4.76%). In second place, along with accommodation (13.13%), there are also food and good sharing and delivery (14.14%). It is possible to see how widespread these three industries are, compared to the others, also in the matrix of cases that has been presented above. Industries like skills, network crowdsourcing and online buying/renting (respectively 8.08%, 8.08% and 5.05%) can be considered with a medium diffusion in the P2P business model. Indeed, nowadays they are developing more and more and are attracting the attention of academics as well. Lastly, all the other sectors presented in the matrix have been grouped under the voice others (9.09%) because they cannot be considered representative of the P2P market since they have been poorly studied and are related to market niches, for which few platforms can be found.

35 LITERATURE REVIEW

For what concerns B2P firms (Graph 3), transport sector still remains the most spread one (62.86%), but the distribution of subcategories is different.

Graph 3 - B2P Industries Distribution Graph 4 - B2P Transport Services Distribution In fact, considering Graph 4, in first place there is vehicle-sharing (77.27%), which is represented by a lot of companies of car-sharing and bike-sharing, in the second place there are ride-sharing firms (22.73%) that allow to share small trips between more individuals at a lower price, while, for what concerns ride-hailing (0.00%), it is not possible to find any examples, since, by nature, it is made up of those individuals who use their own means of transport to gain money, as opposed to B2P platforms which own their fleet of vehicles. The following most popular sectors are food and good sharing and delivery and online buying/renting (respectively 11.43%, 8.57%). For all other sectors, B2P is much more fragmented than P2P, in fact as well as financing and network crowdsourcing which count for 5.71% each, it is possible to recognize many niche industries at the same stage of diffusion grouped in others (5.71%). Based exclusively on literature, an interpretation of the sectoral diffusion of the Sharing Economy for what concerns these two business models can be provided. The industries in which the Sharing Economy is mostly spread concern very common and widespread sectors even before the introduction of IT platforms such as transport, food take-away and accommodation. Therefore, alongside the traditional players, new ones have joined the businesses introducing new competitive levers mainly based on technology development. Considering the transportation sector, the biggest difference, given by the introduction of the Sharing Economy, has been the opening of this service also for people with a lower per capita income. Thinking about Italy, for example, the taxi service has always been used by medium-high spending customers, while thanks to the low prices of Uber even people with a lower standard of living have started to use it. By focusing instead on the food and good delivery sector, in addition to being a service that has always been used by the consumer, it has expanded the demand-supply connection network. In fact, whereas before restaurants delivered home generally within a radius of 10 km, the use of riders made

36 LITERATURE REVIEW possible to widen the covered perimeter. A peculiar characteristic of this industry is that riders, by moving with the backpack supplied by the related enterprise, become a source of advertising, therefore firms enjoy an almost-free advertisement that feeds itself. Lastly, also the tertiary sector services, represented by hotels and residences, have always existed. The use of platforms to book was a consumer upgrade in order to consult different offers in the shortest time. In all three sectors, an incremental innovation took place. The same cannot be said for the skill sector, for example. In fact, opening one’s own house to an unknown individual, who is not employed by any company, is something very radical compared to the traditional mentality. Concluding the analysis carried out, it is possible to affirm that the most widespread sectors are those in which the Sharing Economy has brought about an incremental innovation that has refined and facilitated the use of a service for the consumer. Exploring now the other two types of platforms, it is evident, directly from the matrix, that P2B still remains quite a niche typology, concentrated mostly on the network crowdsourcing, food and good delivery and skills. The importance of these industries may lie in the fact that they correspond to the typical services that an individual can offer to a firm. B2B platforms, instead, focus on the manufacturing industry, even if it is possible to find studies also on financing, food and goods delivery and network crowdsourcing, indeed these are the common services that can be shared between enterprises.

2. Sharing platform models vs operations pillars The second matrix where researches and articles have been disposed is built on axes related to operations pillars and sharing business models. Through a general overview, it can be firstly noticed that the distribution of papers is mainly along the first two rows. Indeed, as already introduced in the previous section, sharing platforms are mostly based on P2P and B2P interaction models. The second observation to make is how papers are distributed on operations pillars. Indeed, each sharing platform model is characterised by distinctive features, deriving by the type of actors involved and the way they are mediated. As a consequence, these peculiarities are reflected in the operations pillars discussed in the various studies. Besides the fact that all the four sharing models have many papers listed under general study, due to the generality of the topics considered in some articles, capacity management and demand management are two themes discussed with approximately the same frequency by P2P and B2P studies (respectively, 8.33% and 4.76% vs 10.87% and 4.35%). This stems from the fact that platforms based on those sharing models typically have to deal with the matching problem that is less relevant in B2B and P2B cases. Indeed, B2B models have to find the way of sharing resources rather

37 LITERATURE REVIEW than dealing with the dynamics of a varying supply and unpredictable demand. The same is for P2B cases, where a private helps the organisation providing a service, thus the platform has no need of matching. However, the pillar that is mostly considered in both P2P and B2P cases is pricing system (20.24% and 17.38%). Indeed, P2P platforms, like Uber, do not own inventory or physical assets that can be directly managed, but they act as mediators of two sides. In this context, sharing price and wages are the most effective tools through which influence demand level and the number of working agents, and at the same time maximising the profit. For what concerns B2P cases, the price setting for profit maximisation and for demand satisfaction is at the basis as for every kind of business. Then, each row identifies the operations pillar tailored on the sharing model it refers to. Papers on P2P platforms are characterised by quality (13.10%) and queue management pillars (8.33%). As already said, those apps play an intermediary role, and this gives them a great responsibility. Indeed, gathering feedbacks through reputation systems and keeping track of agents’ behaviours is the way through which they can guarantee safety to customers, thus enabling trust and loyalty to the platform. The brand image of this kind of company depends on the conduct of disparate employees and is strictly related to the measurement of their performances and the customers’ perceptions. As a consequence, P2P firms are adjusted and updated in order to guarantee a high service level. In addition to quality management, the other aforementioned distinct pillar in P2P is queue management. Actually, the queuing theory is well-suited to represent the dynamics between customers and service-providers, by interpreting the latter as servers waiting to process the former. This approach is mostly used in P2P platforms probably due to the intermediary role of the app which has to match two sides affected by variability and randomness; thus, the queueing theory is a way to schematise many possible dynamics to be studied and consequently controlled in real cases. Graph 5 provides the sum-up of the distribution of P2P operations pillars.

3,6% 1,2% 1,2% 2,4% 3,6% Pricing system Quality management 2,4% Capacity management 20,2% Demand management Queue management Regulation system 13,1% Sustainability Operations management 13,1% General study 6,0% Design management Measurement of customer satisfaction 6,0% 8,3% Logistics / distribution channel 6,0% Game theory 4,8% 8,3% Innovation management Asset management

Graph 5 - P2P Operations Pillars Distribution

38 LITERATURE REVIEW

Moving towards the B2P model, a distinctive feature with respect to the previous one is the presence of inventory and employees that the firm can directly influence. Indeed, against the few papers on asset management in the P2P sector (1.19%), this operations pillar represents one of the main topics of B2P platforms (10.87%) which actually own assets that need to be maintained. Moreover, also resource planning & control becomes a relevant topic (6.52%), since the company has the possibility to directly control and reallocate its resources. The last operations pillar characterising B2P cases is innovation management (10.87%); indeed, although the Sharing Economy phenomenon is innovative for all the platforms, many papers analyse how already existing enterprises deal with the innovation brought by Sharing Economy and how it affects the relationship with its customers and their business strategies. The general overview of the distribution of B2P cases in the operations topics is displayed in Graph 6.

2,17% 2,17% 2,17% 4,35% Pricing system Innovation management 17,4% Capacity management

8,7% Asset management Resource planning & control Demand management 6,5% 10,9% Quality management Sustainability 6,5% Operations management General study 10,9% 6,5% Regulation system Logistics / distribution channels 4,3% 10,9% Queue management 6,5% Game theory

Graph 6 - B2P Operations Pillars Distribution The B2B sharing model, since it entails companies sharing resources to support their own production processes, covers pillars typically related to internal operations5. In this view, along with capacity management (7.14%), logistics & distribution channel gains importance (21.43%) as it refers to the management of resources distribution and delivery between organisations. Another relevant pillar is sustainability (14.29%), since nowadays Corporate Environmental Responsibility 6 is crucial for many businesses.

Finally, few papers are about the P2B sharing model, thus it is difficult to present a complete analysis on this field.

5 Internal operations are those activities that do not deal with external context, linked to the interaction with customers. 6 C.E.R. refers to a company's duties to abstain from damaging natural environment.

39 LITERATURE REVIEW

At this point of the discussion, a general perspective on the matrix can be resumed and five pillars of interest can be identified. Design management, lean management, service operations strategy, world class service companies and yield management correspond to the five columns with one or no paper. As regard most of them, it is not that strange that they are unfilled. First of all, sharing platforms, by leveraging on unused capacity, do not design new products or develop business projects, thus design management practices are not common in Sharing Economy Services. Then, besides the fast growth of many sharing firms around the world, no definition of a complete service strategy is provided, neither at local level nor at global one; indeed, the definition of any world class service enterprise in Sharing Economy is in its infancy. The last two operations pillars, lean management and yield management, corresponding to two of the most relevant topics of operations management, are not spread in a Sharing Economy system, to the knowledge of this research. Indeed, while yield management has not yet been applied to any form of Sharing Economy, the first attempt to apply lean management has been found in the construction industry in China, where, in order to reduce both wastes and lead time, a form of construction called “pre-fabricated” was studied.

3. Industries vs operations pillars The last pair-wise comparison considers industries and operations pillars. From the first matrix, it has emerged that there are some high-standing industries to focus on, namely accommodation, transport, food and good delivery and sharing, representing more than two-thirds of total papers. So, the analysis will consider just these sectors. At first glance, like in the previous analysis, some operations pillars are endemic of the markets in which the sharing platform competes. Papers about accommodation chiefly discuss quality management rather than asset and capacity management. Indeed, in the case of house and room sharing, the theme of trust is fundamental both for the host and the guest. This market is not affected by the dynamics of supply and demand, as for transport industry, but by the factors that bring people to trust each other. For this reason, one of the few papers about customer satisfaction is in accommodation. As concerns transport, the matrix confirms that the operations pillars pertinent to this sector are capacity, demand and asset management along with pricing system, quality and queue management. However, the perspective provided by this third classification allows to identify two new operations pillars of interest and to better interpret them. The first one is regulation system, where the debate between ride-hailing platforms and taxi-drivers is still open and unsolved in many countries [40A]. The second one is sustainability; indeed, one of the advantages deriving from ride-sharing is the reduction of cars on roads and consequently of traffic congestion and pollution [16A][47A].

40 LITERATURE REVIEW

The last noteworthy industry is the one related to delivery and sharing of food and goods where, as might be expected, logistics & distribution channel and pricing system are the main pillars. Indeed, in this category, companies and platforms need to manage the way products are distributed and to influence the number of working couriers by acting on pricing system or to find the way to compete against traditional business models, studying the optimal price.

41 LITERATURE REVIEW

YIELD YIELD YIELD YIELD

MANAGEMENT

MANAGEMENT

65, 72, 73 72, 65,

UNCLASSIFIED 22, 37, 53, 56, 57, 57, 56, 53, 37, 22,

SERVICE SERVICE SERVICE SERVICE COMPANIES

WORLD CLASS CLASS WORLD

COMPANIES

WORLD CLASS CLASS WORLD

71 68, 95 68,

57

70, 11C 70,

57 UTILITIES SERVICE SERVICE

STRATEGY SERVICE SERVICE

OPERATIONS OPERATIONS

STRATEGY OPERATIONS OPERATIONS 33 25 56 6C 13B

11B

56

25

6C 32

QUEUE QUEUE

4, 32 4,

QUEUE QUEUE

11B, 13B 11B, SPACES

MANAGEMENT 7B, 24, 25, 33, 5B,

24, 25, 33, 5B, 7B, 7B, 24, 25, 33, 5B, MANAGEMENT 38 14

9C 1C

56

14

81 1C 75, 79 76, 9C 81, 10B

QUALITY QUALITY

QUALITY QUALITY 76, 9C, 10B 76, 9C, 27, 38, 77, 78, 80

MANAGEMENT

14, 27, 38, 75, 76,

MANAGEMENT

77, 78, 79, 80, 10B, 77, 78, 79, 80, 10B,

14

SKILLS

4B, 11B, 12B 11B, 4B, 4, 14, 17, 18, 49, 49, 18, 17, 14, 4, 50 4B

4B

30 54 30, 54 28, 74 20, 4B

9B, 16B 9B, 50, 9B 50, 78, 4B, 13B 50, 78, 4B, OPERATIONS OPERATIONS

MANAGEMENT

OPERATIONS OPERATIONS

20, 28, 50, 74, 4B 20, 28, 50, 74, MANAGEMENT

18 41 51 18 71 72

38, 10C 38, 18B, 19B 18B,

18, 51 16, 18 18, 47

71, 72 41, 64

18, 64, 66 18, 31, 34

18, 31, 34

5C, 6C, 7C, 17B, 5C,6C, 7C, 17B,

3, 4, 5, 13, 18, 18, 13, 5, 4, 3, 31, 16, 18, 47, 51, 66

SUSTAINABILITY

VEHICLE SHARING VEHICLE

34, 42, 58, 2C, 58, 42, 34, 4C, SUSTAINABILITY 36

13, 59 13, 76 36, 39, 46

SYSTEM

SYSTEM

REGULATION REGULATION

REGULATION REGULATION 36, 39, 40, 46, 59 67 41

20B 12B

25 41 25, 12B 11B, 12B 11B, SYSTEM PRICING PRICING 2C, 6C, 19B 6C, 2C,

23, 14B, 15B 23, 14B,

SYSTEM

RIDE-SOURCING PRICING PRICING

2, 23, 3C, 2B, 3B 2B, 2, 23, 3C, 14B, 15B, 20B 15B, 14B,

24, 25, 26, 40, 44, 67, 2C, 3C, 6C, 6C, 3C, 67, 2C,

2B, 5B, 6B, 7B, 8B, 8B, 7B, 6B, 5B, 2B,

7B, 8B, 11B, 12B, 12B, 11B, 8B, 7B,

23, 24, 25, 26, 40, 23, 24, 25, 26,

44, 2B, 3B, 5B, 6B, 6B, 5B, 3B, 44, 2B,

6B, 7B, 8B, 12B, 13B 12B, 8B, 7B, 6B,

12B, 14B, 15B, 19B 15B, 14B, 12B,

77, 78, 80, 2B, 4B, 5B, 5B, 4B, 2B, 80, 78, 77,

1, 4, 17, 18, 24, 24, 18, 17, 4, 1, 26, 25,

45, 46, 47, 49, 50, 50, 49, 47, 46, 45, 59, 55, 27, 33, 36, 38, 39, 39, 38, 36, 33, 27, 44, 40, 54 32 35

9C 32 41, 86

21, 64, 66

41, 54, 64 21, 32, 66 CHANNELS

LOGISTICS/ LOGISTICS/

CHANNELS LOGISTICS/ LOGISTICS/

DISTRIBUTION DISTRIBUTION

76

DISTRIBUTION DISTRIBUTION

Comparison Matrix Comparison

50, 76, 9C 76, 50,

4, 16, 17, 38, 38, 17, 16, 4,

17, 18, 35, 50, 50, 35, 18, 17, RIDE-SHARING 6

52 50 68 65 wise Comparison Matrix Comparison wise

16B

2 wise wise

42, 50

wise ComparisonMatrixwise

6, 61

-

-

50, 65 - INNOVATION INNOVATION

MANAGEMENT

INNOVATION INNOVATION

2, 42, 52, 68, 16B

INDUSTRY

MANAGEMENT 15B

OPERATIONS PILLAR OPERATIONS

OPERATIONS PILLAR OPERATIONS

ONLINE ONLINE

9B, 14B, 15B 14B, 9B,

23, 49, 51, 14B, 14B, 51, 49, 23, BUYING/RENTING

41 59

First First Pair Third Pair

21B 11B

41

67

-

22, 53, 73

-

Second Pair

22, 53, 73

59, 11B, 21B 59, 11B, -

GAME THEORY GAME

6

4

GAME THEORY GAME

5

15, 48 15,

61, 81 61,

Table

Table

10B, 16B 10B, NETWORK NETWORK

43 67

Table

82, 10B, 21B 10B, 82, 43

ASSET ASSET

8, 15, 18, 51, 63, 63, 51, 18, 15, 8,

ASSET ASSET CROWDSOURCING CROWDSOURCING 5, 31, 34, 6C

MANAGEMENT

5, 31, 34, 6C, 7C 5, 31, 34, 6C, MANAGEMENT 4 37 19 49

11C

48 37

4, 17, 60 4, 17, 63 4, 17, 49 19, 60 4, 17, 103 4, 17, 49, 69 4, 89, 5C, 7C 4, 89, 5C,

8, 48, 61, 63, 82

4, 17, 5C, 11C 4, 17, 5C, 63, 69, 82, 11C GENERAL STUDY GENERAL

1, 4, 17, 36, 45, 49, 55

6, 19, 41, 62 41, 19, 6,

GENERAL STUDY GENERAL

1, 4, 8, 17, 45, 49, 55, MANUFACTURING

15 15 80

15 15, 80

CUSTOMER CUSTOMER CUSTOMER

SATISFACTION SATISFACTION

MEASUREMENT OFMEASUREMENT

MEASUREMENT OF MEASUREMENT

SHARING

2, 4, 18, 3C, 18, 4, 2, 21C

FOOD AND GOOD GOOD FOODAND

28, 74, 1C, 2B, 3B 1C, 74, 28, 2B, 2, 4, 17, 18, 21, 21, 18, 17, 4, 2, 23,

62 62

LEAN LEAN LEAN LEAN

MANAGEMENT MANAGEMENT

MANAGEMENT MANAGEMENT

35

25, 30 25,

54, 64 54,

DELIVERY 63, 66, 12B 66, 63, 3, 18B, 19B 3, 18B,

CONTROL CONTROL FOOD AND GOOD GOOD FOODAND

RESOURCE RESOURCE

3, 18B, 19B 3, 18B, CONTROL CONTROL

PLANNING & PLANNING

RESOURCE RESOURCE

PLANNING & PLANNING

60

52 9C

21B 14B 14B

DESIGN DESIGN

DESIGN DESIGN

21B, 14B 21B,

FINANCING 4, 17, 20, 4B, 4B, 20, 17, 4,

MANAGEMENT MANAGEMENT

DEMAND 4C, 17B, 4C,

4C, 17B, 18B 17B, 4C, DEMAND DEMAND

40, 5B, 7B, 8B 7B, 40, 5B, 40, 5B, 7B, 8B 7B, 40, 5B,

MANAGEMENT

15

MANAGEMENT

79, 4B, 20B 4B, 79,

4, 15, 17, 18, 33, 33, 18, 17, 15, 4, 36, 43, 49, 69, 75, 75, 69, 49, 43, 36,

18 18 18 18 56

ACCOMMODATION

56

25 19B 25, 12B 18, 12B

12B, 13B 12B, 12B, 13B 12B, CAPACITY CAPACITY

17B, 18B, 19B 18B, 17B, CAPACITY CAPACITY 18, 58, 4C, 10C, 10C, 18, 58, 4C, MANAGEMENT

18, 25, 5B, 6B, 8B, 8B, 6B, 18, 25, 5B,

58, 4C, 17B, 18B, 18B, 17B, 58, 4C,

MANAGEMENT

25, 10C, 5B, 6B, 8B, 8B, 6B, 5B, 25, 10C,

P2P

P2B

B2P

B2B

P2P

P2B

B2P B2B SKILLS ONLINE ONLINE SPACES SHARING UTILITIES DELIVERY FINANCING NETWORK- UNCLASSIFIED

RIDE-SHARING UNCLASSIFIED

RIDE-SOURCING

BUYING/RENTING MANUFACTURING

FOOD GOODS AND FOOD GOODS AND UNCLASSIFIED VEHICLE SHARING VEHICLE

CROWD SOURCING SOURCING CROWD ACCOMMODATION

SHARING PLATFORM MODELS MODELS PLATFORM SHARING INDUSTRIES MODELS PLATFORM SHARING

42 LITERATURE REVIEW

2.3. Papers Summary

The last step in order to complete the literature review of operations management in Sharing Economy Services is the discussion of the papers presented and classified in the previous sections. In line with the analyses done so far, articles, conferences and book chapters are described according to the operational pillar they refer to.

2.3.1. Capacity Management Capacity management is an important topic especially for B2P vehicle-based platforms. Starting from the bike system, D. Freund, S.G. Henderson, E. O’Mahony and D.B. Shmoys, 2019 [58A] adopt the integer linear programming in order to study how to reallocate the capacity among stations minimising the User Dissatisfaction Functions (UDF). The programming model is also used by D. Freund, S.G. Henderson and D.B. Shmoys, 2019 [18B] combined with simulation methods aiming to define how to reallocate the system by moving bikes to match demand. The same problem is addressed by M.C. Chou, Q. Liu, C. Teo and D. Yeo, 2019 [17B] who study the impact of periodic redistribution and location of bicycles in the network to support a higher number of flows. Redistribution of resources is also introduced in X. Zhao, M. Han, Q. Deng and K. Xue, 2019 [4C], who identifies the factors affecting supply side that make the company re-allocate its bicycles trying to satisfy as much demand as possible. Moving to ride-sharing system, R. Ibrahim, 2017 [25A] suggests managerial insights both on long- term staffing decisions and short-term control levers mainly acting on agents’ compensation and delays announcements to impatient customer. In another study, S. Banerjee and R. Johari, 2019 [5B] deeply discuss the characteristics of ride-sharing and ride-hailing platforms, the related empty-car rebalancing problem and the dispatching policies. Similarly, L. He, H. Mak and Y. Rong, 2019 [19B] identify strategic and operational decisions to submit in order to deal with capacity-management. The former comprises service region design and fleet sizing, the latter embeds fleet repositioning, dynamic pricing and reservation system. Capacity management is also important in crowded cities, where traffic congestion is the major problem. As a consequence, S. An, D. Nam and R. Jayakrishnan, 2019 [10C] propose to integrate a peer-to-peer ride-sharing system with a fleet service to cover the unmatched riders. Indeed, the matching issue is another topic important for this pillar; Y. Chen, M. Hu and Y. Zhou, 2019 [8B] define an optimal matching policy that maximises social welfare. Considering self-scheduling capacity, I. Gurvich, M. Lariviere, and A. Moreno, 2019 [12B] present three levers to deal with it in car-sharing systems, namely pool size, compensation and the maximum

43 LITERATURE REVIEW number of available drivers, in order to guarantee a good service level and to generate profit. In another article, G.P. Cachon, K.M. Daniels and R. Lobel, 2017 [6B] model surge-pricing as a lever to influence capacity in function of demand variation. To conclude, referring to random capacity, R. Ibrahim, 2019 [13B] concerns with the effective management of those service systems.

2.3.2. Demand Management Demand management is essential in every type of business and for this reason is studied by multiple authors. S. Banerjee and R. Johari, 2019 [5B] deeply analyse the implication of demand modulation via pricing, changing the rates at which rides are requested, and via dispatching policies. In another article developed by J. Bai, K.C. So, C.S. Tang, X. Chen and H. Wang, 2019 [7B], demand is the main factor affecting the levels of price, wage and pay-out ratio. Moreover, they characterise clients as wait-time sensitive and re-interpreted the results on pricing in this view. Concerning the satisfaction of customers, Y. Chen, M. Hu and Y. Zhou, 2019 [8B], considering the matching side, introduce a passenger typology who is forward-looking and decides when to strategically request the sharing service, according to his willingness to pay, while X. Zhao, M. Han, Q. Deng and K. Xue, 2019 [4C] propose an analysis of the factors affecting customer demand and supply for a bike-sharing platform in Chicago, where enterprises tend to introduce more bicycles to satisfy the demand with the result of idle accumulation of resources. Thus, based on Coweb model, the authors test the demand-supply relationship using a linear regression model to propose the optimal amount of bicycles to satisfy the requests. In another research, H. Wang and H. Yang, 2019 [40A] study which methods transport firms use to estimate spatio-temporal demand. The estimation is important in order to understand how many vehicles the company needs to dispatch and where to redistribute it, as studied by M.C. Chou, Q. Liu, C. Teo and D. Yeo, 2019 [17B]

2.3.3. Asset Management Asset management is a main operation for organisations such as Enjoy, MiMoto, Car2Go who own a fleet of vehicles to share. The main problem these organisations face every day is the refuelling system, meaning how, when and where refuel vehicles that are parked around the city. Z. Meng, E.Y. Li and R. Qui, 2019 [34A] explore ways in which free-floating car-sharing firms (FFCS) can improve their approaches to fuel-level management, continuing to address threats to environmental sustainability. Furthermore, according to this article they can also design a refuelling recommendation strategy to provide customers with rewards for refuelling, and therefore reduce companies’ effort to manage this problem.

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Platforms owning electric vehicles have to deal with their charging system. I. Semanjski and S. Gautama, 2016 [5A] assess for the first time the impact of driving and charging behaviour on EVs batteries, also considering the economic impact deriving from the battery management on this kind of vehicles. EVs platforms can not only provide transportation services to drivers and passengers, but also energy supplies to the power grid through appropriate participation in electricity markets, as shown by Mamalis, S. Bose and L.R. Varshney, 2019 [6C] who explore a new way for their utilization. Also Y. Zhang, W. Li and X. Du, 2019 [7C] present this opportunity applied to the new trend of Energy Internet, where, for example, charging facilities may be used during their idle state. For what concerns bike-sharing companies, instead, the main asset management topic regards the recycling activities. It is the case of X. Lai, Z. Sun, J. Liu and G. Wu’s study, 2020 [31A] who analyse how to improve the bicycles recycle efficiency focusing on quality rather than on quantity of the assets. Asset management also affects other industries. In fact, according to D. Ki and S. Lee, 2019 [43A], accommodation firms, despite they do not own any asset, must pose attention to the spatial distribution of their resources. Airbnb, for instance, has many properties concentrated in residential areas generating urban problems, so proper action should be adopted to cope with this issue.

2.3.4. Resource Planning & Control Among the managerial challenges up to sharing platforms, dispatching of resources implies the strategic planning and control of service region design. In their studies, L. He, H-Y Mak, Y. Rong and Z-J M. Shen, 2016 [19B][3A] address this topic for one-way electric vehicles sharing system with its related operational challenges: first, customer pattern uncertainty and second, unavailability of data in the first development stage. Along with this, the platform has to deal with the trade-off between maximisation of customer catchment and operations costs controlling. In another study, D. Freund, S. G. Henderson and D. B. Shmoys, 2019 [18B] discuss planning methods for bike-sharing systems station-based not only in terms of number of docks to be installed and how to rebalance the system in order to match the demand, but also regarding the expansion possibility.

2.3.5. Queue Management Queueing models are created in different articles in order to study the dynamics of customers and agents joining the service and the contribution of the factors influencing it. S. Banerjee and R. Johari, 2019 [5B] apply the queue theory to ride-sharing dynamics, trying to estimate the effects on pricing and dispatching policy of factors that are state-independent and state-dependent. In another study, H.R. Sayarshad and H.O. Gao, 2018 [24A] use a multi-server system under infinite-horizon look-

45 LITERATURE REVIEW ahead to schematize the dynamics of a ride-hailing platform. Also R. Ibrahim’s study, 2017 [25A] is based on queueing theory with many-servers, however he adds the dynamics of impatient customers and random number of servers. In another study, J. Bai, K.C. So, C.S. Tang, X. Chen and H. Wang, 2019 [7B] model the impact of passenger arrivals on the pricing system of a self-scheduling platform considering customers sensitive to price and waiting time and providers as earning sensitive. This approach can also be applied to platforms providing jobs, like Upwork; Y-J Chen, C. Maglaras, and g. Vulcano, 2019 [11B] represent a marketplace where the supplier owns a specific competence and the customer is a potential buyer, choosing the most suitable service provider. In another article, S. Kim and C.Y. Yeun, 2019 [33A] analytically delineate through two queueing models the dynamics between resource owners and consumers with the aim of studying the operating costs; the techniques adopted include a classical Markov process of the single channel queueing system, semi-Markov process and semi-regenerative process. Considering the cloud sharing industry, G. Darzanos, I. Koutsopoulos and G. D. Stamoulis, 2019 [56A] model a queueing theory-driven model for cloud service providers (CSPs), using a M/M/1 queueing system that is used in order to formulate and study revenue and cost functions. In another article, R. Ibrahim, 2019 [13B] relies on a queueing-theoretic framework with a random number of self-scheduling servers and impatient clients in order to study the performance of the platforms in managing problems, such as staffing and system delays. Finally, T. Mamalis, S. Bose and L.R. Varshney, 2019 [6C] apply queue modelling to electric vehicles platforms to capture the salient features of EV charging processes and schematize the possibility of using EVs for two services, transportation and grid services.

2.3.6. Logistics / Distribution Channels The aim of the research of S. Melo, J. Macedo and P. Baptista, 2019 [32A] is to assess the impact of distribution sharing solutions under a public good perspective, by estimating the impacts of sharing logistics parking infrastructure owned by public authorities. The analysis is conducted in terms of better environmental, energy and traffic performances. Logistics is also concerned about the effects on sustainability; specifically, H.B. Rai, S. Verlinde, J. Merckx and C. Macharis, 2017 [66A] define a new solution of distribution channel, called “Crowd Logistics”, together with all factors determining its potential sustainability. In another article, D. Zhao, Y. Xue, C. Cao and H. Han, 2019 [41A] create a mathematical model in order to analyse the channel selection of manufacturers for achieving sustainable operations, also taking into account charging modes, access requirements, commission rates. From another point of view, the Sharing Economy is studied by L. Tian and B. Jiang, 2017 [21A] who focus on the effects of C2C goods

46 LITERATURE REVIEW sharing on the distribution channels of both the manufacturer and the retailer, considering the trade- off between cannibalization and value-enhancement effects. In conclusion, a real application of Sharing Economy in logistics is proposed by H. Ma, C.W. Wong, L.C. Leung and S. Chung, 2019 [54A] who develop a facility sharing strategy for container terminals. They demonstrate that the Sharing Economy could be a pragmatic approach to share facility among the operators in HKP (Hong Kong Port).

2.3.7. Sustainability The concepts of Sharing Economy and sustainability are strongly intertwined, since this phenomenon made many sectors more sustainable, starting with the transport one. S. Gössling and C.M. Hall, 2019 [18A] conceptualize the Sharing Economy structure and evolution, and investigate its sustainable dimensions, including perspectives on resource use, ownership, participation and control, and the distribution of profits. Still adopting a general perspective, C.J.C. Jabbour &co 2019 [72A] focus on the integration of circular economy and Sharing Economy considering South American companies, in particular on the Brazilian ones due to the importance of BRIC and specify that in order to shift from linear manufacturing to a shared economy, every manufacturing firm needs to start the change from the design phase. In the bike-sharing sector, X. Lai, Z. Sun, J. Liu and G. Wu, 2020 [31A] study how to improve resource recycle efficiency considering costs and benefits coming from this business and computing the profit equation in order to identify the optimal value to improve efficiency. The analysis was extended to two real cases (OFO and Mobike), finding some areas of improvement in order to exploit the recycle activity as much as possible. In another paper, H. Cai, X. Wang, P. Adriaens and M. Xu, 2019 [47A] want to quantify the environmental benefits of ride-sharing in urban cities, taking into account the heterogenous individual travel demands. They create an optimisation model in order to maximize the total avoided VMT (vehicle-miles travelled) after identifying all the sharable trips. Still looking at the vehicle-sharing industry, Z. Meng, E.Y. Li and R. Qiu, 2019 [34A] explore ways in which car-sharing companies can improve their approaches to fuel-level management, continuing to address threats to environmental sustainability. Ride-sharing practices allow important improvements, such as the reduction of pollution due to a reduction in traffic congestion. In this sense, ride-sharing can also be interpreted as car-pooling, whose impact is analysed by M. Do and H.Y. Jung, 2018 [16A] in terms of economic and environmental benefits. Moving to crowd logistics, H.B. Rai, S. Verlinde, J. Mercks and C. Macharis, 2017 [66A] identify which factors determine its potential sustainability, while Y. Niu, Z. Yang, P. Chen and J. Xiao, 2017 [64A] build a greenhouse gas emissions mathematical model for third-party logistics. The problem

47 LITERATURE REVIEW considered in this paper is to construct open routes for vehicles to visit all customers within their time windows, minimising the total cost involving the fuel emissions cost and the driver wages. Concerning the energy sector, K. Alanne and S. Cao, 2016 [71A] present the concept of zero-energy hydrogen in order to find a way to integrate housing and personal mobility in the energy balance. In another article by H. Ma, C. W. Wong, L.C. Leung and S. Chung, 2019 [54A] a quasi-experimental setup is used to highlight the potential of internet-based matching platforms, in particular Craigslist in the US market, to prevent waste and deliver environmental benefits through the creation of C2C used-goods closed-loop supply chains (CLSCs). Operations sustainability is also considered in the choice of the optimal pricing strategy as mentioned by D. Zhao, Y. Xue, C. Cao and H. Han, 2019 [41A].

2.3.8. Innovation Management Independently from the type of sectors, the social innovation in Sharing Economy falls under the name of Open Social Innovation (OSI), as studied by J. J. Yun, K. Park, C. Im, C. Shin and X. Zhao, 2017 [65A]. Indeed, it explains the key success factors of OSI, based on ten Korean social enterprises, and the concrete dynamics behind it. The same open context is addressed by L. Ales, S. Cho and E. Körpeoğlu, 2019 [16B] who present a general model framework capturing the main features of an innovation contest7. Then, depending on the industries, different innovative solutions where studied in the articles found. Starting from the fashion sharing sector, B.E. Jin and D.C. Shin, 2020 [2A] study platforms working in this industry which are based on collaborative consumption and, thus, can profitably deliver their value propositions as they do not own products. Not having inventory implies no inventory management issues, normally resulting from demand volatility that typically affects this industry. For what concerns the ICT sector, according to J. Liu, M. Chen and L. Wang, 2019 [6A], cloud computing technology achieves a high degree of sharing and collaborative use of manufacturing resources and services. It realizes the collaborative production of information across enterprises in the process of product design and manufacturing and provides customized on-demand services for users. In the bike-sharing industry, innovation management linked to ICT technologies was important, especially in the past, in order to discover new ways to track bicycles not only to avoid theft, but also for demand management. Thus, according to Z. Liu, L. Ma, Y. Zhu and W. Ji, 2019 [42A], a Chinese firm has established an intelligent big data platform called “Hubble System”, that not only monitors

7 i.e. Where an organiser seeks solutions to an innovation-related problem from a group of independent agents

48 LITERATURE REVIEW bikes in real time, but also carries out data analysis of users’ records and predicts citizen riding behaviour. Remaining on the urban and metropolitan areas scenario, G. Ambrosino, J. D. Nelson, M. Boero and I. Pettinelli, 2016 [50A] describe the possibility to integrate different urban transport means, for which the large-scale Agency can play a fundamental role with respect to the expansion of public transport services and the integration and coordination of the different modes and operators. Moving on to the energy sector, a new business model based on the shared battery paradigm is studied by P. Lombardi and F. Schwabe, 2016 [68A] which describes an energy storage operator who is offers his storage system to different kinds of customers. Regarding innovation in financial industry, D. Gong, S. Liu, J. Liu and L. Ren, 2019 [52A] present and study, though a theoretical model, an innovative financing scheme called platform-based financing that helps SMS (small and medium sellers) who cannot have access to a commercial banking loan. Lastly, considering the network crowdsourcing industry, H. Xiao, G. Xiaomin and Z. Pengzhu, 2018 [61A] explain the coordinated innovation mode of this sector and provide theoretical and practical reference for multiple innovation modes.

2.3.9. Quality Management Quality management is a very important pillar for every kind of service or product sold since it is one of the key factors evaluated by customers. A peculiarity of the Sharing Economy phenomenon is that the service provided by these platforms strongly depends on employees’ performances which cannot be directly influenced. Thus, monitoring tools, such as reputation and motivation systems, are needed. One of the huge difficulties arising from gig economy is the job quality level. A.J. Wood, M. Graham, V. Lehdonvirta and I. Hjorth, 2018 [14A] address through interviews how gig economy is perceived by workers in Southeast Asia and Sub-Saharan Africa and identify algorithmic management as a helpful tool to monitor workers performances and support their time-schedule. W. Zuo, W. Zhu, S. Chen and X. He, 2019 [27A] collect data through electronic word of mouth (eWOM) and process chain network to identify the quality issue of a car-sharing platform with the aim of proposing four service quality improvements: virtual site for passengers to board, driver selection by the customer, fare objection for drivers’ detouring and confirming getting on/off functions. The use of eWOM is also adopted by W. Zuo, W. Zhu, S. Chen and X. He, 2019 [78A], who apply Long Short Time Memory (LSTM) text classification, sensitive analysis and text mining of eWOM of the online ride-hailing platform Didi, in order to propose service quality optimisation suggestions based on process chain network (PCN). In addition, S. Jang, M. Farajallah and K.K.F. So, 2020 [77A] present different quality cues that affect travellers’ purchase decisions about shared

49 LITERATURE REVIEW products and services provided by heterogeneous sellers. A set of multiple cues may exert independent and interactive effects on consumers’ product-quality assessment and subsequent purchase decisions. In another study, E. Sthapit, P. Björk and J.J. Barreto, 2020 [79A] propose managerial hints to improve the service quality in P2P accommodation industry by studying the impact of negative memorable experiences in the UK and the USA. Still considering the P2P business model, but moving to the transport sector, X. Cheng, S. Fu and G. de Vreede, 2018 [80A] focus on ride-sharing platforms to investigate which are the influencing factors of service quality during the ride and of the app itself. Furthermore, they also study the interaction between service quality, satisfaction and loyalty. Considering online car-hailing, W. Zuo, W. Zhu, S. Chen and X. He, 2020 [76A] study how to deal with its service issues, such as trust and privacy, based on Process Chain Network. Some researchers have found that reputation indicators have a positive impact on the trust of platforms, and reputation systems may incentive companies to ensure a high level of service quality. Indeed, the study of K. Bimpikis and Y. Papanastasiou, 2019 [10B] aims at generating an efficient reputation system, in the same way M. Basili and M. A. Rossi, 2019 [38A] focus on its design to actively perform a “regulatory” role by excluding from the platform users with ratings below a given threshold and by establishing drivers' behavioural rules that link their performances to the remuneration. Also motivation is impactful in terms of quality. It is the main focus of another paper, in which T. Zhang, D. Bufquin and C. Lu, 2019 [75A] choose Airbnb as case study in order to build a theoretical framework to discuss the main motivational factors through hosts interviewed. In addition, Y. Hua, X. Cheng, T. Hou and R. Lou, 2019 [9C] aim to analyse the impact of drivers moving from one platform to another and sharing negative experience, concluding that it is essential for Internet taxi firms to establish ways of motivating drivers to engage in their work. Quality management is also addressed by global scientific researches. It is the case of T. Dedeurwaerdere, P. Melindi-Ghidi and A. Broggiato, 2016 [81A], who analysed the results of a world-wide survey of managers and users of microbial culture collections to understand whether Sharing Economy practice is applicable or not, remarking the need of a common quality standard for the materials sharing. Finally, the Sharing Economy concept can be applied also to second-hand markets, as in the conference paper where Y. Yang, Q. Sum and S. Ba, 2019 [1C] find a way to standardise the market in order to guarantee maintenance and high-quality products, despite it is applied to second-hand appliances.

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2.3.10. Measurement of Customer Satisfaction X. Xu and C. Lee, 2020 [12A] examine electric word of mouth in three types of platforms working in accommodation industry. They identify the best response that managers should provide according to the customers’ feedbacks and which are the influential factors affecting the overall consumer satisfaction, which in turn can generate loyalty effect. To this purpose, X. Cheng, S. Fu and G. de Vreede, 2018 [80A] focus on how service quality influences customer satisfaction in ride-sharing platforms.

2.3.11. Design Management In online advertising, impressions are sold via real-time auctions which are organised by central platforms. Thus, S. R. Balseiro, O. Candogan and H. Gurkan, 2019 [21B] analyse the mechanism design problem of an intermediary who offers a contract to an advertiser with a private budget and a private targeting criterion. In another study, M. Hu, M. Shi and J. Wu, 2019 [14B] seek to understand the impact of all-or-nothing mechanisms on consumer behaviour, as well as the optimal design of such mechanisms, from the perspective of third-party platforms like Groupon and Kickstarter.

2.3.12. Service Operations Strategy The only paper falling inside this pillar has been developed by J. M. Field &co, 2018 [57A]. His purpose is to present exciting and innovative research questions in service operations. The methodology is based on Delphi study, stated that three or four topics within each theme are selected and multiple questions for each topic are proposed. This article has been generated since in many Sharing Economy Services customers act as both service providers and receivers, creating a duality of customers co-production at both ends of the delivery system.

2.3.13. Pricing System Platforms can adopt many pricing policies, according to their needs, but three of the most common ones are well explained by X. Lin and Y. Zhou, 2018 [26A] who provide a detailed definition of surge-pricing8, static pricing9 and dynamic pricing10. They are also studied by H. Wang and H. Yang, 2019 [40A] to discover which are the features behind them, by which the platform adjusts both the prices and wages dynamically depending on real-time supply and demand information, considering both platform performance and social welfare. In another study, S. Banerjee and R. Johari, 2019 [5B],

8 The platform pays a fixed commission but varies the sharing price depending on demand conditions. 9 The platform pays to providers a fixed compensation of the fixed price charged to customers. 10 The platform can adjust both wages and sharing price.

51 LITERATURE REVIEW who based their research on ride-sharing platforms, compare static and dynamic pricing considering their impact on the total rate of rides in the system and their robustness, that is the matching rate in function of passenger arrivals rate. Dynamic pricing is suitable for free-floating systems where prices can be set in order to encourage customers to move vehicles and rebalance their fleet, L. He, H-Y Mak and Y. Rong, 2019 [19B]. However, papers on this topic are mainly about surge-pricing. In particular, G.P. Cachon, K.M. Daniels and R. Lobel, 2019 [6B] focused on it in platforms with self- scheduling capacity, demonstrating that, although this pricing policy is criticized due to the concern for consumers’ welfare, their model makes the customers better off both during low and peak demand. While, in another research, R. Ibrahim, 2017 [25A] supposes that managers can control the compensation offered to drivers of a ride-sharing platform in order to optimise the staffing level; he presents surge-pricing as a possible solution even if this lever has some restriction about when and how often can be used. Thus, he proposes new methods to influence capacity or demand. D. Zhao, Y. Xue, C. Cao and H. Han, 2019 [41A] present optimal pricing strategies, needed to keep a balance between profitability and attracting participants, of both manufacturer and suppliers, associated to the channel strategic choice of the manufacturer for achieving sustainable operations. A similar study conducted by Y. Chen, M. Hu and Y. Zhou, 2019 [8B] considers a platform that coordinates the matching of customer demand with crowdsourced supply by optimally setting prices, considering many possible scenarios where both demand and supply functions change. The contribution brought by X. Qiao, D. Shi and F. Xu, 2019 [2C] in the definition of the optimal pricing strategy considers the customer utility function split into two periods where he can decide whether to buy or rent the car. In other studies, J. Bai, K.C. So, C.S. Tang, X. Chen and H. Wang, 2019 [7B] examine how on- demand service firms should set their price, wage and pay-out ratio, that is the percentage of price collected from customers to the providers, while B. Jiand and L. Tian, 2019 [3B] understand the impact of P2P sharing products on the enterprise’s pricing strategy, profit, consumer surplus and social welfare, considering as main influential factors transaction costs, price variation, that is the firm’s marginal cost, and quality level. I. Gurvich, M. Lariviere, and A. Moreno, 2019 [12B] model the profit of a car-sharing platform with self-scheduled capacity considering it function of the number of active agents, the pool size, and the given compensation. About compensation, Y-J Chen, C. Maglaras, and G. Vulcano (2019) [11B] propose a compensation-while-idling mechanism applied to an aggregated market, that is modelled as a marketplace where buyers submit a request-for-quote and suppliers compete for these requests, to overcome the inefficiency arising from competition among suppliers.

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Pricing modulation and optimal profit setting depend also on the business model of the platform, whether it is for-profit (FP) or not-for-profit (NFP). Actually, in food leftover markets, FP and NFP platforms fix different prices according to different objective functions, respectively profit and demand maximization, as demonstrated by J. Yu, 2019 [3C]. Indeed, in some cases the revenue optimisation price is not related to company surplus, rather to social welfare, like for H.R. Sayarshad and H.O. Gao, 2018 [24A] or Y. Chen, M. Hu and Y. Zhou, 2019 [8B]. Researches classified under pricing system also discuss about whether it is convenient or not to join the Sharing Economy model both from renter and owner sides. S. Benjaafar, G. Kong, X. Li and C. Courcoubetis’s study, 2018 [2B] a parallel analysis of a system with and without collaborative consumption is carried out, along with the identification of the equilibrium of ownership, usage level, consumer surplus and social welfare in function of price and commission rate. The remaining papers about pricing system are divided according to the industry they refer to. Concerning the transport sector, multiple articles are found. S. He and K. G. Shin, 2019 [44A] propose CAPrice, a novel adaptive pricing scheme for urban Mobility-on-Demand (MOD) network. Based on more accurate demand-supply predictions, CAPrice formulates a joint optimisation framework, anticipating prices and subsidies towards incoming ride-requests and thus incentivizing drivers more responsively to customers. T. Mamalis, S. Bose and L.R. Varshney, 2019 [6C] study through queueing system the optimal pricing and battery splitting to maximize the revenue for an electric vehicle platform dealing with two markets, transport and energy and power grid service. Moving to spacecraft trading industry, U. Pica and A. Golkar, 2017 [67A] develop an agent-based framework to simulate and evaluate pricing mechanisms of commodities. In the context of crowdfunding, M. Hu, M. Shi and J. Wu, 2019 [14B] study how all-or-nothing mechanisms may affect a creator’s pricing decisions on the basis of a two-period game where cohorts of buyers arrive at a crowdfunding project and make sign-up decisions sequentially. Results show a peculiarity of this industry, where, even when product options are the same, high-type buyers may still choose the high-price option. Considering, instead, platforms belonging to the online group buying sector, S. Marinesi, K. Girotra and S. Netessine, 2019 [15B] analyse the use of threshold discounting, the practice of offering service at a discounted price only if at least a given number of customers show interest in it, also comparing it with the traditional approach, such as slow period discounting or closing. Switching to accommodation platforms, a study conducted by J. Li, A. Moreno and D. J. Zhang, 2019 [20B], using a data set of prices and availability of listings on Airbnb, demonstrates that performance differences between professional and nonprofessional hosts can be partly explained by pricing inefficiencies.

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Finally, the disruption of Sharing Economy also reached fashion industry. In this context, T. Choi and Y. He, 2019 [23A] compared operations, optimal service charging scheme and role of marketing effect of fashion companies with and without collaborative consumption model, with particular focus on the effect on price.

2.3.14. Regulation System Since the Sharing Economy is still an emergent phenomenon, most countries do not know how to deal with and to regulate, due to its high level of innovation. This issue is perceived in many countries and studied by many authors, mainly developed in the transport and accommodation sectors. Indeed, all the articles found are related to them. A. Tham, 2016, [36A] investigates the approaches to regulate Airbnb and Uber from cross-national perspective involving Australia and Singapore, while M. J. Mohamed, T. Rye and A. Fonzone, 2019 [39A] study how much transport authorities and operators in London understand the impact of Uber services, which may depend on the city context in terms of size, densities and level of public transport availability. Concerning China, J. Li and L. Hou, 2019 [46A] aim to carry out an evaluation on the taxi reform in order to see whether the current regulatory framework is appropriate to be applied to the ride-hailing industry. The emergence of ride-hailing systems and the ways in which they differ from conventional taxi systems raises controversial issues and regulatory problems, such as unclear regulation of labor relations between platforms and drivers, “gray” tax enforcement on driver income and other societal and environmental impacts studied by H. Wang and H. Yang, 2019 [40A]. Moving to ride-sharing companies, Q. Sun, Y. He, Y. Wang and F. Ma, 2019 [59A] mean to implement regulations dealing with the interaction between all the stakeholders involved. Considering dock-less bike systems, the main problem is the creation of public nuisances caused by safe un-parking, which is studied by R. Chen, 2019 [13A] who suggests how to solve it by providing the instalment of monitoring systems, considering privacy issues.

2.3.15. Game Theory D. Zhao, Y. Xue, C. Cao and H. Han, 2019 [41A] adopt the game theory in order to study the interaction among all the stakeholders involved in the channel selection and the pricing strategies decision of manufacturers who want to achieve sustainable operations. The same theoretic model is used by Q. Meng &co, 2019 [53A] to examine the cooperation among multiple organisations in the Sharing Economy. A similar study is developed S.R. Balseiro, O. Candogan and H. Gurkan, 2019 [21B], where the game theory is used to model the strategic interaction among the intermediaries of

54 LITERATURE REVIEW a chain network. In another article, the mentioned method is addressed by U. Pica and A. Golkar, 2017 [67A] in order to design optimal interaction strategies for sealed-bid reverse auctions. A new approach that combines the game theory with the dynamic evolution process, named “Evolution Game Theory”, is used to perform other analysis. According to Q. Sun, Y. He, Y. Wang and F. Ma, 2019 [59A], the implementation of ride-sharing regulation can be seen as a game process between the related companies and the government. The same technique is applied by L. Wei and Y. Yang, 2018 [73A] in order to model the degree of socialization of two players’ production materials in order to understand their degree of public benefit of their respective Sharing Economy. Y-J Chen, C. Maglaras, and G. Vulcano, 2019 [11B] based their analysis on platforms providing jobs and, through game theory application, they studied the competitive equilibrium of an aggregated market place under congestion effects, fixing the service rate and the static price in a successive game. To conclude, a schematization on the main insights and findings of all production researches about game theory applied to Sharing Economy and circular supply chains has been developed by T. Choi, A. A. Taleizadeh and X. Yue, 2020 [22A]

2.3.16. Lean Management According to G. Xu, M. Li, C. Chen and Y. Wei, 2018 [62A], the Sharing Economy is seen as a facilitating means for lean management. Indeed, prefabricated construction industry in China is based on building blocks that are manufactured in factory plants on Make-To-Order basis and then moved to the building site. In order to allow this process, it is fundamental to collect real time information regarding the project and coming from different SMEs, using an integrated cloud-based IoT platform.

2.3.17. Operations Management This paragraph groups all those articles that analyse the operations management in general, with no particular reference to a specific pillar. M. Albergaria and C.J. Chiappetta Jabbour, 2019 [28A] present the advantages of the application of big data analytics capabilities (BDAC) to shared resources in a library. This procedure is suggested by the authors for operations management in Sharing Economy platforms. In another article, S. Benjaafar and M. Hu, 2019 [74A] present three canonical applications in order to highlight the main features of Sharing Economy based on the operations management concept. Considering a specific sector like the transport one, G. Ambrosino, J.D. Nelson, M. Boero and I. Pettinelli, 2016 [50A] describe a scenario for urban and metropolitan areas where flexible mobility services (FTS) and the large-scale Agency can play a fundamental role with respect to the expansion of public transport services and the integration and coordination of the different modes and operators.

55 LITERATURE REVIEW

While focusing on port operations, H. Ma, C.W. Wong, L.C. Leung and S. Chung, 2019 [54A] provide detailed managerial insights to manage this kind of operations within the context of Sharing Economy. Among the performances tracked by platforms, the study of S. Li, W. Wu, Y. Xia, S. Wang and M. A. Douglas, 2019 [30A] provide three main capabilities, namely resource matching, drivers and rout management and risk control on private data, operational behaviours and payment methods. Their analysis revealed three value creation mechanisms: accuracy and effectiveness of matching, driver agility and driver compliance. Looking at the research of G. Allon, A. Bassamboo and E.B. Çil, 2019 [9B], not always an efficient operations management can be beneficial for the business, indeed the authors show that operational efficiency may be detrimental to the overall efficiency of the marketplace. Furthermore, they give a suggestion about the benefits to complement the operational efficiency by enabling communication among firm agents. In particular, T.I. Tunca, 2019 [4B] identifies five operational efficiencies that Sharing Economy platforms can benefit from. They are related to sunk and fixed costs, bit sized resources, human idle time, networks to lower entry barrier into workforce and market, and to assign new operational and economic roles to people. Operational efficiency, along with economic one, is also addressed in finance services, where Y. Gao, S. Yu and Y. Shiue, 2018 [20A] have analysed the performances of P2P platforms in China.

2.3.18. General Study This section embeds articles about Sharing Economy without entering into detail in any specific operations pillar. The first part consists of those articles that do not consider any particular sector. The research of L.S. Revenko and N.S. Revenko, 2019 [4A] provides definitions, sectors classification in which Sharing Economy has been developing with the related pros and cons of adopting this method against normal business operations. In another article, K.G. Abraham, J.C. Haltiwanger, K. Sandusky and J.R. Spletzer, 2017 [37A] aim to clarify what different sources of data explain the changes in the prevalence and nature of both gig employment and non-employee work arrangements, more generally over time. Still in general, R. Perren and R. V. Kozinets, 2018 [17A] offer an improved conceptualization of the Sharing Economy phenomenon by addressing those forms that can really be defined as exchanges between peers. To this concern, they introduced the term “later exchange” to represent the equivalent level of the exchangers and classify the different platforms according to two axes, platform intermediation and consociality. U.M. Apte and M.M. Davis, 2019 [49A] present the operations of a large number of SES companies and create a new format of business model to adapt the original one to the Sharing Economy industry.

56 LITERATURE REVIEW

Moving to something more specific, many authors discuss about Sharing Economy in developing countries. Ransford A. Acheampong and Alhassan Siiba, 2019 [1A] introduce, for the first time, the adoption factors of car-sharing in Ghana and provide practical policy implications for its transport services. Focusing on Cape Town, three papers have been found. In the first one, A. Pollio, 2019 [45A] speaks about how Uber created a market in this city through strategic manoeuvres that beget specific technical features, and how its ride-sharing transactions have become asymmetric conversions of value during which various forms of gain can be produced. In another article, P. Carmody and A. Fortuin, 2019 [55A] study how ride-sharing apps are affecting the nature of work and employment relations for drivers in the African city and it also explores how they represent a new form of capital- virtual capital. The last one focuses on the accommodation industry; indeed, the objective of G. Visser, I. Erasmus and M. Miller, 2017 [69A] is to offer an exploratory analysis of the emergence and organisational character of Airbnb in the city. Mixing together transport and accommodation industries, Y. Kojima, K. Hayashi, K. Akamatu and K. Hasegawa, 2019 [5C] study how to adapt and improve rental cycle system in the tourism sector. Considering the good sharing sector, N. Karacapilidis, E. Adamides, M. Tzagarakis and C.P. Pappis, 2011 [8A] present CoPe_it!, one of the first web-based tool that supports share of knowledge and argumentative discussions rather than just data. It is one of the first forms of argumentative collaboration for decision making in manufacturing processes. For what concerns the network crowdsourcing industry, L.F.A. Leon and S. Quinn, 2018 [48A] present online services centred on providing street-level imagery through close association with OpenStreetMap platform and crowd-mapping community. In another article, H. Ta, T.L. Esper and A.R. Hofer, 2018 [63A] investigate the effects of various crowdsourced delivery system designs related to driver disclosure and ethnicity on customers’ attitudes towards the drivers and the retailers. Overall, the research shows that crowdsourced delivery may create unexpected and challenging social dilemmas for operations managers. On the contrary, S.L. Javernpaa and K.R. Lang, 2011 [82A] study online communities and their challenge to overcome organisational boundaries to release creative content production. Moving to the financial sector, J.M. Lehmann and P. Smets, 2019 [60A] analyse some self-help initiatives taken by self-help groups among Ethiopians and Ghanaians living in the Netherlands, placing them within the contemporary financial landscape, showing a more sustainable financial system. The last paper regards the utilities sector, where Y. Tang, Q. Zhang, H. Li, Y. Li and B. Liu, 2018 [11C] propose four scenarios under which make an economic analysis on repurposed EV batteries in a distributed PV (photovoltaic) system under sharing business models, after the liberalization in the electricity market by the Chinese government.

57 LITERATURE REVIEW

2.4. Research Questions

A clear overview has been provided about how operations topics are debated in Sharing Economy Services. However, throughout all the studies presented up to now, it has never been defined how sharing platforms structure their operations. Indeed, operations management is a crucial function for companies to deliver a valuable service to clients and this aspect gains particular importance in platforms, since Sharing Economy is purely hinged on customer experience rather than on products offer. To this extent, the first research question addressed regards how operations are organised and how their structure changes according to the sharing business model adopted by the platform. Furthermore, this research means to better investigate the pillar of lean management that till now has been barely examined. Therefore, the second research question evaluates if lean practices could be applied to Sharing Economy platforms and which benefits, and drawbacks, may arise from its implementation. However, since operations and its practices change accordingly to the industry where the enterprise competes, this research is restricted to transport sector, that is also the most representative of Sharing Economy Services.

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3. ANALYSIS

Considering the focus of this scientific research on the transportation sector, it is appropriate to deeper characterise it. As already mentioned in section 2.2.3.1, three sub-categories can be distinguished: ride-hailing, ride-sharing, and vehicles-sharing according to the provided service. Moreover, as stated by the classification of sharing models in paragraph 2.1.4, this kind of platforms can adopt P2P, B2P, B2B, and P2B business models [34A]. In particular, B2P car-sharing companies can be further distinguished into two group: station-based and free-floating. The first one requires the users to initiate and terminate the rental at the same locations, while the latter allows customers to pick up and drop off vehicles at any point within city limits. Since P2P and B2P are the main Sharing Economy models in the transport sector, from this moment on, this paper will concentrate on these, leaving aside B2B and P2B judged to be a niche in this market.

3.1. Platform Classification and Characteristics

Despite P2P sharing model is peculiar of ride-hailing platforms, while B2P is typical of vehicle- sharing, as demonstrated in the first comparison matrix in section 2.2.3.2, Figure 6 shows through some examples how the transportation sector, where the enterprise competes, does not prevent the platform to use a sharing model rather than another.

Figure 6 - Sharing Platform Model Examples

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There are some common features, underlying ride-sharing and ride-hailing services, that are critical to support and guarantee the matching between drivers and passengers [5B]. First of all, platforms need to track hundreds of movements of both sides, thus data collection and analytics are essential to run their business and afford graphical representations and reliable predictive models. The other critical factor is real-time operations and control; indeed, they deal with rebalancing and real-time dispatching issues, hence they need to react fast. Lastly, since ride-sharing platforms typically create a marketplace between two sides, rather than employ and directly manage drivers, market design concerning the long-term scenario is crucial. Although these characteristics are presented just for two transportation sub-categories out of three, the remaining one might share them, too. Actually, also vehicle-sharing organisations are concerned about real-time repositioning and, consequently, they need to collect data and handle it to perform in short- and long-term. This question sets the starting point of the scientific research. Therefore, among the collected papers, those referring to transportation industry have been systematically analysed with the aim of outlining strategic, tactical, and operational decisions taken by these organisations. Then, to better clarify the output of this first step, Key Performance Indicators (KPIs) have been defined, considering, whenever it was possible, the ones already displayed by the authors, otherwise they were deducted.

3.2. Analysis of Key Performance Indicators and Stressed Operations Pillars

KPIs are distinguished between internal and external. Internal ones refer to the performances traced by the platform to check the efficiency of the service delivered in order to be competitive in the market; to this concern, profit, usage level and matching per minute are some examples. External KPIs consist in parameters evaluated by customers to compare the different available transportation services, making them prefer an app rather than another one. For instance, these indicators are drivers’ reputation, sharing price or trip availability. Moreover, among them, it is possible to identify six groups according to the underlying area of interest: 1. service coverage, availability & matching → it is the core of transportation platforms and of Sharing Economy in general. Indeed, these KPIs track the service availability, hence the coverage level, through dispatching policies and fleet repositioning, along with the matching efficiency and timeliness; 2. economic impact → from one side, the company looks at its business profitability, through wages and prices that maximise profit, while, from the other side, customers and drivers ponder whether it is convenient or not to join the service, basing their choice on sharing price and the costs they suffer;

60 ANALYSIS

3. environmental sustainability → environmental benefits triggered by the adoption of shared mobility are taken into account by both internal and external perspectives; indeed, the evaluation of these benefits by customers is then reflected into the brand image; 4. platform quality → KPIs introduced to monitor the app efficiency in delivering the service, thus it might include information availability and easiness of payment system; 5. reputation → it entails the performances to assess the platform reputation, like customers’ feedbacks, application scores, and claims; 6. safety & security → indicators that guarantee customers’ safety during trips and personal data security, such as safety perception and information disclosure. Figure 7 reports the legend with the abbreviations utilised in future graphs to indicate the clusters names discussed above.

Figure 7 - Legend for KPIs Groups

Once the KPIs groups have been set, the first part of the analysis can be structured. Indeed, the first research question aims at identifying how operations are organised; however, since there is no clear definition of it in any paper, through the KPIs analysis, it is possible to verify if there exists a relation between the sharing model adopted by the platforms and the performances monitored by its operations. Therefore, the following part considers a parallel analysis of P2P and B2P cases11, structured on their KPIs, also referring to strategic, tactical, or operational levels, and the stressed operations pillars. Then, a more detailed analysis will be presented, focusing on the transportation sub-categories of each sharing model case.

3.2.1. P2P vs B2P KPIs Analysis Every company deals with decisions at three levels, strategic, tactical, and operational, to maintain a sustainable and profitable business. As already introduced in the section about operations management, the actions performed by the operations function implement what forecasted for the medium-term period on tactical level, which, in turn, interprets the business strategy. The strategic decisions, taken by the top management, set what to reach on a long-term scale, with significant and

11 B2B and P2B cases are disregarded since they are not so frequent; indeed, no papers were about them.

61 ANALYSIS non-reversible effects on the final goals of the organisation. These considerations typically aim at identifying potential markets to compete in and the related entry-strategy. To this purpose, a market analysis is carried out, and, among other factors, the Sharing Economy platforms in transport sector assess to what extent citizens are keen on using their service and their level of vehicle-sharing adoption[1A]. Otherwise, the entry-decision may be related to the possibility to extend the business to another type of market, in particular this option is up to electric vehicles station-based enterprises which could enter the energy market, providing both transportation and grid services [7C]. Other strategic decisions regard how to manage regulatory issues and how to adapt to laws and restrictions imposed by governments [13A]. By the way, once that the market has been identified, other decisions must be addressed, concerning the strategic planning of service region design and fleet sizing. Then, all these choices are interpreted at the tactical level as medium-term results and translated into daily activities at operational level. Therefore, the former includes decisions linked to price setting, wages and capacity levels, whereas the latter is more specific to single manoeuvres like the number of active agents or available vehicles per day and the dynamic adjustments of service price. Though, in order to verify the effectiveness of the decisions implemented in the short- and medium- term, companies monitor their performances through different KPIs. Starting from a general overview of P2P and B2P cases, Graph 7 and Graph 8 display the frequency of KPIs distributed on the different groups identified in the previous section. It can be noticed that they are mainly traced to assess service coverage, availability & matching (service CAM), by both organisation types and from both perspectives, internal and external ones.

60 56 55 50 45 40 35 35 30 26 25 20 15 14 14 13 15 11 9 10 7 4 4 5 0 CAM ECO ENV QUA REP SAF

External Internal

Graph 7 - P2P KPIs Distribution

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50 45 43 40 35 30 25

20 17 15

10 7 4 4 4 5 3 3 2 2 2 3 0 CAM ECO ENV QUA REP SAF

External Internal

Graph 8 - B2P KPIs Distribution

Indeed, as already discussed, a crucial issue for transportation companies is matching, where different metrics correspond to the different ways to provide the service, according to the sharing model adopted. In P2P cases, performances are more about time delivery and availability of service. The internal metrics consist of matching per minute, estimated time-of-arrival, demand forecast accuracy, passenger waiting time and requests per minute. While, on the other side, customers evaluate waiting time, time of matching, trip availability and integration rate, that is the possibility to match different mobility services, for example by having available a ride-sharing service at a point in which it is not possible to continue by public transports. Switching to B2P platforms, they cope also with physical assets and the related services, because, for example, they need to re-dislocate vehicles in town or in their stations to reach a good service availability. To this purpose, internal KPIs address fleet utilisation and repositioning, dealing with the minimisation of redistribution travel time and the availability of docks or bikes per station. On the contrary, considering the state of health of their assets, B2P organisations check, for traditional vehicles, maintenance system efficiency and, for electric ones, indicators like the state of health of the battery, that is the percentage of rated capacity, the state of charge, meaning the energy left in the battery, and the charging speed. Then, since EVs batteries are affected by how they are utilised, these firms also forecast and monitor customers’ behaviours. Users, from their side, evaluate bike and dock availability for service by bicycles, refuelling time for traditional cars, available driving time and waiting time at charging station for EVs. Then, going more in detail in the analysis, P2P is particularly concerned about reputational KPIs. Actually, the other core issue for these platforms is to enable trust between strangers, aspect that does not affect B2P cases. Indeed, through feedbacks, customers can evaluate their trips and drivers,

63 ANALYSIS allowing the platform, from one side, to deliver more information to potential passengers about the service and, from the other side, to process data to find out company efficiency level and drivers’ performances. Of course, also B2P platforms have rating systems to evaluate customers’ satisfaction in service coverage and availability. Moreover, it must be considered that the intermediary role played by these platforms, tries to enable the interaction between the two sides in the easiest way possible. Thus, the apps need to manage a large amount of private data in order to provide easy payment and to improve matching, by processing customers’ locations and feedbacks. In this view, KPIs on security of personal data are monitored by both cases, minimising the risk linked to account protection and payment credentials. However, these KPIs are also intended for “personal” safety that, from B2P side, is guaranteed through vehicles maintenance to reduce accidents risk. Furthermore, all the collected data is presented on the app to convey the service in the most effective way. Consequently, many KPIs are recorded for platform quality level. These indicators refer to the app itself, such as the easiness of use, payment or request, and to the service quality, such as delays announcements, possibility to choose the driver or fares for detouring, that is the platform controlling if the driver deliberately lengthens the trip to obtain a higher remuneration. Actually, the other crucial aspect taken into account by all the three actors, the platform, the driver and the passenger, is the economic impact. First of all, from the company perspective, the most common KPI monitored under this aspect is profit, which in the papers is typically interpreted in terms of maximisation of the objective function of sharing price and operative costs. In addition, P2P organisations, in order to verify their profitability, consider the occupancy vehicle level and, since they have to pay their “employees”, the drivers, they also evaluate the agents’ wage level and the pay-out ratio, that is the percentage of the revenues left to service providers. Moreover, they keep track of the transaction costs and the payment commissions. From customers’ perspective, the passengers consider the service price and their inconvenience costs, while the drivers evaluate commission rate and the cost related to car usage. This last cost, that is an external KPI for P2P platforms, becomes internal in B2P, as it is up to the firm and directly impacts on its profit, rather than on the driver’s remuneration. Moreover, among asset management costs, also expenditures to guarantee the service availability are considered, like that for removal of broken bikes or recycling costs. The last KPIs group includes environmental sustainability indicators. Shared mobility allows to reduce cars on roads, thus the traffic congestion and pollution. Consequently, sharing platforms can convert their performances into environmental benefits, such as in terms of reduced CO2 [1], that are valuable to the company brand image.

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To summarise what discussed so far, KPIs in P2P platforms chiefly monitor service coverage, availability & matching, economic impact, and reputation, while those of B2P are equally distributed among the different groups but with a predominance in service CAM. Focusing on the different transportation sub-categories, Graph 9 and Graph 10 represent the distribution of the KPIs for ride-hailing and ride-sharing solutions in P2P, confirming the predominance of the three main groups for the first one, and of reputation and service CAM for the second one. However, in this last case, just one paper is about KPIs on economic impact; this may be due to the lower number of articles considered on this topic (7) against those on ride-hailing (28).

50 46 45 40 35 30

25 22 19 20 14 15 13 13 9 10 7 6 5 5 1 1 0 CAM ECO ENV QUA REP SAF

External Internal

Graph 9 - P2P Ride-hailing KPIs Distribution

15 13

10

7 6

5 4 3 3 2 2 2 2 1 0 0 CAM ECO ENV QUA REP SAF

External Internal

Graph 10 - P2P Ride-sharing KPIs Distribution

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About P2P station-based and free-floating models, just two papers have been found, one each; the former has KPIs on reputation, the latter on service CAM, but these are too few articles to generalise a pattern in monitored performances. Passing to B2P cases, most papers are applied to free-floating solution and Graph 11 shows its KPIs, where, for sure, the most critical ones are still related to service CAM category, concerning vehicles dispatching and rebalancing, meaning to transfer idle resources towards areas with high demand.

20 18

15

10

6 5 4 3 3 3 2 2 2 2 0 0 0 CAM ECO ENV QUA REP SAF

External Internal

Graph 11 - B2P Free floating Vehicle-sharing KPIs Distribution

Also all the four papers on station-based model present indicators of that category, concerning the availability of the service and the waiting time related to its readiness. To conclude, ride-sharing solution in B2P case treats with approximately the same frequency about reputation, environmental sustainability, platform quality and service coverage, availability and matching.

3.2.2. P2P vs B2P Stressed Operations Pillars The analysis of the KPIs monitored by transportation platforms allows to identify, under a different reading key, the stressed operations necessary to successfully run their business. By recalling the analysis done in section 2.2.3.2., Graph 5 and Graph 6 summarise the distribution of the operational pillars for P2P and B2P solutions respectively. Indeed, the major ones for P2P case are pricing system and quality, capacity, demand and queue management to which correspond the performances in P2P transport companies assessing respectively the economic impact, the reputation and platform quality, and the effectiveness of capacity-demand matching. Graph 12 confirms the same distribution for the operations pillars in this specific mobility sector.

66 ANALYSIS

3,3%1,7% Pricing system 10,0% 20,0% Quality management Capacity management 8,3% Demand management Queue management 3,3% Regulation system 13,3% Sustainability 8,3% Operations management General study Innovation 10,0% 13,3% Game theory 8,3%

Graph 12 - Operations Pillars Distribution in Transport P2P Platforms

Studying the B2P case, the major cluster is pricing, followed by innovation management, and all those operations related to the management of resources from both sides, the company and the customers (Graph 6) However, the importance of the operations pillars in B2P platforms changes when focusing on transport industry, as represented in Graph 13. While pricing system leaves the first place to capacity management, innovation management gives it away to asset management and resource planning & control. Actually, more than half of the papers on transport are about operations managing organisation resources, reflecting the predominance of KPIs about service coverage, availability & matching in B2P case.

Capacity management 12,9% 16,1% Asset management 3,2% Pricing system Resource planning & control 3,2% Demand management 3,2% 12,9% Regulation system 3,2% Sustainability Innovation 6,5% Quality management 9,7% Queue management 6,5% Operations management 6,5% Logistics / distribution channels 9,7% 6,5% General study

Graph 13 - Operations Pillars Distribution in Transport B2P Platforms

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3.3. Real Case Studies

To deepen this research, some interviews have been performed with Sharing Economy companies adopting B2P or P2P business models. Before proceeding with the explanation of their operations management system and the measured KPIs, the firms are presented in terms of foundation year and operational headquarters, service provided and dimensions. The latter has been defined based on the number of employees per organisation.

B2P companies: ▪ Jump → founded in 2010 and acquired by Uber in 2018, it is a dock-less scooter and electric bicycle sharing system operating in many countries at international level. In Italy, it is present in Rome. ▪ ReachNow → it was an American car-sharing service platform founded in 2016 by the BMW Group and defunct in 2019 following a strong competition with Uber and Lyft. The company could rely on 51-200 employees. ▪ MiMoto → it is an Italian firm founded in 2017 offering a dock-less electric scooter service in the cities of Milan, Turin and Genoa. Given the recent foundation, it is still small in size, indeed it counts 11-50 staff members. ▪ Share Now → it is a German car-sharing enterprise, formed in 2018 from the merger of Car2go and DriveNow. Its vehicles are spread in 18 cities across Europe making it one of the largest car-sharing service in the world with 501-1,000 workers.

P2P companies: ▪ Uber → Uber Technologies, Inc. is an American multinational ride-hailing firm founded in 2009 and operating in different sectors such as the food delivery one. With an overall annual revenue equal to 14.15 billion USD, it is the largest P2P organisations in the transport sector with an organic of 22,263 employees worldwide. ▪ Little Ride → it is a pan-African mobility platform founded in 2016 and one of the world’s largest ride-hailing ones, serving cities across Kenya, Uganda, Zambia, and Tanzania. It has 51-200 employees. ▪ Mondo Ride → founded in 2015 in Dubai, it is a multinational online ride-sharing company based in Nairobi, Kenya, relying on 51-200 employees. ▪ Jugnoo → founded in 2014 in India, it is the 3rd largest ride-hailing firm, operating in more than 50 cities pan-India with around 201-500 employees.

68 ANALYSIS

▪ Bolt → it is an Estonian transportation platform founded in 2013 and headquartered in Tallinn. It provides a service range from ride-hailing to micro-mobility and food delivery. It is the first competitor of Uber and counts 1,001-5,000 employees. ▪ Gett → previously known as GetTaxi, it is an Israeli on-demand mobility organisation founded in 2010 that connects customers with transportation, goods and services. It has an organic of around 1,001-5,000 employees.

Such a diversified scenario, displayed in Figure 8, with firms operating in different countries and with different sizes, has permitted to cover a large part of the possible facets of operations management.

Figure 8 - Geographical Map of Case Studies

69 ANALYSIS

3.3.1. Key Performance Indicators Keeping the KPIs as a reading key for operations description, two analyses are carried out corresponding to within-case study, to highlight differences and similarities among companies adopting the same sharing model, and cross-case study, to compare B2P against P2P scenario. Indeed, once the interviews have been concluded, the resulting indicators have been distinguished into internal and external, and classified according to the six groups introduced in section 3.2.

3.3.1.1. Analysis Within-Case Studies Proceeding in this way, it is easier to conduct the analysis at a macro-level to firstly verify the consistency between literature and real cases, and secondly, to move to within-case one. However, before starting with the discussion, some considerations are needed. First of all, the interviewed firms do not cover all the possible sharing models; in B2P case, only free-floating vehicle-sharing is represented, while for P2P only ride-hailing, which are, by the way, the most frequent and representative for these categories. Hence, only the parameters of the corresponding sharing models will be used for the comparison with literature papers. The second point is about the distinction between internal and external KPIs. Since the metrics are discovered through direct interviews to companies’ managers, there is a predominance of internal ones; indeed, those external might be better discovered through questionnaires to customers. However, to the purpose of this research, just the internal ones are mainly of interest in order to outline operations.

Below the two scenarios are analysed, however for a complete overview of all the literature and real- case indicators, the B2P and P2P KPIs lists are provided in Appendix B and Appendix C.

3.3.1.1.1. B2P – Literature vs Real Case Study The comparison between literature and real case studies can be set, starting from B2P sharing model. Graph 14 and Graph 15 report the distribution for each KPIs category in both cases. Although the greater piece corresponds to service coverage, availability & matching for the two sides, the remaining percentages are differently distributed. In Graph 14, there is quite the same frequency among the remaining categories, except for platform quality that was never treated. In Graph 15, there is a predominance of economic and reputational KPIs, while nothing was about environmental impact. However, key performances are monitored in order to verify the achievement of strategic goals and organisation’s mission, which can be summarised by Critical Success Factors (CSFs). CSFs are the process characteristics or variables that should be achieved to positively succeed in the process outcome.

70 ANALYSIS

2,2% 10,0% 11,1% CAM CAM 6,7% 13,3% ECO ECO ENV ENV 6,7% QUA QUA 55,6% 60,0% REP 24,4% REP 10,0% SAF SAF

Graph 14 - Literature Internal B2P KPIs Distribution Graph 15 - Real-case Internal B2P KPIs Distribution Therefore, those that can be derived for B2P Sharing Economy platforms, operating in the transportation sector, are mainly two: reliability and readiness. The first one refers to service availability, that means finding an available vehicle close in the area, and to safety during the ride; while, the second one implies optimal conditions of ready-to-rent cars in order to successfully end a trip.

After a general overview of the results of B2P real cases, a systemic analysis and comparison with literature KPIs can be set, following the six classes. As concerns the main category, in both cases it is possible to identify parameters related either to refuelling or to charging. Indeed, in literature they are described quite generically as Maintenance service efficiency and Recharge system efficiency, whereas through interviews it was possible to better define them. Table 7 explicates the formula for the calculation whenever it is not immediate, the measure of unit of the indicators, the frequency of their monitoring, the functions and the geographical level that are mostly interested in their supervision and are affected by their values.

Table 7 – B2P KPIs Datasheet 1

71 ANALYSIS

Through these performances, the platforms can evaluate the efficiency of their repair system and the quality of the intervention by considering the kilometres between two consecutive repairs; indeed, it is not just a matter of making the vehicle available to customer, but to reduce the number of interventions, as they imply many additional operations linked to collection of vehicles to be repaired and re-located in town. Nevertheless, even if real cases monitor internal indicators linked to system efficiency, literature papers take into account performances related to customer experience, like Charging station waiting time. However, this aspect could be disregarded since leaving cars with low fuel levels in parking spots discourages customers from renting the vehicles or result in the cars being driven shorter distances, thus dampening the profitability of the business; indeed, drivers may not be willing to waste time in refuelling the car. Other two parameters suitable for B2P case are Fleet utilisation and Resource saturation level, as companies are concerned about the exploitation of their assets. In real examples, the managers presented very detailed KPIs, displayed in Table 8.

Table 8 - B2P KPIs Datasheet 2

These indicators are relevant since they are functional to fleet repositioning, meaning the optimal cars disposition. Indeed, Ready-to-be-rented rate is an index of loss, which considers vehicles that are unavailable because they need some interventions or they are dislocated in bad positions, meaning that those cars cannot be used unless they are positioned in areas with high Utilisation rate, thus increasing the Ready-to-rent rate and profitability. Moreover, Share Now indicated three unusual parameters: How far the car remains in the same spot, How fast the car is picked-up and Maximum distance to walk until you get the first car. The first two are interesting as they are useful to identify areas which are likely profitable, hence affecting ready-to-rent and utilisation rates; whereas, the last one is useful for car positioning close to intense-usage areas and it converts a vehicle from ready-to- rent to ready-to-be-rented, or vice versa, as it affects customer’s willingness to take it.

Also moving to the reputation KPIs analysis, there is high consistency between literature and real case. They both check customer satisfaction and app rating. Furthermore, literature papers monitor Number of customers lost due to unfuelled (or uncharged) vehicles while Share Now evaluates Frequency of ride-failure due to car default, that is quite similar. Moreover, It checks cleanliness level that, along with the previous indicator, is representative of readiness CSF. Indeed, either the car

72 ANALYSIS is dirty, or it is un-fuelled or broken, they are invalidating platform reputation, as they negatively affect the vehicle readiness. As regard economic metrics, instead, despite Profit and Revenues per trip, no costs are similar to literature, although their diversity allows to cover a vast range of cost items sustained by B2P companies. Table 9 summarises the cost items, indicating whether they were identified in literature or in interviews and the related vehicle, albeit they can all be applied to every transportation service, like scooters and electing vehicles. Moreover, the associated Operations are deduced; however, the cost items will be further detailed along with the Operations description and according to the type of vehicle shared by the platforms in section 3.3.2.1.1, to better understand how Operations are structured and carried out.

Table 9 - Literature and Real-cases Cost Items

All these costs form the Direct cost per vehicle and, in order to understand the profitability for each of them, it is compared with Revenues per trip so to compute Return per car as follow:

푁 ∑푖=1(푅푒푣푒푛푢푒푠 푝푒푟 푡푟𝑖푝푖) − 푇표푡푎푙 푐표푠푡 푝푒푟 푐푎푟 푇표푡푎푙 푐표푠푡 푝푒푟 푐푎푟 ∑푁 (푆푒푟푣𝑖푐푒 푝푟𝑖푐푒 ∗ 푟푒푛푡𝑖푛푔 푡𝑖푚푒 ) − (퐷𝑖푟푒푐푡 푐표푠푡 푝푒푟 푐푎푟 + 푂푣푒푟ℎ푒푎푑 푝푒푟 푐푎푟) = 푖=1 푖 , 퐷𝑖푟푒푐푡 푐표푠푡 푝푒푟 푐푎푟 + 푂푣푒푟ℎ푒푎푑 푝푒푟 푐푎푟 where N is the total number of trips performed by a car along its lifetime, and overheads considers taxes, legal costs, depreciation and inspection related to the car. That formula is further broken down into Rate of return per car per day, in Share Now case.

Concluding with the remaining categories, some considerations are in order. No KPI is presented about environmental impact by interviewed firms. However, it is reasonable to assume that environmental metrics are not presented since it was not the aim of the interviews; indeed, by doing web-researches, each organisation provides information about their sustainability and carbon emission savings [2][3]. As concerns platform quality, despite it was not possible to identify any

73 ANALYSIS parameter in literature, Share Now explained three interesting ones: How easy is to sign-in, considering uploading of ID, driving licence and payment mode, Reachability of call-centres, to support customer issues, and Reception of smartphones and technology. The last one traces customer experience, as it considers how fast the vehicle can be opened and how fast it takes to get started. This parameter, indeed, highly impacts on readiness CSF, as the easiest is to start the journey, the higher will be customer satisfaction and retention. Moreover, smartphone reachability is linked to a safety &security indicator, that is How long it takes to open the vehicle. Indeed, the higher the former, the shorter the latter and the driver is sitting safe in the car as soon as possible. However, in literature additional safety performances are monitored, like Platform privacy control and Risk of accidents, that probably are considered also by real enterprises. Indeed, privacy control is a prerequisite of apps entailing payment modes and private data insertion, and all the KPIs related to maintenance and repair aim at diminishing the risk of accidents.

3.3.1.1.2. B2P – Within-Case Analysis The KPIs analysis for B2P case is applied specifically to the free-floating sharing model, as explained in the introduction of the section. Nevertheless, the interviewed organisations cover a range of vehicles to study: • Jump → electric-bike-sharing; • MiMoto → electric-scooter-sharing; • ReachNow and Share Now → car-sharing. This list is useful to run within-case analysis among real cases that can be set according to the different kinds of transportation services. There can be identified two categories: 2- and 4-wheeled vehicles.

Starting from the most relevant KPIs group, service coverage, availability & matching, the first difference to notice is that car-sharing firms track performances linked to idle capacity. For example, Share Now monitors How fast the car is picked-up and How far the car is remaining in the same spot; these parameters are disregarded by 2-wheeled platforms, or, at least, they are not presented as crucial. This aspect derives from the asset cost; indeed, an idle scooter is less expensive than an idle car, as the investment is lower. Another issue linked to this kind of B2P organisations is parking; although safe parking-lots have to be detected also for bikes and motors, finding them for cars is more challenging. Consequently, Share Now traces how easy is to find it. Furthermore, from within-case analysis, it stands that car-sharing platforms do not monitor any indicator on repair and maintenance systems. This depends by the fact that these companies outsource those operations, while Jump and MiMoto manage them internally. Indeed, the two both check repair

74 ANALYSIS and maintenance efficiency in terms of quantity, like Number of bikes repaired per hour or Number of slots loaded by operator per shift, and quality, since both trace KMs run between two consecutives battery changes (repairs) on the same scooter (bike). From the other side, car-sharing platforms are interested in service availability and, through other KPIs, can monitor the efficiency of repairer suppliers without being specific, as required by 2-whelled enterprises.. Indeed, ReachNow traces Ready-to-rent and Ready-to-be-rented cars and, through their comparison It is able to understand the impact of supplier efficiency. In fact, as Ready-to-be-rented cars are those that cannot be used 푅푒푎푑푦−푡표−푏푒−푟푒푛푡푒푑 푐푎푟푠 because they need interventions or need to be rebalanced, if the ratio is 푇표푡푎푙 푛푢푚푏푒푟 표푓 푐푎푟푠 highly lower than 1, it means that either the repairer or the balancer supplier (or both) are not delivering the service efficiently, because if Ready-to-be-rented cars is high, it implies that few vehicles, of the total fleet, are available to the users. Whereas, Share Now tracks Repairing Time which is an index of idle time for Its vehicles, where the shorter, the better and How many times a car cannot be used due to a default. However, independently on how they are monitored, these metrics are all linked to readiness CSF, as either though in-house repairs or though outsourcing, the interest concerns vehicles available to be used.

Moving to the second relevant KPIs category, economic impact, it is still reflected the difference between in-house and outsourced operations; indeed, MiMoto traces Maintenance and Rebalancing cost, while Share Now Cost of insurance and damages which are less specific and that can be related to payments to suppliers. Instead, still referring to the different impact of asset investment according to vehicle type, 4-wheeled platforms, unlike 2-wheeled ones, monitor Return per car and Direct cost per vehicle. This is due to the greater costs up to cars than those for bikes and scooter, hence also evaluating the return per car is an index of profitability and investment recovery.

Concluding with the remaining categories, few indicators were presented, indeed all together they correspond to the 20% of the identified KPIs. Probably, the core for B2P firms is linked mainly to service availability and the related economical aspect. Perhaps, these platforms focus less on reputational aspects and safety perception as they do not have to convince their customers to get into cars with strangers, as for P2P case. Therefore, their reputational indicators are Customer satisfaction level, Stars rating, Churn rate, since it is anyway important to evaluate customer experience.

To sum up, few indicators are in common among the B2P platforms, unless they share the same vehicle type and business practice. Indeed, there are similarities among 2-wheeled means with in- house operations management and 4-wheeled vehicles which outsource them.

75 ANALYSIS

3.3.1.1.3. P2P – Literature vs Real-Case Study Moving to P2P scenario, Graph 17 displays the KPIs distribution in the six groups, where it can be noticed the predominance of service coverage, availability & matching and reputation. This is in line with divisions observed in literature, as reported in Graph 16, and with the kind of service these enterprises deliver.

7,0%

CAM CAM 30,4% 19,0% ECO ECO 46,0% ENV ENV QUA 53,6% QUA REP REP 13,0% 7,1% SAF SAF 8,9%

1,0% 14,0%

Graph 16 - Literature Internal P2P KPIs Distribution Graph 17 - Real-case Internal P2P KPIs Distribution As for B2P case, also in P2P the KPIs are monitored in function of CSFs. The two cases share one of them, reliability; indeed, also these platforms need to guarantee trip availability, in the sense of match availability, and safety during the ride. However, rather than readiness, these enterprises rely on timeliness, meaning the availability of the service in the shortest time possible. In this perspective, the predominance of indicators about service coverage, availability & matching and reputation is consistent.

Starting from service CAM, this is the main category in both scenarios and many metrics are in common; Table 10 reports the datasheets for these indicators.

Table 10 - P2P KPIs Datasheet 1

76 ANALYSIS

However, there are some differences between the two cases. Thanks to the interviews, it was possible to identify some new indicators, never resulting in any article. The concept of ridership was introduced, which is monitored by Gett as the average number of rides per rider. This number allows to trace the demand and verify how it is distributed on a daily basis. From the driver’s side, instead, two performances, observed by Bolt, are of interest: CIAN and Fast time trip. The first one evaluates how many drivers enrolling in the platform stay active or drop-off. This indicator is more representative of the actual capacity, indeed it is not simply the number of carmen available on the app but it allows to verify which is the effective active part. The second one, Fast time trip, traces which on-boarded drivers delivered the fast trips during the past weeks; this measure embeds three components (1) how much reactive the pilots are in accepting the ride, (2) how long they make the customer wait and (3) the trip duration. However, as concern last two points, the platform takes into consideration if drivers are coming from far areas and where they are expected to go, hence through ETA (Estimated Time of Arrival) they can evaluate if they are performing well, considering their position. This indictor is then used to guarantee rides to those well-performing operators. In this way, the app pursues an effective incentive mechanism. The pilot is willing to provide a fast service in order to make sure to get trips, thus gains; moreover, knowing about this opportunity, all the drivers are pushed to perform better, resulting in a shorter ETA for the customers, hence improving the service quality and customer satisfaction. Besides these new measures, there are some KPIs in literature which can be useful in real life. It is reasonable to assume that part of them, which was not presented during interviews, is monitored anyway, like Matching time and Request per minute. Also, Integration rate between different mobility services is an uncommon one to consider. Although it requires a high effort in terms of data analysis, it gives the opportunity to expand the business by matching with public transportation thus allowing to continue the journey where trains and airplanes stop. Consequently, it needs a devoted operations team, as it is the case of Uber which has a department completely dedicated to airport monitoring, but this analysis is postponed to section 3.3.2.1.7.

77 ANALYSIS

The second category of importance is reputation. By comparing the key performances of the two sections, there is high consistency between them; indeed, they have different names but similar intent, as they all aim at measuring the service quality. Table 11 provides the datasheets with the analysis of the main indicators.

Table 11 - P2P KPIs Datasheet 2

These parameters are typical of marketing function, rather than operations department. Indeed, they are useful to evaluate the app usage and the service spread. However, in Sharing Economy services, they assume a new meaning. For example, Drivers’ and Riders’ churn rate represent respectively the capacity and demand level variation. Indeed, since each driver corresponds to a car, meaning a capacity unit, their abandon activates operational levers in order to guarantee the service. Uber, for instance, analyses the reasons behind a ride cancellation by the rider or the driver. The former could decide to withdraw the request as the carman is taking too long to arrive; this may be because he took the wrong direction, or he left too late. According to the cause, different corrective actions can be taken in order to adjust the service quality. From the driver’s side, cancellations are linked to “cherry- picking”12 phenomenon; indeed, they can decide which trips to accept, so that they typically take only the shorter and more profitable. However, this results in less service coverage. To face this issue, Uber reveals the journey destination only once the pilot accepts the ride, but this practice is applied wherever it is legislatively feasible. Also Reactivation rate has a new meaning in this sector; indeed, reactivation can be monitored per driver over a daily or a weekly period in order to understand if some carmen are covering recurring periods of activation to meet the demand or if some moments are un-served. The aim of the platform is to understand how it is able to match with the requests. Another impact of reputational KPIs on operations derives from drivers’ and riders’ rating. Indeed, the peculiarity of the P2P model is that the two sides evaluate each other. This allows to better manage

12 Competitive advantages that cause a firm's customers to select that company's products or services. It is the main reason why customers purchase it.

78 ANALYSIS matching. For example, Uber automatically never assigns you to a side that you rated with few stars, neither for the rider nor for the driver. While, Little Ride calls back customers who assigned low scores and ask more details to understand which countermeasures to take.

As concern quality platform group, the real-case KPIs identified fell under reputation category, too. Indeed, they are both indices of service quality, thus impact on platform reputation, and app efficiency in delivering the service. These indicators are related to the time needed to resolve customers’ problems, Timeliness of company response to customer’s questions and Turn-around time for resolving issues, and to app quality, Request to sessions. The last one needs further explanations; it is computed as: 푁푢푚푏푒푟 표푓 푟𝑖푑푒푟푠′푟푒푞푢푒푠푡푠

푆ℎ표푝푝𝑖푛푔 푠푒푠푠𝑖표푛 and it indicates how many customers, opening the app, demand for a ride over the total accesses. It is an index of quality platform as it measures the app ability in catching the riders, making them rent a trip. The KPIs identified in literature differ from those resulting from interviews. They are chiefly related to external performances that, as already introduced at the beginning of this sub-chapter, could be easily evaluated through questionnaires to customers rather than through managers’ interviews. Indeed, they are linked to on-app experience like Ease of payment, Ease of platform use, Ease of request, Price disclosure, etc. In addition, it can be deduced that the company do not directly monitor these metrics as they are embedded in other ones; for example, Request to sessions implies that whenever it is lower than 100%, the riders may have found some difficulties on the platform to request a trip. Also App rating is linked to platform quality and availability of information on it. For sure, detailing parameters on this aspect could bring some insights for app improvement. After all, P2P organisations are applications delivering a transportation service and their interface is the “business card” to present the enterprise and retain a customer, making him choose one solution from that of a competitor.

The economic KPIs in literature do not observe the same indicators in real cases. In the first context, they are more detailed since they are in function of revenues or profit, like Sharing price, Driver’s wage, Transaction cost and so on. Instead, by discussing with companies’ managers, they talked about revenues in general terms. This is explained by the fact that many articles had the aim of finding the optimal price or profit in function of disparate variables, among which operational costs, while managers focused on other more relevant KPIs which, in turn, lead to higher profit. However, there is an indicator of interest that is the emblem of Uber’s pricing strategy, the Surge. Although it is

79 ANALYSIS classified as an economic parameter, it is an operational lever to balance demand with capacity. It is a multiplier applied to service price; during a peak in demand, satisfied by a lower capacity level, Uber increases it, thus consequently the price, and the request is skimmed. As shown in Graph 18, given at time t a supply and demand level equal respectively to S and Q, by applying the surge on price P, P* is obtained and the demand lowers to Q*. The marginal gain up to the Platform is the blue rectangle.

Graph 18 - Surge Lever Effect on Demand In this way, the demand is assured, and the Company increases Its marginal profit, indeed surge- pricing states that the firm pays a fixed compensation of a variable tariff to drivers, hence no additional gain is up to them.

Finally, few indicators are on safety & security and environmental impact. In numerical terms, this lower number derives from the higher amount of papers read against the interviewed enterprises. Moreover, the percentages in Graph 16 and Graph 17 take into account how many times a KPIs category is encountered in papers, thus every key performance is counted as many times as it appears in the different readings. For this reason, many are recurring. Moreover, as concern environmental impact for real cases, the same reasons for B2P scenario are valid: it was not the topic of the questions and, by checking on websites, data on sustainability can be gathered [4]. Whereas, as concern safety & security, although it is not treated during interviews, it does not mean that it is not relevant. Still relying on web-researches, the importance given to this aspect is easily verifiable: Uber has a dedicated section in the app where riders can share their real-time position to familiars or ask for assistance clicking on the icon [5]. These movements in the app can be easily traced. A simple indicator is: 푁푢푚푏푒푟 표푓 푎푠푠𝑖푠푡푎푛푐푒 푟푒푞푢푒푠푡푠

푇표푡푎푙 푟𝑖푑푒푠 and its distribution on drivers, in order to verify if there is any countermeasure to take.

80 ANALYSIS

3.3.1.1.4. P2P – Within-Case Analysis After the comparison between KPIs in literature and real life, it is compelling to deepen the analysis for real cases. The interviewed companies are all based on the ride-hailing model, so that it is possible to identify which parameters characterise these platforms, while it is unfeasible to verify how they change according to different sharing models. Nevertheless, they are in different developing phases, as they were not founded in the same period, and, moreover, they are operating in diverse countries. In this perspective, it turns interesting to understand how monitored parameters change. However, before characterising the firms to better interpret the analysis, two considerations are in order. First of all, the interviews to Bolt, Jugnoo and Gett are not so exhaustive to be completely considered in this section; indeed, the latter replied by chat with macro-KPIs, thus they are not too much detailed to be considered as representative for Gett case, while the former were embedded in the interviews to Little Ride, as the managers previously worked for them, but the questions were only partly explained for Bolt and Jugnoo. Therefore, in this analysis, there are, on one side, Uber who was founded in 2009, count on more than 20,000 employees and is spread in 77 countries; on the other side, African platforms (Little and Mondo Ride) established in 2015/2016, with around 200 workers and operating in East Africa. In the second place, within-case study is carried out just for two KPIs groups, service coverage, availability & matching and reputation. Indeed, all the metrics linked to platform quality are considered also as reputational indicators and no performance is monitored for environmental impact and safety & security. Whereas, for economic impact, all the firms check revenues and diverse cost items, but the only noteworthy aspect is the Surge which is taken into account just by Uber. Table 12 summarises the KPIs mostly shared by these organisations.

Table 12 - Common KPIs among P2P Platforms

81 ANALYSIS

Roughly, it can be noticed that these ones are similar to those adopted in marketing monitoring. However, as already previously explained, they assume a different meaning in Sharing Economy services, as they aim at representing the real business dimension in terms of demand and capacity levels. What is interesting is that Uber did not indicate Number of drivers and riders as a KPI, but it is difficult to think that this simple data is not visible to its teams, but rather that it is assumed as not “key”. Indeed, by moving the analysis on the differences between Uber and African companies, many points can be highlighted. Unlike the latter, in Uber dashboard, the KPIs cover the whole customer experience, from the pick-up point, passing through the journey, ending with the final rating of both sides. Moreover, these indicators are more refined than the African ones. In the initial phase, when the customer is searching for a ride, Uber monitors how many riders are looking for an Uber over the active ones, so to have an idea of the matching ability and to understand the service coverage. It also has some pre-established pick-up points, for example in airports, where, for each of them, the number of arrivals and exits are traced. Another relevant parameter is ETA (Estimated Time of Arrival) which highly impacts on customer experience and service perception. Indeed, as already said at the beginning of the analysis, a CSF for transportation platform is timeliness which is linked to the easiest parameter to be monitored by customers: time of arrival. Furthermore, waiting time has a psychological weight on client perception that threatens to compromise the whole service. Thus, ETA turns to be a relevant parameter to consider. Then, once the ride has been completed, Uber pushes on ratings from both sides, the driver and the rider, to improve next experiences. Moreover, It is the only platform which analyses both drivers’ and riders’ churn rate, understanding the reasons behind their abandon. Moving to African companies, the majority of their metrics are general KPIs to assess the volume of their business. For example, among the indicators presented by Mondo Ride, there are the number of drivers recruited, activated and trained per day. This level of detail for drivers was never discussed by Uber’s manager. Moreover, they evaluate Hours spent by drivers on the platform, Number of customers on board, Number of trips and requests. All these indicators aim at assessing the service coverage and the ability of the organisation in providing the service. Actually, Mondo Ride and Little Ride were founded respectively in 2015 and 2016, thus they are in the introductory phase of their business, moving to growth. This trend is reflected by two indicators, Drivers growth rate and Download rate per week, which traced how fast the service is spreading in terms of supply and demand. For sure, they guarantee anyway a minimum service quality; indeed, Little Ride fixes a Baseline, which is the minimum acceptable mark that driver’s rating must achieve and check the Estimated Time Variable, that is ETA variability, as they do not want frustrated passengers.

82 ANALYSIS

The within-case analysis revealed that platforms give different importance to their KPIs. Indeed, many parameters presented by African firms are not considered “key” for Uber, because it is in another business phase, as displayed in Figure 9.

Figure 9 - Business Growth Phases of P2P Platforms

Therefore, since they monitor performances according to the corresponding business phases in which they are, they have different operational objectives. Uber focuses on service quality, verifying whether both sides are satisfies or not, and on service coverage in order to arrive everywhere and at first. On the contrary, Little Ride aims at service building and spreading, focusing on customer acquisition and satisfaction, so to ensure their retention. They both need to leverage on their capacity and service quality, but while Uber can rely on a stable fleet, African enterprises need to build it. However, how operations are managed in order to reach successfully their goals, is postponed to section 3.3.2.1.5.

3.3.1.2. Analysis cross-case studies Cross-case analysis aims at identifying communalities and diversities between transportation sharing models. Indeed, after having presented the key performances and understood which operations hide behind their monitoring, it is now possible to extend the analysis to all the encountered transport categories and complete the study. Therefore, through KPIs examination, three main differences can be highlighted.

Timeliness vs Readiness At the beginning of each sub-chapter dedicated to within-case analysis, the Critical Success Factors for B2P and P2P cases have been introduced. Although they share reliability, the assigned meaning is slightly different. In the first case it is about car availability, whereas, in the second, about match availability. B2P, indeed, measures Ready-to-rent rate, corresponding to the vehicles percentage that

83 ANALYSIS is being used or available to be booked in any given moment; thus, the service is reliable if the company provides a high ready-to-rent index, because it means that the customer easily finds a car. P2P, instead, checks Completion rate, meaning the number of fulfilled requests over the total ones; therefore, a high rate corresponds to high matching ability. Indeed, whenever the rider or the driver gives up, the match is unsuccessful. It is perceivable that the operational levers to increase these indices are different relatively to the sharing model adopted, but this question is postponed to cross-case analysis between operations. Then, each of the organisation typology has a proper CSF. B2P focuses on readiness; the interviewed firms control service quality like Refuelling level, How often the car cannot be used due to a default, and cleanliness level. These are the main performances that a car needs to satisfy. P2P centres on timeliness, though ETA and Waiting time, but also in terms of customer assistance, with Turn-around time for resolving issues. After all, considering the way the B2P service is provided, it has no sense to discuss about timeliness, as these platforms deal with stable vehicles that customers have to reach. Therefore, it is rather about proximity, which is embedded in the reliability concept; indeed, the service is reliable if it is able to cover high-demanded areas and make the renter find a means to book. Hence, B2P enterprises trace Cars placement.

Intangible vs Tangible Assets Still focusing on the service provided, the great difference between the two scenarios is vehicle ownership, which highly impacts economic indicators. In percentage terms, the 24% of the KPIs monitored by B2P firms is about this aspect, against the 9% of P2P ones. Indeed, the former deal with costs related to car maintenance and rebalancing, while the only cost item presented by P2P managers is marketing. For sure, these last companies bear other expenditures which are in function of the operations they manage. However, a deeper economic analysis is delayed to operations description and cross-case study. At least, it is evident that the two platform types sustained two different investments, vehicle fleet against IT technology, for which their profitability is assessed. B2P tracks Return per car, evaluating the yield per each single small investment, while P2P measures Total gross booking per trip, where the enterprise earns the service fee. Moreover, both typologies monitor trip duration, as they both gain according to the time spent for the ride; however, B2P calculates it in terms of Trip length, as it has costs linked to car movement, unlike P2P. In addition, the big difference between tangible and intangible asset implies that B2P organisation deals with idle capacity, which is a core pillar on which Sharing Economy services are built. Indeed, the concept of “sharing” in these platforms is embedded in the utilisation of the same vehicles among

84 ANALYSIS disparate actors. On the contrary, P2P firms never suffer of idleness, meaning when the capacity exceeds the demand (there are more active drivers than requests), and, better saying, they do not have to cover any cost, because carmen are paid per trip. This is not valid for B2P, which actually checks Utilisation rate and Idle time.

“Drivers and Riders” vs “Customer” The distinction between driver and rider is intrinsic in the nature of P2P ride-hailing service. Indeed, since the enterprise does not own any vehicle, it must rely on car owners who provide the trip for them. Consequently, in order to verify if the available fleet is enough to satisfy the demand, P2P organisations monitor both Number of drivers and riders. From their side, B2P ones, simply deal with customer, where the passenger and the pilot coincide; therefore, they are interested in tracing the demand in terms of Active customers per month that is compared with Cars availability. The same differentiation is reflected in reputational indicators. Since P2P interacts with two customer types, it verifies if they are both satisfied; in fact, their retention is important since the two of them contribute to platform profitability: the renter acquires the service that, without drivers, cannot be delivered. Whereas, B2P simply asks for app rating which entails in-car experience and ease of app usage. Moreover, the overlapping between pilot and passenger in B2P, and their distinction in P2P case, affects also the safety perception. For the latter, the danger is perceived inside the vehicle, indeed P2P platform has to provide the service in the clearest way possible, presenting real-time data and assistance during the trip, as already explained in the chapter above. Indeed, one of the interviewed companies indicated Service safety as a parameter evaluated by riders. While, for the former, the danger is outside the car. Share Now checks How long it takes to open the vehicle; even though this indicator impacts on customer experience, since the easiest to get in the car, the higher the satisfaction, it is also an index of safety, since many means are used in night hours to go back home. Therefore, the earlier in the vehicle is the better.

Despite the main differences listed above, there are some KPIs which are adopted by either B2P or P2P case, but are suitable for both, as Conversion, Retention, Churn and Reactivation rate, App rating and Download rate. As it can be noticed, they are all common marketing parameters. There are some indicators, instead, which have the same aim but are shaped on the context they refer to. In P2P, Number of trips, which measures the business volume, is represented in B2P by TAVD (Travels per Active Vehicle per Day). Then, in P2P case, Fast time trip is an index of service efficiency, since it keeps track of the drivers performing the best journeys, in B2P it corresponds to

85 ANALYSIS

How fast the car is picked-up, as it is a sign of efficient car positioning. Finally, since in P2P customers are influenced by timeliness, ETA is the external KPI through which they evaluate to take a ride; in B2P the equivalent weighted parameter is the distance to walk until getting the closest car. To conclude, having in mind the service features deriving from performances analysis, it is possible to better interpret the distribution of the six KPIs categories for the two sharing models, displayed in Graph 19.

100% 2% 7% 7% 90% 11% 80% 30%

70% 24% ENV 60% 9% SAF

50% QUA REP 40% ECO 30% CAM 57% 54% 20%

10%

0% B2P P2P

Graph 19 - Cross-case KPIs Distribution Comparison As preannounced by the within-case studies, the relevant aspect for both businesses is service coverage, availability & matching; however, as resulted by the identified CSFs and, consequently, the related key performances measured, the operations behind this KPIs group are expected to be diverse. The second class for B2P case is economic impact that is substituted in P2P one by reputation. Indeed, according to transportation service characteristics, the pertinence of the KPIs changes. Companies owning assets are more interested in cost sustained and their coverage, rather than app reputation. This does not imply that this aspect loses of importance, otherwise the enterprise would not have any client. However, there are clusters of indicators more relevant in one case than the other. For instance, reputation in P2P is more crucial because the platform has to enable trust between strangers, whereas, for B2P case, economic impact represents its third category. The last more debated group is platform quality. Besides the different service properties, the main communicational channel through which the firms get in touch with their customers is the app; thus, its interface needs to be managed.

86 ANALYSIS

3.3.2. Operations Management 3.3.2.1. Analysis Within-Case studies The first analysis conducted aims to evaluate the level of heterogeneity and homogeneity between the types of operations and how they are managed by interviewed companies, maintaining a separation based on business model.

3.3.2.1.1. B2P – Operations Management Description The operations that are managed by B2P organisations are mainly divided into three areas: (1) moving vehicles from a starting point Xi to a final Xf to stimulate demand, commonly called rebalancing, (2) refuelling means with electricity or petrol to make them always ready-to-use for the customer and (3) carrying out maintenance activities to ensure their safety and restore them. From the description detected through the interviews, it was discovered that the specificity of these operations, their applicability and how they are carried out are influenced by the vehicles’ typology, meaning to 2- or 4-wheeled. Therefore, during the analysis, the activities related to both types will be explained in detail. Furthermore, to give consistency also in monetary terms, the related cost functions will be inferred to establish the main cost drivers and whenever one type of mean is more convenient than another. In addition, the business practice adopted by the interviewed firms (in-house versus outsourcing) will be explored to identify pros and cons of each alternative and any patterns related to their size.

Rebalancing It is an activity consisting in moving vehicles from a low- to a high-demand zone. It is performed since it is important to guarantee an offer to the customer and, thus, satisfy his request by making him find a ready-to-use vehicle in his vicinity. The areas where demand is concentrated varies throughout the day. They can follow either daily pattern, such as in the morning the demand is concentrated in residential areas for people commuting to work, whereas in the late-afternoon it turns in business areas for workers returning home; or they may be occasional following a mass event. The possibility of rebalancing the fleet strongly depends on the type of vehicle. Indeed, it is typical of platforms offering bikes and scooters that can rely on mass transport, while it is critical for car-sharing firms which have to perform a one-at-a-time mobility. In addition, the size of the organisation strongly affects how the operation is carried out. There is a direct proportional relationship between the number of vehicles to be moved and the enterprise size: the more a company is spread over the territory and consolidated, the more means it has at its disposal and the more it has to rebalance the

87 ANALYSIS fleet in order to satisfy the customers’ requests. Thus, depending on the three possible growth stages of a firm (small, medium, large), rebalancing can be done differently: • stage 1 → the workers are in charge of moving the vehicles around the city. Usually, two operators go to point Xi to pick up the vehicle to be moved, one of them drives it till the parking point Xf, followed by the colleague who brings the operator to point Xi+1 for the subsequent transport; • stage 2 → a van is used to load the vehicles to be moved. Thus, an operator touches all points Xi, where the vehicles are parked, loads them on the truck and takes them to the respective points Xf; • stage 3 → following the same logic of stage 2, a tow truck is used for a mass displacement. The difference compared to the previous one is given by the quantity moved simultaneously, which requires a larger mean at an advanced stage of growth. For better understanding, Figure 10 and Figure 11 graphically display the concepts described above.

Figure 10 - Rebalancing Stage 1 Figure 11 - Rebalancing Stage 2 and 3

Depending on the type of transport used, the firm incurs different costs. At stage 1, the cost function is the sum of three components: • the cost of moving the vehicle which is a function of movement time that varies from vehicle to vehicle in terms of distance to travel and traffic intensity encountered. This cost is valued at the hourly salary of the two operators who perform the activity, but also at the loss of income incurred, due to the fact that during the rebalancing period the vehicle cannot be used by the customer; • the cost of reaching the i+1 vehicle, after parking the previous one. It is computed as the cost of the two operators multiplied by the time to reach the i+1 point, which still depends on the distance to travel and traffic intensity. During this task, there is no loss of earnings for the company as no vehicle is occupied; • additional costs such as gasoline, battery replacement etc.

88 ANALYSIS

푁 [∑ 푇 ∗ ( 2 ∗ 퐶 ∗ 60 + 푃푟𝑖푐푒 )] 푚표푣푒푚푒푛푡[푚𝑖푛/푣푒ℎ𝑖푐푙푒] 표푝푒푟푎푡표푟푠 표푝푒푟푎푡표푟 € [푚푖푛⁄ ] [€⁄ ] [ ⁄ℎ] ℎ 푚푖푛 푖=1 푁 + [∗ ∑ 푇 ∗ (2 ∗ 퐶 ∗ 60 )] 푟푒푎푐ℎ 푝표푖푛푡[푚𝑖푛/푣푒ℎ𝑖푐푙푒] 표푝푒푟푎푡표푟푠 표푝푒푟푎푡표푟 € [푚푖푛⁄ ] [ ⁄ℎ] ℎ 푖=1

+ 퐴푑푑𝑖푡𝑖표푛푎푙 퐶표푠푡푠 [€], where N is the number of vehicles to be moved.

The additional costs component is inserted in this formula, as well as in all the others described in this chapter, for completeness. However, they are normally negligible compared to the others which are described in detail.

Considering, instead, stage 2 and 3, the cost function is relatively formed by the same components. The main difference lies in the quantity of vehicles transported simultaneously and in the cost of renting related to the typology of truck leased to perform the activity. Indeed, a tow truck has a higher leasing cost than a van, but allows to move more 2-wheeled means at the same time. The overall cost, in addition, is a function of the time employed to perform a tour, meaning the time to collect all the means to be repositioned and unload them to their destinations.

퐶 + ∑푁 푇 ∗ 퐶 ∗ 푁°표푝푒푟푎푡표푟푠 ∗ 60 + 퐴푑푑𝑖푡𝑖표푛푎푙 퐶표푠푡푠 , 푟푒푛푡푖푛푔[€] 푖=1 푚표푣푒푚푒푛푡[푚𝑖푛/푡표푢푟] 표푝푒푟푎푡표푟 € [푚푖푛⁄ ] [€] [ ⁄ℎ] ℎ where N is the number of vehicles to be moved and under the hypothesis that only one vehicle is rented to perform. Indeed, it is reasonable to assume that only one team is dedicated to perform this activity along the working day. In this cost function, the number of vehicles does not appear directly, but it is incorporated in the time components. Indeed, the higher the number of means to be rebalanced, the higher the time to complete the activities, thus the higher the cost of rebalancing.

The primary benefit of moving vehicles in groups comes from exploiting economies of scale. Indeed, the rebalancing cost is spread across multiple means, making this operation cheaper for the firm. This is also important from a lean management perspective. As will be explained in the next section, rebalancing can be classified as a waste called Muda Type 1, a non-value-added activity necessary for the customer. Indeed, even if rebalancing does not add value to the service, if the client does not find a vehicle nearby, he may decide not to use the service or to switch to competitors. Those wastes cannot be eliminated as they are necessary for the customer, but they must be reduced as much as possible aiming to improve their efficiency and effectiveness.

89 ANALYSIS

Since economies of scale cannot be applied to 4-wheeled vehicles, car-sharing companies do not manage this operations and limit rebalancing as a secondary activity performed downstream of others that involve the movement of the vehicle such as a repair in the workshop. As a solution, ReachNow offers to its customers incentives to park the car in a more prosperous area. Furthermore, concerning lean management, relying on customer, this task would no longer be a waste.

Refuelling Refuelling requires two different activities depending on the vehicle type: feeding using petrol or electricity or replacing the battery, respectively related to 4- and 2-wheeled vehicles. Given their diversity, they are performed differently. Considering the first one, a truck moves from car to car to refuel them, usually during the night shift when the demand level is below-average and the traffic intensity is almost nil. Assuming that just those vehicles whose petrol level is below a certain threshold μ are refuelled, the associated cost function is given by: • truck cost meaning the rental or purchase one; • refuelling cost which is a function of the time to move between vehicles and that to refuel them. Examining the former, it is reasonable to assume that a mathematical algorithm is applied considering the positions in which the cars to be refuelled are located in order to compute the optimal path to follow, minimising the travel time; • raw material cost whose drivers are the type of source that feeds the vehicle, which usually can be gas or fuel, and the necessary quantity. This one, under the initial assumption that vehicles are refuelled below a certain petrol-threshold μ, can be figured out as average of the requirements; • additional costs.

퐶 + [∑푁 (푇 + 푇 ) ∗ 퐶 ∗ 푁°표푝푒푟푎푡표푟푠 ∗ 60 ] + 푡푟푢푐푘[€] 푖=1 푟푒푓푢푒푙푙푖푛푔[푚𝑖푛] 푡푟푎푣푒푙[푚𝑖푛] 표푝푒푟푎푡표푟 € [푚푖푛⁄ ] [ ⁄ℎ] ℎ 푄푢푎푛푡푖푡푦̅̅̅̅̅̅̅̅̅̅̅̅ ∗ 퐶 + 퐴푑푑𝑖푡𝑖표푛푎푙 퐶표푠푡푠 , 푟푎푤 푚푎푡푒푟푖푎푙 [푙𝑖푡푟푒푠] 푟푎푤 푚푎푡푒푟푖푎푙 € [€] [ ⁄푙𝑖푡푟푒푠] where N is the number of vehicles to be moved.

In order to get savings on this cost, Share Now encourages the customer to refuel the car at the end of the trip. Therefore, It allocates on each vehicle a kind of credit card that allows the client to feed it going to a partner petrol station.

90 ANALYSIS

Switching to 2-wheeled vehicles, there are two main components related to how this activity is performed. One that can be considered a visible back-office activity, since it is carried out on-site and it can be noticed by the customer, related to battery change, while the other is a completely back- office task since it takes place in the company’s battery warehouse and concerns their recharging. The cost associated to the first one has the same drivers of the previous formula: travel and refuelling time. However, the latter in this case is called change time in relation to the task actually performed and can be considered as an average as it is a repetitive activity whose time depends on the effectiveness of the operator. Due to its nature, the concept of learning curve can be associated to it. Indeed, as the task is repeated, the employee learns how to complete it quickly, therefore reducing the amount of time needed for a unit of output. The cost associated to the back-end activity is, instead, given by the battery capacity, which is a measure of the quantity of electrical energy stored and is expressed in Ampere-hours (Ah). In order to convert this unit of measurement into kWh which is used to value the unitary cost, the following formula is used: 푊 퐴 ∗ 푉표푙푡푎푔푒 = ℎ = 푘푊 ℎ 1000 ℎ

If for the on-site activity the initial investment is given by the truck, for this one it is also given by the necessary infrastructure to recharge several batteries at the same time. This device is formed by different slots where to position the batteries to recharge. The charging time is not included in the cost function because it is not the driver of its cost, but it is necessary to compute the slot requirement. Indeed, the latter depends on the daily recharging demand and the charging time, calculated as:

퐵푎푡푡푒푟푦 퐶푎푝푎푐𝑖푡푦[푘푊ℎ] 푇푐ℎ푎푟푔푖푛푔[ℎ] = 퐶ℎ푎푟푔𝑖푛푔 푆푡푎푡𝑖표푛 푃표푤푒푟 [푘푊]

Thus, the number of slots necessary can be counted as follow:

퐴푣푎𝑖푙푎푏푙푒 푇𝑖푚푒 ℎ [ ⁄푑푎푦] (푁° 푟푒푐ℎ푎푟푔푒푎푏푙푒 푏푎푡푡푒푟푦 푏푎푡푡푒푟푖푒푠 ) = [ ⁄푑푎푦] 푤푖푡ℎ 1 푠푙표푡 푇푐ℎ푎푟푔푖푛푔 ℎ [ ⁄푏푎푡푡푒푟푦]

퐵푎푡푡푒푟푦 퐷푎𝑖푙푦 푅푒푞푢𝑖푟푒푚푒푛푡푠 푏푎푡푡푒푟푖푒푠 [ ⁄푑푎푦] 푁° 푆푙표푡푠 = 푁° 푟푒푐ℎ푎푟푔푒푎푏푙푒 푏푎푡푡푒푟푦 푏푎푡푡푒푟푖푒푠 [ ⁄푑푎푦] determining the necessary infrastructure investment.

91 ANALYSIS

The overall cost function is, therefore, equal to:

푁 {퐶 + [(푇̅ ∗ 푁°푏푎푡푡푒푟𝑖푒푠) + ∑ 푇 ] ∗ 퐶 ∗ 푁°표푝푒푟푎푡표푟푠 ∗ 60 } 푡푟푢푐푘 [€] 푐ℎ푎푛푔푒 푚𝑖푛 푡푟푎푣푒푙[푚𝑖푛] 표푝푒푟푎푡표푟 € [푚푖푛⁄ ] [ ⁄푏푎푡푡푒푟푦] [ ⁄ℎ] ℎ 푖=1

+ {( 푁°푏푎푡푡푒푟𝑖푒푠 ∗ 퐵푎푡푡푒푟푦 퐶푎푝푎푐𝑖푡푦 ∗ 퐶 ) + 퐶 } + 퐴푑푑𝑖푡𝑖표푛푎푙 퐶표푠푡푠 [푘푊ℎ ] 푒푙푒푐푡푟푖푐푖푡푦 € 푖푛푓푟푎푠푡푟푢푐푡푢푟푒[€] [€], ⁄푏푎푡푡푒푟푦 [ ⁄푘푊ℎ] where N is the number of vehicles to be moved and under the hypothesis that a vehicle requires one and only one battery.

The main issue to deal with for operations managers is related to the inventory rotation. Indeed, in order to be fast and efficient in the change, operators usually take the battery closest to the exit point of the warehouse, thus adopting a LIFO (last-in, first-out) rather than FIFO logic (first-in, first-out). This behaviour generates problems in warehouse rotation, as the use of batteries is not evenly distributed among the stocks but is concentrated only on a part, as well as their wear. Changing the arrangement of the batteries inside the warehouse, favouring the adoption of the correct policy, could be a solution to be examined.

Maintenance The last important operation managed by all B2P organisations is maintenance, whose way of being carried out does not depend on the type of vehicle, unlike the others, but it is divided into two classes according to the type of repairs to which refer to and that can be small or big. The first concerns small damage such as a mirror to be repaired or the brakes and it is done on-site by mechanical operators. Generally, it is performed in conjunction with other activities such as refuelling or battery change. The second, on the other hand, regards macro-replacements and requires the transport of the vehicle in a workshop, usually the company’s one or an affiliate, in order to have professional repairs following serious accidents. These two types of maintenance fall into the category called incidental which provides to act after the failure of a component. There is another class of maintenance named preventive which aims to replace an element before it gets damaged. It is applied when the affected elements compromise the operation of a vehicle. It is carried out inside a factory and, as will be explained in paragraph 3.3.2.1.2, is generally outsourced to partner organisations.

Considering the maintenance at break, the cost function incurred by the companies follows the type of repair performed. Starting from the small one, the main cost components are mainly driven by the

92 ANALYSIS time spent in reaching the vehicle, analyse the problem and solve it, and to the purchase of the needed mechanical components j-th.

{[퐶표푝푒푟푎푡표푟 푒 ∗ 푁°표푝푒푟푎푡표푟푠 ∗ (푇푡푟푎푣푒푙[푚𝑖푛] + 푇푎푛푎푙푦푠푖푠[푚𝑖푛] + 푇푟푒푝푎푖푟[푚𝑖푛] )] [ ⁄ℎ]

푘 + ∑ 퐶 ∗ 푁°푝𝑖푒푐푒푠 , 푐표푚푝표푛푒푛푡,푗 € 푐표푚푝표푛푒푛푡,푗} [ ⁄푝𝑖푒푐푒] 푗=1 푣푒ℎ푖푐푙푒 where k is the number of damages to be mended. This cost is sustained for each vehicle that needs to be repaired. The formula is equal to that related to big mends, for which it is necessary to double the travel time component since the operator needs not only to reach the vehicle, but also to bring it to the workshop. Furthermore, in this case, the time requested for the damage analysis is higher in accordance with the extent of the problem.

{[퐶 ∗ 푁°표푝푒푟푎푡표푟푠 ∗ (2 ∗ 푇 + 푇 + 푇 )] 표푝푒푟푎푡표푟 푒 푡푟푎푣푒푙[푚𝑖푛] 푎푛푎푙푦푠푖푠[푚𝑖푛] 푟푒푝푎푖푟[푚𝑖푛] [ ⁄ℎ]

푘 + ∑ 퐶 ∗ 푁°푝𝑖푒푐푒푠 } , 푐표푚푝표푛푒푛푡,푗 € 푐표푚푝표푛푒푛푡,푗 [ ⁄푝𝑖푒푐푒] 푗=1 푣푒ℎ푖푐푙푒 where k is the number of damages to be mended. Difference in costs is also given by components standardisation and tasks repetition which benefit small repairs. Indeed, compared to major ones, they are more frequent, similar and generally related to the same mechanical elements.

Concerning the transport, it would be necessary to consider also those additional costs included in the cost functions described for refuelling. However, while for the previous operations transport represented an integral part for carrying out the necessary tasks, in this case it is negligible. The most important operations described in this paragraph show the flexibility of the 2-wheleed fleet compared to the 4-wheeled ones. In fact, smaller and lighter vehicles can be moved more easily and supplied even during the day as the use of a truck is not required. Thus, the costs incidence on companies’ profits is also lower compared to car-sharing. However, the ease of performing operations as well as the customer demand is highly dependent on the weather conditions. Indeed, in case of rain for instance, moving vehicles or replacing the battery becomes more complicated, also considering the safety of the operators, and often avoided. The only operations whose characteristics do not

93 ANALYSIS change between vehicles type is maintenance, for which activities are performed in the same way and bad weather conditions are adverse in both cases. Nevertheless, car-sharing incurs higher costs due to the expensiveness of means’ components compared to bicycles and scooters, and to the activities that must be performed which require often experience and higher times. In the following Table 13, the analysis carried out, costs aside, has been summarized according to two classifications: performing driver, meaning the factor that leads an operation to be conducted differently, and performing companies, related to the type of company managing it under the vehicle type division.

Table 13 - B2P Operations Drivers

Among the operations that firms have to manage, there are some sporadic, unmanaged on a daily basis and fewer widespread among organisations that are reported for completeness. Alongside routine maintenance, there is an extraordinary one called recovering. It is an activity carried out following the theft of a vehicle or its inappropriate use, such as smoking inside it. These situations require the platform to localise the vehicle using the GPS device and restore it, guaranteeing to next customer a satisfying experience. This operation highlights another critical issue to manage: the supervision of customers’ behaviours. Indeed, incorrect demeanour not only generates vehicles problems which are reflected in extra costs, but risks to damage the corporate image as well as the other customers’ experience. Another operation that will be better explained in chapter 5.4.2.1, given its importance during the pandemic, is cleaning. Some enterprises, like Jump and ReachNow, mop the means in conjunction with other activities such as small repairs or battery change. Being carried out in parallel, it does not produce a significant additional cost, which is only function of the time to perform the task and the material used, whose both magnitude is smaller than that of the parallel activity. The last useful tasks to mention are the operational and financial management of the fleet. There are teams dedicated to buying new vehicles and selling old ones, trying to earn their residual value.

94 ANALYSIS

3.3.2.1.2. B2P - Business Practices Adopted After having described the most important operations, the way in which they are carried out, and the respective costs incurred by the companies, it is important to understand the policy adopted by the firms interviewed, what are the relevant decision-making drivers and whether the decision is influenced by the corporate size.

Outsourcing is the business practice of hiring a party outside a company to perform services that traditionally were realised in-house by the organisation’s own employees. It is undertaken as a cost- cutting measure since it can help businesses reduce labour costs significantly. In addition, an enterprise can employ an outsourcing strategy to better focus on the core aspects of its business. However, it does have also disadvantages, among which one of the most important is the addiction to a third party. In fact, if the latter stopped offering a certain service, the company’s business would suffer. As a solution, many firms, including those surveyed, do not just rely on one supplier, but sign contracts with multiple local ones.

For each operation, a two-dimensional matrix is created using as axes the business practice adopted and the company interviewed divided by the vehicle type, as shown in Table 14.

Table 14 - Business Practice Adopted

95 ANALYSIS

As seen in paragraph 3.3.2.1.1, the main driver of difference is the type of vehicle. Therefore, within this analysis, it is possible to adopt a cross-case perspective based on it, in order to infer the motivations towards a business practice rather than another.

Table 14 shows that car-sharing platforms outsource every operation. Leaving aside rebalancing, which will be discussed briefly later, the decision is coherent with the competences and the infrastructures requested by those activities. Indeed, starting with refuelling, whereas 2-wheeled enterprises only require a multi-charging device and a power socket, for 4-wheeled ones is essential to have a tanker and the related fuel inside. While the former can be purchased once and amortised over time, the latter must be continuously supplied by a third party, making the previous investment avoidable. Therefore, the supplier from which only gas and fuel would be purchased is the same from which the entire service is bought. The principle is the same applied to a truck that supplies a petrol station. The difference lies in the fact that the activity is not concentrated in a single point, but the tanker has to refuel many cars dispersed around the city, thus the supplier does not benefit from the centralisation of the service. This operation, however important, is not a core competence for car- sharing firms. In fact, in the event that a supplier stops offering a service, incentives can be given to the customer to refuel the car at the end of the trip. The same can be used to reduce the dependency on the supplier. Despite the operation externalisation, the organisation continues to be in charge of its management. Indeed, taking as example ReachNow, It generates a ticket for refuelling, sending it to a local partner that becomes in charge of the task. The same happens for repairs. Switching to maintenance considerations, the required skills to perform the activity are more specific compared to the 2-wheeled case, since the components of a car are more complex than those of a scooter. Although some small tasks are normally done in-house, such as changing tires, others require skilled mechanics. Therefore, car-sharing platforms rely on dealerships for all minor repairs and on specialized workshops to mend serious damages or carry out preventive maintenance, normally affiliated on the basis of the car manufacturer to which the fleet belongs. In this case, the outsourcing decision is safer compared to other operations. Indeed, the maintenance and repair market is very large. This makes the partnership in favour of car-sharing companies which can rely on low switching cost, finding easily another supplier in case of contract breach with the current one. Having a higher bargaining power, enterprises externalise to those suppliers also rebalancing service. Indeed, they instruct the dealer where to park the car, so that it stands in a high-demand point. Being a sporadic operation, performed downstream maintenance, the decision to externalise it, to the companies that deal with the latter, is consistent. Considering 2-wheeled firms, on the other hand, for which this

96 ANALYSIS operation is managed also from a tactical-strategic point of view, outsourcing is not a plausible choice. There is a team of operations managers who use KPIs and tools, such as heat maps, to define a strategic plan to rebalance the fleet. Still analysing scooter- and bike-sharing platforms and focusing on the other two operations, the possibility to have a mix of business practices is wider. Indeed, MiMoto represents an example of almost fully integrated organisation, relying on a third party only for major repairs following a serious crash, while Jump adopts a higher outsourcing level, externalising refuelling. Starting to examine the first example, this Company has a team of mechanics which are in charge of all repairs in-house. However, when a mend is too extensive, requiring skilled labour, the outsourcing decision is mandatory. Given its low probability of occurrence, it is reasonable to assume that MiMoto does not have real partnership contracts with mechanical workshops, but as a customer contacts a trusted one or follows the price trend. Jump, instead, does not outsource any part of maintenance. Indeed, sharing bicycles, the repairs are certainly the easiest among the three types of vehicle; thus, a team of mechanical cyclists is enough also to perform the preventive maintenance which is not outsource as in the case of car-sharing. The investment It tries to save on is related to refuelling. Not requiring skilled labour but having to purchase the charging infrastructure and to perform it frequently (every 2 days the battery must be changed), outsourcing allows for cost-cutting.

Lastly, analysing the business practices from the corporate size point of view, it is possible to state that there is a clear link between the two. Indeed, smaller organisations like MiMoto which is concentrated in few cities (Milan mainly and Genoa secondly) prefer in-house solutions because the size of the business, and accordingly that of costs, is not so great to make managers lean towards outsourcing. Instead, for enterprises like Jump whose business is distributed all over the world, making more strategic decisions becomes essential to continue growing and expanding. Indeed, for them, saving means having economic possibilities to invest in other markets and continue to grow. It is the logic that gave birth to Jump itself, being a recent subsidiary of Uber. The same reasons underlie the choice of the 4-wheeled firms interviewed. They are spread across entire continents, ReachNow in North America and Share Now in Europe. Thus, they outsource the non-strategic operations on which they can save money, limiting the investment to the physical fleet only. The choice of complete outsourcing proved to be safer for ReachNow following its failure. Indeed, having only cars as assets to manage, bankruptcy was less significant economically compared to the case of fully in-house.

97 ANALYSIS

In conclusion, the way operations are managed, as well as the business practices adopted, strongly depends on the country in which the business is located and specifically on the city. For instance, considering the first level of differentiation, Italy, where Sharing Economy is expanded to a limited extent, has smaller companies that rely mainly on in-house operations, compared to other countries such as Spain, Poland etc.

3.3.2.1.3. B2P – New KPIs Development Having defined the Operations for the B2P model and having already discussed most of the monitored KPIs, it is now possible to broaden the analysis and introduce additional indicators that firms are supposed to track or should adopt. First, the platforms keep track of their vehicles position, located around the city. Hence, they can check the demand trend of a zone throughout the day and the week, plotting it on a two-axis chart, as in Graph 20, that is just representative.

Graph 20 - Rented Vehicles Daily Distribution

Furthermore, they can map the points where more pick-ups and drop-offs occur, always according to the time and the day. By matching this data, enterprises can optimise rebalancing. Dwelling on this operation, some additional KPIs can be developed, like Rebalancing index that is computed as:

푁푢푚푏푒푟 표푓 푣푒ℎ𝑖푐푙푒푠 푚표푣푒푑 푓푟표푚 푥 푡표 푥 1 2 푁푢푚푏푒푟 표푓 푣푒ℎ𝑖푐푙푒푠 푡ℎ푎푡 푤𝑖푙푙 푏푒 푝𝑖푐푘푒푑 − 푢푝 푓푟표푚 푥2

If it is equal to 1, it is advisable to rebalance, otherwise, when it is higher, the numerator must be reduced, so that the entire number decreases, and no more vehicles than needed are moved. However,

98 ANALYSIS for this activity to be convenient, it is necessary to verify whether another indicator is greater than 1, that is:

퐸푥푝푒푐푡푒푑 푟푒푣푒푛푢푒푠 푓푟표푚 푥 푝표푠𝑖푡𝑖표푛 − 퐸푥푝푒푐푡푒푑 푟푒푣푒푛푢푒푠 푓푟표푚 푥 푝표푠𝑖푡𝑖표푛 2 1 푅푒푏푎푙푎푛푐𝑖푛푔 푐표푠푡 푓푟표푚 푥1 푡표 푥2

This parameter examines whether the convenience resulting from the new position covers the related costs in moving. Otherwise, as already described in chapter 3.3.2.1.1, the enterprise can incentivise the users to carry out this task, and through the following formula, it can evaluate its benefit:

∑푁 푝푟𝑖푐푒 𝑖푛푐푒푛푡𝑖푣푒푠 ( 푖=1 푖) , 푅푒푏푎푙푎푛푐𝑖푛푔 푐표푠푡 푡 where N is the total incentives provided until time t, that is the period considered for the computation. A further KPI for this activity is Maximum distance to accept to rent. Indeed, when the user logs-in and checks the closest means, through this data he decides whether to block it or not. This parameter is important because it tells the company the limit where to reposition the fleet. Therefore, it can aim to reduce these costs, for example if, at a certain time of the day in a city zone, the maximum distance is very large, the rebalancing will require less monetary effort. In some cases, when the route to walk is too long, the user does not block the vehicle, however the platform can geolocate him and, by measuring its distance from the closest means, ponder to extend the service coverage in new areas. Indeed, if many users log-in always in the same place searching unsuccessfully for a suitable ride nearby, it suggests the firm that a new area of interest is forming. Along with the time taken to reach the vehicle, there are other three timelines to consider in the customer journey: • Time to Rent → the notification of car, bike or scooter availability and booking blocking; • Time to Open the Vehicle → already indicated by Share Now; • Time to Close the Vehicle. These three elements affect users’ experience and it is up to the organisation to ensure their speed, improving the technology reachability. Moving to a second B2P activity, some KPIs can be inserted for refuelling. As already discussed, this operation depends on the vehicle type. In case of 4-wheeled ones, where a truck is required, the ratio below can be used to verify when it is more convenient to refuel:

푁푢푚푏푒푟 표푓 푣푒ℎ𝑖푐푙푒푠 푡표 푟푒푓푢푒푙

푁푢푚푏푒푟 표푓 푣푒ℎ𝑖푐푙푒푠 푎 푡푟푢푐푘 푐푎푛 푟푒푓푢푒푙

99 ANALYSIS

This index must be contextualised, considering where the means are positioned and how much unfuelled they are. If many are in high-demanded areas and, in subsequent uses, the tank will be empty, then it is advisable to do this activity, otherwise it can be postponed to a second intervention. Finally, in order to understand if the car, bike or scooter will be able to cover the expected distance to travel, a good parameter is:

푅푒푚푎𝑖푛𝑖푛푔 푓푢푒푙 (푏푎푡푡푒푟푦) 푑푢푟푎푡𝑖표푛 ( ) , 푇퐴푉퐷 ∗ 퐴푣푒푟푎푔푒 푡푟𝑖푝 푑𝑖푠푡푎푛푐푒 푣푒ℎ푖푐푙푒 where TAVD is the Travels per Active Vehicle per Day (ref. 3.3.2.1.3). To conclude, for what concerns maintenance, the KPIs indicated by managers are exhaustive. Indeed, KMs run between two consecutive repairs is a quality index but it can also be converted into Time to Failure, indicating the Reliability of the means. Also assets are well monitored, such as through Battery tracking to check how long a battery lasts and its use, and costs, with Rotation index of battery stock and Maintenance cost. Therefore, the improvement that can be made in this situation is the planning of preventive maintenance. In fact, thanks to the enabling technologies of Industry 4.0, the platform can remotely monitor the driver behaviours, and forecast tire or brake wear, thus preventing puncture or breakage, increasing customer safety, too. This activity is more suitable for 4-wheeled vehicles, rather than 2-wheelers, where a breakdown, which requires corrective intervention, is more expensive and can be riskier to customers.

3.3.2.1.4. Insights on B2P Station-based Sharing-model As mentioned in the previous chapters, there are two types of vehicle-sharing companies: station- based and free-floating. The firms interviewed belong to the second class; therefore, based on the physical difference between the two models, some inferences about their operations can be deducted. In order to be consistent with the analysis of previous models, a comment about KPI is introduced. Literature papers are very helpful for station-based case (ref. Appendix B). To begin, they assess the Number of available docks per station and keep track of Vehicles utilisation and Trip length. This last parameter is very useful in order to evaluate the travelled radius by the users and verify if other strategic bases have to be installed. Moreover, it is an index for wear monitoring, because, according to the kilometres run, some maintenance actions have to be planned. Indeed, it is possible to identify two means types to intervene on, electric (both 2- and 4-wheeled) and push-vehicles. In literature, in particular, some studies discussed about the former version, introducing indicators like State of battery health, State of charge, meaning the energy left in the battery, and Charging events per day. Consequently, the Charging duration is calculated in function of those metrics; it is an index of efficiency and it allows, from one side, to guarantee a fully-available vehicle to users in the shortest

100 ANALYSIS time possible, and, from the other side, to verify if customers’ travel behaviours stressed and ruined the battery efficiency. Taking as reference the B2P free-floating case, additional KPIs can be deducted. Indeed, it is reasonable to assume that also station-based platforms evaluate the quality of the interventions, verifying the Trips run between two consecutive repairs, as well as their quantity, for example with the Number of vehicles repaired per day. Among the activities borne by the platform in analysis, the literature indicator Redistribution travel time heralds rebalancing as another core operation. Indeed, also in this case, there some strategic points where to move vehicles in order to satisfy the demand, according to the period of the day and the season. Therefore, in order to evaluate the efficacy of stages positioning, at first, and vehicles distribution per station, later, further KPIs can be developed. As regard the first aspect, Pick-up (drop- off) frequency per station indicates how many means are picked-up (dropped-off) per day per each installed base and it is an index of good positioning as it signifies that those vehicles are often rented (delivered), making that stage a crucial reference point. As concern the second one, meaning vehicles disposition along the available docks, it can be assumed that the first means picked-up are the outermost ones, because they are more easily accessible; this must be considered during the arrangement of the means in a station, whenever it happens after an intervention done in the workshop that implies their repositioning by the company’s operators. Monitoring Pick-up frequency per vehicle and Pick-up frequency per dock allows to position those with high intense usage in the ports with low frequency, and vice versa, to evenly distribute the wear occurrence on means. In light of what introduced by the KPIs analysis, further considerations can be done on operational aspect. Firstly, not being dispersed around the city, but being concentrated at the dock stations, all operations that require to visit multiple vehicles per day, such as refuelling, benefit from centralisation. Thus, it is not necessary to complete a tour around the city, but just reach the respective docks. By analysing the single operations, refuelling strictly done by operators could be avoided if the parking lots were powered by electricity. This solution would be valid for all types of means (bicycles, scooters and cars), under the hypothesis that they are electric. While centralisation benefits refuelling in any case, since it is linked to the dock and not to the vehicle, maintenance strongly depends by the positions of the latter. In this sense, several mends can be performed simultaneously assuming that means are located to the same station. Considering rebalancing, instead, since vehicles are parked at dock stations, it is not done with the same frequency as free-floating. Nonetheless, it is important for the customer to find the vehicle at the nearest dock, but also to find a park in the stations he reaches to leave it. In this sense, rebalancing among dock stations can be done in order to add vehicles from high-demand departure points and remove them from high-demand destination points, respectively to ensure available vehicles and parks to the client. Alongside rebalancing, it is essential

101 ANALYSIS for these companies to strategically decide the location where to place the various dock stations as well as the respective number of slots.

3.3.2.1.5. P2P – Operations Management Description From systematic literature review to KPI analysis, it is easily understandable which is the core of P2P transportation operations: matching. However, it is just the outcome of additional aspects that need to be handled for both the rider and the driver, corresponding respectively to demand and capacity management. Firms, working in this industry according to this business model, share the same operations types, which will be systematically presented below. But the way in which they are structured and managed sets them apart. Indeed, as already introduced in section 3.3.1.1.4, these platforms are in different business phases; therefore, while B2P operations are handled differently based on the type of vehicle shared, those of P2P firms strongly depend on corporate size and structure. For completeness, a review of organisations’ data is reported below in Table 15 and make reference to Figure 9 as a reminder.

Table 15 - Data Summary of P2P Platforms

A peculiarity of P2P platforms is the management of two sides: the driver and the rider. Hence, also operations can be distinguished according to these categories. However, besides the differentiation, they influence each other. Therefore, considering the first side, activities comprise drivers’ recruiting and training, drivers’ behaviour management and assistance, fleet and capacity management. While, those related to the second one are customer care and rides acquisition. Finally, pricing is an operation that takes into account both sides. To be consistent with the analysis in B2P paragraph, the operations are listed below and discussed also in economic terms to evaluate the different management decisions taken by the platforms, interpreted in light of their growing phase.

102 ANALYSIS

Drivers’ Recruiting and Training Drivers’ recruiting consists in the identification of supply areas where to hire, whether to increase the capacity level in existing regions or extend the business in new ones. In drivers’ selection, specific criteria are evaluated to authenticate that the platform has the right set of carmen. This phase is followed by their training, which implies courses either one-by-one for African organisations or online for Uber, where it is taught to soon-to-be-drivers how to use the application, receive the trips, end a ride, collect money and check statements. In addition, they are formed on interaction with customers; indeed, service quality is an order winner for P2P transportation market. Since these enterprises provide the same service type, the key aspect is how it is provided and the related rider’s experience. Actually, the driver is the direct interface of the customer with the company, providing the core service (the trip) and this strongly affects brand image. Therefore, it is important to select reliable employees and train them according to the standard quality level the firm is willing to give. The difference in the training mode between African platforms and Uber derives from the corporate size and, consequently, from the number of operators to form. Indeed, it is assumable that the formers deal with few pilots to train, since they can rely on one-by-one courses, while this is impossible for the latter who counts on hundreds of new drivers. In this way, once the online course has been designed, the costs up to Uber are lower than African platforms, as it can take advantage from economies of scale, training multiple pilots simultaneously. Indeed, Uber cost function is structured into three terms:

퐶푑푒푣푒푙표푝푚푒푛푡 + 퐶푡푒푎푚 + 퐶푢푝푑푎푡푖푛푔

The first is the development cost and it is in function of four items: • personnel → a cross-functional team developed the concept and designed the online course; hence this cost includes the programmer who developed the app, the designer who drew the interface, the HR and the team devoted to drivers’ management who personalised the course according to different legislation and cultural aspects; • format → the more the course is designed with audio, video, gamification13 and simulation, the more it will cost; • duration → the longer, the more expensive; • personalisation → at global level, this aspect highly impacts on the cost as it must be customised according to the different legislations and regulations in the countries; then, once established, the course itself is the same for all the carmen.

13 The application of typical elements of game playing (e.g. point scoring, competition with others, rules of play) to other areas of activity, typically as an online marketing technique to encourage engagement with a product or service.

103 ANALYSIS

Cteam is the cost the Enterprise sustains for the team in charge of drivers’ recruiting and training, which, among the activities they carried out, also takes care of issues encountered during the courses. However, this expenditure is not completely charged to this cost function, as they are paid for other activities.

Finally, Cupdating refers to course adjustments, according to new regulations or quality standard that need to be taught. Hence, this cost is still in function of the same parameters of the first one. The African annual training cost, instead, can be computed as:

̅̅̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 퐶푡푟푎푖푛푒푟[€/ℎ] ∗ 푡푐표푢푟푠푒[ℎ] ∗ 푁°푎푐푞푢푖푟푒푑 푑푟푖푣푒푟푠 + 퐶푚푎푡푒푟푖푎푙[€] ∗ 푁°푎푐푞푢푖푟푒푑 푑푟푖푣푒푟푠 + 표푣푒푟ℎ푒푎푑

The course is provided and managed in-house, thus it is possible to assume that an employer is in charge of this operation and he is paid on an hourly basis, rather than with an annual salary, since training period depends on the number of drivers to form. Moreover, overheads include the investment to prepare the staff, the offices where to submit the courses, and the support materials. As remarked by the two cost functions, the first one is independent from drivers number and, even though it requires higher investment, the business volume allows to spread it as the number of arrivals increases, and exploit economies of scale. While the second one is in linear relation with the number of drivers and cannot rely on centralisation advantages, unless it started organising courses with more people at once.

Drivers’ Behaviour Management and Assistance Even once drivers’ profile is online, the firms keep on monitoring their behaviours and performances. As introduced in KPIs analysis, both Uber and Little Ride intervene on carmen’s conduct whenever it is needed. Indeed, starting from their rating and by deepening the reasons behind low scores, the companies can evaluate to take corrective actions, retraining or suspending them. To this purpose, they fix a minimum mark that each driver must get to keep on working for them. This is always in line with guaranteeing service quality as an order winner. Furthermore, the platforms provide assistance to drivers whenever they require it, for instance, if they have problems with the app or they need to update expired documents. Therefore, in economic terms, this operation depends on the autonomation level. If drivers’ rating and customer feedbacks analysis is automatically monitored, the cost up to the company is mainly related to algorithm development and to design of a devoted section on the platform; the algorithm collects data, clustering per carman and trip, computes the average score and tracks the ratings trend. Then, either the application or an employee can get in touch with the drivers of interest. In the first case, the app sends a warning to the pilot about his negative trend, asking for a feedback to be

104 ANALYSIS evaluated automatically or by the team. In case of recidivism, a team member can intervene. In the second scenario, the employer directly calls him to investigate the reasons behind his behaviour. When the enterprise is really at introduction phase of its business, it may happen that data analysis is carried out by the operator rather than the platform, requiring less investment but higher effort in terms of time and level of detail. As a result, the cost formula can be computed as sum of three components:

퐶푝푙푎푡푓표푟푚 + (퐶푑푎푡푎 푎푛푎푙푦푠푖푠)푎푢푡표푛표푚푎푡푖표푛 푙푒푣푒푙 + 퐶푡푒푎푚

Fleet Management Although P2P organisations do not own any car, some adjustments are entailed. Indeed, even though they do not directly control the vehicles, they must provide a minimum quality standard, which is partly linked to car conditions. To this purpose, African platforms do random inspections and provide incentives for repairs. On the contrary, Uber carries out checks through vehicles registrations. Also in this operation, the business volume affects the way it is performed. Uber handles a global fleet that is too numerous to be able to control enough cars to guarantee a minimum quality level. Hence, also this activity is done remotely and automatically. Indeed, an algorithm can compare data inserted by drivers with parameters set by Uber, evaluate if the car meets the standards and eventually send a notice. Like in the previous case, the cost is partly linked to algorithm development and partly to personnel, since the assistance centre is responsible for contacting the driver. Thus, the resulting formula has the same items:

퐶푝푙푎푡푓표푟푚 + (퐶푑푎푡푎 푎푛푎푙푦푠푖푠)푎푢푡표푚푎푡푖표푛 푙푒푣푒푙 + 퐶푡푒푎푚

Instead, the cost up to African firms could be linked to the team, if it directly contacts the car-owner, or to the development of an IT function that randomly sends an inspection notice to carmen. Moreover, they had to pay mechanics who supply car inspections. This last cost depends on the number of vehicles to detect. Thus, assuming that the notice for car inspection is automatically send by the platform, the cost function turns to be:

퐶푓푢푛푐푡푖표푛 푑푒푣푒푙표푝푚푒푛푡 + 퐶푚푒푐ℎ푎푛푖푐

= 퐶 + (퐶 € ∗ 푁° 푐푎푟 ) 푓푢푛푐푡푖표푛 푑푒푣푒푙표푝푚푒푛푡 푖푛푠푝푒푐푡푖표푛[ ] [ ] 푐푎푟 푦푒푎푟

The two solutions provided by Uber and African companies are the result of a trade-off between quantity and quality. Indeed, Uber deals with high quantity, hence in order to inspect all, It has to

105 ANALYSIS sacrifice quality, by looking just at information available on car documentation. Little and Mondo Ride, instead, who manage less vehicles, can focus more on quality aspects, even though inspection is done by sample, therefore it is not an exhaustive analysis. Graph 21 represents the trade-off.

Graph 21 - Trade-off Between Quality and Quantity for P2P Firms

However, in the specific case of Uber Black, the luxury version of the service, the Company has to guarantee a higher quality level. Hence, It annually renews the car standards necessary to offer the service for the following year and “enforces” quality improvement. Indeed, this service consists in car rental with professional drivers, thus it requires higher standards. Therefore, Uber offers favoured leasing and warns them in advance in order not to catch aback existing pilots.

Capacity Management In service industry, this activity entails the control and prediction of service performances. Hence, KPIs like the number of activated drivers and the fulfilment rate evaluate the capacity level, which is preparatory to understand if some interventions are needed, comparing them with pre-fixed target levels and actual demand. Capacity management can be proactive; for example, Little Ride, by following the ride requests trend, adjusts recruitment frequency from daily to weekly basis, so that demand and capacity go hand-by-hand. Uber, instead, can rely on fleet partners for Uber Black, whenever it is legally allowed. These partners are agencies who bought a certain amount of driving licenses and, according to standards set by the Platform, manage part of the fleet.

Overall, Uber and African organisations deal with the same operations on the driver’s side, but the business size and, consequently, the corresponding growing phase, affect their management. On one hand, Uber gains advantage from centralising some standardisable activities and managing them remotely, leveraging on economies of scale. On the other, Little and Mondo Ride, which are spread just in East Africa, can adopt direct personal communication with their drivers. This can stem from

106 ANALYSIS two reasons: (1) since they are at introduction phase, moving to grow (ref. Figure 9), the first rides are fundamental to guarantee customer acquisition and retention. In this perspective, drivers’ training and cars inspections are crucial; hence, by executing one-by-one courses and visual checks, the attention is more focused, thus effective; (2) the drivers’ volume is not so high to require a centralised team devoted to these operations and, from the other way around, the Enterprises do not have the organisational and economic competences to support and manage the investments that Uber did.

Pricing Although pricing is an activity carried out at tactical level, its setting in transportation sharing services is changed according to daily demand conditions. Therefore, it is as an operational lever to affect the balance between offer and request; Uber, for instance, exploits surge pricing thanks to a real-time monitoring system, as already explained in KPIs analysis. This operation is a bridge between driver and rider because, on one side, by tracking the costs up to car owner, the tariff has to be profitable for them to retain agents in the platform and guarantee the service, on the other, the sharing price has to be adjusted to demand elasticity in order to attract riders and ensure profitability to the firm.

Customer Care Customer care is a crucial operation, as it strongly impacts his loyalty and retention. Indeed, African companies monitor the time to resolve customers’ issue when their claims are conveyed to call- centres, calling them back to investigate reasons behind a negative feedback on the app. Moreover, the platform makes sure to guarantee communication with drivers properly, to ensure an optimal experience. Uber, on its part, has a dedicated team for customer care, called Centre of Excellence, and monitors riders’ feedbacks and ratings to improve matching quality. For example, if the rider assigned a low score to a driver, it will be never matched to him again. Therefore, as for pilots’ assistance, the costs sustained by the firms depend on two factors; on one side, customer care management is supported by IT tools and algorithm to collect and analyse data, whereas on the other side, a team is dedicated to get in touch with them and resolve their issues. This last cost is formed by two groups: call-centre operators who take care of minor problems and perform interviews, and a specialised team that is responsible for interpreting all the customers’ data and take corrective actions. The formula can be generalised as follow, where the cost of the algorithm depends on the autonomation level:

퐶푝푙푎푡푓표푟푚 + (퐶푑푎푡푎 푎푛푎푙푦푠푖푠)푎푢푡표푛표푚푎푡푖표푛 푙푒푣푒푙 + 퐶푡푒푎푚 + 퐶푐푎푙푙−푐푒푛푡푟푒

Compared to the previous similar cost functions, it only adds Ccall-centre.

107 ANALYSIS

Rides Acquisition It entails the analysis of data about demand, meaning service requests and app usage. Many KPIs monitor this operational aspect, like the number of requests, the download rate and how many users search for an Uber over the active ones. Rides acquisition is linked to capacity management, as it is the corresponding part of matching, Therefore, despite demand analysis, this operation includes strategies to balance it with offer. For instance, Little Ride has two customer segments, the “retailers” which are occasional riders, and the “corporate” which pay a monthly subscription; based on the segmentation, different discounts are assigned to incentivise requests. Once again, the costs for demand study and management are linked to the responsible team and data analytics.

Therefore, as it has been highlighted through operations description and cost formulas definition, all these activities are underlain by a major crucial one, that is data collection and analytics. Indeed, these platforms build their offer based on real-time monitoring of a daily variable demand and unfixed capacity. Thus, trying to monitor their distribution according to different time of the day, seasonality, weather conditions and special events, results of vital importance. Data collection derives from two streams. On one side, the platforms gather many useful data from their apps and users’ smartphones, for instance by exploiting their GPS positions, studying how customers move in the app and clustering general information of their profiles. This allows to better characterise the demand, the offer, and how they behave according to the pre-mentioned parameters. Consequently, the enterprises can decide how to incentivise or de-incentivise them and get the best match. Indeed, P2P organisations deal with variable demand as well as for traditional businesses but, unlike them, they cannot decide when to schedule a shift, or its duration, so that also monitoring drivers’ behaviours gets great relevance. The other stream of data can be acquired on the market. Many platforms collect data, apparently un- useful, that always find a suitable acquirer. Therefore, P2P organisations are interested in (1) road conditions and traffic congestions, easily understandable from Google, (2) weather conditions, from related apps, or (3) transportation services usage according to different countries. Depending on team structure according to the company, data analytics is carried out through algorithm and specialised operators. Therefore, P2P platforms sustain mainly two bigger costs which are distributed among firm functions. Overall, the platform represents an intermediator between demand and offer, but it has to keep customer retention from both sides to guarantee the service coverage and profitability. Thus, knowing both riders and drivers and trying to forecast their changes is crucial. Indeed, as stated by Robert Kaplan and David Norton, creators of the balanced scorecard: “If you cannot measure it, you cannot manage it”.

108 ANALYSIS

3.3.2.1.6. P2P – Business Practices Adopted Through interviews, it was possible to discover that P2P organisations managed all the operations in- house, not relying on outsourcing. Keeping everything within the corporate borders allows to establish synergies between the activities, since the two sides of the platform are strictly intertwined. Furthermore, the app represents the beating heart around which every service is offered. Thus, outsourcing a key competence would be meaningless and in-house strategy allows also to enhance economies of scale and of learning.

All the operations described above are mainly back-office activities. Indeed, since the enterprise has low influence on carmen, it struggles to manage in back-ground whatever it is feasible and to take care of them. On one side, by looking after them with the assistance centre, it tries to establish a form of loyalty. Indeed, the drivers deliver the hard part of the service, that is also the core one. Being the only point of contact with the customers during the trip, it is important to create a sense of belonging to the platforms in order to make them deliver the service in the best possible way. On the other side, the training and formation of front-end operators (the drivers) is done with the intent of leaving to their discretion the minimum essential part of the service (the trip), while the rest of the customer’s experience is internally managed in back-office. Indeed, everything concerning the journey is tracked internally and the customer can directly communicate with the platform through the app or the dedicated centres. In conclusion, by leveraging on data analysis in back-office operations, it is possible to provide a good service without owning any car.

3.3.2.1.7. P2P – Operations Structure The overall operations managed by P2P platforms have been described. However, the operational structures assumed by Uber, Bolt and African Companies are completely different and it is explained by their different stage of growth, as already demonstrated by KPI analysis. Indeed, during Bolt and Little Ride interview, the manager stated that: “the difference between the two firms is not in operations, but in the team. Indeed, Bolt has a big team, so there is the head of customer care, the head of supply etc., while in Little there is a small team, so there is just one head for every operation.” Despite the growing phase, this is also linked to the areas covered by the transportation service. Little and Mondo Ride serve East-Africa only, while Bolt is established in 35 countries and 150 cities, operating in Europe, Africa, Western Asia and North America. The spread of Uber is even larger, covering 69 countries and more than 10,000 cities. As a consequence, Its organisation is deeply structured as schematised in Figure 12.

109 ANALYSIS

Figure 12 - Uber Organisational Structure

There are four layers, whose size depends on the geographical coverage, focused on the area they serve and specialised on specific operations. The idea behind Uber strategy is to centralise back-end activities, that are standardisable and applicable to larger areas, and personalise the service at country- level based on different cultures and habits.

First of all, a team is assigned to each mega-region: Asian Pacific, EMEA, and the US. Then, each of them is split into regions, for instance Western-Southern Europe for EMEA, which are further divided into sub-regions, like South-East Europe among Western-Southern countries. The region has a central hub, called Central-Ops, situated in Amsterdam for Europe, where everything that is standardisable is centrally managed. However, although they are centralised, each team is highly specialised by sector. This is possible because they deal with less volume variability, since they are addressing larger areas. In this way, they are able to exploit both economies of scale and of learning. This team is responsible of monitoring service usage and traces features linked to other transportation services in the countries. It is also in charge of analyse pricing and economic factors; on one side, it studies costs up to drivers, like leasing, refuelling and maintenance, while, on the other one, it analyses demand characteristics, such as the willingness-to-pay according to GDPs, demand elasticity and share distribution according to price changes. Each sub-region, instead, targets specific operations. For instance, one manager is responsible for competitive analyses, monitoring the other market players’ actions, prices and promotions. Another one focuses on airports, checking the related inside flows and data. In this way, it is possible to study the pick-up position, based on the number of arrivals and exits per each one, and to bring Uber drivers closer to the airports according to airplanes landings. The last layer corresponds to the single countries, each assigned to a team who follows marketplace dynamics on a daily basis and balances demand-offer according to real-time monitoring, time of day

110 ANALYSIS and season. In addition, there is a section devoted to riders and drivers’ management, called Community Operations, and abbreviated to Com.Ops., which is divided into: • Green Light Hub assisting drivers on a country base, as with the updating of car standards; • Centre of Excellence (COE) that supports clients, for example when a rider forgets personal devices on an Uber taxi. These teams turn into practice the dictates of Operations department and apply solutions to solve problems in specific countries. For instance, the Ops team sets quality standards and minimum level of KPIs to achieve, such as which types of car to activate for Uber Black or the minimum acceptable cancellation and acceptance rates. Consequently, the Italian Com.Ops has the responsibility to communicate the directives to drivers and support them. This overall disposition of operations allows to exploit, in the best way, data collection and management. Indeed, at regional level, data are collected, and the Ops team classifies and handles the main factors of demand and capacity management. Then, everything more specific is further detailed and delegated to single countries, managed by Com.Ops teams.

3.3.2.1.8. P2P – New KPIs Development After KPIs and Operations descriptions, also for P2P case, it is now possible to extend the study to further indicators. Starting from service coverage, P2P platforms monitor capacity and demand levels, hence drivers and riders’ trend. Like in the B2P case, they can plot on a two-axis graph the Number of active drivers during the hours of the day, as representative displayed in Graph 22. The same can be done for riders.

Graph 22 - Active Drivers Daily Distribution

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The graph changes according to the day of the week, as it is reasonable to assume that Tuesday night- hours do not have the same peaks of Saturday ones. In addition, a map can indicate the areas that are most covered (or requested), depending on the period of the day and the week. This monitoring of the two sides allows the firm to verify its matching effectiveness and coverage capability. Moreover, by cataloguing each pilot, it can register their period of activity in terms of days, hours, duration and location, trying to understand their scheduling and the covered areas. In this way, in case of abandon of important drivers, the company reacts to this lack and contacts other ones, expanding their average travelled distance. Indeed, by tracking the Maximum covered distance by a driver, the platform can also select those who are willing to travel longer spaces and, therefore, it knows that they will accept the far rides, reducing the time needed to find a suitable match. Indeed, different time items made up ETA (Estimated Time of Arrival), which affects customer’s waiting: • Average Time to Response (ATR); • Time to Leave (TTL); • Trip Duration. To these ones, the Time to Meet (TTM) is added, which corresponds to the time it takes for the driver and the rider to meet each other. It can sometimes depend on the quality of the app and the reception of the GSP, which the organisation has to track in order to improve it. Returning to the list above, ATR and TTL are indicators of carmen’s responsiveness, and they are the ones that the enterprise must monitor when it wants to ensure timely response to the rider's match. In fact, the ATR indicates the average time that the driver takes to accept or refuse the trip; the TTL, instead, is the time that elapses between the trip acceptance and the journey start. Since the pilot is paid per trip, and not by time, this does not affect his earnings but impacts on customer's waiting time, and consequently the reputation of the company, that must be preserved. Indeed, the rider can decide to abandon the trip, while the driver is on his way; in this case, the platform has to trace the Average Time to Abandon, meaning the time elapsed between the matching and the trip churn, when the client is not willing to wait anymore. Thus, by comparing it with the ETA of the available cars, it can verify in advance whether a given customer is likely to cancel the race or not, wasting time and money for both the firm and the driver.

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Focusing on drivers’ training, there are some parameters that indicate the effectiveness of the course, such as:

푁푢푚푏푒푟 표푓 푛푒푤 푎푐푡𝑖푣푒 푑푟𝑖푣푒푟푠 ( ) , 푁푢푚푏푒푟 표푓 푎푡푡푒푛푑𝑖푛푔 푑푟𝑖푣푒푟푠 푡 and 푁푢푚푏푒푟 표푓 푟푒푝푒푎푡𝑖푛푔 푑푟𝑖푣푒푟푠 ( ) , 푁푢푚푏푒푟 표푓 푎푡푡푒푛푑𝑖푛푔 푑푟𝑖푣푒푟푠 푡 where t stands for a reference period, like a month or a week.

The former allows to understand how many drivers who have attended the course have become active on the service, hence, by investigating the causes of others’ abandon, the course can be improved. The latter indicates how many drivers of the attending ones of period t had to repeat the course. If this ratio is high, it means that either the course has not been well assimilated, because it is poorly structured, or the selected drivers are not diligent, and therefore the recruitment criteria must be reviewed. Finally, other quality indices can be designed for customer care. For example, the following one:

푁푢푚푏푒푟 표푓 푝푟표푐푒푠푠푒푑 푟푒푞푢푒푠푡푠 ( ) , 푁푢푚푏푒푟 표푓 푟푒푞푢푒푠푡푠 𝑖푛 𝑖푛푝푢푡 푡 where t is a reference period, combined with Number of returned requests, assesses the efficiency of the customer centre in solving their issues; the same indicator can be computed for driver assistance service.

3.3.2.2. Analysis Cross-Case Studies So far, a detailed description of operations management and the related business practices adopted have been discussed according to the features characterising B2P free-floating vehicle-sharing and P2P ride-hailing. Therefore, to exhaustively conclude the analysis, cross-case homogeneity and heterogeneity between operational activities are browsed.

3.3.2.2.1. B2P vs P2P – Operations Management Description Before starting with the analysis, Table 16 notes the operations identified for the two business models. Considering them, since the aim of the two company types is the same, there is quite a parallelism between their operations. Indeed, whether by owning the asset or indirectly supplying it to customers, their objective is to provide a transportation service, guaranteeing a reliable and pleasant trip.

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Table 16 - Operations Summary

Both platform types, as traditional businesses, deal with demand-capacity relation. In B2P case, rebalancing is the related operations, reflected in P2P by rides acquisition and capacity management. According to this connection, there are mainly two scenarios to handle: when the request exceeds the offer, and vice versa. Depending on the business model adopted, different countermeasures are taken. When the demand overtakes the available vehicles, in B2P context it means that few cars are close to high-requiring areas; thus, companies opt for their movement, trying to increase local capacity levels. However, as explained in section 3.3.2.1.1, this activity implies costs and partial gain loss due to vehicles occupancy during the transportation. Nevertheless, the movement is evaluated in function of the expected revenues obtained thanks to the new position. Other strategies the platforms can implement are the activation of some means “hired” on the app or incentivising the customers to rent far cars, by offering a discounted tariff. This same scenario is translated in P2P context when there is a peak in the requests due to a special event. These platforms apply the surge, focusing on the riders with the highest willingness-to-pay predisposition; in this way, the organisations not only do not suffer additional costs but also manage to increase profit, since the service fee is amplified. However, this strategy can be pursued also by B2P firms when, once the fleet has been moved, the price per minute can vary in function of the time of the day and special events to balance demand with offer. When the misalignment between the two is due to less capacity, in P2P scenario it means fewer active drivers on the platform, therefore the firms can decide to incentivise them, implying more costs but satisfying more rides. This condition is interpreted in B2P service as the overall fleet that is not enough to cover all the requests and the cars are all rented. In this case, platforms have no levers on

114 ANALYSIS their side, but this situation is assumable to be not so frequent, or, at least, that the firms follow the demand trend and evaluate the convenience in purchasing new vehicles. The opposite case, when the capacity out-reaches the demand, is translated into a cost for B2P firms, since they are sustaining an expense linked to vehicles idleness. Firms operating with P2P model, instead, have a great advantage deriving from lack of cars: they do not suffer of extra-inventory. Indeed, drivers are paid per trip, rather than according to the time spent on the platform, thus even if the capacity is higher than the demand, the extra-pilots do not correspond to a cash outflow.

Considering support activities like refuelling, maintenance and cleanliness, no team is responsible for their implementation in P2P organisations, as they are completely charged to vehicle owners. Nevertheless, they are not totally disregarded. Indeed, although they do not imply direct costs for the firms, they affect driver’s willingness to take part in the transportation service; to this purpose, the ride price is set taking into account the cost impact on carmen’s profitability. Moreover, also P2P platforms have to “maintain” their service, acting on pilots’ conduct. Indeed, they keep track of drivers’ behaviours and car conditions so to evaluate the implementation of corrective actions, when their ratings go below a defined threshold, or preventive ones, when they have to deal with new regulations or they notify in advance new car standards to drivers, like for Uber Black. Actually, both the enterprise types aim at furnishing high service quality, thus they need to preserve and improve it.

The communalities and diversities in operations are also reflected in firms’ expenditures. There are some costs which are likely sustained by both sharing models. They all rely on an application to communicate with customers (meaning riders for P2P), hence platform costs are up to both of them. Then, data analytics and IT infrastructures are essential to successfully run this business. Indeed, either to track vehicles disposition or drivers’ conduct, information from installed instruments or personal devices are collected and browsed. Finally, even if customer care in B2P firms has not been discussed, it happens that renters need assistance for issues arisen during or after a ride. Therefore, the costs related to these operations, which were mainly presented in P2P section, are also up to B2P platforms. The distinction between the two cost lists derives from other operations. The operational expenditures of B2P case linked to rebalancing, refuelling and maintenance, correspond to carmen’s wages, incentives, training and inspections in P2P. However, the costs balance hangs on B2P side; in fact, with the costs in common listed above and the same impact of the overall operational expenditures (OPEX) costs (considering the items described before for the specific sharing model), by adding the fleet investment, the expenditure up to B2P platform is more onerous. Indeed, the only investment borne by P2P firms is the development of the driver section on the app and a greater effort

115 ANALYSIS in data analysis, which does not correspond to a fleet investment for two reasons: (1) it is independent from the number of cars (alias drivers), (2) it is the amplification of an already existing application, therefore it is not developed out of nothing. However, to balance the scale, the opportunity for operations outsourcing, thus for cost cutting, is more likely in B2P sector than in P2P one, but this analysis is deferred to next section on business practices adopted. Indeed, before concluding, an observation is in order. Although the creation and management of a B2P model seems more expensive, this company type has to deal with lower variability and uncertainty on capacity side. Indeed, in order to guarantee profitability, P2P platforms require higher effort and specialised teams to manage variable front-end operators.

3.3.2.2.2. B2P vs P2P – Business Practices Adopted By definition, considering the characteristics of the transportation service offered by B2P platforms, all the operations are back-office activities. Indeed, these organisations do not have any point of sale and, unlike P2P ones, passengers do not interact with any pilot, as they coincide. Furthermore, even if some activities can be executed in front of customers, like on-site repairs and maintenance, they do not imply direct interaction. Therefore, unless support activity like customer assistance centre, no task is done in front-office. This is not true for P2P firms which leave front-end contacts to drivers. However, B2P back-end activities, unlike P2P ones, can be easily outsourced as they are not part of core operations. For example, both Share Now and ReachNow assigned to third parties the execution of rebalancing, refuelling and maintenance, going through cost cutting and expert suppliers, reaching also a higher quality intervention, thanks to their specialisation. They retain in-house the management, keeping the decisional power. For P2P case, instead, outsourcing is not a good option. There are strong synergies between the two sides matched by the platform; therefore, assigning one operation to an external independent unit can result in knowledge dispersion and interferences in communication, loosing effectiveness. For instance, if data analysis about customer experience and satisfaction is assigned to an external party, when a rider gives a negative rating to a driver, the possibility not to be matched with that pilot again could not be that immediate if the external collaborator does not instantly communicate this un- matching for future trips. A further example, if the demand monitoring is left to an external independent unit, the surge-pricing lever could miss of effectiveness, because it is applied according to precise periods of the day to balance demand-offer, hence lose of fast application. Indeed, one of the greatest advantages exploited by P2P firms is economies of learning that, in order to be reached, must be managed inside by studying and improving the service offered to both sides. The only

116 ANALYSIS activities that could be outsourced are those which are less crucial for data analytics, like cars inspection and call-centres for data collection. Therefore, B2P enterprises can rely on cost saving resulting from the outsourcing of independent operations deriving from economies of scale exploited by third parties, while P2P ones can take advantage from economies of learning by keeping them in-side.

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4. LEAN MANAGEMENT APPLICATION

In the literature review carried out, it was highlighted that no past study had dealt with the application of lean management in the Sharing Economy. Therefore, as further research question of this university work, in addition to operations managed, the analysis of how Japanese philosophy can be adopted by transport B2P and P2P services has been set.

4.1. Lean Management Philosophy

Lean management is a Japanese production method derived from Toyota in the 1930s of running an organisation supporting the concept of continuous improvement. The ongoing effort aims to improve products, services or processes and requires incremental improvement over time to increase their efficiency and quality. This is accomplished by analysing a business process, revising it or eliminating any steps that do not create value for customers and factors that waste time, effort and money.

4.1.2. Principles Lean management principles are five: • identify value → review products and services from the standpoint of the end customer. The unique value is defined in terms of what the company is making, why and for whom; • map value stream → value stream mapping enables to understand how value flows through the organisation. It outlines every step of the process for each part of the business, such as production, research and development, marketing and human resources; • create flow → make the value-creating steps occur in tight sequence, finding ways to maximize efficiencies and reducing waste; • establish pull → the idea is to let the customer pulling. Instead of investing in materials, production and inventories to be ready for the customer’s order, lean managers can use the customer’s needs to direct the system, saving cost, space, time and resources; • seek perfection → is important to find areas of improvement and implement meaningful change. Therefore, each process is constantly analysed in order to tight flow and deliver value as the customer needs. The goal is not perfection, but rather, the pursuit of it, a concept known as continuous improvement.

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4.1.3. Value-Added vs Non-Value-Added Activities Lean manufacturing is a systematic method for the elimination of wastes within a manufacturing process. From the point of view of the customer, value is any process or action that he would be willing to pay. Thus, according to this definition, tasks carried out to provide a product or a service can be divided into two groups: value-added activities, which increase the benefit of a good or a service to a customer and non-value-added activities, which do not augment the worth of what is delivered to him. Among the latter, two clusters can be recognizable: those which are unnecessary and must be removed to raise the profitability of a business by costs reduction and efficiency enhancement; and those that do not create value for the customer but are necessary to provide a product or a service under the present operating system. They cannot be eliminated in the short term, but it may be possible by changing processes or equipment in the long run.

4.1.4. Muri, Mura, Muda Toyota’s production system was developed around the elimination of three types of wastes defined using Japanese names: • Muda → ⪻ non-value-adding work ⪼. There are two types of Muda, Type 1 and Type 2. The first one includes non-value-added activities in the processes that are necessary for the end customer, while those of Type 2 are unnecessary and must be eliminated. There are seven categories of waste under Muda Type 2: - transport → unnecessary movement of people or parts between processes; - inventory → excess products and materials that are not processed; - motion → unnecessary movement of people, parts or machines within a process; - waiting → time spent waiting for a work cycle to be completed; - overproduction → products are produced in a quantity higher than customers demand; - over-processing → processing beyond the standard required by the customer; - defects → product or service failure to meet customer expectations. Although according to the Japanese philosophy, motion and transportation differ mainly on the typology of resource that is moved (respectively operators and materials), the selected interpretation, described above, is more congruent with the Sharing Economy context, especially in P2P scenario. • Mura → ⪻ unevenness ⪼. It is the reason behind the seven wastes mentioned above. Hence, Mura drives and leads to Muda. The goal of lean management is to level out the workload in order not to have unevenness or waste accumulation. Just-In-Time system is the technique

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adopted to limit overproduction and inventories by delivering the right part, at the right amount, and at the right time. • Muri → ⪻ overburden ⪼. It exists when machines or operators are utilized for more than 100% capability to complete a task in an unsustainable way. It can result from Mura and in some cases can be caused by excessive removal of Muda from the process. Standardise work can help avoid Muri by designing the processes to evenly distribute the workload among the workforce and the equipment.

After introducing lean philosophy theoretically, in the next paragraph the operations will be classified following the customer’s point of view, and the seven wastes typical of productive system will be reflected into the transport sector of the Sharing Economy.

4.2. Lean Management Concepts Applied in Sharing Economy

Since the operation analysis in chapter 3.3.2 was carried out dividing platforms according to the business models adopted, the same approach will be adopted in this part. Indeed, as described, the business model typology strongly affects the operations managed to provide the service, whose purpose is similar, but the necessary activities to perform are strongly different.

4.2.1. Value-Added vs Non-Value-Added Operations The Sharing Economy operations managed by B2P and P2P companies can be analysed according to the value produced for the customer. Starting from the first business and focusing on the three most important activities, the first reflection concerns the absence of completely unnecessary operations. Indeed, those with no-added value for the customer, rebalancing and maintenance, are necessary to provide the service. Regarding the first one, despite being important for the client to find a vehicle nearby in high-demand areas, he is not willing to pay for this activity, as it is the basis of the service offered itself. The same reasoning can be done for maintenance, which even in production systems is considered an activity that cannot be completely eliminated, but does not increase the value of the product or service. In the case of vehicle- sharing firms, the need level of repair activities highly depends on the extent of the damage. For instance, mending a mirror is more relevant and urgent than a rifling also for a customer point of view, focused on his own safety. Therefore, both rebalancing and maintenance are necessary to retain the customer. Refuelling, instead, is of a different nature. This activity is of added value for the client, since not all enterprises carry it out. Many firms, including some interviewed, use incentives to make

120 LEAN MANAGEMENT APPLICATION the client in charge of this activity, thus saving on management costs. Hence, finding a refuelled vehicle becomes a differentiating factor on the market, which increases the service level offered and, accordingly, leads the customer to pay a premium price.

Switching to P2P organisations, to classify the operations it is necessary to study which ones improve the customer experience above the standard level. However, also for lean practices, the discussion has to keep two perspectives, since these platforms deal with riders and drivers’ side. By taking rider’s point of view and starting with the operations to manage the drivers, the unique point of contact with the client, their training can be divided into two levels: one that is related to their teaching in using the app, accepting rides etc., which is not of added-value since it is linked to a basic activity to offer the service, while the second, linked to driver’s behaviour and way of accommodating customer requests that may improve the rider experience. For example, the driver can decide to add extra intermediate stops, on customer request, and keep the same price of the initial standard trip. The added-value, in this case, is not at corporate level, but could be reflected in a higher hourly price for some carmen compared to the base rate. This premium price could be assigned to drivers in relation to their rating level. Still considering activities linked to drivers, fleet management is a control activity for the company to ensure the driver has documents in order and the cars meet certain standards. Both do not represent an added-value of the service, but aim to avoid problems or claims as a result of negative experiences. Concerning cars standard, furthermore, the client pays a price according to the type of vehicle chosen. Therefore, performing the ride with a higher-level car is inherent in the paid price. Finally, moving to activities strictly related to rider assistance, all these tasks are value-adding, because the customer is addressing the platform due to an issue that, otherwise, would not be solved. Coming to driver’s side, the value for his perspective consists in the activities that make him prefer to work for a platform rather than another. In his view, driver’s training becomes value-adding, since he is learning how to perform the job for free. Moreover, also the assistance provided to him, as well as the discounts and favoured leasing offered, are activities that add value to his introduction and retention in the service delivery. Finally, data collection and analytics, which underlie the matching management by leveraging on demand and capacity levers, is a necessary non-value-adding operation. Indeed, it is fundamental to gather information in order to perform all the activities without generating any waste and weaken customer experience, but the customer is willing to pay for this task when it turns into a strategic level to improve matching efficiency, shortening the waiting time and increasing the journey pleasure.

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Indeed, it is according to this efficiency degree that the rider decides to require the service from one competitor rather than another. Before concluding, a further point to discuss is in order. According to lean practices, the production is triggered by the customer order, which represents the process pacemaker. This is reflected in P2P service by the driver who remains unproductive until a ride request activates him. However, the carman himself decides when to be online in the app, not the firm. Therefore, this is a distorted lean concept because it is the customer that activates the service process, but the platform just conveys the requests to “switch-on” any idle driver, without having any decisional power on capacity level. To better explain this concept, bringing it back to a production scenario, it is like a customer order that triggers a Production-ordering Kanban to which corresponds the driver’s decision to start the process or not.

Table 17 is a sum-up of the discussion above.

Table 17 - VA and NVA Activities Summary

4.2.2. Waste Occurring in SES Transport Sector Seven waste types can be reflected in the transport sector of the Sharing Economy in order to explore which ones are present, and consequently how they can be eliminated from a lean perspective.

Focusing on B2P operations, both refuelling related to 2-wheeled vehicles and maintenance, in general, are conducted relying on inventories. The stock of components as well as that of batteries are wasteful. In the first case, the problem could be eliminated ordering parts on a daily base, when a

122 LEAN MANAGEMENT APPLICATION breakdown occurs relying entering into partnership with Just-In-Time suppliers; while, in the second case, by changing the type of refuelling. Instead of replacement, the battery placed on the vehicle can be recharged directly on-site, providing operators with a wireless charging device. In this way, they would perform the same tour around means of as before, but it would change the activity carried out. Strictly related to inventories, transport represents another waste to be eliminated. While for on-site maintenance, moving the material is necessary to carry out the activities, and it is less time-consuming and costly than transporting vehicles to the warehouse, transport around the workshop must be eliminated. For this purpose, the layout has to be changed looking at the lean manufacturing principles. Tied to maintenance activities also waiting and defects are possible types of waste. A broken vehicle can wait to be processed because of the lack of material or overburden workers and a mend may not be done properly causing reworks. The latter are very important for enterprises that monitor specific KPIs, as seen in chapter 3.3.1.1.2, to measure maintenance processing efficiency. In order to eliminate both, balancing the process using takt time concept, improving operator task reliability, implementing Standard Operating Procedures to ensure that standards quality and methods are clear can be possible solutions. Furthermore, for those firms who have not yet adopted lean management techniques, creating the flow can bring enormous benefits in terms of time, cost and quality for maintenance activity. In the next paragraph, considering the example of Jump, this solution will be analysed.

For P2P organisations, instead, many types of waste are nullified as most of them do not require manual activities performed by operators. Thus, their centralisation on the platform allows to have a leaner management by nature and with less Muda Type 2 tasks. Moreover, also from customer perspective, peer-transport service brings benefits, reducing time losses. Indeed, as stated in a PWC research [1], reporting a manager's words: “That time you spent driving previously, now it’s downtime. You have an extra hour and a half in your day where you can be doing whatever you want.” Nevertheless, from service delivery viewpoint, the firm has great room of improvement. The most salient waste is relative to drivers’ capacity. When it exceeds the riders’ demand, there is a service type of overproduction. Indeed, managing more drivers than necessary has a cost for the firm that has invested in their training. Moreover, this waste can damage the company’s capacity itself in the long run, as many drivers who receive a low or nil number of rides could decide to switch to competitors’ app. On the contrary, the opposite relationship, with demand greater than capacity, causes waiting. A part waiting to be processed by a machine and a customer waiting to be processed by a platform do not have the same impact on enterprise’s profitability. Indeed, the customer perception of delay is different in the two situations. In the first one, it is indirect because the client

123 LEAN MANAGEMENT APPLICATION detects the delay only on the delivery data if it is not respected. However, the perceived delay may not be equal to the real one, since it can be absorbed along the production chain. In the P2P case, the customer is directly impacted by the matching process. Thus, a too long wait can lead him to give up the service. In both waste types, the strongest influential factor is the human version of demand and capacity. This makes the way of tackling it more complicated, since it is necessary to manage people feelings. Finally, also when capacity level is able to satisfy the demand, the customer waits for the request to be accepted. Indeed, according to service process, the platform assigns the ride to a driver and, depending on his reactiveness in responding to the job opportunity, the rider is affected by the waiting time. Every time that a carman does not accept the request or takes it too long, the firm has to convey it to the next pilot and so on. This increases waiting and it is up to the organisation finding a way to optimise this process. Moreover, although a productive process does not exist, the service provided corresponds to the trip, where waste can be generated. Indeed, the driver could move away from an optimal existing path; this implies more costs on his side and, on corporate level, lengthening the trip means subtracting time to other rides, thus affecting profit, and increasing the travel time to passengers. Therefore, this waste is part of motion typology. Un-necessary motion arises also on rider’s side, whenever he needs to move towards a pick-up point or away from an over-crowded area so to simplify the meeting. Another loss coming from trip is transport typology. The pilots need to move from drop-off point to pick-up one in order to collect the next customer. This journey implies waste since (1) it generates costs for the driver, mainly linked to fuel, (2) it corresponds to an unproductive time for the company, as the carman is inactive, and (3) it affects next rider’s waiting time. The main lever to nullify this loss is the design of an optimal set of trips which minimises the empty journeys by notifying to drivers the closest requests according to the model introduced in section 4.4.3. Finally, also defects can be formed; since they are divergences from customer expectation, despite drivers’ training, some misalignments from firm’s standard may occur and be negatively perceived by riders. For instance, this aspect strongly affects the luxury version of Uber, Uber Black, where the platform annually renews quality standard with the aim of encountering customers’ expectations. For what concern basic service, instead, KPIs on drivers’ conduct are monitored to strive to improve the service delivery.

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In Table 18, the wastes of P2P and B2P organisations are summarised.

Table 18 - Seven Wastes Reflected in B2P and P2P Companies

4.3. Real-Cases Adopting Lean

Starting with B2P platforms, the information collected during the interviews brought out the almost nil degree of diffusion of this production model among them. Indeed, only Jump has instilled it in the management of some operations. This uneven propagation is also characterised by the business practice adopted by the organisations. Indeed, for those such as ReachNow that rely only on outsourcing, lean management makes no sense to be adopted. The unexpected thing resides in MiMoto. Indeed, despite its operations are almost completely internalised, it has not yet introduced lean methodology to perform its activities. Being a very young firm and not associated with any multinational one, unlike Jump, it is reasonable to assume that it has not yet perceived the urgency to reduce costs, time and wastes. Many enterprises pass through various growth stages and evolve many times among different production models before landing to this.

Analysing Jump, in accordance with its young stage of growth, it has profitable lean margins which require the adoption of Japanese methodology not only in operations management, but at corporate level. In this scenario, every small improvement can brush up the company’s profit. Therefore, Jump acted on two levels according to the place where activities are performed: - in-house → the firm has reduced the level of job enlargement14 related to repairs. At the beginning, a mechanic was responsible for all the repairs to be done on a vehicle up to the final test. Whereas, with the Eldorado project, the lean concept was introduced inside the workshop. First of all, the flow was created, placing along a line all the necessary steps to

14 Job enlargement is a job design technique wherein there is an increase in the number of tasks associated with a certain job. It means increasing quantitatively the scope of one worker’s duties and responsibilities.

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repair a vehicle. Then, all the phases are performed by a worker highly specialized in a particular mend. This solution allows to quicker guarantee high-level repairs at the same time. In addition, it allows to reduce waste because parts coming from previous repairs can be used for future ones, thanks to the mechanic wide vision on all spare parts related to his area. However, the extreme focus of one operator on one type of repair reduces his motivation. Lack of motivation can damage the efficiency of the work and its quality, hence particular attention must be paid. - on-site → the firm aims to increase the level of effectiveness and efficiency. Before the introduction of the lean methodology, each operator was assigned to a specific zone of the city and could move freely within it to carry out the necessary activities. After the change, begin assigned to the same city area, the worker must follow a path optimised dynamically by a server that updates data in real time based on bicycles movement. Thus, the algorithm adds or eliminates a stop if a vehicle is requested by a customer to start a trip or is parked at the end of a trip.

Considering P2P organisations, lean management is also adopted by Uber and Little Ride. However, being the only investment of P2P firms related to the platform, lean management is applied directly through it or at corporate level changing its mindset. Uber is an example of the first application. Indeed, during the matching process, an algorithm is fed with several factors including the driver arrival time at the meeting point, the customer arrival time at the destination one, general wastes etc. The objective is to assign a carman to a client maximizing the economic ride convenience, thus minimising cost under a certain customer price. Among all the organisation, Little Ride has instead spread the pure Japanese principle stating that continuous improvement ideas can come from any company level according to a top-down-bottom- up approach. Furthermore, respecting the customer point of view, each new service added to the enterprise’s portfolio, as well as only a new feature, must be created increasing the value for the client and reducing the lead time.

The low application of lean management, especially by B2P platforms, leaves ample space for this research. As a consequence, in the next paragraph, the typical tools of this discipline have been studied, keeping in mind the Sharing Economy principles and objectives, in order to make them applicable to these realities.

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4.4. Exploiting Lean Tools There are many tools associated to lean management philosophy developed by Toyota since the 1930s. These instruments are most effective if they are implemented together, however many can be used on their own to solve specific issues within a business. Indeed, the operations managed by Sharing Economy organisations are not related to production processes, making difficult to trace the canonical concepts of linear flow and value stream. As a consequence, methods and procedures can be applied according to a lean perspective of processes improvement, but they must be adopted to the specific scenarios.

4.4.1. Maintenance The first operations that will be studied in a lean perspective, together with the tools that can be applied, is maintenance. Indeed, being a necessary operation, but not with added value, reducing the related time, waste and costs is important as they are not balanced by a premium price paid by the customer. Moreover, it is an activity on which companies, such as Jump, already made some lean improvement changes. According to the place where repairs are carried out, lean discipline can be more or less introduced. Considering those in-house, it is important to start with the definition of the production model adopted inside the factory, that influences which tools can be used.

The repair process is a non-linear procedure where the flow is much more similar to a job shop, thus each department is dedicated to a specific vehicle component, such as in Jump case. Indeed, it is not reasonable to arrange the mending process along a line, as lean management stated, since not all means require the same repairs, they may not have to visit all departments, thus their mending sequence may be different. Furthermore, within the same department, some mends could take more or less long times depending on their magnitude. However, to support continuous flow, departments can be organised as a cellular manufacturing based on the parts they repair in order to minimise travel time and, at the same time, maximizing the variety of mends performed, making as little waste as possible. In order to decide which repairs to perform within the same cell, it is possible to rely on the type of component to be mended, as mentioned with Jump, or the Rank Order Clustering can be used. The ROC is an algorithm referring to production flow analysis, that uses the automation of cluster study by computing binary weight from a machine-part matrix in order to group in the same cell repairs that are performed with the same equipment. In Appendix D the algorithm steps are reported.

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Regardless of the criteria used to create the cells, one of their biggest advantages is the amount of flexibility given by the layout. Indeed, each department is considered as a cell, accomplishing a certain task, whose internal work stations are usually arranged in a U-formation, since this allows the operators to move less and watch over the entire process. In Figure 13, the devised layout is designed.

Figure 13 - Cellular Manufacturing - B2P Case

The vehicle moves from one cell to the next, completing in each part of the maintenance process. Two different points of entry and exit of vehicles have been placed. This choice allows to minimise the related interference by adopting a traversal routing policy, according to which a means enters the factory, moves around the different layers of cell stopping when necessary, and gets out from the other side without never coming back. The same policy is applied to factory operating maintenance activities on 4-wheeled vehicles. However, in case of cars, which are heavier and take up more space, a fixed position maintenance is suggested, whose layout is shown in Figure 14. After placing the machine in a free position, operators perform the necessary repairs. Since each one can require a different series of interventions, a small equipment warehouse is located near each station. This allows to reduce waste related to transportation. The level of waste reduction strongly depends on the workers specialisation. Indeed, if they can perform any repairs on a vehicle, maximising job enlargement, they can be assigned to a specific station, avoiding moving around the factory. However, unskilled operators, increase the time required to carry out activities and may increase waste linked to reworks, defects. Therefore, it is a trade-off to manage. Furthermore, it is also important to shorten motion inside each station.

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Figure 14 - Fixed Repair Position - B2P Case

Comparing the two layouts, in the cellular one, vehicles move around cells based on the own repair’s path to follow. In the other, the flow of people moving from one position to another must be optimised, following different scheduling policy. However, a single scheduling point allows to determine the maintenance path for each vehicle/operator, depending the case considered, in order to balance the workload of each cell and/or the number of operators working on the same means. In addition to a factor scheduling, a daily scheduling must be planned also for the activities to perform inside each cell/position. A lean management tool that can be adopted as scheduling system to manage the vehicles’ flow is Kanban. Furthermore, this method is helpful to achieve Just-In-Time by controlling the entire value chain from the supplier. The first step is the introduction of Kanban board to visualise the workflow. It is a simple whiteboard with sticky notes or cards, each one representing a task. The board is divided into three columns named: • to do → grouping repairs not yet started; • doing → consisting of repairs in process; • done → listing repairs completely.

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The simple visualisation allows to deal transparently with work distribution and bottlenecks if any. According to the concept of flow, the cards has to pass through the system as evenly as possible without long waiting times or blockages. Everything that hinders the flow must be critically examined. Kanban methodology proves to be an effective scheduling tool in relation to the analysis done so far, as it can be applied both at factory level, managing the flow around stations of operators and/or vehicles respectively in fixed repair position and cellular manufacturing layout and at single- department level to schedule the repairs within them.

Scheduling the flow turns to be important since it strongly influences the factory layout and the arrangement of the stations within. Spaghetti Chart is a useful methodology to visualise flows of materials, workers and information inside a plant, by mapping all the movements (Muda Type 2) performed. In maintenance case, it can be used to evaluate areas of improvements in the layout. For instance, assuming that the operators are specialised according to the type of repair as in Jump case, they are assigned to a specific cell. Therefore, flow is limited to vehicle one and can be traced in order to arrange the cells minimising the means distance to travel around the factory. Various factors affect the station layout and are highlighted by the flow, such as entities interference, repair typologies frequency, presence of bottlenecks etc.

Flows of people can be caused by the need of bringing necessary tools and spare parts from the warehouse to the actual working point. At single-cell level, Single-Minute digit Exchange of Die (SMED) is useful due to the necessary set-up required by passing from one vehicle to the next. Indeed, mechanical equipment as well as components may need to be changed. This methodology provides a rapid and efficient way of converting a manufacturing process from running the current product to running the next one. The time spent to stop producing one part and start to produce another through the same machine is called changeover15, which must take less than 10 minutes, according to this method. A quick set-up time, indeed, is key to reduce production lot sized and thereby reducing uneven flow (Mura), production loss and output variability. Looking at the maintenance process, the changeover requires simpler and more manual tasks compared to a manufacturing one, which generally involves changing machines settings. Therefore, this type of set-up, not linked to automatic equipment, take by nature less than 10 minutes, according

15 It can be divided into three Ups: (1) clean-up product, materials and components from the line, (2) set-up the process converting the equipment and (3) start-up, meaning the time spent fine tuning the equipment after it has been restarted to solve quality problems.

130 LEAN MANAGEMENT APPLICATION to the components characteristics to be transported (small and relatively high) and the absence of the start-up phase. Hence, in this scenario, One-Touch Exchange of Die (OTED) can be applied, which has the same operational principles of SMED, but a shorter execution time, equal to 100 seconds. This reasoning is also in line with the B2C companies’ type of business, for which vehicles do not represent a product, but the capacity generating revenues. Therefore, OTED can be applied to reduce changeover time inside each department, in order to reduce the overall repair lead time and put the vehicles back on the road quickly, thus allowing the customer to use them. Lots of benefits are obtained by reducing the time dedicated to changeovers. Firstly, it allows to increase capacity in terms of available working time. As a consequence, the number of vehicles repaired in one day rises and the offer for the customer is restored more quickly. Then it improves production flow inside each department, which is vital to pursue Just-In-Time maintenance relying on JIT suppliers. This enables to reduce inventories within each shop and the Work-In-Progress among the plant, meaning those vehicles that have started the repair process and wait between one department and the next. Hence, also lead time decreases, defined as the time between the initiation and completion of a production process. Furthermore, this approach allows to improve quality of repairs, as problems emerge faster than before, thus there are less scrap and reworks, determining waste reduction. As overall consequence, costs decrease and profit increases. The same reason can be done for 4-wheeled vehicles. Indeed, the designed layout, where each location is equipped with a small warehouse, allows to reduce the distance the operator has to travel to reach the store; thus, reducing the overall changeover time.

To further reduce this time, 5s methodology can be introduced in order to organise the general workspace in an efficient and effective way. The name of this method is given by a list of five Japanese words starting with the letter “S”, displayed in Table 19, each referring to a specific activity.

Table 19 - 5s Vocabulary

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The first phase consists of sorting all items needed to perform an activity in a location, removing those unnecessary. The objective is to reduce time loss looking for a tool, reducing the possibility of distraction by the unnecessary ones. Furthermore, this allows to increase the useful space available and, from a safety perspective, to eliminate possible obstacles. During the second step, the necessary items are placed in the optimal position for fulfilling their function in the workplace. The criteria to choose the correct location is related to the workflow, which must be smooth and easy. Then, it is necessary to clean and inspect the workplace and the equipment on a regular basis in order to monitor that first-step goals are achieved (hence, production efficiency and safety improvement, errors and defects prevention and waste reduction) and to keep workplace safe and clean. When these objectives are achieved, anyone not familiar to the environment is able to detect any problem within 50 feet in 5 seconds. After that it is important to standardise the processes and to schedule the repetition of these first three S-practices. The last step regards the establishment of workers’ self-discipline in order to ensure they follow this approach. Introducing this methodology within each department of the maintenance job shop allows also to eliminate the waste related to motion. Indeed, bringing close to each station the necessary items avoids the operator to moving around the department, or worse around the factory. Considering this, time reduction is higher than that foreseen by 5s application only.

In order to eliminate completely changeovers, the Mixed Model Production can be introduced. It is the practice of producing several distinct models of a product on the same assembly line without changeovers. The objective is to smooth demand on upstream manufacturing cells or suppliers and thereby reduce inventory, eliminate changeovers and improve kanban operation. According to this, this tool can be used in order to define a sequence of repairs to be performed within a cell, or a fixed maintenance station, without stoppages related to set-up. This is possible, for instance, finding those mends that share the same equipment and arranging them one after the other along a timeline. Although it seems overly complicated due to difficulties in part stocking, set-up, skills, training etc., it allows to get the highest benefits both inside the cell and along the entire maintenance process.

Maintenance activities, being performed mainly by operators, are subject to human errors. Poka-yoke (mistake-proofing) is a Japanese mechanism that supports factory workers in avoiding mistakes. Generally, it refers to any behaviour-shaping constraint designed in order to effectively make

132 LEAN MANAGEMENT APPLICATION impossible to commit mistakes in a given process. This methodology is very important for mainly two reasons: the first is related to the huge economic impact of manufacturing defects, while the second is inherent to operator safety. Maintenance involves the use of many mechanical tools, thus introducing poka-yoke control inside the workshop is important, especially in 4-wheeled case. For instance, the magnitude of physical damage occurred by a car falling off the kickstand can potentially cause loss of life. In order to set behaviour-shaping constraints, the relationships between defects and human errors must be detected. A matrix, displayed in Table 20, structured with these two as axes, can be helpful.

Strong connected Connected

Table 20 - Human Errors vs Causes of Defect Matrix

Each cell of the matrix may be completed with a box, black or white, or may be left empty. Drawing a black square signifies that a human error is strongly connected to the cause of a defect, a white square still represents a connection but lighter and, lastly, leaving the cell empty means that there is no connection between the error and the cause. After exploring each relation, six principles, explained in Table 21, can be used in order to avoid or minimise the occurrence of mistakes.

Table 21 - Poka-yoke Six Principles

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Poka-yoke ensures that the process is designed in order to prevent mistakes before they occur. When this is not possible, it performs a detective function, correcting and eliminating defects. In many situations, the choice depends on economic and feasibility factors. Each control mechanism can be created in three ways: 1. contact method → physical components characteristics allow to distinguish the correct position or prevent the connection among objects that cause malfunctions; 2. fixed-value method → check if a certain number of operations have been performed; 3. motion-step method → control whether all steps of a given process have been carried out in their correct sequence. Considering the maintenance process, an instrument that allows to apply the second and the third method simultaneously is the check-list. For instance, knowing the tasks to be performed during a specific repair, a sheet showing the sequence of required activities is attached to each vehicle; then, the operator, to whom the mean is assigned, must tick each task after performing. Concluding, mistake-proofing systems give the possibility to avoid or control human defects. However, their introduction depends on the occurrence frequency of errors. Indeed, they are required when mistakes happen often, while those occasional may deserve just a warning.

Still focusing on employees, Total Quality Management (TQM) can be used in order to install and maintain an environment where operators continuously improve their ability to perform high-quality repairs. As the lean management principle teaches, also in this case every activity undertaken must be customer-oriented, meaning that the client determines the level of maintenance quality, and whether the efforts are worthwhile. The repair process, restoring the functionality of the vehicle, re- establishes its general safety and that of the customer driving it. Therefore, although it is implicit in the service offered and the customer is not willing to pay an extra price, it is an activity that must be carried out. Not investing in mends quality, and the way they are performed, can internally undermine the profitability of the service offered. Indeed, poorly maintained and unsafe vehicles make clients switch to competitors’ solution. Adopting TQM, moreover, requires the involvement of the total workforce, which is obtained with empowerment16. This aspect is relevant as operators are the corporate part that best knows the process and can suggest areas of improvement. It is the top-down- bottom-up approach introduced by Little Ride in his organisation according to a lean perspective, as explained paragraph 4.3. Hence, TQM is a process conducted throughout the firm.

16 Employee Empowerment is a set of measures designed to increase the degree of autonomy and responsibility for decision-making regarding their specific organisational tasks. Self-managed work teams are one form of empowerment.

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The last considerable aspect of this method is the dependency on continuous improvement culture, which is essential to increase competitiveness. Indeed, fostering B2P companies to be both analytical and creative in finding new or better ways to perform repairs allows to be more competitive on the market. Considering the lean management characteristics of the maintenance process, mentioned above several times, improvement changes must be done to increase repairs quality, pursuing a cost- leadership strategy17.

TQM shares much in common with Six Sigma improvement process. However, it focuses on ensuring that internal guidelines and process standards reduce errors, while the other tries to reduce defects. However, since maintenance is not the core service of B2P firms and big interventions are generally outsourced, the establishment of this methodology would be too much laborious, as it implies the definition of an ad-hoc team for small repairs that do not bring high benefits. Rather, its mindset can be partly introduced as a control activity to evaluate whether the workers are meeting quality standards or if some deviations are occurring.

These considerations can help B2P enterprises to start introducing lean management within their organisations, or to increase their degree of adoption. The possibility to apply this methodology is higher in 2-wheeled firms rather than in 4-wheeled ones. Indeed, the first ones, relying on a mixed- model of business practices, have operations to manage internally such as maintenance. For the others, instead, this research may be a change of direction in the internalisation of some activities or in establishing close partnerships with mechanics who can introduce these tools in managing their workshops.

Considering on-site maintenance, fewer tools can be applied, since the activity is simpler compared to that carried out in factory and is influenced by many external factors. For instance, the path to reach the various vehicles, and the related time, is wasteful. However, its minimisation using an optimisation algorithm is highly dependent on traffic conditions. Focusing on those activities on which it is actually possible to save time, 5s can help to reduce the changeover time. Indeed, from one vehicle and the next one, the type of repair can change, hence different tools and spare parts are required. The 5s concept can be used in order to organise the equipment inside the van, decreasing the related searching time. This tool is useful for both station-

17 It is one of the two Porter’s competitive advantage typologies. A company has a cost advantage if all the cost sustained to carry out its activities are lower than those of its competitors.

135 LEAN MANAGEMENT APPLICATION based and free-floating platforms. Indeed, under the assumption that maintenance is performed by operators in couple, changing equipment during the travel in between represents the optimal situation for the latter, as the changeover time is masked with the displacement one. However, for a matter of security, it is not possible to execute activities on a moving van. As a consequence, finding a way to reduce the set-up time on-site is necessary. The Poka-yoke technique can be also functional to the 5s one in this case. Indeed, relying on the contact method, the internal space of the toolbox can be shaped in order to recall the right utensil to insert, avoiding confusion. In this way, searching time would be reduced and the absence of any necessary tools would be immediately grasped thanks to the high-level of order. In the figures below, a schematic example is provided. Figure 15 shows the concept in practical terms, whereas Figure 16 demonstrate its efficacy. Indeed, it enables to immediately find the needed tools and eventually which are missing.

Figure 15 – Toolbox with shaped spaces Figure 16 – Filled toolbox

The same concept can be applied to the maintenance performed inside the factories, using tool- trolleys, that can be easily moved from one station to another, on which deposit the shaped toolboxes. However, the possible interdependence between these two utensils has been explained at this point as it is more relevant for the on-site activity rather than for the in-house one, for mainly two reasons: 1. being vehicles parked on the street and visible to the customer, it is important to quickly restore them and make them ready-to-use; 2. the limited ergonomics of the van, compared to the internal space of the plant, requires the best possible organisation of the space.

Together with the technique explained above, Mixed Model Processing can also be applied in order to find the maximum number of mends to perform in sequence before a set-up is required. This technique is ideal for station-based firms since the sequence is limited to the vehicles parked in a dock, whereas, for the others, it refers to all means to be repaired in a city or in a specific zone of it.

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Thus, the application of the model must be combined with the path to travel which in turn depends on the traffic conditions.

Despite the combination adopted by companies (2- or 4-wheeled vs station-based or free-floating), in all cases the standardisation of vehicles allows to reduce the variety of equipment used as well as that of spare parts and additional components. Indeed, typically a B2P firm offers a fleet of identical means. This enables to sustain a changeover time lower than a situation characterised by higher variety.

4.4.2. Rebalancing & Refuelling Considering the other two operations (refuelling and rebalancing), a minor analysis can be conducted as they are already lean, meaning that they do not require large tasks.

Focusing on refuelling, the takt time concept can be introduced in order to determine the task duration needed to match the demand, represented by the number of vehicles to replenish in a given day. This tool is more relevant for 2-wheeled means compared to 4-wheeled ones. Indeed, in the latter case, the activity is carried out in a linear way during the night shift which reduces traffic conditions issue to consider in the optimisation of the vehicle visit sequence. Moreover, as analysed in paragraph 3.3.2.1.2, it is an operation reasonably outsourced by enterprises. Hence, takt time can be computed to balance the workload among on-site operators replacing batteries according to the following formula:

퐴푣푎𝑖푎푙푏푙푒 푤표푟푘𝑖푛푔 푡𝑖푚푒 푇 = [푚𝑖푛⁄ ] 퐷푒푚푎푛푑 푢푛𝑖푡푠

Despite defining the demand as the number of vehicles to be replenished in a given day, it must be considered indirectly also the customer demand which influences the period within which a means must be restored to its ready-to-use function. Once a takt system is implemented, many benefits are gained. Indeed, it allows to identify which operator is a bottleneck18 and on which organisations to focus attention. Furthermore, being related only to value-added work, it motivates the firm to focus on non-value adding task elimination. Another advantage, already implicit in the nature of this operation, is given from economies of learning. Indeed, requiring a set of similar tasks to perform daily in the same way, it enables to increase the productivity of workers. However, even if indirectly in this specific case, takt time is tied to customer demand. Hence, when there is a huge increment in

18 Operator that perform activities in a time higher than planned.

137 LEAN MANAGEMENT APPLICATION clients requests, the planned time has to decrease, defining the reorganisation of the procedure followed to replace the battery in order to take a shorter takt time. In case this is not feasible, another operator has to be added to the team. Furthermore, a lower planned time put considerable stress on the workers, increasing the likelihood of errors. This, together with the task repetitiveness, diminishes motivation and sometimes may enhance absenteeism. Concluding, the takt time concept does not consider human factors, such as an unexpected break or a brief rest period. This is especially critical in operations involving significant physical labour. Thus, the workers team must be capable to operate above the peak. The takt time concept is worthwhile also for rebalancing. However, it is more complex as the traffic intensity of the areas from where vehicles are moved, that of the destination zone and of the path in between must be considered. In both cases, the effective takt time varies daily according to the customers demand, which in turns changes the number of vehicles that need to be refuelled or moved. Furthermore, it is highly dependent on daily variability of possible special events, such as football matches, which increase the service requests, especially in determinate city areas. However, analysing data and trends, it is possible to compute a standard takt time for normal activities and forecast an extraordinary one.

SMED technique can be also useful in reducing the time to change the battery on a vehicle. Indeed, following the pit-stop concept for F1, battery replacement is the equivalent of a machine downtime, being a period during which it cannot be used by the customer. Therefore, the aim is to get the vehicle back ready-to-use as quick as possible. Accordingly, there is a series of principles, adapted to be suitable for B2P companies, that enables to eliminate wastes. • Precise locations → it is important to arrange the spare batteries and necessary tools inside the dedicated compartment in order not to lose time in searching them. To this purpose, a minor form of 5s can help in the arrangement of objects, especially in case of large compartment as that of a van. • Procedure standardisation → on-site, replacement activity must follow a uniform standard method, whereas any attempt to tinker with the equipment, in order to improve the procedure, has to take place in controlled sessions inside the factory. Furthermore, benefit from economies of learning, the more the operator practises, the lower the time required. • Adapt the equipment → the place where battery is inserted and connected must be designed with quick release levers in order to reduce the number of tools to use and speed up the procedure itself.

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As for maintenance changeover, the nature of the activity allows to introduce OTED instead of SMED. Indeed, as already mentioned, reducing the overall mending time is relevant to restore the vehicle quickly making it available to the customer. The importance is even higher compared to a manufacturing scenario. Indeed, traditional firms, adopting lean management, start to produce a product only after receiving an order from the customer, in accordance with the pull-system theory. Therefore, these firms earn revenues before producing. B2P sharing firms, instead, need ready-to- rent means in order to generate revenues, thus all those activities that support the provision of the service must minimise the downtime of vehicles.

4.4.3. Drivers’ Behaviour Management and Trip Optimisation All the tools discussed so far, which exhaustively represent the set of existing lean practices, encounter difficulties in the application to P2P case. Indeed, they are mainly related to manual activities that are not performed in these platforms. However, preserving value-adding operations and eliminating non-value-adding ones is their same intent. Therefore, practical utilisation of lean instruments aims at identifying the recurring issues in rider and driver monitoring and the related root-causes to eliminate.

FMEA (Failure Mode and Effect Analysis) can be shaped on this context and utilised, for instance, for rides cancellations analysis. Indeed, the “failure” corresponds to trip churn by the customer or the driver and generates “failure effects”. According to their impact, the severity (S) index is assigned. Possible consequences could be (1) the rider switching to another available driver, hence implying low severity, or (2) he could leave the platform and move to a competitor’s one, implying high severity due to customer loss. Each failure has a “potential cause” that led to certain results. Still keeping the same examples of above, (1) the driver was in delay or (2) at the end of the trip, the pilot had a rude attitude that negatively affected customer experience. The occurrence (O) value corresponds to each cause, where the higher indicates the more recurring. Finally, the company has to detect which are the control activities that make it prevent issues arise. Detection (D) is the index for this last step, where the higher is the harder to spot. Figure 17 represents how FMEA is structured.

Figure 17 - FMEA Format

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The last column, RPN (Risk Priority Number), prioritises which problems to address first, usually represented by the highest scores. Indeed, FMEA continues as shown in Figure 18, where some recommended actions are indicated, like driver re-training. Once implemented, their effectiveness needs to be checked.

Figure 18 - FMEA Complete Format

Another useful practice to adopt is six-sigma that, combined with lean methodology, allows to reduce Muda. According to the first principle of lean, identify the value, six-sigma is built starting from the Voice of Customer. Indeed, the firm collects and analyses riders’ statements and comments that reflect their perception of the service experience. Through their browsing, key travellers’ issues can be identified and Critical to Quality (CtQ) parameters set. These are specific, precise and measurable expectations that a client has regarding the service. By referring to paragraph 3.3.1.1.4, the CtQ indicators in P2P transportation context could be ETA (Estimated Time of Arrival) and drivers’ rating. These parameters can be utilised as the focal point of six-sigma methodology. Indeed, the leading formula is process capability, that is a measure of how much a given process is capable of realising the specifications established for its output and, consequently, to generate acceptable outcomes. Thus, P2P platforms can fix a minimum driver’s rating to guarantee a certain quality level or an ETA to be respected for each ride type, meaning trips categories grouped according to service criteria like distance and weather conditions.

Assuming that ratings and ETA are normally distributed, with the related mean µ and standard deviation σ, the process capability corresponds to:

푈푆퐿 − 퐿푆퐿 퐶푝 = , 6휎 where USL and LSL stand for Upper and Lower Service Level, set by the firm according to market requirements, while 6σ represents the process (thus trip performances) variation. A good level of Cp is over 1, as it implies that the process variability (6σ) is able to meet market tolerance (USL-LSL), as schematised in Figure 19.

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Figure 19 - Process Width and Market Tolerance Window Representation

To this indicator, Cpk is added which verifies if the process is centred or is shifting, as it represents the deviation of its average value respect to the target of the specification. In fact, it is computed as follow:

푈푆퐿 − µ µ − 퐿푆퐿 퐶푝 = min [ ; ] 푘 3휎 3휎

Therefore, the pro in implementing this methodology is the definition of a structured approach centred on Voice of Customer that permits to constantly check the distortions and, eventually, correct and improve the process. Moreover, data collection and analysis operation is an enabler for this method, since the platform is already gathering detailed information. However, it requires high economic and organisational effort that is enhanced in P2P scenario; these firms, indeed, are dealing with a service that is highly variable and depends on many parameters hard to control, like weather conditions and human variability itself. Nevertheless, exactly its implementation can push them in monitoring uncontrollable factors, trying to build a six-sigma procedure also for these adverse conditions, improving overall the service.

Many wastes in P2P sector are generated during trip delivery and, to eliminate them, some insights are provided. The potential of data collection and analysis, where the platforms are able to handle huge amount of data coming from disparate sources, allows to make the enterprise compute the best possible trip for drivers, aiming at minimising the losses, like motion, waiting and transport. As concern the first one, the driver follows the optimal path definition from an existing GPS navigator, like Google Maps or Waze. However, if some accidents arise along the journey, the firm cannot act on them and it is up to driver’s experience understanding how to resolve the anomaly. Another un-necessary motion is required to customers who have to move towards an area in order to simplify the meeting with the car. Although it affects his experience, it does not imply any cost to the

141 LEAN MANAGEMENT APPLICATION organisation, unless the rider decides to abandon the trip, as he needs to walk for too long. The P2P platform facilitates the meeting, fixing some pick-up points, as already Uber does. By analysing the data regarding the roads that are mostly travelled and the crossing points between them, the platform can identify optimal meeting points for the two sides. It is like using the Spaghetti Chart with the intent to verify which paths are recursively repeated and crossed. Moving to transport waste, the previous consideration about pick-up points may prove useful. Indeed, unsaturated drivers can wait the next request close to a pick-up point and the app itself can indicate to them which are the most likely ones to be activated, according to the period of the day, weather condition and special events. Otherwise, the best approach to reduce it is the optimal trips scheduling. Reasonably, the app already assigns rides according to drivers’ proximity, thus decreasing this waste. However, since the carmen independently decide whether to accept the trip or not, the request can be conveyed to a more distant operator, hence transport will never be completely eliminated. For the last one, waiting, the main process phase that the firm can improve is request allocation. The company decides the driver that is going to complete the request, considering two parameters in order to find the best allocation. For example, if there is a request at point P waiting to be accepted, there could be three available drivers whose distances from P are X1, X2, and X3. Thus, the times they take to reach the customer are equal to t1, t2, and t3, according to their average travel speed. In addition, the firm can evaluate the Average Time to Response (ATTR) and the Average Time to Leave (ATTL) (ref. 3.3.2.1.8), that is how long each driver takes to accept or reject the ride and to leave. Therefore, the objective function to evaluate in order to choose the first driver to assign the request is:

푚𝑖푛푖(퐴푇푇푅 + 퐴푇푇퐿 + 푇푟𝑖푝 퐷푢푟푎푡𝑖표푛)푖 = 푚𝑖푛푖(퐸푇퐴)푖 ∀ 𝑖 = 1, 2, … , 푁 , where i is the available drivers in the area of interest. This waste could be further improved with the good predisposition of pick-up points and driver waiting to be matched nearby.

4.4.4. Establishing Continuous Improvement Coherently with lean philosophy, all the tools explained above must be gradually introduced. In particular, if lean practices have never been adopted in the company, their instalment requires organisational effort that has to be pursued in small bites every day, according to kai zen approach. Among the procedures described for maintenance, it is possible to distinguish some interventions that imply bigger changes, like the new workshop layout or the new managerial approaches, as TQM. Therefore, their introduction has to be evaluated according to a cost-benefit analysis, verifying if the

142 LEAN MANAGEMENT APPLICATION brought advantages balance the related expenditures. The other tools described for mending are visual controls or simpler applications, as 5S and poka-yoke, requiring lower efforts. The countermeasures identified for refuelling and rebalancing are easier to implement, as they are mainly related to time-improvement through a new modus-operandi to teach to operators. However, since these new methods are designed considering customers’ arrivals time, which is variable, establishing continuous improvement for these operations is important in order to respect the time limits, such as the takt time and SMED in the refuelling case. The same holds for P2P trip optimisation, whose performance improvement directly affects customer experience; hence, the identification of the root-causes generating failures along the service and the realisation of corrective actions pursue continuous improvement. Actually, all these tools has to be applied in this perspective, following Deming cycle which must be started from the first application: (P) plan the interventions, structuring the steps, identifying the operations involved and the responsible; (D) turns into practice what planned and (C) check if the expected targets have been achieved. (A) Once taken corrective actions, wherever needed, standardise the procedure to move to next implementation. The PDCA cycle is at the basis of establishing virtuous improvement, since it is recursively repeated every time a programme is completed. The last issue to consider in the establishment of lean instruments is resistance to change. The introduction of new procedures always finds obstacles in operators. However, workers, who perfectly know the tasks to perform, gain of importance in lean philosophy because they are empowered by the fact that continuous improvement relies in their hands.

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5. COVID vs SES

In the current year, countries around the world have faced a severe pandemic named COVID-19, a deadly virus commonly called Coronavirus. The pandemic started in China, in the city of Wuhan in December 2019 and within a few months it spread all over the world, affecting the various countries with more or less seriously. Data dating back to 6 May 2020 report the total number of infected people worldwide, since the start of the pandemic, equal to 3.76 million and the number of deaths equal to 264,000. The severity of this pandemic and its rapid spread is due to transmission methods. Indeed, coronaviruses are a large family of viruses known to cause diseases ranging from the common cold to more serious health problems such as Middle Eastern Respiratory Syndrome (MERS) and Acute Severe Respiratory Syndrome (SARS). It is mainly spread through close contact with a sick person. The primary way is the breath droplets of infected people, for example through saliva, coughing and sneezing, direct personal hand contacts, thus by touching contaminated (not yet washed) hands with mouth, nose or eyes. The governments had to intervene quickly by limiting contacts between people as much as possible. The measure implemented to contain the epidemic has been called lockdown. A lockdown, formally, is a prison protocol that usually prevents people, information or cargo from leaving an area; when people must stay where they are and may not enter or exit a building, the prohibition becomes a full lockdown. Starting from the Chinese government, and consequently the ones of the other infected countries, they prohibited movement within the country and closed the borders to avoid exchanges also with other nations. Only organisations producing basic goods and retailers selling them, such as tobacconists and supermarkets have remained open, nevertheless guaranteeing the security measures established by the government. All the other firms started to work at home, if possible, through smart-working. Coronavirus has not only affected the traditional businesses, but also the Sharing Economy for which the impact has been different according to the sector and even harder compared to the traditional one. This chapter wants to study which industries have been most involved and why, and how other firms have reinvented themselves in order to continue working.

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5.1. Characteristics Collapse

According to the Brunel University of London [6], the Sharing Economy is one of the businesses that has been profoundly disrupted by the measures taken in order to slow down and overcome the pandemic, indeed its own characteristics from strengths became weaknesses.

As discussed in paragraph 2.1.5, the four main characteristics of the Sharing Economy are: • the presence of a digital platform; • it is mostly based on peer-to-peer transactions, where both suppliers and consumers are individuals or micro-entrepreneurs; • the emphasis on temporary access instead of ownership; • it enables access to unused capacity or idle capacity that can be physical or human based. It is possible to explain for each feature which external forces caused its failure. First of all, the lockdown makes impossible to deliver or consume the services provided by the platforms. Even if the app works and remains functioning, it is not possible to rent a property on Airbnb or book a ride on Uber because it is not allowed to go anywhere. As a consequence, there is no demand for suppliers, thus implying huge economic losses for them. Secondly, since the virus began to spread, it has become necessary to establish rules that prevent contagion when people are forced to leave the house to go to supermarkets or to work. The regulations concern the safety distance between individuals as well as the adoption of some PPE to limit infections such as personal masks and sanitising gel. In light of this, the peer-to-peer business model seems risky since touch and cleanliness are the drivers defining the virus transmission. The pandemic also questioned the emphasis on having access to a resource. Indeed, the property seems to be more appealing not only in terms of cleanliness control, but also in terms of availability and economic security. Furthermore, even if unused capacity cannot be used during the lockdown, individuals who own the asset will be able to benefit from it, compared to those companies that rely on the goods of third parties such as Uber.

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5.2. Sectorial Impact

As mentioned at the beginning, the crisis of the Sharing Economy has not affected all the sectors in which it is present in the same way. Therefore, in the following paragraphs, the impact on the main industries is discussed.

5.2.1. Accommodation The most suffering sector is the tourism one. Only in Italy, it is estimated that the drop in foreign revenues will be 20 million euros to which the 46 million euros lost internally must be added [7]. The confirmation is provided by Brian Chesky, Airbnb Administrator, which had to fire 1,900 out of 7,500 employees, equal to 25.33% reduction in workforce. Indeed, Airbnb, as well as Booking.com, registered 85% decrement in bookings and 90% of reservations cancelled. Thus, the enterprises expect less than half of 2019 revenues. This huge loss is not only due to the lockdown established by governments, cancelling flights and prohibiting travels among countries by closing borders but is also caused by the business model of the accommodation sector. Indeed, people do not feel safe to stay in a house where other people have sojourned and who may have sanitation problems.

5.2.2. Transportation The transport sector also experiences a sharp slowdown. Andrea Saviane, country manager of BlaBlaCar, wonders if people would still like to voyage together or not. Until now, the lockdown downsized till zero the trips per day of the company, who before recorded one trip per minute [8]. The same reason is at the basis of the uncertain future of Uber. Indeed Dara Khrosrowshahi, CEO of Uber, stated that the number of sharing rides per day diminished in the U.S. about 83% [9] and he indicated as main driver of this crisis the trust of the customers and their feelings of safety to join in shared cars as well as to catch the bus and the underground. Due to demand reduction, Uber had to dismiss some of Its employees. In order to support them in finding a new job, It created a talent directory to help other enterprises and recruiters easily connect. Fortunately, some enterprises in the transport sector managed to remain active. It is the case of Mobike. The organisation decided to offer the service for free to all the healthcare professionals at the beginning of the pandemic, and now it is working with the municipality of Milan in order to organise a service that can reduce the influx of people on public transport. It is indeed true that if cars, public transport and apartments are seen as unsafe, open air vehicles are perceived as cleaner and less subject to the risk of contagion. The big challenge that this firm, and its competitors, is facing now is

146 COVID vs SES how to attract a new market segment, made up of people who previously did not rely on the Sharing Economy transport. For what concerns the scooter sharing, such as MiMoto, despite being perceived as safer than cars, the sanitization problems are higher compared to simple bikes. Even if the company has provided the means with a sanitizing pack containing some personal devices such as medical cap and hand gel, the problem still remains for the helmet used by a lot of people. For this reason, the organisation would like to help users to buy their personal one. The solution to improve the service and make it safer is also optimal in view of the imminent future, where people will prefer alternative means compared to public transport, perceived as insecure.

5.2.3. Food and Good Delivery The sector that suffered the least impact was that of food and goods delivery that actually turned out to be an opportunity for people who did not feel safe to leave their houses, thus preferring to receive goods at home. According to Netcomm Forum [10] by the beginning of 2020, in Italy two-million new consumers joined online markets and 1.3 million of them right during the sanitary emergency and the quarantine. For companies operating in this sector, such as Just Eat, Glovo, Deliveroo, Foodinho, the main challenge has been adjusting in time to the safety measures put in place by the government to minimise the infections. Therefore, they had to provide riders with the necessary PPE like personal masks, whose supplies were difficult to find especially at the beginning of the pandemic, and they had to change also their working method. Glovo, first proposed the Contactless Delivery Service, a delivery method that limits contacts to the maximum both in the collection and delivery phase of the order. The sealed bag containing the order is placed by the restaurant owner on an external support, while during delivery, after removing the digital signature, the customer can agree with the rider, either on the phone or on the intercom, to have the bag delivered to the door and avoid another contact opportunity. However, as many restaurants and cafes lean on delivery platforms services in order to keep their business open, some sharing companies have started taking advantage by this situation increasing the fee charged on restaurateurs up to 35%, squeezing their profit [10].

5.2.4. Space As far as this sector is concerned, what decreed its crisis was the choice of many organisations to take advantage of smart-working. WeWork is an American company that rent spaces for co-working and during the lockdown not only faced a reduction in demand, but also the customer refusal to pay the monthly rent. In fact, the latter problem has been experienced in many sectors, even in the traditional

147 COVID vs SES ones, where many shops, bars, restaurants were unable to pay the monthly rent having no income. The same problem is experienced also in the private sector, where many families have not been able to pay the rent of their homes, not having a salary after the closure of firms and factories. The uncertain future that firms like WeWork must manage is whether people would like to return to work in common places rather than in their own homes. Therefore, as in the other cases, the main driver seems to be people's trust [7].

5.2.5. Crowdfunding Although many sharing companies suffered coronavirus implications, crowdfunding reveals all its potentialities. Indeed, many campaigns were created by privates and organisations in order to relief the multiple damages caused by the pandemic. Analysing GoFundMe platform, there are at least 13 thousand campaigns supporting initiatives against coronavirus [11]. Most of fundraising campaigns aims at supporting expenses in hospitals and clinics to afford all the materials needed to guarantee treatments to patients, from PPEs to machineries, due to the increasing scarcity of resources caused by the growth of infected people. However, the boom of initiatives needed to be managed in order not to make the donors run into fake campaigns. To this purpose, the team Trust&Safety of GoFundMe is responsible for monitoring and controlling all the documents and information. Campaigns were started also with the aim of providing economic supports to privates. Considering again the health-care system, in countries like the United States, people do not have the access to free health-treatments and, due to implication on work conditions caused by pandemic, they may not afford them. Crowdfunding platforms can also support not-for-profit organisations, proving food and meals, and people without any unemployment benefits, like part-time employees, freelancers, contractors, or gig workers. Of course, these services can gain a marginal profit by donations. For example, GoFundMe has the option to leave a “tip” from 5% to 10% at the payment moment and imposes an obligatory commission to cover transaction costs [12].

5.3. Research Question

Following the health emergency that has plagued the economies of all countries on a global level, this work will also focus on a third research questions: how operations in the transport sector have changed following the pandemic and what strategic, tactic and operative choices can be made by Sharing Economy companies to face this crisis and remain productive, also taking into account all those security measures that have been enacted by the governments to preserve the health of all citizens and that must be respected in the short-medium term future.

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5.4. Analysis

In order to analyse the countermeasures undertaken by interviewed firms to deal with the Coronavirus pandemic, it is necessary to divide the time horizon into two phases. The first one linked to those possible solutions adopted during the lockdown to maintain business continuity and not zero monthly revenues; and the second phase related to those new operations that the firms had to install in order to adapt to the anti-contagion policies in force in the countries in which they offer their services. Therefore, it is possible to affirm that in the first phase the decisions taken by the enterprises are strategic rather than tactical even if short-term oriented, since they are related to the company’s strategy to survive in the medium-short term. On the contrary, the decisions of phase two are mostly operative, aimed at making the daily operating processes more efficient.

5.4.1. During Lockdown To proceed in a chronological order, and, at the same time, to make a comparison between B2P and P2P organisations, the strategic initiatives of the first period are analysed. After explaining the countermeasures undertaken, their impact on firms’ operations is derived.

Starting to consider B2P business model, some platforms have close agreements with local municipalities in order to provide transport service to healthcare personnel, food-commercial workers etc. Jump, at the request of the Rome municipality, has kept the service active with a limited fleet guaranteeing a free bike-sharing service to hospital staff and a very reduced price to other workers. In this way, with the first service the company got brand image benefits, while with the second one it maintained quarterly revenues, albeit partially. Other enterprises have exploited business synergies dedicating their fleet to other sectors, establishing short-term partnership relations with those that could work but needed transportation means, such as GoVolt which has made the scooters available to Dominos and the Italian Red Cross for home delivery services, thus maintaining continuous operations, although with a lower profit. The common denominator of these bargains, regardless of the third party, is made up of two components: business and operations continuity. The first one is related to the main objective of each for-profit platform and is necessary to ensure liquidity in the long run to pay employee wages, supplier invoices etc., the second one is very important for the functionality of the vehicles. Indeed, staying unused for too long reduces battery life and increases repair and maintenance costs that the company has to bear. This problem does not affect P2P organisations, for which only business continuity is relevant, not having owned vehicles. The countermeasures adopted by P2P firms were analogous to those described above but developed in a

149 COVID vs SES different way according to their business model. Uber, according to the policy adopted for Jump, provided NCC free-service in Rome for Spallanzani and Sacco hospital workers with the service named Uber Medics and a reduced price for normal workers. In addition, it started to cooperate with Eataly to provide home food delivery, switching drivers from the chauffer function to the delivery one. In other countries where Uber provides also Uber Eats services, it encouraged the use of this platform for take-away delivery, such as Gett in America. These firms have benefited from the economies of scope created around their platform, in fact by owning and managing one single app they are able to provide more distinct services, reducing the production cost, instead of offering them separately. Considering Little Ride, the African company interviewed, it adopted an approach similar to those of Jump in Rome, making an agreement with the Kenyan government. However, since it concerns a cultural context profoundly different from the European and American one, it will be treated in a dedicated part, paragraph 5.4.3, in order not to disperse the analysis and explain in detail how It got organised.

In the following Table 22, a cross-business model analysis is summarized in order to highlight analogies and differences between the strategies adopted.

Table 22 – Strategies Adopted During COVID-19 Lockdown

Considering the operations described in chapter 3.3.2, for each business model it is possible to analyse the operations managed continuously albeit in smaller volume due to the demand reduction and the unnecessary ones.

5.4.1.1. B2P Referring to the typical operations managed by B2P organisations, which are rebalancing, maintenance and refuelling, this paragraph analyses the reasons that led them to remain active or to be temporarily dismissed. Starting from the first one, due to the collapse in customers’ requests, it is reasonable to assume that it was not necessary as its main objective is to trigger demand. Indeed, there was no need to satisfy a higher number of customers, since people could only leave the house for strictly necessary reasons and, in most cases, they used their own means perceived as safer.

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However, considering the service offered to hospital staff, it is reasonable to assume that this operation was partially managed by enterprises as Jump in order to encourage vehicles usage by those workers after the agreement with the Rome municipality. Thus, the Firm may have used some employees to move part of the fleet near the two hospitals of interest. Despite this type of rebalancing does not bring directly economic benefits as the service offered is free, it carries on a return of image, important for the recovery phase, and preserves the functionality of motor vehicles. Moving to maintenance activities, it is necessary to consider the two types of repairs, discussed in chapter 3.3.2.1.1, separately. The operational needs linked to big mends have definitely diminished since the lockdown led to a reduction in road accidents, for instance in Italy they have decreased by 70% [13] from March to May compared to the previous year. Contrariwise, those related to small repairs have been conducted, but in a different way. Indeed, keeping part of the fleet active, it is reasonable to assume that some little mends were needed. The criticality encountered in managing this operation is related to the blocking of logistics services during the lockdown. As a matter of fact, deliveries remained active only for essential goods and were only allowed within state borders following their closure. Therefore, in the event that vehicles needed repairs that required the supply of some parts, it is presumable that companies withdrew the damaged vehicle from the market to replace it with one in stock. In fact, many firms, having reduced their fleet, had stocks of these. Concerning the refuelling phase, a distinction based on vehicle type is required. Car-sharing enterprises may incentivise customers to refuel the vehicle before ending the ride using bonus points or discounts. In that case, using a truck, as in everyday business, would be highly inefficient given the low volume of cars used. Truck-refuelling makes sense to optimise costs and times when the number of vehicles to be supplied is high. If not, operational costs are not repaid since they would be distributed on a small number of machines, thus to cover them the companies has to increase the customer’s price. This strategy is completely opposite to the one adopted which instead aims to decrease the price even in the face of the economic crisis experienced. Concerning two-wheels vehicle-sharing platforms, the replacement of scooters and electric bikes battery can be handled in two ways depending on the service offered. Regarding short-term partnership situations, a batteries stock can be supplied to partner organisations, making them in charge of this operation or the intervention of some employees can be scheduled. Operation delegating may be beneficial for the firms which would save the cost of employees, but it could be difficult to manage for enterprises like the Red Cross, whose workload doubled during the pandemic. Furthermore, it depends on how the batteries are charged: if it is required a special device it may not be realistic to install it in the partner headquarters only for few months, whereas it may be suitable using mobile devices connected to

151 COVID vs SES electric outlets. On the other hand, using some company’s operators is a cost for the firm which must be covered relying only on partnership revenues since traditional rides are almost nil.

In Table 23, operations managed by B2P platforms during the lockdown are summarized. It highlights as there were no additional activities for this business model, but only traditional operations that were temporarily dismissed or managed in an alternative way.

Table 23 - Operations Managed During COVID-19 Lockdown - B2P case

A final reasoning to underline is that unlike P2Ps, they did not have to manage the distribution of PP2 during this phase. Indeed, an intensive delivery was not required for the few operators that remained active who did not have a direct contact with the customer.

5.4.1.2. P2P Considering the operations in place for P2P cases, management of the platform not only remained active, but it has also been enhanced in order to provide some services. Since rides requests have decreased, the need to manage traditional operations such as matching rate, customers’ claims etc. diminished. Indeed, the working volume of these activities has the client as main driver, involved in a relationship of direct proportionality. Therefore, according to demand decrease, also workloads diminished. Organisations who benefit from economies of scope, such as Uber with Uber Eats, having a high demand in home-delivery services, shifted platform operators from the chauffer service to the delivery one in order to deal with a growing demand pattern. Furthermore, to satisfy the huge requests, enterprises may have increased their drivers base. Hence, onboarding drivers and training remained active albeit partially. They allowed the selection of new drivers and their formation, especially important in this period where the conduct rules are not only aimed at proving a good customer experience, but also at respecting the hygienic-sanitary rules. For this reason, firms had to provide an additional training to both new and existing drivers, about the policies and guidelines to be followed during the service dispatching. Moreover, in order to keep the service active, companies had to provide a personal mask to their drivers. Hence, they needed to manage the PPE delivery, whose administration was often made difficult by logistical problems occurring during the pandemic due to border closure.

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Those operations are conducted also in case of partnerships formation, which require new platform developments too. Indeed, taking as reference the partnership between Uber and Eataly, the Sharing Economy firm had to connect its app to the Eataly online site to receive order requests and allow the match with its drivers. In this phase, where business survival was important, secondary activities were put aside to better focus on which service to establish to recover at least partially monthly revenues and retain drivers, to have a consistent offer base for the recovery phase. Consequently, business opportunity searching has been exploited, sacrificing operations related to drivers and client rating etc. which instead become very important in the reboot phase, as explained in the next paragraph. There have also been operations which, although important especially to recover customers’ trust such as fleet management, were not possible to manage according to government restrictions that limited contact between people as much as possible. The only possibility, considering short implementation time and cost, was to recreate them in a virtual way. Hence, to manage a virtual inspection of a driver’s car, possible only with highly developed platforms like Uber. It is essential to consider short implementation time because the pandemic arrived unexpectedly, and companies had to take countermeasures that were fast to be applied and cheap. According to this, they had to adapt their operations shortly incorporating both effectiveness and efficiency. Hence, unnecessary operations were temporarily dismissed to be resumed during the recovery phase.

In Table 24, the operations managed by P2P cases during the lockdown phase are highlighted.

Table 24 - Operations Managed During COVID-19 Lockdown - P2P Case

Finally, to conclude the analysis of this paragraph, it is possible to compare the strategies adopted by both types of platforms. Since a cross-case study of traditional operations has already been done in chapter 3.3.2.2 and there were no additional operations to compare except for the PPE distribution, the examination will be limited to the reactive approaches adopted, which are full of analogies. First of all, both business models have to plan alternative services leveraging on the potentiality of their assets in order to survive in the short-term. B2Ps have rented their fleets to support the growing business of other organisations, whereas P2Ps have exploited the potential of their app enlarging the matching function. Indeed, in the driver-platform-chain, a new player has emerged: supermarket companies. These strategic decisions were based on lateral business diversification, meaning to

153 COVID vs SES develop new services by expanding into other sectors, food & goods delivery in this case, more or less similar to that of belonging, pursuing a horizontal integration. Economies of scope are already exploited by large P2P enterprises such as Uber and Gett, while for B2P it is an innovative solution. Secondly, pricing strategies were not only useful to support the local community and get image benefits, but also as a leverage for drivers retaining in case of P2P and for vehicle functionality continuity. Both are important in view of the recovery phase. Indeed, drivers as well vehicles are the offering driver of Sharing Economy Services in the transport sector. Not having them means not being able to satisfy the demand and to make the business run. Hence, pricing strategies prove to be useful both for internal and external purposes.

Drivers retaining deserves a note of respect, since the pandemic revealed the uncertainty of P2P business for free workers. Indeed, not having a work contract which regulates the relationship with the platform, they cannot rely on economic government aids unlike B2P operators. Thus, having drivers who have made the Sharing Economy a full-time job, it was essential to take countermeasures that helped them to work, during the lockdown months. This allowed the drivers to survive economically and maintain confidence in the business which is important for the firms to ensure coverage of the service during the recovery period.

5.4.2. During the Recovery Phase During the recovery phase, in addition to resuming the usual business, two main initiatives of the first phase continued: pricing strategies and local agreements with municipalities. Indeed, both P2P and B2P organisations leveraged on prices aiming at boosting the demand by supporting local communities and, therefore, encouraging the use of the services offered. In addition, local municipalities established long term agreements with B2P platforms, whose vehicles can support the workload of public transport which is not able to satisfy the demand due to the establishment of anti- contagion policies that reduced available places on means. Downstream the lockdown phase, in order to provide customer services, companies had to establish new operations, beside the traditional ones, to comply with current hygiene and sanitation regulations. Additional operations are presented below, together with a cost analysis in order to give meaning also in monetary terms. The economic analysis is performed considering how operations are supposed to be integrated with the traditional ones.

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5.4.2.1. Vehicle Disinfection Starting from the B2P firms interviewed, the operations typology installed depends strongly on the type of vehicles used to provide the transport service. Therefore, in the first part of the analysis of which countermeasures have been adopted by those enterprises, a classification based on vehicles typology will be carried out. Car-sharing businesses, such as ReachNow, have integrated the vehicle cleaning phase to the normal refuelling operation in order to optimise times and costs. Mathematical optimisation, in addition to be a useful process in any business, is even more important in this historical situation in order to face the economic crisis following the pandemic. The critical issue to manage, in this case, is that the components of the machines are not designed to be cleaned frequently with chemicals, therefore the problem of components wear must be considered and how sanitization increases this process. A solution may be to install a sensor inside the car able to measure customer’s body temperature and, if this exceeds 37°C, block the vehicle after the usage in order to sanitise it before another trip. Companies offering scooter-sharing, instead, have invested in providing their vehicles with a sanitizing kit containing hand sanitizer gel and, for scooters only, the disposable cap for the helmet. It is reasonable to assume that the refuelling activities of this kit are carried out in parallel with the replacement of the battery, in an optimising perspective. The most critical aspect to manage is the use of the helmet, therefore, some Italian scooter-sharing platforms, such as eCooltra, have offered their customers a sales-package that facilitates the purchase of a personal helmet in affiliated stores. Lastly, considering that bicycles do not have safe places to store the sanitization kit and to avoid theft, the operation adopted was similar to the one of car-sharing. Enterprises as Jump employed those full- time employees, who previously managed rebalancing activities, in bicycle hygiene. Despite having sacrificed an activity that increased the matching rate between supply and demand, this personnel conversion allowed to support only an additional cost related to the material for sanitation. The work- conversion of these employees must not be considered as a non-value-added activity, since the most impactful issue that the organisations need to face, is the loss of customer's trust, therefore this operation is needed to make the vehicle safer and consequently increase customers confidence.

The difference between the two operations set up, respectively vehicle cleaning and sanitizing kit distribution, is reflected in the costs that B2P companies have to sustain. Addressing the first one in relation with car-sharing, under the assumption that is done in parallel with refuelling, the costs related to truck ride are not differential for the enterprise compared to the period before the Coronavirus pandemic. Therefore, the cost function associated to this operation is composed by two parts: one fixed represented by the cost sustained to get supplies of sanitized

155 COVID vs SES products and the other variable according to the number of vehicles that must be sanitized. Indeed, due to the low demand, not all vehicles will be used. Therefore, the algorithm which optimises the truck route, will generate a path touching only used vehicles.

1 (퐶푙푒푎푛𝑖푛푔푇𝑖푚푒 푚푖푛 ∗ 푁° 푉푒ℎ𝑖푐푙푒[푣푒ℎ푖푐푙푒]) ∗ ⁄ ∗ 푂푝푒푟푎푡표푟퐶표푠푡 € + 퐶푙푒푎푛𝑖푛푔 푝푟표푑푢푐푡푠[€] [ ⁄푣푒ℎ푖푐푙푒] 60 [ ⁄ℎ]

The cost function is valid for those companies that internalised the refuelling activity, while for those who relied on third-parties agreements, the cleaning cost would be an additional service bought by the partner organisation and would be included in the monthly invoice. Indeed, platforms offering vehicle-sharing have only one more operation to manage: maintenance which is not carried out on all vehicles every day, but only when there are reported repairs to be done. For this reason, this activity is often outsourced as well. Thus, there are no internal operators to dedicate to this activity and hire someone only for the recovery period could be more dispendious since it will end to be managed when the pandemic is over. The decision of which solution to adopt is considerable because the costs associated to additional operations need to be rewarded with revenues which are below the annual average. Therefore, it is essential to find a way to ensure vehicle safety, while remaining affordable. Negative and positive reflections can be done for both business practices. In fact, enterprises that have established external contracts can outsource also this new operation, but suffering from prices rising as a result of the economic crisis and especially in case of short-term partnership. In this regard, long-term agreements are safer than short-term ones. On the contrary, firms with internal operations can dedicate their employees to cleaning vehicles but relieving them of other assignments. The possibility of coupling the cleaning operations with others within the company such as rebalancing or maintenance is not sustainable for car-sharing. Indeed, the first one is not often performed by car-sharing platforms due to the high costs and times of moving and parking the vehicles in addition to an offer reduction since during the transport the mean is unusable by customers. The second one is not performed on all vehicles every day, therefore the integration would lead not to sanitizing all the vehicles on a daily basis.

The cost function would be the same for bike-sharing organisations, but having exchanged rebalancing operators for cleaning activity, they only have to bear the cost related to materials. Indeed, the personnel cost is not differential from the period prior the pandemic. Hence, the cost function is limited to:

퐶푙푒푎푛𝑖푛푔 푝푟표푑푢푐푡푠[€]

156 COVID vs SES

The last consideration regards the possibility to substitute the cleaning operations by distributing the sanitizing kits for scooters as well. For bicycles, as already mentioned, it is not a suitable option since there is not a safe place in the vehicle to store them, and the risk of theft would be high. Concerning car-sharing, after an intensive initial delivery, there would be the need to refill them. While for firms adopting outsourcing, it is a matter of externalising another operation to be combined with refuelling. Therefore, it can be indistinctly the vehicle sanitation as well as the kit provision since the third-party will be in charge of organising the execution of the activity. For integrated organisations, the kit control would still be done in parallel with the refuelling. In fact, as already mentioned, it is the only operation performed on a daily basis, while the others are arranged only on request. Thus, the cost borne by the company is given by the cost of material, assuming that one kit is formed by disposable products in equal numbers, therefore by inserting n doses, after nth ride it must be refilled.

(퐾𝑖푡 퐶표푠푡 € ∗ 푁° 푉푒ℎ𝑖푐푙푒 ∗ 1 푘푖푡 ) [ ] [푣푒ℎ푖푐푙푒] [ ] 푘푖푡 푣푒ℎ푖푐푙푒 푒푣푒푟푦 푛 푟푖푑푒푠

Since the kit provision takes an infinitesimal time, especially compared to the one of cleaning, the cost function does not consider it.

Thus, the choice of which operation to establish has to be based on a cost-benefit analysis, where the costs are given by the functions described above which also contemplate the time effort in monetary terms, and the benefit can be measured as security level perceived by the customer. In this way, both an internal economic perspective and an external one related to customer satisfaction are studied. Of course, the maximum benefit would be obtained sanitizing the means after each ride. However, as emerged during the interviews, this is not sustainable by enterprises since it would require more operators of those hired and would reduce the supply as the vehicles would be blocked between one use and the next. The issue is partially solved by distributing the kit, since each customer at his discretion could clean the vehicle before and after the usage.

Considering scooter-sharing platforms, the double choice between cleaning vehicle and sanitizing kit is not possible due to the helmet which is used by many customers. Therefore, it is necessary to provide protective devices, relying on the sanitizing kit. Resuming the cost function described above, in an optimising perspective, assuming an average ride duration in kilometres equal to S, n could be calculated in order to be equal to the minimum number of trips within which the battery must be changed to ensure that all customer find the kit available.

157 COVID vs SES

The decision to measure the ride in term of distance is justified by the fact that the KPIs monitored by 2-wheels sharing companies like Jump and MiMoto use kilometres as unite of measurement.

Figure 20 - Optimal Situation

Having displayed in Figure 20 the optimal situation, according to which after n rides, the operator has to change the battery and restore the kit, the worst case, shown in Figure 21, can be analysed:

Figure 21 - Worst Situation

Indeed, if a customer takes a trip whose distance is equal to n rides, the operator has to immediately change the battery and has to supply only the kit with the missing unit.

Although considering an average duration balances the overall situation, for completeness, all the scenarios in the middle that may happen are analysed and shown respectively in: - Figure 22 → if the distance of several rides is above the average S, the battery change will take place before the n ride, therefore all customers will find the full kit which must be supplied with n-x pieces, where x represents the number of rides prior to the change, hence the number of customers each one using one disposable kit-piece. - Figure 23 → if the distance of several rides is below the average S, the battery change will take place exactly at ride n bringing the situation back to the optimal scenario.

Figure 22 - Realistic Situation (1)

Figure 23 - Realistic Situation (2)

Thus, it has been proved that under the value of n, all customers will find the disposable kit available, recovering, at the same time, their trust in the Sharing Economy.

158 COVID vs SES

To conclude the excursus on sanitation, the post-pandemic period has shown that open-vehicles are perceived more protective compared to cars. Hence, in this situation, a radical innovation in the automotive market shows its importance. Indeed, an autonomous car can be prepared remotely by a platform which memorises the customer’s favourite drive-position and preferences concerning music or air conditioning thus avoiding the client having to touch car components, expect for the steering wheel. Considering P2P organisations, they do not need to manage this operation as it is the driver himself who must take care of vehicle disinfection. However, since the driver represents the only physical interface with the customer and it is essential to regain the client’s trust, especially considering closed- vehicles, firms may intensify the fleet management performing a spread on-road sample check of drivers’ cars to ensure compliance with the rules.

5.4.2.2. PPE Distribution In order to recover the business, it is necessary to be compliant with current legislation which states the use of personal masks among others. Both B2P and P2P platforms needed to firstly communicate the safety measures to their employees (drivers as well) and to provide the necessary PPE. The capillary distribution of masks is operationally expensive in terms of supply and logistics management, but given the diversity of the two business models, it was handled differently. In the first case the companies have to rely not only on the shipment from the supplier to their headquarters, but also on local courier services to reach their drivers. Indeed, given the diffusion of the drivers in the area covered by the service and the importance of not creating gatherings, it is unrealistic to think that enterprises have brought drivers to a meeting point. Even assuming a scheduling, the number of drivers is too high to be able to ensure a fast and effective distribution being in line with the timing for resuming the daily trips level.

Since this operation is managed differently by P2P and B2P organisations, they sustained different costs. Being the simplest case, B2P will be firstly analysed. Those firms have central meeting points for employees, which can be offices and/or workshops. Thus, in order to distribute the necessary PPE, they only need to manage devices supplies which, sorted in large batches, based on the size of the company, may allow to benefit from downward price. The cost function is therefore composed by a variable component equal to:

푃푃퐸 푝푟𝑖푐푒 € ∗ 푁°퐵푎푡푐ℎ[ ] [ ] 푏푎푡푐ℎ 푏푎푡푐ℎ

159 COVID vs SES

Where the quantity to be ordered can be computed as:

푃푃퐸 퐷푒푚푎푛푑 푁° 푃푃퐸푝푒푟 푒푚푝푙표푦푒푒 ∗ 푁°퐸푚푝푙표푦푒푒 = 푄푢푎푛푡𝑖푡푦 푝푒푟 푏푎푡푐ℎ 푄푢푎푛푡𝑖푡푦 푝푒푟 푏푎푡푐ℎ

In addition to the pure cost of material, the firm may have to bear also logistic costs which, according to the supplier’s policy, can be fixed, if they do not depend on the number of batches sent but refer directly to the shipment, or variable.

Therefore, two cost functions are possible:

푃푃퐸 푝푟𝑖푐푒 € ∗ 푁°퐵푎푡푐ℎ[ ] + 푆ℎ𝑖푝푚푒푛푡 퐶표푠푡[ ] [ ] 푏푎푡푐ℎ € 푏푎푡푐ℎ

푃푃퐸 푝푟𝑖푐푒 € ∗ 푁°퐵푎푡푐ℎ[ ] + 푈푛𝑖푡푎푟푦 퐿표푔𝑖푠푡𝑖푐 퐶표푠푡 € ∗ 푁°퐵푎푡푐ℎ[ ] [ ] 푏푎푡푐ℎ [ ] 푏푎푡푐ℎ 푏푎푡푐ℎ 푏푎푡푐ℎ

The same economic function is the basis of P2P organisations, which, in addition, must bear a second logistic cost due to the fact that the drivers are not conveyed daily to a single place, but are dispersed in the territory. Therefore, it is necessary to add another component related to the local delivery performed relying on small couriers which optimise the path in the delivery area. The second logistic cost is variable as it depends on the number of points (drivers) that must be touched in in our tour.

(푃푃퐸 푝푟𝑖푐푒 € + 푈푛𝑖푡푎푟푦 퐿표푔𝑖푠푡𝑖푐 퐶표푠푡 € )푁°퐵푎푡푐ℎ[ ] + 퐿표푐푎푙 퐷푒푙𝑖푣푒푟푦[ ] [ ] [ ] 푏푎푡푐ℎ € 푏푎푡푐ℎ 푏푎푡푐ℎ

푃푃퐸 푝푟𝑖푐푒 € ∗ 푁°퐵푎푡푐ℎ[ ] + 푆ℎ𝑖푝푚푒푛푡 퐶표푠푡[ ] + 퐿표푐푎푙 퐷푒푙𝑖푣푒푟푦 [ ] 푏푎푡푐ℎ € [€] 푏푎푡푐ℎ

This operation brings out one of the pitfalls of the P2P model. In fact, neglecting office employees, peer-companies rely on free workers and cannot benefit from savings linked to distribution centralisation.

5.4.2.3. PPE Compliance Check For the disinfection of vehicles fleet management may be exploited as explained in paragraph 5.4.1.2, to ensure the use of PPE by drivers, and consequently increase the client’s sense of security in sharing cars, it was necessary to improve the technology behind the platform. Enterprises such as Uber set up a facial recognition system that identifies whether the driver is wearing a mask or not, and in the latter case blocking the use of the app not to receive ride requests.

160 COVID vs SES

P2P firms are focused on the technology of the platform and pursue its continuous improvement to make it more effective and efficient, being the only interface with both the customers and the drivers on which they can rely for matching rides and receiving payments. Hence, it can be assumed that they have a specialised function inside the organisation, formed by dedicated teams performing these activities. In this way, the cost to develop the face recognition system for mask wearing can be incorporated into the normal costs that the company incurs. Especially in view of the fact that, in order to be available in the recovery phase, it must have been developed during the lockdown, thus in a period where there was not a lot of work for the platform and the teams had free time to devote.

In Table 25 below, the additional operations enterprises needed to manage have been classified according to the business model to which they refer to.

Table 25 - Operations Managed During Recovery Phase - B2P and P2P Case

Concluding, since B2P and P2P firms rely on technology platforms and manage them by remote work, it is plausible to assume that virtual work has been easily established. The greatest difficulties they found was to resume the business by getting customers confident with the safety of their vehicles, and considering this for open-vehicles has been easier. While the first one needed to install new operations managing on-site employees and those in the workshops, the second business models had to rely on drivers and the operations were aimed at checking the compliances of their behaviour.

5.4.3. African Situation Having interviewed an East African organisation based in Nairobi, in order to understand how their operations had changed during the pandemic period, it is necessary to explain the restrictions taken by the Kenyan government. As mentioned by the citizens interviewed, looking at the situation in Asia as well as in Europe, the authorities had time to organise some preventive actions in order to reduce the impact of COVID-19 and its spread. Following the behaviour of the countries previously infected, on March 15, President Uhuru Kenyatta imposed the lockdown and ten days later it also set up the curfew. The curfew is an order specifying a time during which certain regulations are applied, and typically requires individuals to stay in their houses. In Kenya, the Government established the dusk

161 COVID vs SES to dawn curfew based on which citizens had to stay indoors between 7pm and 5am. Only workers carrying out essential jobs and services could move. Starting to talk about the similarities with the western enterprises interviewed, Little Ride has made an agreement with the government to continue working and to offer transportation services also during curfew hours. Furthermore, it has integrated horizontally, entering in the food and goods delivery sector, in order to benefit from economies of scope and make sure that both drivers and customers were satisfied. In this scenario, ride-hailing platforms need to face a glaring challenge in addition to demand reduction: the security and reliability issues as mentioned in the analysis above. Thus, to prevent driver misconduct, the firm identified only essential drivers to keep active and tightened its hiring SOPs19, in order to adequate their behaviour respecting COVID-19 regulations. The essential drivers were chosen not only relying on their attitude, but also on the type of cars they owned. Indeed, the government allowed the usage of vehicles having seven seats, named seven sita cars, in order to ensure enough distance between the driver and the client. The innovative feature among the solutions adopted is represented by the partnership established by Little Ride with the local hospitals. Indeed, the health care system in Africa, as in other developing countries, is very different from the European one and, in particular, the Italian one. Nairobi hospitals have private ambulances that are managed by a switchboard to assist emergencies. Service improvement during the pandemic relied on P2P firms in order to support local communities. Therefore, the Little Ride app has been connected to the hospitals’ switchboard. In this way, the customer logging into the application can access to traditional ride services or to emergency ones, since the platform forwards the help request to the switchboard. This curious service is in line with the global vision of the continent, summarized in the motto “with Africa, build Africa”, “with Kenya, build Kenya”, pursuing economy growth and the creation of entry barriers for foreigners.

19 SOP (Standard Operating Procedure) is a procedure specific to a company’s operation that describes the activities necessary to complete tasks in accordance with industry regulations, provincial laws or even just your own standards for running your business. They are written guidelines in order to clarify, step by step, what to do in every need. Every company has its own SOPs since they represent the pillars of CRO (Chief Risk Officer).

162 CONCLUSIONS

6. CONCLUSIONS

The recent global crisis has contributed to the change in the economic landscape as well as to the cultural perception of good consumption. As Rachel Botsman stated, people around the world have for too long ignored the negative consequences of modern consumerism and, in the last 50 years, they have consumed more goods and services than all previous generations combined together. The circulation, the purchase, the sale, the appropriation of goods and icons represented the universal language used by the whole society to communicate today. The transition from a model based on consumerism to one based on sharing and exchange represents a change of a global and systemic nature. In this study, the operational facets of the Sharing Economy were addressed with particular attention to the transport sector. Indeed, the smart mobility is changing the way people move around the city thanks to the digital technologies, which are fundamental to have “greener” and more sustainable cities. For more than a decade, sharing transport has become part of everyday life where the expression "take the car" has been replaced by "take an Uber". Albeit its rapid and disruptive growth, great potentialities lie behind this business model, to cover a literature gap was fundamental to study the operational side that supports this system, giving also an engineering contribution. In particular, this thesis, in line with the first research question, had the intent to understand which are the operations that has to be managed by Sharing Economy firms, how they are organised and how their structure changes according to the sharing model adopted by the mobility platform. In accordance with this purpose, ten case studies have been investigated grouped into two clusters: P2P ride-haling and B2P free-floating vehicle-sharing. Together they represent a big portion of the transport industry. Indeed, many players adopt these two business structures which provide great flexibility and the possibility to widen the customer base. Furthermore, an accurate analysis of the KPIs, monitored by those companies, was used as a reading key in order to provide a practical and economic interpretation of the operations. The results, divided by the research question to which they refer to, are presented below.

First of all, the consistency between the observations deriving from real cases and literature papers was verified. Starting with B2P platforms, by recalling Graph 13, there is correspondence between the leading KPIs groups identified through interviews and the main operations pillars observed in the readings about transportation industry. In B2P case, the main performance categories are service

163 CONCLUSIONS coverage, availability & matching (55.6%) and economic impact (24.4%) (ref. Graph 15), where the former reflects capacity management (16.1%), asset management (12.9%) and resource planning & control (9.7%), while the latter corresponds to pricing system (9.7%). In particular, as regard the first two pillars, the performances monitored in real cases preluded maintenance and refuelling activities. Indeed, this type of organisations has to deal with operations devoted to the fleet they own, in order to guarantee the service continuity and the customer safety. Therefore, the deriving result is linked to the activities for capacity and asset management. As concern the third one, resource planning & control, the corresponding task is the fleet rebalancing which, in KPI perspective, is evaluated by the firms through the monitoring of means usage frequency and, only for car-sharing companies, through the tracking of their idle time too. Indeed, this research demonstrated that the monitored parameters vary according to the vehicle type owned by the enterprise; on one hand, car-sharing platforms are concerned about idleness, while, on the other one, 2-wheelers are interested in maintenance and recharging efficiency. This aspect is also reflected by the economic KPIs and the corresponding tracked expenditures. This distinction is also at the basis of the operations comparison in B2P scenario, where for refuelling and rebalancing activities, the procedure changes according to this aspect, whereas this is not valid for maintenance, whose discriminating factor is the damage extent. However, through the economic analysis that was carried out in parallel to operations description, it is possible to derive that for all the three main tasks the expenditures related to 4-wheeled means are greater than those for 2-wheeled ones. Consequently, in the study of the business practices adopted, this work showed that, in order to pursue cost-savings, car-sharing platforms are more inclined in outsourcing, consequently they are not concerned about maintenance and refuelling interventions efficiency, unlike bike- and scooter- sharing enterprises who run them internally. Moving to P2P case and recalling KPIs analysis (ref. Graph 17), the leading groups are service coverage, availability & matching (53.6%), reputation (30.4%) and economic impact (8.9%). These ones are more or less represented in the operations pillars resulting from the literature studies, as displayed in Graph 12. They are respectively, pricing system (20.0%), quality (13.3%) and demand- capacity management (8.3% and 13.3%). The predominance of the first pillar is not much consistent with the leading KPIs category, but this may be because many papers aimed at optimising company’s profit manipulating the function items. Nevertheless, the second piece of the graph is highly coherent with the key indicators. Indeed, in order to guarantee the service quality, reputational performances are crucial, since drivers’ and riders’ feedback are useful in order to improve their experience. Moreover, the importance attributed to this aspect is highlighted by the teams devoted to both the customers and the pilots’ assistance. Furthermore, the operational levers impact, on one side, on

164 CONCLUSIONS timeliness matching to improve service quality and, on the other one, on demand-capacity management. Therefore, the resulting operations are coherent with the ones derived from literature, indeed carmen’s recruitment, training and behaviour monitoring are part of quality management, and surge-pricing, capacity levers and rides acquisition correspond to demand-capacity management. In conclusion, the main differences from KPIs and Operations analyses were derived. First of all, they have slightly different priorities in terms of performance goals. Indeed, the B2P side aims at providing reliable and ready service, meaning handling the fleet in order to provide the vehicles in optimal conditions and as closest to the customers as possible. The P2P one, instead, focuses on timeliness and reliability in the sense of finding the suitable match that minimises the expected time of arrival and maximises the riders’ satisfaction. The second difference stems from the customer type, where, unlike B2P firms, P2P must deal with both riders and drivers. This is also reflected by the activities handled. Indeed, for instance, P2P platforms have two customer centres, one for each side. Finally, the major diversity is given by tangible and intangible assets which implies different strategic decisions on operations practices. In fact, it has been demonstrated that the outsourcing method is more likely for B2P cases rather than P2P ones, whose strong synergies among the two sides prevent this business practice. Moreover, since the former deal with higher total costs, outsourcing is also a strategy for cost-cutting. Another noteworthy point, deriving from this distinction, is related to the IT infrastructure, that for P2P platforms is supposed to be more relevant since, given the lack of the asset ownership, they are supposed to rely on the effective real-time data collection and processing in order to deliver the best service possible.

In light of these results, it is easily understandable that they are filling missing literature gaps. Indeed, some KPIs were already presented, although they were not the core theme; for instance, in the studies developed by Y. Lia, M. K Lima, Y. Tanc, Sir Y. Leeb, M-L Tsengd, 2020 [35A] and by Z. Liu, L. Ma, Y. Zhu and W. Ji, 2019 [42A] some insights, preluding indicators to monitor, were already introduced about system efficiency for maintenance and recharging, respectively. More papers were about rebalancing issues, like the one by L. He, H-Y Mak, Y. Rong, Z-J M. Shen, 2016 [3A] but few indicators were practically explained. As concerns the P2P section, instead, H. Wang and H. Yang, 2019 [40A] already discussed about ETA and matching ability, W. Zuoa, W. Zhua, S. Chena, and X. Heb, 2019 [78A] applied LSTM text classification, sentiment analysis and text mining of eWOM of the online car-hailing, taking into consideration the driver’s reputation and X. Lin and Y. Zhou, 2018 [26A] already introduced surge multiplicator to evaluate the corresponding pricing strategy. Therefore, KPIs were just introductory to the main topic development. On the contrary, in this

165 CONCLUSIONS research, they are systematically presented and analysed in depth, illustrating their interconnections and functionalities. Moving to operations, as already explained, the KPI analysis was preparatory since, in literature, few papers clearly discussed about them. Therefore, it was necessary to find a reading key. Indeed, although rebalancing had already been discussed in some articles studying the optimal disposition, like L. He, H-Y Mak, and Y. Rong, 2019 [19B], none had ever gone into detail explaining how these operations took place in practice. Furthermore, the remaining activities have never been examined, as well as the minor ones, like recovering, supervision and cleaning. Therefore, the results brought by this paper concern the practical point of view of operations and the discussion regarding the convenience in adopting certain business practices, depending on the vehicle offered, 2-wheeled or 4-wheeled. The same holds for the P2P literature. H. Wang and H. Yang, 2019 [40A] partly discussed about P2P operations platform, while many readings were about pricing policies, like the one developed by G.P. Cachon, K.M. Daniels and R. Lobel, 2017 [6B] modelling surge-pricing lever, anticipating the related operations. W. Zuo, W. Zhu, S. Chen, X. He, 2020 [76A] presented reputation indicators positive impact on the trust in the platforms. However, this aspect was often discussed in terms of quality management without detailing which operations and internal mechanisms enable it. The practical implications arising from this part of the research allow to clarify the mechanisms behind the development of disruptive sharing powers. From the KPIs point of view, this permits to evaluate which are the business parameters that monitor the efficiency of maintenance, refuelling and rebalancing operations for the B2P case. For the P2P one, instead, it enables to structure ad-hoc dashboards, depending on the growth stage of the enterprise, indeed another result of section 3.3.1.1.4 and section 3.3.2.1.5 was the discussion of performances and operations management according to the business phase. Furthermore, given the outcomes of this research, it is now possible to verify what requirements are needed and how to structure the related operations. In fact, despite more than ten years of history, new mobility solutions are being introduced, from small vehicles in the city such as scooters, to large ones like Uber Elevate, who is looking to the future on-demand urban aviation.

The second research question derives from the gap arisen in the systematic literature review, where among all the papers read, lean philosophy was applied only to a single case, developed by G. Xua, M. Lic, C-H Chenb, Y. Weid, 2018 [62A]. However, since its founding in the Toyota system, lean thinking inspired many sectors. Indeed, although it started from the manufacturing automotive system, its five leading principles and the concepts of waste reduction and value-adding activities improvement can be universally applied. This work demonstrated how these aspects can also be shaped on the sharing mobility industry: no intervention, whether on a physical machine or on service

166 CONCLUSIONS delivery, is perfect, but everything can be perfected. Consequently, lean tools have been tailored on B2P tasks and, minimally, to P2P ones. Also in this situation, the application of the lean tools is affected by the distinctive features of B2P and P2P organisations. Indeed, thanks to its nature, it was easier to develop instruments for B2P scenario, as many operations are similar to those performed in typical manufacturing plants, such as maintenance which is the one that allowed for more applications. On the contrary, for P2P context, it was harder, mainly due to the lack of manual activities. However, since lean principles strive to reduce waste through continuous improvements, it is possible to shape procedures for root-causes detection in order to establish PDCA cycles and eliminate errors arising during the service process. Hence, it can be concluded that large B2P companies, which tend to register more losses, can consider the adoption of lean practices to make manual activities more efficiently and obtain cost-saving, also depending on the strategy adopted, either in-house or outsourcing. P2P firms, on the other hand, can apply small improvements tools to identify areas on which to intervene, like through FMEA, and they can adopt the sig-sigma and the optimal path definition and rides allocation, but only after developing a good data collection and automatic analysis apparatus to rely on. It is therefore the contribution of this second section to inspire Sharing Economy Services, and the emerging ones, to adopt these practices.

Finally, given the unusual period in which the development of this work took place, it was in its interest to include operational COVID-19 implications into the analysis. Indeed, due to the consequences of the lockdown, the world gradually froze, causing demand drops for most of these economies and economic problems to gig-workers who relied on the salary deriving from sharing- platforms jobs. Therefore, since no article in the literature has yet been developed on this topic because of its extraordinary nature, the reactivity of firms’ operations in the mobility sector was examined. The related research was performed in order to deeply analyse the enterprises’ operational response to the challenges of two distinct periods: during the lockdown and during the recovery phase. Both P2P and B2P organisations have undertaken similar strategies to keep their business alive during the lockdown phase. However, the first ones had also benefits from economies of scope thanks to their multiple expansion into later sectors. This was not possible for B2P platforms due to their low service diversification level. Considering the operational side, during the lockdown many operations were set aside to let companies focus on those strictly necessary to pursue the strategic choices adopted. Nevertheless, P2P organisations had to manage an additional activity related to the PPE distribution. Indeed, in accordance with the policies issued by the government, for those firms offering a service

167 CONCLUSIONS relying on the provision of third parties, such as food & goods delivery, ride-hailing etc. it was mandatory to supply personal masks as well as any other personal device needed to prevent contagion. This operation was extended to B2P companies too during the recovery phase, together with others. The additional activities of the second period were established to provide the service in the safest possible way, in compliance with regulations and with the aim of restoring customer trust in the shared mobility services. Indeed, everything was aimed at protecting the customer by sanitising vehicles, in B2P cases, or by monitoring the drivers’ behaviour, in P2P ones. Furthermore, the cost functions of all the additional operations have been deduced in order to characterise them also from an economic point of view. Given the huge loss of revenues suffered by enterprises, it was essential to minimise these additional costs, finding the optimal performing way. Aware of the consequences generated by this unusual aspect, enterprises can learn how to gain flexibility to respond effectively to large exogenous market changes and prepare a plan for future events that may involve a second sudden drop in demand and avoid bankruptcy.

The analysis conducted in this research has some limitations. First of all, the cost functions have been deduced according to the operations described and, consequently, the cross-case analysis based on the economic aspect is not very quantitative. However, it was not the main object of the thesis to evaluate the economic convenience of adopting one business model rather than another. The same goes for the adoption of lean practices: although they lead to a reduction in waste, in some cases their preparation and adoption require an investment that is up to organisations expenditures evaluation. Finally, given the managers’ availability, the interviews were mainly conducted on two models, ride- hailing and free-floating vehicle-sharing. Albeit they represent the two models typically adopted by the respective platform models, P2P and B2P, it would be interesting for future researches to extend the analysis to other ones such as ride-sharing and station-based. Nevertheless, it can be considered that operations valid for ride-hailing are very common to ride-sharing and some insights have been introduced for station-based. Therefore, a future research direction will be to explore other sharing platform models or economically deepen operations, verifying their effectiveness in adopting certain practices.

168 Appendix A

Appendix A Other authors’ definitions

▪ Aloni, E. (2016) “an economic activity in which web platforms, facilitate peer-to-peer exchanges of diverse types of goods and services” ▪ Barnes, S. & Mattsonn, J. (2016) “involves access-based consumption of products or services that can be online or offline” ▪ Habibi, M.R, Davison, A., & Laroche, M. (2017) “describes the phenomenon as peer to peer sharing of access to under-utilized goods and services, which prioritized utilization and accessibility over ownership, either for free or for a fee” ▪ Hamari (2016) “the peer-to-peer based activity of obtaining, giving or sharing the access to goods and services, coordinated through community-based online services” ▪ Heinrichs, H. (2013) “individuals exchanging, redistributing, renting, sharing and donating information, goods and talent” ▪ Shaheen, S., Chan, N.D., Gaynor, T. (2016) “a popularized term for consumption focus on access to goods and services through borrowing and renting rather than owning them” (Steven Kane Curtis and Matthias Lehner, 2019, “Defining the Sharing Economy for Sustainability”)

169 Appendix B

Appendix B B2P literature key performance indicators Paper Sharing KPI KPI type KPIs group code model 50 Ride-sharing Information availability External/Internal QUA 50 Ride-sharing Users accessibility External/Internal QUA Integration rate between different mobility 50 Ride-sharing External/Internal CAM services 76 Ride-sharing Consumer Trust External REP 76 Ride-sharing Environmental benefits External ENV 76 Ride-sharing Growth rate due to customer satisfaction Internal REP 76 Ride-sharing Process inefficiencies Internal CAM 76 Ride-sharing Reputation indicator External/Internal REP 76 Ride-sharing eWOM benefit Internal REP 76 Ride-sharing Brand benefit Internal ENV 5 Station-based Distribution per daily trip External/Internal CAM 5 Station-based Trip length External/Internal CAM 5 Station-based Charging duration External/Internal CAM 5 Station-based Charging events per day External/Internal CAM 58 Station-based Number of stockouts Internal CAM 58 Station-based Inventory level Internal CAM 58 Station-based Bike availability External/Internal CAM 58 Station-based Dock availability External/Internal CAM 5 Station-based State of battery health (% of rated capacity) Internal CAM 5 Station-based State of charge (energy left in the battery) Internal CAM 17B Station-based Environmental benefits External ENV 17B Station-based Bike availability External/Internal CAM 17B Station-based Dock availability External/Internal CAM 17B Station-based Number of bicycle docks per station Internal CAM 17B Station-based Bicycle deployment rate Internal CAM 17B Station-based Bicycle utilisation rate Internal CAM 17B Station-based Brand benefit Internal ENV 18B Station-based Bike availability External/Internal CAM 18B Station-based Dock availability External/Internal CAM 18B Station-based Redistribution travel time Internal CAM 18B Station-based Number of stockouts Internal CAM 18B Station-based Birth rate Internal CAM 18B Station-based Death rate Internal CAM 18B Station-based Number of bicycle docks per station Internal CAM 18B Station-based Number of bikes per station Internal CAM 3 Free-floating Customer satisfaction External/Internal REP 3 Free-floating Environmental benefits External ENV 3 Free-floating Fleet utilisation Internal CAM 3 Free-floating Charging speed Internal CAM 3 Free-floating Fleet repositioning Internal CAM 13 Free-floating Cost of removal Internal ECO

170 Appendix B

Paper Sharing KPI KPI type KPIs group code model 31 Free-floating Platform reputation External/Internal REP 31 Free-floating Recovered bicycles Internal ENV 31 Free-floating Recycling cost Internal ECO 42 Free-floating Process reliability External/Internal SAF 42 Free-floating Bike availability External/Internal CAM 42 Free-floating Platform privacy control Internal SAF 42 Free-floating Forecast accuracy of riding behaviours Internal CAM 42 Free-floating Maintenance system efficiency Internal CAM 42 Free-floating Users' breakdown accuracy reports Internal CAM 35 Free-floating Battery capacity External/Internal CAM 35 Free-floating Charging station waiting time External/Internal CAM 35 Free-floating Recharge system efficiency Internal CAM Number of customers lost due to uncharged 35 Free-floating Internal REP vehicles 35 Free-floating CO2 emissions level External/Internal ENV 34 Free-floating Refuelling time External/Internal CAM 34 Free-floating Risk of accidents External/Internal SAF 34 Free-floating Refuel system efficiency Internal CAM Number of customers lost due to unfuelled 34 Free-floating Internal REP vehicles 2C Free-floating Buying vs renting car cost External ECO 2C Free-floating Profit Internal ECO 4C Free-floating Sharing price External ECO 4C Free-floating Resource saturation level Internal CAM Supply capacity f(bicycle introduction, 4C Free-floating Internal CAM repair, transport) 6C Free-floating Sharing price External ECO 6C Free-floating Available driving time External/Internal CAM 19B Free-floating Service availability External/Internal CAM 19B Free-floating Fleet sizing Internal CAM 19B Free-floating Fleet repositioning Internal CAM

B2P real-cases key performance indicators

Jump - Free-floating Electric-Bike-sharing KPI KPI type KPIs group Travels per Active Vehicle per Day Internal CAM Number of swats changed per hour Internal CAM Number of bikes repaired per hour Internal CAM KMs run between two consecutive repairs on the same bike Internal CAM Customer satisfaction level Internal REP Company profitability Internal ECO

171 Appendix B

MiMoto - Free-floating Electric-Scooter-sharing KPI KPI type KPIs group KMs run between two consecutives battery changes on the same scooter Internal CAM Number of slots loaded by the operator per shift Internal CAM Maintenance cost Internal ECO Rebalancing cost Internal ECO Rotation index of battery stock Internal CAM Battery tracking Internal CAM

ReachNow - Free-floating Car-sharing KPI KPI type KPIs group Ready-to-rent rate Internal CAM Ready-to-be-rented rate Internal CAM Utilisation rate Internal CAM

Share Now - Free-floating Car-sharing KPI KPI type KPIs group Return per car Internal ECO Rate of return per car per day Internal ECO Direct cost per vehicle Internal ECO Revenues per trip Internal ECO Cost of insurance Internal ECO Cost of damages Internal ECO Stars rating External/Internal REP Cars availability External/Internal CAM Service availability External/Internal CAM Returns for the customer External/Internal ECO Trip length Internal CAM Idle time Internal CAM Utilisation rate Internal CAM Repairing time Internal CAM Churn rate Internal REP Active customer per month Internal CAM Conquest cost for loyalty Internal ECO How far is the car remaining in the same spot Internal CAM How fast the car is picked-up Internal CAM Demand vs supply balance Internal CAM Cleanliness levels External/Internal REP Maximum distance to walk until you get the first car External/Internal CAM Car placement External/Internal CAM How long it takes to open the vehicle External/Internal SAF Reception of smartphones and technology External/Internal QUA Refuelling level External/Internal CAM How easy is to find a parking lot External/Internal CAM How easy is to sign-in (ID, driver licence, payment mode) External/Internal QUA Reachability of call centres External/Internal QUA How often the customer cannot use the car due to a default External/Internal REP

172 Appendix C

Appendix C P2P literature key performance indicators

Paper Sharing KPI KPI type KPIs group code model 16 Ride-sharing Environmental Pollution External ENV 16 Ride-sharing Usage level Internal CAM 38 Ride-sharing Drivers' reputation External/Internal REP 38 Ride-sharing Rating system efficiency Internal REP 38 Ride-sharing Switching cost Internal REP 50 Ride-sharing Information availability External QUA 50 Ride-sharing User accessibility External QUA Integration rate between different mobility 50 Ride-sharing External/Internal CAM services 76 Ride-sharing Customer satisfaction External/Internal REP 76 Ride-sharing Service reliability External/Internal CAM 76 Ride-sharing Consumer Trust External REP 76 Ride-sharing Environmental benefits External ENV 76 Ride-sharing Growth rate due to customer satisfaction Internal REP 76 Ride-sharing Process inefficiencies Internal CAM 76 Ride-sharing Reputation indicator External/Internal REP 76 Ride-sharing eWOM benefit Internal REP 76 Ride-sharing Brand benefit Internal ENV 77 Ride-sharing Information disclosure External/Internal SAF 77 Ride-sharing Safety perception External SAF 77 Ride-sharing Drivers' reputation External/Internal REP 77 Ride-sharing Safety security level Internal SAF 77 Ride-sharing Switching cost Internal REP 77 Ride-sharing Rating system efficiency Internal REP 9C Ride-sharing Drivers' reputation External/Internal REP 9C Ride-sharing Brand benefit Internal ENV 9C Ride-sharing CO2 emissions level External/Internal ENV 9C Ride-sharing Drivers' motivation level Internal REP 9C Ride-sharing Drivers' work engagement Internal REP 5B Ride-sharing Sharing price External ECO 5B Ride-sharing Service coverage External/Internal CAM 5B Ride-sharing Service availability External/Internal CAM 5B Ride-sharing Data analytics Internal QUA 5B Ride-sharing Real-time control Internal QUA 5B Ride-sharing Throughput rate Internal CAM 10C Free-floating Covered unmatched rides Internal CAM 10C Free-floating Vehicle availability Internal CAM 10C Free-floating Demand forecast accuracy Internal CAM 38 Station-based Drivers' reputation External/Internal REP 38 Station-based Switching cost Internal REP 38 Station-based Rating system efficiency Internal REP

173 Appendix C

Paper Sharing KPI KPI type KPIs group code model 1 Ride-hailing Profit Internal ECO 24 Ride-hailing Sharing price External ECO 24 Ride-hailing Service coverage External/Internal CAM 24 Ride-hailing Service availability External/Internal CAM Vehicle routing f(tour length, customer 24 Ride-hailing External CAM delay) 26 Ride-hailing Profit Internal ECO 26 Ride-hailing Driver's wage Internal ECO 27 Ride-hailing eWOM tracking Internal REP Service quality f(fares for de-touring, 27 Ride-hailing fairness of paid price, possibility to External/Internal QUA choose the driver) 25 Ride-hailing Service availability External/Internal CAM 25 Ride-hailing Staff cost Internal ECO 25 Ride-hailing Agents' compensation Internal ECO 2B Ride-hailing Commission rate External ECO 2B Ride-hailing Owner costs External ECO 2B Ride-hailing Sharing price External ECO 2B Ride-hailing Renter's inconvenience cost External ECO 2B Ride-hailing Time to find the service External/Internal CAM 2B Ride-hailing Usage level Internal CAM 2B Ride-hailing Time to find a renter Internal CAM 2B Ride-hailing Ownership level Internal CAM 2B Ride-hailing Usage level Internal CAM 5B Ride-hailing Sharing price External ECO 5B Ride-hailing Service coverage External/Internal CAM 5B Ride-hailing Service availability External/Internal CAM 5B Ride-hailing Data analytics Internal QUA 5B Ride-hailing Real-time control Internal QUA 5B Ride-hailing Throughput rate (rides rate) Internal CAM 6B Ride-hailing Sharing price External ECO 6B Ride-hailing Service availability External/Internal CAM 7B Ride-hailing Sharing price External ECO 7B Ride-hailing Waiting time External/Internal CAM 7B Ride-hailing Pay-out ratio Internal ECO 7B Ride-hailing Throughput rate Internal CAM 8B Ride-hailing Sharing price External ECO 8B Ride-hailing Driver's wage Internal ECO 12B Ride-hailing Service availability External/Internal CAM Cost of relinquishing direct control of 12B Ride-hailing Internal ECO capacity 36 Ride-hailing Sharing price External ECO 36 Ride-hailing Ease of platform use External/Internal QUA 36 Ride-hailing Adoption rate Internal CAM 36 Ride-hailing Number of drivers Internal CAM 36 Ride-hailing Technological development level Internal QUA

174 Appendix C

Paper Sharing KPI KPI type KPIs group code model 38 Ride-hailing Driver's reputation External/Internal REP 38 Ride-hailing Rating system efficiency Internal REP 38 Ride-hailing Switching cost Internal REP 39 Ride-hailing Ease of request External/Internal QUA 39 Ride-hailing Ease of payment External/Internal QUA 39 Ride-hailing Trip safety External/Internal SAF 39 Ride-hailing Matching per minute Internal CAM 39 Ride-hailing Waiting time External/Internal CAM Number of times a driver has not the right 39 Ride-hailing External/Internal QUA change 39 Ride-hailing Number of claims Internal REP 40 Ride-hailing Waiting time External/Internal CAM 40 Ride-hailing Information disclosure External/Internal SAF 40 Ride-hailing Matching time External/Internal CAM 40 Ride-hailing Loyalty (customer retention) External/Internal REP 40 Ride-hailing Matching per minute Internal CAM 40 Ride-hailing Estimate time-of-arrival Internal CAM 40 Ride-hailing Demand forecast accuracy Internal CAM 40 Ride-hailing Request per minute Internal CAM 40 Ride-hailing Premium price Internal ECO 44 Ride-hailing Price disclosure External/Internal QUA 44 Ride-hailing Trip availability External/Internal CAM 44 Ride-hailing Demand forecast accuracy Internal CAM 44 Ride-hailing Request per minute Internal CAM 44 Ride-hailing Pricing system accuracy Internal ECO 45 Ride-hailing Trip availability External/Internal CAM 45 Ride-hailing Technological safety External SAF Capability to incorporate calculations in the 45 Ride-hailing Internal QUA software 45 Ride-hailing Request per minute Internal CAM 45 Ride-hailing System security level Internal SAF 45 Ride-hailing Service failure rate Internal REP 45 Ride-hailing Number of claims Internal REP 47 Ride-hailing Environmental benefits External ENV 47 Ride-hailing Travel costs External ECO 47 Ride-hailing Parking research time External/Internal REP 47 Ride-hailing Matching time External/Internal CAM 47 Ride-hailing Brand Benefits Internal ENV 47 Ride-hailing Occupancy Vehicle Internal ECO Number of times a customer does not find a 47 Ride-hailing Internal REP parking spot 47 Ride-hailing Demand forecast accuracy Internal CAM 47 Ride-hailing Request per minute Internal CAM

175 Appendix C

Paper Sharing KPI KPI type KPIs group code model 49 Ride-hailing Sharing price External ECO 49 Ride-hailing Payment security External/Internal SAF 49 Ride-hailing Service availability External/Internal CAM 49 Ride-hailing Service reliability External/Internal CAM 49 Ride-hailing Reputation External/Internal REP 49 Ride-hailing Transaction costs Internal ECO 49 Ride-hailing Pricing system accuracy Internal ECO 49 Ride-hailing System security level Internal SAF 49 Ride-hailing Demand forecast accuracy Internal CAM 49 Ride-hailing Resource utilisation Internal CAM 49 Ride-hailing Number of drivers available Internal CAM 49 Ride-hailing Switching cost Internal REP 50 Ride-hailing Information availability External/Internal QUA 50 Ride-hailing Users accessibility External/Internal QUA Integration rate between different mobility 50 Ride-hailing External/Internal CAM services 55 Ride-hailing Sharing price External ECO 55 Ride-hailing Information disclosure External/Internal SAF 55 Ride-hailing Response rate External/Internal QUA 55 Ride-hailing Time to queries about issues External/Internal REP 55 Ride-hailing Commission per trip Internal ECO 55 Ride-hailing Safety security level Internal SAF 55 Ride-hailing Number of claims Internal REP 55 Ride-hailing Response system efficiency Internal REP 55 Ride-hailing Number of unpaid trips Internal REP 13B Ride-hailing Service availability External/Internal CAM 13B Ride-hailing Waiting time External/Internal CAM 13B Ride-hailing Matching time External/Internal CAM 13B Ride-hailing Number of drivers per shift Internal CAM 13B Ride-hailing Demand forecast accuracy Internal CAM 13B Ride-hailing Matching per minute Internal CAM 78 Ride-hailing Driver's reputation External/Internal REP 78 Ride-hailing Service failure rate Internal CAM 78 Ride-hailing eWOM benefits Internal REP 78 Ride-hailing Rating system efficiency Internal REP

P2P real-cases key performance indicators

Bolt - Ride-hailing KPIs KPI KPI type group CIAN Internal CAM Fast time trip Internal CAM Turn-around time for resolving issue External/Internal QUA/REP

176 Appendix C

Uber - Ride-hailing KPI KPI type KPIs group Number of trips Internal CAM Average trip duration Internal CAM Estimated Time of Arrival - ETA External/Internal CAM Completion rate Internal CAM Drivers’ churn rate Internal REP Riders’ churn rate Internal REP Requests to sessions Internal QUA/REP How many search for an Uber over the active ones Internal CAM Driver’s rating External/Internal REP Rider’s rating Internal REP Number of arrival (or exit) per pick-up point Internal CAM Total gross booking Internal ECO Surge Internal ECO

Gett - Ride-hailing KPI KPI type KPIs group Number of drivers Internal CAM Number of riders Internal CAM Number of trips Internal CAM Completion rate Internal CAM Ridership Internal CAM Conversion rate Internal REP Retention rate Internal REP Churn rate Internal REP Reactivation rate Internal REP Revenues Internal ECO

Mondo Ride - Ride-hailing KPI KPI type KPIs group Number of requests Internal CAM Fulfilment rate Internal CAM Number of recruited drivers per day Internal CAM Number of activated drivers per day Internal CAM Number of trained drivers per day Internal CAM Hours spent on the platform by the driver Internal CAM App rating External/Internal REP Service availability External CAM Service affordability External ECO Service reliability External CAM Service safety External SAF

177 Appendix C

Little Ride - Ride-hailing KPI KPI type KPIs group Driver’s rating External/Internal REP Baseline (minimum mark) Internal REP Number of acquired drivers Internal CAM Number of requests Internal CAM Fulfilment rate Internal CAM Drivers growth rate Internal CAM Waiting time External/Internal CAM Turn-around time for resolving issues External/Internal QUA/REP Little Ride CORPORATE Revenues Internal ECO Number of customers on-board Internal CAM Marketing costs Internal ECO Number of trips Internal CAM Timeliness of company response to customer’s questions External/Internal QUA/REP Disclosure level Internal CAM Little Ride RETAILER Customer segmentation target Internal CAM Customer conversion rate Internal REP Download rate per week Internal CAM Estimated Time Variable External/Internal CAM Ratings External/Internal REP

178 Appendix D

Appendix D Rank Order Clustering

Given a binary repair-equipment n-by-m matrix 푏푖푝, the algorithm is characterised by the following steps: 푚 푚−푝 1. for each row i, compute the number ∑푝=1(푏푖푝 ∗ 2 ); 2. order rows according to descending numbers computed in step 1; 푛 푛−푖 3. for each column p, compute the number ∑푖=1(푏푖푝 ∗ 2 ); 4. order columns according to descending numbers compute in step 3; 5. if on steps 2 and 4 no reordering happened, go to step 6. Otherwise go to step 1; 6. stop. where n is the number of columns and m is the number of rows.

Equipment 1 Equipment 2 Equipment 3 … Equipment n Repair 1 Repair 2 Repair 3 …

Repair m

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Conference papers 1C. Yang, Y., Sun, Q. & Ba, S. 2019, "Research on the Development Strategy of Shared Appliances under the Low Tide of China's Shared Economic", IOP Conference Series: Materials Science and Engineering. 2C. Qiao, X., Shi, D. & Xu, F. 2019, "Optimal pricing strategy and economic effect of product sharing based on the analysis of B2C sharing platform", 2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019. 3C. Yu, J. 2019, "Who gets the benefit from food leftover sharing platform's operations?", 2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019. 4C. Zhao, X., Han, M., Deng, Q. & Xue, K. 2019, "Decisions on supply and demand of bicycle- sharing service based on cobweb model", 2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019. 5C. Kojima, Y., Hayashi, K., Akamatu, K., Hasegawa, K. & Tanaka, M.S. 2019, "A study of rental cycle system using location information and contribution to city planning", 2019 IEEE 1st Global Conference on Life Sciences and Technologies, LifeTech 2019, pp. 14. 6C. Mamalis, T., Bose, S. & Varshney, L.R. 2019, "Business-to-Peer Carsharing Systems with Electric Vehicles", 2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019. 7C. Zhang, Y., Li, W. & Du, X. 2019, "Outlook of Electric Vehicles and Grid Interaction in Energy Internet", 2019 3rd IEEE International Conference on Green Energy and Applications, ICGEA 2019, pp. 120. 9C. Hua, Y., Cheng, X., Hou, T. & Luo, R. 2020, "Monetary Rewards, Intrinsic Motivators, and Work Engagement in the IT-Enabled Sharing Economy: A Mixed-Methods Investigation of Internet Taxi Drivers*", Decision Sciences, vol. 51, no. 3, pp. 755-785. 10C. An, S., Nam, D. & Jayakrishnan, R. 2019, "Impacts of integrating shared autonomous vehicles into a peer-to-peer ridesharing system", Procedia Computer Science, pp. 511. 11C. Tang, Y., Zhang, Q., Li, H., Li, Y. & Liu, B. 2019, "Economic analysis on repurposed EV batteries in a distributed PV system under sharing business models", Energy Procedia, pp. 4304.

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Book chapters 2B. Benjaafar, S., Kong, G., Li X., Courboubetis, C. 2019, “Peer-to-peer product sharing”, Sharing economy – Making supply meet demand, vol. 6, pp. 11-36. 3B. Jiang, B., Tian, L. 2019, “The strategic and economic implications of consumer-to-consumer product sharing”, Sharing economy – Making supply meet demand, vol. 6, pp. 37-54. 4B. Tunca, T. I. 2019, “Operational factors in the Sharing Economy: a framework”, Sharing economy – Making demand meet supply, vol. 6, pp. 55-72. 5B. Banerjee, S., Johari, R. 2019, “Ride sharing”, Sharing economy – Making supply meet demand, vol. 6, pp. 73-100. 6B. Cachon, G. P., Daniels, M., Lobel, R. 2019, “The role of surge pricing on a service platform with self-scheduling capacity”, Sharing economy – Making supply meet demand, vol. 6, pp. 101-114. 7B. Bai, J., So, K. C., Tabg, C. S., Chen, X. 2019, “Time-based payout ratio for coordinating supply and demand on an on-demand service platform”, Sharing economy – Making supply meet demand, vol. 6, pp. 115-136. 8B. Chen, Y., Hu, m., Zhou y. 2019, “Pricing and matching in the Sharing Economy”, Sharing economy – Making supply meet demand, vol. 6, pp. 137-164. 9B. Allon, G., Bassamboo, A., Çil, E. B. 2019, “Large-scale service marketplaces: the role of the moderating firm”, Sharing economy – Making supply meet demand, vol. 6, pp. 165-192. 10B. Bimpikis, K., Papanastasiou, Y. 2019, “Introducing exploration in service platforms”, Sharing economy – Making supply meet demand, vol. 6, pp. 193-216. 11B. Chen, Y-, Maglaras, C., Vulcano G. 2019, “Design of an aggregated marketplace under congestion effects: asymptotic analysis and equilibrium characterization”, Sharing economy – Making supply meet demand, vol. 6, pp. 217-248. 12B. Gurvich, I., Moreno, A. 2019, “Operations in the on-demand economy: staffing services with self-scheduling capacity”, Sharing economy – Making supply meet demand, vol. 6, pp. 249- 278. 13B. Ibrahim, R. 2019, “On queues with a random capacity: some theory, and an application”, Sharing economy – Making supply meet demand, vol. 6, pp. 279-318. 14B. Hu, M., Shi, M., Wu, J. 2019, “Online group buying and crowdfunding: two cases of all-or- nothing mechanisms”, Sharing economy – Making supply meet demand, vol. 6, pp. 319-346. 15B. Marinesi, S., Girotra, K., Netessine, S. 2019, “Threshold discounting: operational benefits, potential drawbacks and optimal design”, Sharing economy – Making supply meet demand, vol. 6, pp. 347-378.

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16B. Ales, L., Cho, S-, Körpeo˘glu, E. 2019, “Innovation and crowdsourcing contests”, Sharing economy – Making supply meet demand, vol. 6, pp. 379-408. 17B. Chou, M. C., Liu, Q., Teo, C-, Yeo, D. 2019, “Models for effective deployment and redistribution of shared bicycles with location choices”, Sharing economy – Making supply meet demand, vol. 6, pp. 409-434. 18B. Freund, D., Henderson S. G., Shmoys, D. 2019, “Bike sharing”, Sharing economy – Making supply meet demand, vol. 6, pp. 435-460. 19B. He, L., Mak, H-, Rong, Y. 2019, “Operations management f vehicle sharing systems”, Sharing economy – Making supply meet demand, vol. 6, pp. 461-484. 20B. Li, J., Moreno, A., Zhang, D. J. 2019, “Agent pricing in the Sharing Economy: evidence of Airbnb”, Sharing economy – Making supply meet demand, vol. 6, pp. 485-501. 21B. Balseiro, S. R., Candogan, O., Gurkan, H. 2019, “Intermediation in online advertising”, Sharing economy – Making supply meet demand, vol. 6, pp. 502-528.

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Articles Not Found For completeness, the articles and conferences that were not downloadable from Scopus are listed below, as they are included in the number indicated in Table 2 - Articles & Conferences Distribution.

- Riemensperger, F. & Falk, S. 2020, "How to capture the B2B platform opportunity", Electronic Markets, vol. 30, no. 1, pp. 61-63. - Seilonen, I. 1997, Distributed and collaborative production management systems in discrete part manufacturing. A review of research and technology. - Hu, X.-. 2004, "Design of integrated platform for collaborative production of telecommunication industry based on e-stamps", Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, vol. 10, no. 9, pp. 1149-1153. - Favilla Jr., J.R. & Colbert, C. 2008, "Steeling the show", ABB Review, , no. SPEC. ISS., pp. 42- 46. - Wang, J., Pan, K., Liu, A. & Wang, X. 2018, "The Model and Solution for Collaborative Production Planning with Order Splitting in Cloud Manufacturing Platform", Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, vol. 52, no. 12, pp. 1655-1662. - Wang, X., Chai, X., Zhang, C. & Zhao, X. 2019, "Collaborative production planning algorithm for cross-enterprises in cloud manufacturing", Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, vol. 25, no. 2, pp. 412-420. - Revenko, L.S. & Revenko, N.S. 2019, "Sharing economy phenomenon in the digitization era", Journal of Siberian Federal University - Humanities and Social Sciences, vol. 12, no. 4, pp. 678-700. - Birdsall, M. 2014, "Carsharing in a Sharing Economy", ITE Journal (Institute of Transportation Engineers), vol. 84, no. 4, pp. 37-40. - Choi, Y.-. & Choi, S.-. 2019, "Comparative study of crossing the chasm in applying smart factory system for SMEs", International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 8, pp. 1017-1024. - Li, L., Wang, X., Lin, Y., Zhou, F. & Chen, S. 2019, "Cooperative game-based profit allocation for joint distribution alliance under online shopping environment: A case in Southwest China", Asia Pacific Journal of Marketing and Logistics, vol. 31, no. 2, pp. 302-326. - Tian, X.-., Wu, R.-. & Lee, J.-. 2017, "Use intention of chauffeured car services by O2O and Sharing Economy", Journal of Distribution Science, vol. 15, no. 12, pp. 73-84. - Kim, J., Ishikawa, Y. & Ikeda, T. 2019, "Realization of Sharing Economy centered on on-demand transportation services", Fujitsu Scientific and Technical Journal, vol. 55, no. 1, pp. 45-52.

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- Qi, W., Li, L., Liu, S. & Shen, Z.-.M. 2018, "Shared mobility for last-mile delivery: Design, operational prescriptions, and environmental impact", Manufacturing and Service Operations Management, vol. 20, no. 4, pp. 737-751. - Choi, H.R., Cho, M.J., Lee, K., Hong, S.G. & Woo, C.R. 2014, "The business model for the Sharing Economy between SMEs", WSEAS Transactions on Business and Economics, vol. 11, no. 1, pp. 625-634. - Nowag, J. 2018, "When sharing platforms fix sellers' prices", Journal of Antitrust Enforcement, vol. 6, no. 3, pp. 382-408. - Pettersen, L. 2017, "Sorting things out: A typology of the digital collaborative economy", First Monday, vol. 22, no. 8.

192 BIBLIOGRAPHY

Conferences Not Found - Yu, R., Long, X. & Li, J. 2019, "Driving style analyses for car-sharing users utilizing low- frequency trajectory data", ICTIS 2019 - 5th International Conference on Transportation Information and Safety, pp. 927. - Zehng, H. & Han, J. 2019, "Exploring the impact of street renovation on the surrounding store economy: A case study of street reconstruction in Tianhougong area of Quanzhou", 12th International Space Syntax Symposium, SSS 2019. - "Industry 4.0 standards for rideshare aggregation management", 2019, Proceedings of the International Astronautical Congress, IAC. - Shen, B., Shan, Y., Jia, Y., Xie, D. & Zhu, S. 2019, Modeling the Cashflow Management of Bike Sharing Industry. - Yang, Y., Sun, Q. & Ba, S. 2019, "Research on the Development Strategy of Shared Appliances under the Low Tide of China's Shared Economic", IOP Conference Series: Materials Science and Engineering. - Fan, S., Xu, G. & Ai, Q. 2019, "Multi-agent Cooperative Interaction Mechanism in a Community Integrated Energy System Using Nash Bargaining Theory", iSPEC 2019 - 2019 IEEE Sustainable Power and Energy Conference: Grid Modernization for Energy Revolution, Proceedings, pp. 1273. - Geiger, S., Schall, D., Meixner, S. & Egger, A. 2019, "Process traceability in distributed manufacturing using blockchains", Proceedings of the ACM Symposium on Applied Computing, pp. 417. - Uskenbayeva, R.K., Kuandykov, A.A., Rakhmetulayeva, S.B. & Bolshibayeva, A.K. 2019, "Properties of platforms for the transformation and automation of business processes", International Conference on Control, Automation and Systems, pp. 29. - Treviño, C.P.S., Rubio, L.E.O. & Ramírez, J.D. 2019, "Using IT-based solutions to improve logistic operations of a scrap collection system", Proceedings of the International Conference on Industrial Engineering and Operations Management, pp. 443.

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