REWARDING PARTICIPATION IN SERVICE VALUE NETWORKS An Approach to Incentivize the Joint Provisioning of Complex E-Services
Tobias Conte1, Benjamin Blau2, Gerhard Satzger2, Clemens van Dinther3, Christof Weinhardt3
1
FZI Forschungszentrum Informatik (Research Center for Information Technology), Karlsruhe, Germany,
[email protected] 2
Karlsruhe Service Research Institute (KSRI), Karlsruhe Institute of Technology (KIT), Germany, {
[email protected]|
[email protected]} 3
Institute of Information Systems and Management (IISM), Karlsruhe Institute of Technology (KIT), Germany, {vandinther|weinhardt}@kit.edu
Parts of this article have been presented previously at the 15th Americas Conference on Information Systems (AMCIS), San Francisco, California, August 6 – 9, 2009.
1
Abstract: Products increasingly turn into services. A prime example for this trend is the software industry. Specialized vendors leverage their core competencies in service value networks that offer joint complex eservices to customers. Such service value networks are in their infancy, yet both academics and practitioners lack approaches to formalize and economically analyze them. To resolve this deficit, we introduce a formalization of service value networks. Our objective is to reward service providers not only for their inclusion in a particular service rendered, but also for their mere presence in the network. Purpose of such a scheme is to incentivize vendors to participate in the networked value creation and to enable platform operators to enforce certain network characteristics. To this end, we introduce a metric to express the contribution of service providers to the whole network – the power ratio. For service value networks with power ratio based incentives, we conduct simulations to study their evolution and to analyze the power ratio’s ability to foster competition. Keywords: Service value networks, revenue distribution, network evolution, service ecosystem, joint provisioning of e-services
2
1 Introduction Customers’ demand for sophisticated, individualized services has considerably been rising in recent years. One of the most powerful approaches to handle complexity is modularity, that is composing complex services from smaller subsystems that are designed independently, yet function together as a whole (Baldwin and Clark, 2000). In contrast to earlier notions of controlling the whole value chain by vertically integrating value creation, companies today focus on their core competencies. Open standards and service-oriented architectures have emerged as important building blocks for innovative service networks tying together the competencies of specialized contributors. In related but non-core activities, assets of partners are being leveraged. That way, the network is able to “pick, plug, and play” business processes (van Heck and Vervest, 2007). The above-mentioned development and the advent of service value networks is illustrated in Figure 1.
Figure 1. From hard-wired value chains to adaptive service value networks
Source: Blau et al. (2009b) Literature generally postulates a shaper-adapter configuration in such networks where one or more focal companies control the central element of the network (Tapscott et al., 2000). For example, the shaper imposes standards or offers a common marketplace – that is generally creates a platform that other members (adapters) of the ecosystem can use to enhance their own performance. A pioneer for above-mentioned developments is the software industry. With the advent of software-as-a-service1 (SaaS) and Web services enabled by Internet standards and interoperability, vendors were able to turn software products into digital services (e-services). Recently, 1
Software-as-a-service is a software distribution model in which applications are hosted, operated, and maintained by a service provider and made available (”one-to-many”) via a network such as the Internet.
3
they took another step forward: By increasingly leveraging their core competencies in service value networks (SVNs), they are able to fulfill specific customer requirements by offering joint complex e-services. Complex or composite e-services typically involve the assembly and invocation of several component services offered by a multitude of partners in order to provide a multi-step business functionality (Papazoglou, 2007). The cutting-edge concept is to serve this specific service request by an agile composition of existing service modules offered by a pool of seller candidates which provide complementary as well as substitutive services (Blau et al., 2009a). Increasingly, service platforms and marketplaces emerge that assemble and manage SVNs – with significant business impact, at this stage already observable in the software industry. When preparing the market entry of an SVN, its operator needs to “get on board” both buyers and sellers in order to establish a successful business (Rochet and Tirole, 2003). The key challenge is to find a way to exploit so-called network effects, i.e. capitalizing on the dependency of a network’s value and the number of participants connected to it (Shapiro and Varian, 1999). While network effects are often discussed for the demand side, they also apply to the supply side of such networks: Not only the quantity of service customers will boost the success of the network, but also the quantity and complementarity of providers as well as the quality of the services offered by them. Thus, a successful SVN requires measures to incentivize participants to join. We present an approach that explicitly concentrates on setting incentives for the service provider side. Its underlying novelty is to not only compensate those who actually contribute to the complex service offered at a time, but also to pay out to service providers that are on standby, that is partners supporting the network’s variety, but actually do not contribute to the complex service rendered. Therefore, we utilize a surplus that is additionally distributed according to network participation. That way, we seek to lower the entry barrier for service vendors that face initial costs when developing services compliant to the platform requirements. Obviously, the surplus has to be sponsored by one of the network participants. In this article we assume that the platform operator grants the surplus, among other things, aiming at promoting alternative paths through the network which should lead to a more balanced network without single providers gaining monopolistic positions. In such balanced networks, the platform operator is no longer dependent on powerful service providers which could impose pressure by bullying the market or by threatening the network with termination of membership.
4
In the following, we do not examine the very impact of such a payment scheme to the formation of SVNs. We rather analyze if such a payout rule can, from the platform operator’s point of view, positively affect the network’s structure. This paper is organized as follows: First, we show that the software industry is a suitable application field for our research. Based on that, we formalize SVNs in the software industry in section 3. After a brief overview of related literature in section 4, we then motivate and formalize our approach to compensate not only allocated providers, but also the partners on standby (Section 5): We introduce a measure (the power ratio) to express the service providers’ contributions to the overall network and consider the possibility of using this power ratio to distribute generated value among all participants in the network. In Section 6, we evaluate these theoretical considerations in a simulation (i) to study the power ratio’s ability to foster efficiency and (ii) to show desirable properties that make the payment scheme based upon the power ratio a promising approach. The paper concludes with a summary and an outline of further extension of this research. 2 E-services as Field of Application Today’s SaaS and Web service market already shows the way towards ecosystem-like structures. We will first classify this market by means of a simple topology and then substantiate our findings through more detailed insights into the field of e-service mashups. 2.1
Market Structure of E-Services
The SaaS and Web service market can roughly be divided into four quadrants as shown in Figure 2.
5
Degree of Interaction
Multitude of Providers Single Provider
Aggregated offering of simple services via a marketplace
Integrated offering of a complex service consisting of simple or complex services via a marketplace
Offering of simple services by single companies
Complex service offerings by single companies
Degree of Composition Complexity
Single Service
Complex Service
Figure 2 Typology of e-services
Source: Blau et al. (2009b) This typology classifies the manifestations of the e-services market by composition complexity of the service and the degree of cross-organizational interaction when developing and offering services. The variety of single Web services offered by individual service providers is already vast. Prime examples are services provided by Google such as Google Maps2, a map service that can be easily embedded in homepages or mashups. On the other hand, not only simple, but also complex e-services supporting multi-step business processes are offered ondemand. For instance, providers like Salesforce.com or Netsuite Inc. successfully entered the business software market with their entirely web based on-demand customer relationship management (CRM) suites. Components offered within these suites can be dynamically composed to customized processes. Additionally, (Web) service marketplaces such as StrikeIron3 evolved, opening up their service integration platform for service providers to offer their services via a joint distribution channel. AppExchange4, the application marketplace offered by salesforce.com, offers a range of pre-integrated complementary services provided by third party providers grouped around the core service Salesforce CRM. However, each and every 2
http://maps.google.com http://www.strikeiron.com 4 http://www.salesforce.com/appexchange 3
6
Web service is based on a common platform and the same core application which significantly facilitates the provisioning of such integrated services. There is further need for research in the field of complex services that are composed of elements provided by different vendors. The research project TEXO5 will put forth an infrastructure to provide complex, composite e-services over the internet by means of the TEXO Service Management Platform. In such highly modularized environments, service intermediaries, linking together demand and supply, play an important role. Service brokers add value by assembling modular services as a central value-added capacity. That way, existing services from different providers can be orchestrated resulting in new and innovative offerings. 2.2
E-Service Mashups as Approach to Integrate Business Services
Typically, situational applications that are needed only for a limited time span do not make it into realization in favor of strategically important applications as part of a company’s development backlog. With the advent of Web 2.0 technologies and the renaissance of HTTP appreciation, the possibilities to build “good enough” applications have greatly increased. The number publicly available mashups6 is considerably increasing7. While the first mashups were dedicated to small consumer applications where simple data (e.g. RSS feeds8) was integrated into the Web browser, mashup technology now promises to integrate enterprise applications. In fact, mashups are now capable of providing solutions for the long tail of applications (Anderson, 2006).
5
TEXO is part of the research program THESEUS which is funded by the German Federal Ministry of Economics and Technology (BMWi) with the goal of developing a new Internet-based infrastructure (http://theseusprogramm.de/scenarios/en/texo). 6 Service mashups are applications or Web sites that aggregate content such as data feeds, applications, widgets, or gadgets from different sources (Merrill 2006). 7 Cp. Programmableweb.org (http:// programmableweb.org/). 8 http://purl.org/rss/1.0/spec/
7
Mass Market
Off-the-Shelf (SaaS)
Niche Market
Situational/Tailored (Service Mashups)
Service Customization Figure 3 E-service mashups address customization needs
As depicted in Figure 3 the mass market exhibits only small degrees of customization but enjoys demand of many customers, that is volume business. Software companies have already been exploiting these market segments. However, there is also a long tail of applications which require highly specialized features. Accordingly, this highly specialized software cannot be offered to many customers in a scalable manner. This long tail of applications is very valuable in a sense that the demand for customized and quality differentiated software is immense, that is value business. The technology of mashups now enables software vendors to exploit the long tail as customization becomes more feasible and cheaper through the aggregation of small services. Big and RESTful Web services encapsulate functionality and put them behind clearly defined interfaces based on SOAP, WSDL and HTTP. Typically, it is distinguished between consumer, data, and enterprise mashups. In fact, consumer mashups combine data elements from different sources and hide them behind a simple user interface. Data mashups combine data streams from different sources into one single data feed with one dedicated user interface attached to it. Enterprise mashups integrate data from internal and external sources creating composite Web applications. Because of its simplicity in setting up composite applications, mashup technologies are expected to evolve significantly. As stated in Section 2.1, Web service delivery platforms and marketplaces evolve to offer customers and providers a solid base to trade above-described modularized services. However, before economically analyzing such emerging SVNs, we need to formalize them.
8
3 Formalization of Service Value Networks A service value network is formed by service providers that contribute to the achievement of an overall goal. For example, this objective can be the flawless execution of a business process in order to provide defined functionality to the customer. Recalling the main characteristics of service value networks, many service providers offer differentiated and specialized services covering various functionalities within the network. The following abstract model is a formalization of a service value network containing a network of service providers. It captures the above-mentioned network’s characteristics using a formal notation. An SVN is described by means of a simplified state chart model (Harel and Naamad, 1996). . State charts have proven to be the preferred choice to specify process models as they expose well-defined semantics and they provide flow constructs offered by prominent process modeling languages (e.g. WS-BPEL) and, therefore, allow for simple serialization in standardized formalisms. Importantly, in our model we focus on the core process of realizing an overall goal without going into process-related details such as parallel or cyclic components. Thus, the logic of the services’ interaction is depicted in a process model aligned with the representation presented in Zeng et al. (2003). According to the definition of SVNs, substitutive as well as complementary services are to be depicted. Thus, we represent a service value network by a directed, k -partite graph. Each partition represents a different functionality requested by the service consumer. For simplicity we assume that each service is owned by a different service provider. Thus, the set of nodes V {v1 , , vn } equals the set of n service providers9 that act in the network. In order to reach the overall goal (that is, an instance of the complex service demanded), exactly one service out of each partition (service cluster) is required. Let vicm denote that vendor vi belongs to cluster cm ( m {1,..., k} ), V cm V includes the set of nodes allocated to cluster cm . Substitutive services are mapped to one and the same functionality cluster. Additionally, we define two designated nodes vs and v f as source and the sink of the network to formalize complex services as an end-to-end connection. These nodes are not considered vendors in the network. Hence, they are not included in V . Source and sink are also not considered a separate cluster10
9
The terms service, service provider and vendor are used interchangeably. In case of vs , there are no incoming edges and one outgoing edge to each node of the first cluster. Analogously, the sink has no outgoing edges and one incoming edge from each of the nodes of the last cluster.
10
9
Let vi be an arbitrary vendor/service in the network. An edge eij denotes an integration relationship between services vi and v j . That is, an edge between two vendors symbolizes the interoperability of their services offered and their willingness to cooperate. Let E be the set of all possible links in a k -partite graph. To represent the process-oriented view, E is further restricted as follows: Edges are only allowed between nodes of consecutive functionality clusters. Therefore, let E : {eij | i {1,..., n}, vi V cm , v j V cm1 } {esj | v j V c1 } be the set of all possible links related to the set of vendors V . That is, we can formally describe the graph G ({V {vs , v f }},{E {eif | vi V ck }) representing the full SVN as follows. Consequently,
G (V , E ) denotes the set of all vendors and their related links.
Besides their very functionality, services are basically characterized by their non-functional attributes such as price or quality measures. We divide these into price and attributes. The set of l attributes A j {a1j ,..., a lj } fully characterizes the configuration A j of service v j . a mj is an attribute value of attribute type m of the configuration of service v j . Each eij E is
annotated with a price pij to apply when v j is being allocated as successor of vi . In more detail, in order to determine pij , service providers incorporate costs that accrue for executing its service at runtime dependent upon service vi . For instance, such costs can originate from data conversion efforts or hardware load. The representation of a detailed cost structure of service providers is intentionally omitted which serves a better understanding and does not restrict the generalization of the model. v f is not considered a service or vendor; thus, the links eif , vi V ck are not assigned prices and are not included in E . We are particularly interested in formalizing instantiable composite services as they symbolize a value creating output of the network. Only possible realizations of complex services, i.e. complete paths from source to sink, create value. However, since vs and v f are not considered vendors, we formally exclude them from paths. Thus, Fl : (Vl , El ) with Vl V and El : v F E (vi ) , where E (vi ) E stands for the set of all incoming links of vi , denotes a i
l
feasible complex service in G . Furthermore, F : {F1 ,..., Fm } defines the set of all m complex services available. Generally, each vi with i s and i f must at least have one incoming and one outgoing edge to be an active node in the network, i.e. to be situated on at least one path Fl F . 10
Figure 4 shows an exemplary formalization of a service value network which can be described as G ({v1 , v2 , v3 , v4 , vs , v f },{es1 , es 2 , e13 , e23 , e24 , e3 f , e4 f }) . The reduced graph G ({v1 , v2 , v3 , v4 },{es1 , es 2 , e13 , e23 , e24 }) shows the network of providers and their connections
which are able to meet the requirement specification by the service requester. There are three paths in G representing a possible realization of a complex service. For instance, such a path is F1 ({v1 , v3 },{es1 , e13 }) . c1 v1
c2 p13
A1
Caption
v3
Integration relationship
A3
ps1 p23
vs
vf
vs
Source node
vf
Sink node Service offer with set of attributes
ps 2
v2
p24
A2
v4 A4
Functionality cluster
Figure 4 Exemplary formalization of a service value network
4 Distribution of Value: Related Literature Before designing a measure that captures service providers’ individual contribution to the
overall network, we proceed with a literature review on different concepts to distribute value and on network formation. In SVNs, we face the typical situation of co-opetition (Nalebuff and Brandenburger, 1996). Co-opetition circumscribes a duality between competition (between substitutive service offers) and cooperation (which is necessary to accomplish complex e-services). The latter aspect suggests to borrow a concept from cooperative game theory when designing a payment scheme. Cooperative game theory offers several concepts that distribute value among individuals or players. Well-known approaches are, for example, the core and stable sets (Von Neumann and Morgenstern, 1944; Gillies, 1959). These concepts map a coalition of players T V into real numbers considering coalition games (V , ) with a finite set of players and a
characteristic function . However, both concepts assign a set of payoffs to players which is frequently empty or ambiguous. The Shapley value differs from these approaches (Shapley, 1953). It always provides a unique solution in form of a single payoff assigned to each player. The Shapley value bases the payout distribution on the average marginal contribution a player
11
adds to a coalition. Hence, it reflects the average power or significance of a player vi V . However, the concept is based on a coalition structure and does not consider restrictions inherent to network topologies. The basic assumption in a coalition is that a player vi V is able to cooperate with any player v j V . As shown in Section 3, this does not hold true, though, for networks, where due to functional or strategic restrictions, not only the players themselves, but also the links between them are of prime importance. Bearing these characteristics in mind, an extension of the Shapley value to network structures is presented in Myerson (1977) transferring the Shapley value to cooperation graphs (Myerson value). Thus, the range of possibilities to form coalitions is reduced to a given topology and its links. The Myerson value returns a positive value whenever a player is pivotal to at least one coalition. This value is then weighted with the probability of the underlying coalition to form - assuming that the sequence of the players to join a coalition is equally likely. These considerations result in the following allocation function Yi for a player vi V as a direct extension to the Shapley value: (1)
T j !( V T j 1)! ( (T j {vi }) (T j )) Yi (V , ) T V \{v } j i V!
However, when applying the Myerson value, an implicit assumption inherited from the use of characteristic functions is that cooperation among more players must always be more fruitful than cooperation with fewer members11. But what if a smaller coalition of players is able to obtain a certain goal more efficiently than a coalition of more players? In other words, considering real world scenarios, larger coalitions might be of lower value than smaller coalitions due to the overhead costs they generate. Yet, the application of proper characteristic functions does not cover this issue. This extension is, in turn, proposed by Jackson (2005) utilizing monotonic covers of value functions. Another closely related field of research is network formation. Jackson and Wolinsky (1996) analyzed the evolution of social and economic networks where self-interested individuals form or sever links. In Jackson and Watts (2002), network formation is dynamically based upon players’ individual improvements resulting from changes in the network topology. Traditionally, breaking relationships can be done unilaterally while the formation of links requires consent from both players (Jackson and Wolinsky, 1996). Bala and Goyal (2000), however, stated that links can be formed by individual decision under certain circumstances.
11
Proper characteristic functions require superadditivity: T1 V and T2 V , (T1 ) (T1 T2 ) (T1 \ T2 ) .
12
This is also the case in SVNs since service providers cannot influence which other services process their outputs. To summarize, we build our model upon Jackson (2005), however, include the characteristics of SVNs as well as the overall network perspective. 5 Rewarding Contributions Our scenario belongs to the class of two-sided markets. In such two-sided markets, the inter-
mediary faces the challenge to attract two sides in order to establish a functioning business. Both sides of the market positively value the number of participants on the other market side. Service consumers favor a multitude of service providers on the platform, fostering variety and competitive prices. At the same time, sellers are only willing to register if they expect to come across many buyers in the market (Caillaud and Jullien. 2003). Thus, we face the famous chicken-egg problem: Both sides need to be brought “on board” (Rochet and Tirole, 2003). In this article, we concentrate on the service provider side, seeking for an incentive scheme that pulls sellers into the SVN. Service providers face fixed costs when designing or adapting services that comply with the requirements imposed by the platform. That is, sellers need to make specific investments prior to any transaction which enhance or facilitate the value of the trade within the platform, but are of considerable less value outside of the platform (Gandal, 2002). Future rewards and transactions in general cannot be ex ante specified with certainty (Rogerson, 1992). These specific investments might prompt sellers not to join an SVN since future revenues are too uncertain compared to the initial investments. Thus, we follow the approach of offering a recurring payment to service providers even if their services are not regularly allocated. That way, we seek to lower the entry barrier for service vendors by granting some income only depending on the success of the whole network – that way also aligning interests among providers. Its underlying novelty is not only to compensate those who actually contribute to the complex service delivered at a time, but also to pay out sellers that are on standby, that is partners supporting the network’s variety, but actually not contributing to the complex service executed. However, before introducing such a payout rule, we develop and evaluate a measure that considers the contribution added to the overall network. We will call this measure the vendors’ power ratio to denote their significance to the value creation in the network. In doing so, we
incorporate important characteristics of service value networks such as prices of services, complex services assembled from several modules, and agile, flexible interchangeability of
service providers. 13
5.1
Measuring Individual Contributions to the Overall Network: The Power Ratio
Based on Jackson (2005), we interpret value functions as objects representing costs and benefits. Let Sk : (Vk , Ek ) with Vk V and Ek : v V E (vi ) , where E (vi ) E denotes the set i
k
of all incoming links of vi be a possible cooperation of service providers present in G . Furthermore, S : {S1 ,..., S2n } defines the set of all cooperations where n | V | . When assigning value to vendors or cooperations, respectively, only complete paths represent complex service instances. Therefore, only those cooperations that incorporate at least one feasible path are assigned a positive value ( Sk ) . That is, a cooperation S j that does not incorporate a path is assigned a value ( S j ) 0 . For cooperations S k S with Fj Sk we need a valuation that is directly dependent on the attributes that create value for the customer. When designing such a value function, one quickly spots that the assumption of superadditivity is not realistic. The best path F * F provided in G is to create the same value as the cooperation that includes the whole network, that is ( F *) ( SG ) . In other words, we suggest waiving the strong assumption of superadditivity in favor of a weaker constraint: We accept
( S1 S 2 ) ( S1 ) ( S2 ) , S1 , S 2 S , as long as ( S1 S 2 ) is not smaller than the most valuable of its components ( S1 ) or ( S2 ) . Generally spoken, that implies: (2)
( i 1 Si ) max( ( S1 ) ... ( Sk )) , Si S k
In order to meet (2), only vendors providing additional value are incorporated in our calculation. Thus, as soon as a cooperation yields more than one path, the path providing the highest value is chosen for the calculation of the value function. For example, consider a vendor vi that enters an existing cooperation S k Fl F . Assume that vi does not provide an additional path and, therefore, does not provide any additional value. On the other hand, if service provider vi joins a cooperation S1 F1 , S1 S , F1 F , thereby accounting for a cooperation
S 2 S with additional path F2 F with ( F1 ) ( F2 ) , then
max ( S 2 ) max{ ( F1 ), ( F2 )} ( F2 ) . As introduced before, we consider prices pij and service attributes Aj as the central indicators for the value that is generated by the network or sub-networks, respectively. The price of a complex service PFl is determined by aggregating the prices pij of services situated on Fl , that is PF1 : e F pij . For simplification, we assume equal service attributes Aj of all serij
l
14
vices. Consequently, value functions are only driven by prices. Therefore, we can introduce the value function as monotonically decreasing with rising prices as a quasi-linear approach
using max ( S k ) min PFl if Fl S k , Fl F , S k S with being the service requester’s Fl
willingness to pay. Thus, assembles as a function : S
that reciprocally accounts for
the prices of services included in the complex service for cooperations S k S as follows: (3)
min PF , if Fl Sk , Fl F , S k S , PF l l Fl
max ( S k ) :
0,
otherwise
Consequently, each service provider that generates a positive value, i.e. PFl holds true for at least one complex service which the respective provider is a part of, is considered a vital service provider. Let be the set of all possible value functions. In order to determine the providers’ power ratios in an SVN, we define a function Y : S
n
. Remember that the basis for our con-
sideration is always the cooperation that includes the full given graph. Incorporating (3) and the concept of considering the overall network, we get (4) as a direct extension of (1). For all cooperations vi can theoretically join (4a), term (4c) takes a positive value whenever vi is pivotal to it. This value is then weighted by the probability of the underlying cooperation to form assuming that the sequence of the vendors to join this cooperation is equally likely (4b). (4)
Yi ( SG , ) S
Vk !( V Vk 1)! max ( Sk ({vi }, E (vi ))) max ( Sk ) k with vi Vk V ! (a)
(b)
(c)
The set of all incoming edges of a node vi is denoted E (vi ) . As soon as a service provider vi enters a cooperation Sk , E (vi ) is also added. Based on (4), we calculate a service provider’s relative share in the overall productivity of the network which we interpret as its power ratio (PR) i relative to the overall network, i.e. i [0;1] : (5)
i ( S G , )
Yi ( SG , ) Y (S , ) i G jV Y j (SG , ) max (SG )
The individual power ratios of the service providers (1 ,..., n ) sum up to 1. Hence, we do not directly distribute the value created via Shapley-style calculations, but rather extract the influence of a single service provider relative to the topology of the whole network. A numerical example is provided in Section 5.3.
15
5.2
A Payment Rule Based on the Power Ratio
Let us now return to the very idea of including our metric as a part of a payment rule that decides how generated revenues are distributed amongst network participants. We propose the following PR-based payment (PRP) rule as a distribution scheme (partially) based upon the power ratio. pij j , if v j , eij F * rjPRP : otherwise j ,
(6)
shall be a surplus that is additionally distributed according to j . Certainly, the sur-
plus has to be sponsored by one of the network participants. Generally, all of the three parties involved are a possibility. In this article we assume that the platform operator sponsors the additional to be distributed12. The assumption can be built upon several rationales. On the one hand, environments like SVNs need to reach a critical mass to be profitable. To reach such a critical mass, the network needs to offer incentives to draw in service providers. Furthermore, customers might prefer purchasing services in networks with alternative offerings, knowing that other providers will dynamically pitch in if an allocated service vendor goes out of business. Several scholars assume that service consumers have preferences for a variety of services (cp. e.g. Chou and Shy, 1990; Church et al., 2002). Hence, the platform operator is willing to push the network structure to a situation where, preferably, as many as possible service providers are linked with each other. On the other hand, PR-based payment might be purposeful for the platform operator to push the network structure to a certain direction. Promoting alternative paths through the network might, for instance, lead to a balanced network without single providers having monopolistic or oligopolistic positions. In such balanced networks, the platform operator is no longer dependent on single service providers which could, having a monopolistic position in the network, impose pressure by bullying the market or by threatening to walk away. In the latter case, the network potentially would cease to exist. The determination of the allocated path or complex service F * is not coupled with the PR and is based on an allocation function that picks the most favorable complex service available in the network available from a requester’s point of view: (7)
12
F * argmax PFl Fl F
Generally, consumers also come into consideration as source for paying to come across a greater variety of providers in the network. Generally, service consumers benefit from a large network since it yields a large amount of services (Chou and Shy, 1990).
16
Hence, a service requester has to indicate his willingness to pay to the service platform operator. If the willingness to pay net of the complex service price yields a negative value, the mechanism does not allocate any complex service. Before proceeding with our research question, we will provide a numerical example to illustrate the power ratio-based payment rule. 5.3
Numerical Example
Assume a company uServ that seeks to purchase a complex service request and order management-service that supports its customer relationship business processes. The company therefore approaches our service platform and marketplace, requesting a solution that (i) handles service requests and orders in a first step and afterwards (ii) confirms the orders. Further assume that, besides the very functionality, uServ is only concerned with the price of the complex service. uServ is willing to pay 50 monetary units for a package including a defined number of usages that perfectly fits their needs. The platform operator publishes uServ’s service request to the pool of service providers registered with the marketplace. He receives five service offers resulting in graph G (cp. Figure 5). For simplicity, we introduce partitions c , each representing a specific class of functionality. In our example, c1 denotes functionality (i), that is handling of service requests and c2 denotes the second functionality (confirmation of orders). G yields three instantiations: Service v1 combined with service v3 as the first possibility, service v2 and service v3 as the second alternative, and finally a composition of service v2 and service v4 . So, the set of paths F is defined by F ( F1 , F2 , F3 ) with F1 ({v1 , v3 },{es1 , e13 }) , F2 ({v2 , v3 },{es 2 , e23 }) , and F3 ({v2 , v4 },{es 2 , e24 }) . Price bids submitted by the service providers can also be found in Figure 5. c1 ps1
10
v1
vs
c2 p13
20
p23
27
v2 ps 2
10
v3
vf
v4
p24
25
Figure 5 Service value network: Numerical example
17
Calculating PFl we can determine the overall value provided by each of the three services as follows:
( F1 ) 50 20 10 20 ( F2 ) 50 27 10 13 . ( F3 ) 50 25 10 15
According to (7), the platform operator will then allocate F * =argmax PFl F1 . Fl F
Since F1 is allocated, the involved service providers v1 and v3 both receive their bids in the first place. In addition, a surplus is distributed via the power ratio. Applying (5), the vector (1 , 2 , 3 , 4 ) assembles as follows: (0.259, 0.242, 0.367, 0.134) . Exemplarily, we show the computation of 4 in detail: 1 1!2!
2!1!
2!1!
4 15 0 15 0 15 13 20 4! 4! 4! Service provider v4 is a pivotal vendor in three cases: The first term denotes the situation where vendor v2 is already in a cooperation (yielding a zero value) and v4 is the second one to enter, thereby accounting for a value of 15. The probability to form given all possible cooperations v4 can join is 2 24 . For the latter, the whole network view is decisive: There is one vendor inside, v4 joins, and afterwards two other vendors can enter in two different sequences. The second term reflects the setting v1 and v2 already in (as substitutes without link account for a zero value) and v4 enters as the third vendor, pushing the cooperation’s value to 15. Thirdly, v2 and v3 are already in, providing F2 with ( F2 ) 13 ; v4 joining raises the value to 15, thus the marginal contribution of v4 is 2. All other combinations either do not provide any value (e.g. v4 joining v1 ) or do not add any additional value such that v4 is not pivotal (e.g. v4 joining v1 , v2 , v3 ). The calculation of the other vendors’ power ratios is carried out analogically. Finally, we divide by ( Fmax ) max ( SG ) 20 to receive the influence of a single vendor relative to the overall topology. Assume that the platform operator grants PFl 30 . Altogether, applying (6), service provider v1 receives a payment of r1 10 0.259 30 17.77 . Service provider v3 obtains t3 20 0.367 30 31.01 . Service providers v2 and v4 which are not allocated only receive
18
their PR-based payment mounting up to t2 0.242 30 7.26 and t4 0.134 30 4.02 , respectively. uServ is charged ps1 p13 30 for the requested service. 5.4
Research Question and Hypotheses
To study the suitability of PR-based revenue distribution, let us assume that service providers within the SVN are fixed. That is, in this article, we do not study the direct effect of PR-based revenue distribution on sellers’ incentives to join the network. Yet, they can decide upon their connectivity to preceding service offerings. We investigate on how service providers in the SVN maximize their utility based on their PR. That is, they are considered as self-interested individuals that form or sever relationships to maximize their benefit. Whether two services are connected basically depends on two factors, namely functional and strategic criteria. Consequently, vendors can decide if they want to be connected to their predecessors or not. Thus, each vendor can choose its linkage strategy to directly anteceding service providers. That is, links can be formed by individual decision since compatibility merely requires the effort of the subsequent service provider that also has to bear the costs (cp. also Bala and Goyal, 2000; Jackson and Watts, 2002). In order to compare our PRP rule with more traditional notions of distributing payments, we consider a simple payment exclusively rewarding the service providers allocated, that is a purely allocation based payment (AP) rule: (8)
p , if eij , v j F * rjAP ij otherwise 0,
The payment function as shown in (8) is derived from the principle of first price auctions. The sellers providing the component services that are situated along F * receive their announced price. The design of our PRP rule aims to support the goals pursued by the platform operator. Firstly, a more distributed compensation shall incentivize participants to join the SVN (O1). Such incentives will be reinforced if there is not only a recurring payment, but also a greater alternation of allocated service providers. Thus, the platform operator is willing to foster network agility (O2). Eventually, from a platform operator’s perspective, it is beneficial to foster variety (O3) within the network to prevent single service providers from winning too much bargaining power. Furthermore, the more intermeshed the SVN, the greater the number of alternative paths which increases reliability by enabling failovers through re-allocation. At first sight, it seems favorable for vendors to be linked to as many other service providers as possible when payments are made based on (6). Having more connections, a service provider is situated on more paths and is thus more often a vital participant when it comes to coopera19
tion formation. However, vendors with a high PR might be reluctant to establish links to service providers with less power. Since the power ratios of all network participants are relative, strengthening others can result in weakening one’s own position. Hence our research question can be formulated as follows: Is a payment scheme incorporating the power ratio feasible to diminish concentration and to foster agility and variety in SVNs? However, before evaluating the suitability of the power ratio as a rule for revenue distribution, we need to verify whether the measure itself sufficiently fosters competition and efficiency of service providers located on and off the best path in a preliminary study. Derived from above-stated research questions, we state the following hypotheses: H1:
For allocated service providers, the power ratio fosters a higher price pressure than for providers not located on the best path.
H2:
PR-based payments account for diminished concentration of distributed payments compared to a purely allocation-based payment (cp. O1).
H3:
PR-based payments account for increased dynamics and agility in the network compared to a purely allocation-based payment (cp. O2).
H4:
PR-based payments account for a higher degree of variety compared to a purely allocation-based payment (cp. O3).
After evaluating hypotheses H2-H4 in a simulation with a relatively high , we vary the level of the surplus in order to verify how different levels of influence H2-H4.
6
Simulation
We apply a simulation approach to study the hypotheses stated in Section 5.3. Service providers form or sever links based on their utility improvement or deterioration based on the action accomplished. We chose simulations to handle the complexity of the payment scheme. Due to the relative Shapley-style calculations, changes of an individual service provider directly impact the payments of all other network participants.
6.1
General Simulation Model
The problem is modeled as an n-person game with each node representing a service provider (cp. Section 3).. To further simplify our model, we assume a universal service requester, i.e.
is sufficiently high and identical for all requesters. Thus, we can again simplify as a
20
function : S
that reciprocally accounts for the prices of services included in the com-
plex service for cooperations S k S as follows: (9)
1 , if Fl Sk , Fl F , S k S max max ( S k ) : Fl Sk Fl pij otherwise 0,
For | V cm | pmax with pmax being the upper boundary of the price interval, (9) yields comparable results to (3) when computing the vendors’ power ratios.
6.2
Specific Simulation Setting and Assessment of Results for H1
For the evaluation of the hypotheses H1 and H2 to H4, we implement two different simulation settings. H1 and H2 to H4 are therefore evaluated separately. 6.2.1
Specification of Simulation Setting for the Evaluation of H1
For the evaluation of H1, no specifications of the general simulation model introduced in Section 6.1 have to be made. The settings are as follows: We create networks with three functionality clusters and | V cm | 3, m {1, 2,3} .We conduct simulations with n 9 nodes in 100 randomly chosen topologies in order to eliminate stochastic dependencies. We randomly draw links with a density of 0.75 in each simulation. The prices to links are drawn from a uniform distribution in the interval [0.1,1] with increments of 0.1 . Each simulation includes 10 runs. For such a run, a random edge eij E is drawn and reset. Its price pij is then incrementally set to pij L : {0.1, 0.2,...,1.0} , meaning that in the first run pij 0.1 , in the second pij 0.2 and so on ending with pij 1.0 in the tenth run. For each of these runs, we calculate the | L | 10 power ratios lj , l L , of vendor v j whom the chosen edge eij is an incoming link for. 6.2.2
Assessment of Results for H1
To assess H1, we turn our attention to how a randomly drawn edge eij with varying prices pij impacts service provider v j 's power ratio. In order to examine the behavior of the power ratio of vendor v j at the moment the link drifts off the best path due to increasing prices, we deleted all topologies where eij is never on the best path. This leaves us with 52 out of 100 arbitrary topologies. Out of these 52, we aggregate all topologies where the cut from eij being on
21
the best path to being off the best path occurs after the same increment. Based on this categorization, we are able to study the change in the power ratio of service provider v j for a certain pij over all aggregated topologies. jp
jp jp 0.1 expresses the relative change of j jp 0.1
with the price associated to eij rising from pij 0.1 to pij . In Tab. 1, we outline the cut pcrit (meaning that the cut occurs after pij pcrit ) in two different ways. In columns 3 and 4 we merely compare the single values jpcrit and jpcrit 0.1 whereas we compare the average of all values before the cut 1 :
2 :
1 {l L | l pcrit }
lL l pcrit
lj and after the cut
1 lj in columns 5 and 6. However, in both of the evaluations, {l L | l pcrit } llLpcrit
we consider the average over all topologies with the same pcrit in the sample. So, as a characteristic measure to evaluate H1, we suggest to compare the delta of PR prior to the abovementioned cut and the post-cut delta of PR. That is, we can re-formulate H1 as follows: H1:
The delta of PR prior to the cut is larger than the post-cut delta of PR.
Due to the fact that 11 topologies exhibit a cut directly after pij 0.1 , we cannot evaluate the PR prior to pcrit so that 41 topologies are finally left for our tests. Tab. 1 Relative change in power ratio (H1)
jpcrit
jpcrit 0.1
1
2
12
-0.1096
-0.0519
-0.1096
-0.0322
0.3
7
-0.0852
-0.0470
-0.0825
-0.0291
0.4
7
-0.1009
-0.0663
-0.0959
-0.0457
0.5
4
-0.0948
-0.0673
-0.0874
-0.0405
0.6
4
-0.0875
-0.0572
-0.0775
-0.0381
0.7
6
-0.0668
-0.0425
-0.0630
-0.0371
0.9
1
-0.1443
-0.0718
-0.1039
-0.0718
-0.0949
-0.0547
-0.0909
-0.0370
Position of cut pcrit
Number of topologies in sample
0.2
Weighted average
Most importantly, j decreases severely (in average 9.49% or 9.09%, respectively, per increment) as long as eij is part of the best path. This phenomenon changes abruptly as soon as 22
eij glides off the best path with the decreases in the power ratio becoming much more moderate (in average 5.47% or 3.70%, respectively, per increment). In order to back up H1, we perform a single-sided paired Wilcoxon rank-sum test13. The underlying data groups are (i) jp and (ii) jp 0.1 of all topologies with pcrit 0.1 . We can conclude that above-described behavior occurs systematically as the Wilcoxon rank-sum test shows significance with p
