BEAM: A framework for business ecosystem analysis and modeling

Transcription

1 BEAM: A framework for busness ecosystem analyss and modelng & C. H. Tan B. K. Ray J. Lee R. Cao W. Dng Ths paper presents a framework for the modelng and analyss of busness model desgns nvolvng a network of nterconnected busness enttes. The framework ncludes an ecosystem-modelng component, a smulaton component, and a servceanalyss component, and ntegrates methods from value network modelng, game theory analyss, and multagent systems. A role-based paradgm s ntroduced for characterzng ecosystem enttes n order to easly allow for the evoluton of the ecosystem and duplcated functonalty for enttes. We show how the framework can be used to provde nsght nto value dstrbuton among the enttes and evaluaton of busness model performance under dfferent scenaros. The methods are llustrated usng a case study of a retal busness-to-busness servce ecosystem. INTRODUCTION As busnesses become more and more modularzed, characterzng entty relatonshps and understandng how busness decsons or actons taken by one entty mpact all of the nterrelated enttes, both wthn and among enterprses, becomes a key challenge.,2 Ignorng these nteractons can lead to unexpected and potentally undesrable outcomes. 3 Tools that help to systematcally characterze the busness ecosystem (or network) and analyze the potental mpact of dfferent busness decsons on each entty n the network are essental for mprovng busness desgn. Fgure shows a sample ecosystem assocated wth hosted busness-to-busness (B2B) transacton servces that facltate suppler-retaler collaboratons. In the retal ndustry, transactons between supplers and retalers are usually supported by a B2B transacton system that provdes order management, settlement, sale reportng, nventory control, and other functons to supplers. Currently, many such applcatons are ndependently bult and hosted by retalers. Supplers have to log on to dfferent systems to conduct transactons wth dfferent retalers and pay monthly fees for each system. In an example scenaro, an nformaton technology (IT) hostng servce provder (Company A) plans to Ó Copyrght 2008 by Internatonal Busness Machnes Corporaton. Copyng n prnted form for prvate use s permtted wthout payment of royalty provded that () each reproducton s done wthout alteraton and (2) the Journal reference and IBM copyrght notce are ncluded on the frst page. The ttle and abstract, but no other portons, of ths paper may be coped or dstrbuted royalty free wthout further permsson by computer-based and other nformaton-servce systems. Permsson to republsh any other porton of the paper must be obtaned from the Edtor /08/$5.00 Ó 2008 IBM IBM SYSTEMS JOURNAL, VOL 47, NO, 2008 TIAN ET AL. 0

2 Customer Go to market Revenue Sales, marketng and dstrbuton Servce Subscrpton fee Applcaton marketplace Applcaton module Developer Lcense fee Soluton Lcense fee Vendors Whle our work s applcable to ecosystems havng busness offerngs of dfferent types, ncludng products and servces, we focus our attenton here on servces ecosystems, whch are of specal nterest n servce scence research. Related servce scence research has studed servces ecosystems from the vewpont of nteractons wthn the network, such as socal network analyss, 5 swarm ntellgence, 6 and network horzon, 7 but has not provded an approach for the systematc analyss of busness models for evolvng servces ecosystems. Fgure Hostng servce ecosystem Revenue Testng servce Tester LITERATURE REVIEW In ths secton, we brefly summarze relevant pror work on value network analyss, game theory, and MAS the three areas that underle our proposed busness ecosystem analyss and modelng framework. develop an ntegrated hostng B2B portal for the retal ndustry. For the supplers, the platform elmnates multple logns for transactons wth dfferent retalers and multple monthly fees pad to use many dfferent transacton systems. For the retalers, the system reduces mantenance costs of the noncore transacton system. The IT servce provder may also beneft f the suppler volume s large. To ntate and grow the servce, effectve busness models are essental. Relevant questons nclude how to desgn a revenue-sharng mechansm wth retalers, how to desgn a chargng model for dversfed supplers, and how to form an effectve busness-partner network coverng all the relevant busness actvtes, such as applcaton development, platform operaton, go-to-market, and delvery. Typcal busness-decson-support frameworks assume that the strategc outcome can be defned ndependently of the reactons of other players. At nearly all levels of the decson process, however, nteracton among players s sgnfcant. We present a set of modelng and analyss methods wthn an ntegrated framework to analyze busness-model desgns nvolvng a network of nterconnected enttes. The framework combnes deas from value network analyss 2 for systematc characterzaton of the ecosystem, game theory 3 for descrbng fundamental entty behavor, and multagent systems 4 (MAS) to provde a computatonal approach for analyzng evolvng busness models. Value chan and network A servce ecosystem can be consdered a value cocreaton confguraton of people, technology, shared nformaton, and value propostons connectng nternal and external servce systems. 8 Ths noton s closely related to the value chan and network concept descrbng the tangble (such as goods, servces, and revenue) and ntangble (such as knowledge) transactons between dfferent busness enttes. In the context of a value chan, Allee 9 descrbes relatonshps between busness enttes by three types of value transactons: goods, servces, and revenue; knowledge; and ntangble value. We make use of these deas n developng the characterzaton of a busness ecosystem as presented n ths paper. In value network research, a busness model s typcally used to descrbe the roles and relatonshps of a company, ts customers, partners, and supplers, ncludng the flow of goods, nformaton, and money among these partes and the fnancal benefts for those nvolved. 0, In ths paper, we are usng the term busness model n the same way, whereas we use the term busness network only when referrng purely to the ecosystem network structure. Game theory In tradtonal economc analyss, nteractons among multple busness partners are often evaluated n the context of a global optmzaton problem n whch demand-supply and cost-prce are balanced. In contrast, a game-theoretc approach takes ndvdual 02 TIAN ET AL. IBM SYSTEMS JOURNAL, VOL 47, NO, 2008

3 preferences and hdden nformaton nto consderaton, provdng addtonal nsghts useful for understandng compettve envronments. Game theory has been used to analyze the behavor of multple partners under nformaton asymmetry or a decentralzed decson-makng envronment n supply chans. 2,3 Ths work provdes valuable nsghts nto tactcal plannng n nventory management, order management, prcng mechansm, buyback strategy, lead tme, and product qualty assurance. In ths paper, we adopt game-theoretc technques for the desgn of busness models n a servce ecosystem. Prevous applcatons of game theory to the analyss of a network-based servces envronment nclude, for example, Baron et al., 4 who studed the combnaton of admsson control wth prcng on customer choce of usage level n an IT hostng servce. Multagent systems Classcal game theory s based on explct analytcal methods, makng t dffcult to extend beyond two or three partcpants. However, computatonal methods are typcally used n the context of MAS, that s, loosely coupled networks of problem-solver enttes (agents) that work together to fnd answers to problems that are beyond the ndvdual capabltes or knowledge of each entty. 5 The term MAS s used for all types of systems composed of multple autonomous components that show the followng characterstcs: 6 Each agent has ncomplete capabltes to solve a problem There s no global system control Data s decentralzed Computaton s asynchronous Recent research n the area of desgnng MAS coordnaton mechansms and agent-decson-process modelng 7 has adopted game-theoretc approaches for analyss of MAS. We use a smlar approach here, provdng a game-theoretc approach for busness-model desgn based on computatonal technques adopted from MAS. The next secton provdes the detals of our approach. MODELING AND COMPUTATIONAL TECHNIQUES FOR BUSINESS GAME ANALYSIS As cted n the prevous secton, much work exsts that s applcable to systematc modelng and analyss of busness strateges. However, a number of challenges arse n applyng exstng technologes to servce ecosystems because they tend to be complex, adaptve systems whose confguratons and boundares change over tme. 8 The three man challenges are:. Allowance for evoluton of and duplcaton n roles over the servce lfe cycle Company A may ntally carry out almost all necessary busness actvtes tself, just as many hostng companes dd n the past. As the busness grows, however, Company A may decde to provde an onlne marketplace and hostng platform and collaborate wth busness partners for other busness actvtes. For example, go-to-market (GTM) partners can market and delver the applcatons on the platform whle related solutons can be developed by vendors. However, many busness actvtes reman unchanged no matter whch enttes play the GTM role. A useful busness ecosystem-modelng tool needs to take these common elements nto account to allow for easy reconfguraton of the network structure as the ecosystem evolves. Later, we ntroduce a rolebased modelng approach n the secton Servce ecosystem modelng to accomplsh ths task. 2. Methods for systematc study of nteractons among many busness enttes Tradtonal game theory analyss typcally focuses on nteractons between two or three partes. The MAS approach allows extenson to multple partes. 7 Here, we provde an ntegrated approach to combne MAS and game-theoretc technques wthn the context of a role-based value network model. 3. Valdaton of the ecosystem model and assumptons We propose an approach that ntegrates the collecton and assessment of data wthn the analyss framework to valdate and refne the model and assumptons. We also ncorporate analyss methods that allow for model uncertanty. Modelng and analyss components Based on the analyss requrements outlned above, Fgure 2 shows the archtecture of our proposed system. At the foundatonal level, the servce ecosystem modeler provdes a way to descrbe the servce ecosystem enttes and the relatonshps among IBM SYSTEMS JOURNAL, VOL 47, NO, 2008 TIAN ET AL. 03

4 Servce Analyss Value Dstrbuton Analyss Servce Ecosystem Dynamcs Smulaton Smulaton Engne Servce Ecosystem Modeler Busness Scenaro Edtor Demand Model Busness Plan External Events Servce Performance Analyss Fgure 2 BEAM system archtecture Busness-Model Desgner Partnershp Revenue-Sharng Mechansm Procurement Commtment Role-based Servce Ecosystem Model Entty Behavor Modeler Senstvty Analyss Customer Targetng Prcng Scheme Capablty Allocaton them (that s, the network structure) through the role-based servce ecosystem model. The busness model desgner s a confguraton component used to specfy attrbutes of the enttes n the network accordng to ther roles. These attrbutes pertan to elements of the busness that are under the control of the busness entty; for example, the prce charged for a servce. In contrast, the busness-scenaro edtor defnes scenaros that have an mpact on the busness and are outsde the control of the nvolved partes; for example, external market events and customer demand. In the layer that smulates the dynamcs of the servce ecosystem, the smulaton engne module calculates the statc and dynamc performance of the servce ecosystem, whle the senstvty analyss module evaluates the model results, allowng for uncertanty n model parameters. At the servce analyss layer, the value-dstrbuton analyss module calculates the value of each busness entty under the assumed busness model based on specfed metrcs; for example, fnancal metrcs such as revenue and cost. The servce performance analyss module provdes a more dynamc pcture of how the servce s provded and consumed, as well as the effectveness of busness models n dfferent busness phases. The entty behavor modeler provdes the capablty to model servce demands, customer choces, partner producton, and compettors based on real operatonal data. These results can be used to populate elements of the servce ecosystem modeler, as well as provde addtonal nsghts nto elements of the busness desgn, such as customer targetng, promoton plannng, servce prcng, and servce-capacty plannng. Other analyss modules can be added to ths layer as needed. Servce ecosystem modelng In a busness servce ecosystem, the functons and actvtes of an entty may overlap wth those of other enttes and evolve over tme. If enttes and ther relatonshps are modeled drectly, t wll be dffcult to change them accordng to an evolvng busness model over the servce lfe cycle. The model dagram may also become complex, as there may be many duplcate model elements. We defne a role as an aggregaton of common functons, ncludng actvtes, decsons, and metrcs. In the role-based servce ecosystem model, each busness entty acts as an ndependent agent that plays multple roles accordng the busnessmodel specfcaton and makes decsons based on ts goals, nformaton set, and constrants by sensng envronmental changes. In ths approach, most of the servce ecosystem attrbutes are specfed n the role model. For busness enttes and ther relatonshps, only nformaton that may dffer from one company to another needs to be specfed, such as the cost of an IT archtect. The basc modelng elements of a role-based servce ecosystem busness model are shown n Table. Addtonal detal s gven n the followng sectons. The fundamental mechansm of busness game analyss s the lnkage between resources, actvtes, and decsons. A busness entty performs actvtes, needs nvestment n resources, and has certan performance metrcs. The specfc busness model determnes the decson rghts allocaton and partnershp model for a busness entty. The decsons mpact actvty executon, whch n turn s quantfed by metrcs. Wth such lnkages, the cost of actvty executon and the mpact of decsons on busness metrcs can be calculated. The busness model governs the relatonshp among agents. 04 TIAN ET AL. IBM SYSTEMS JOURNAL, VOL 47, NO, 2008

5 Table Servce ecosystem model elements Resource Class Concept Propertes Such elements as monetary, human capacty, machne, software, and power that can be consumed n the executon of busness actvtes or nvested to realze a servce Owner Unt cost Actvty A task that uses resources Resource consumpton Decson The selecton of a course of acton among varatons, such as prcng and capablty allocaton Objectve Decson varable set Constrant set Related decson varable Metrc Role Busness entty Busness model Performance ndcator of a busness object (actvty, busness entty, or servce ecosystem) A set of connected actvtes and decsons n a servce ecosystem A general term used for enterprses, busness unts, and regulators The roles and relatonshps of a company, ts customers, partners, and supplers, as well as the flow of goods, nformaton, and money among these partes and the fnancal benefts for those nvolved Busness object Value Actvty lst Decson lst Metrcs lst Goals (e.g., payoff functon, rsk atttudes) Demographc propertes (e.g., capacty, sze, and locaton) Partnershp Decson-makng structure Decson-makng mechansm Model elements Detals of some of the model elements are gven n the followng sectons. Decsons Decsons drectly affect busness-actvty executon. For example, decsons on capablty allocaton drectly determne how resources are utlzed n related busness actvty executon. Accordng to game theory, a decson D comprses four parts: an objectve DO, a decson varable set DV,a constrant set DC, and a related decson varable set RDV assocated wth roles that nfluence the objectve of the role of nterest. That s, D ¼ (DO, DV,DC, RDV ). The ratonal decson-makng process can be expressed as an optmzaton problem: max DO ðdv ; RDV Þ DV DC 0: The value proposton evaluaton of the consumer can be modeled as a customer decson. The decson result wll mpact the volume of servce consumpton (an actvty n the metamodel) whch, n turn, has an mpact on the revenue (metrcs n the metamodel). Roles A role s a set of connected actvtes and decsons wthn the servce ecosystem. Each role s descrbed by the followng three propertes: a metrcs lst used to measure the performance of the role, a decson lst that lsts all the decsons the role can make, and an actvty lst that lsts all the busness actvtes played by the role, where actvtes, metrcs, and decsons are each elements of the ecosystem model. Role k s gven as R k ¼ðfA : rða Þ¼R k g; fd : rðd Þ¼R k g; fm : rðm Þ¼R k gþ: Here, r(a ), r(d ), and r(m ) denote the role assocated wth actvty A, decson D, and metrcs M, respectvely. The metrcs can follow a herarchal structure. Some metrcs can also be drectly lnked to a busness actvty or process or to decson varables. For example, the metrc servce revenue s closely related to the prcng-scheme decson process. The value of a metrc s a functon of decson varables and IBM SYSTEMS JOURNAL, VOL 47, NO, 2008 TIAN ET AL. 05

6 lower-level metrcs or metrcs from other roles that nfluence the current role. Role relatonshps In value network research (e.g., Allee 9 ), role relatonshps are usually categorzed nto two types: transfer lnks and nfluence lnks. Transfer lnk descrbes the flow of products, servces, monetary nstruments, or nformaton between roles. Influence lnk descrbes the nteractons n decson makng and the mpacts on metrcs, whch can happen between roles as well as between the enttes n the same role. An example of an nteracton n decson makng s that the prcng scheme of a servce provder causes changes to the volume of procurement and usage on the part of ts servce consumers. An example of metrcs nfluence s that the sales volume of GTM has a drect mpact on the revenue of the platform operator. There are two types of nfluence lnks between decsons. The frst s a drected lnkage from decson D to D j ( 6¼ j), whch, n the language of game theory, represents a Stackelberg game 2 wth D as the leader and D j as the follower. The second nfluence lnk s an undrected lnkage between decson D to D j whch represents a Nash game. As the set of decson ponts grows, the set of possble combnatons of decson nteractons can become complex. For example, there are eght possble combnatons of the two decson lnkage types descrbed. To reduce complexty, we only allow the followng two possbltes n the secondlevel combnaton: n a Stackelberg game, only the follower decson s a Nash game; n a Nash game, each or one of the decson ponts s a Nash game. Thus, even wth further combnatons, only three types of game structures are possble: a pure Nash game wth two or more players, a pure Stackelberg game (one leader, one follower), and a Stackelberg game wth Nash game embedded n the follower part (one leader, multple followers). Busness enttes In desgnng entty relatonshps, n addton to attrbutes based on the role of an entty we allow entty-specfc attrbutes and relatonshps. Entty relatonshps may need to be consdered n the context of certan decsons. For example, two enttes may be n a wn-wn relatonshp wth regard to one servce, but may be competng wth each other n another servce area. In decson makng, the competton n other areas should be consdered. Busness model The busness model specfes characterstcs of the network from three perspectves:. Specfcaton of how roles are played by the enttes. We use a partnershp model where P j 2 ½0; Š denotes the percentage of the role R played by entty E j ; P 2 R M 3 N wth P N j¼ P j ¼ : 2. Specfcaton of the decson-makng structure, ncludng the followng: Decson rghts allocaton. For example, n servce sales, decson rghts allocaton determnes who wll decde the servce prcng scheme: the platform operator or the GTM entty How often the decson can be revsed 3. Specfcaton of specfc busness strateges ncludng the followng: Demand-sde polces, such as customer targetng, prcng scheme, and servce bundlng Supply-sde decsons, such as revenuesharng mechansms Internal process-related decsons such as capacty-allocaton methods that mpact servce performance (examples of capacty are human captal and computatonal resources) Fgure 3 shows a hgh-level metamodel of the servce ecosystem elements and ndcates the relatonshps among the aforementoned model elements. MAS smulaton analyss of servce ecosystem dynamcs We use smulaton to analyze the servce ecosystem dynamcs after the ecosystem model has been specfed. The busness actvty and performance smulaton wll swtch to decson-makng mode f at least one of followng condtons s satsfed: The busness model changes The decson update tme specfed n the busness model s acheved The trgger condton specfed n the decson models s met A related decson s updated from other agents 06 TIAN ET AL. IBM SYSTEMS JOURNAL, VOL 47, NO, 2008

7 c Busness Model c Servce Ecosystem c Decson c Role Play c Busness Entty 0.. Governed Invest Own c Metrcs c Actvty Consume c Resource Fgure 3 Servce ecosystem metamodel The smulaton swtches to performance mode when ecosystem equlbrum s obtaned. However, before smulaton can be conducted, the role-based ecosystem model must be translated nto an MAS model. To acheve ths, the busness model settng and demand model are translated nto the envronment settng of the MAS model. The busness scenaros (for example, external events and decson updates) are generated as system events of the MAS system. Each busness entty corresponds to an agent. The transformaton from entty-based analyss to role-based analyss s accomplshed as follows:. Attrbute generaton: The decson, metrcs, and actvty lst of an entty s the aggregate of the decsons and metrcs of the roles that the entty plays. That s, the decson and metrcs lst of a busness entty nherted from the role model are expressed as E k ¼ðfD : rðd Þ¼R m and P mk 6¼ 0g; fm : rðd Þ¼R m and P mk 6¼ 0gÞ: 2. Decson nteracton generaton: Splt The lnkages between entty decsons are automatcally generated accordng to the relatonshps n the correspondng role model. That s, f there s a lnkage between D and D j n the role model, the same lnkage wll be assumed between the nstances of these decsons n the dfferent enttes. An undrected lnkage (that s, a Nash game) wll be specfed for nstances correspondng to the same decsons. Merge One busness entty may play multple roles. The decson nteracton between two decsons D to D j wthn the same entty E k s merged as a new decson D new. That s, max DV ; DV j P rðdþ;k DO ðdv ; DV j Þ DC 0 DC j 0: þ P rðdj Þ;k DO j ðdv j ; DV Þ and If there s a drected lnkage from D to D j n a to-bemerged decson D new, then for each D k that has an undrected lnkage wth D j, a drected lnkage from D new to D k wll be generated. For each D k that has a drected lnkage to D j,an undrected lnkage from D new to D k wll be generated. If there s the undrected lnkage from D to D j n a to- IBM SYSTEMS JOURNAL, VOL 47, NO, 2008 TIAN ET AL. 07

8 Role-based model Role Role After splttng Entty After merge Entty Entty 2 Entty Fgure 4 Decson nteracton transformaton Entty 2 Entty Role 3 Role 4 be-merged decson D new, the lnkage wll be retaned between D k and D new, where D k denotes the prevous decson that had the lnkage (drected or undrected) wth D or D j. A smple example s shown n Fgure 4 to llustrate the process Accordng to the assumptons on game structure above, the possble game structures n ths entty model are: A pure Nash game A pure Stackelberg game (one leader, one follower) A Stackelberg game (one leader) wth a Nash game embedded n the follower part (multple followers) A Stackelberg game wth multple leaders A Stackelberg game wth multple leaders wth a Nash game embedded n the follower part (multple followers) Snce the number of layers n these Stackelberg games s no more than two, the game computaton can be transformed to an optmzaton problem. More complex scenaros can be addressed usng computatonal game theory research. 8 Servce analyss Dfferent types of servce analyss can be performed for a specfed busness model at a specfc pont n tme. For nstance, the calculaton procedure to obtan value metrcs (such as revenue, costs, and cash flow) for each entty s shown n Fgure 5. CASE STUDY In ths secton, we analyze the retal B2B servce ecosystem ntroduced at the begnnng of ths paper. We focus our analyss solely on the perspectve of Customer volume Servce utlty Actvty volume Resource consumpton per actvty nstance Revenuesharng mechansm Investment Resource consumpton of an actvty Role defnton (Actvtes by a role) Resource requrement of a role Role model (All the roles by an entty) Cost related wth role change Resource requrement of an entty Unt resource cost of an entty Cost of an entty Prcng model Total revenue Revenue of a role Revenue of an entty Fgure 5 Value dstrbuton calculaton Temporary calculaton results Data from the system ecosystem model 08 TIAN ET AL. IBM SYSTEMS JOURNAL, VOL 47, NO, 2008

9 Table 2 Role model of retal B2B servce ecosystem Category Role Hgh-level Actvty Potental Player (Busness Entty) Decson Servce consumer Servce provder Compettor GTM Delvery Support Platform operator Content provder Servce subscrpton Suppler and retaler Subscrbe decson (subscrbe or Servce usage not, module subscrbed to) Payment Prcng scheme selecton Call for support Servce marketng Company A Jon or not? Servce sales and contractng Busness partner Customer targetng Retaler Marketng effort and staff allocaton Prcng scheme Applcaton confguraton Company A Jon or not? Applcaton mplementaton Busness partner Confgurablty Applcaton upgrade Staff allocaton Call center envronment setup Company A Jon or not? Onlne support Busness partner Support servce level On-ste support Support resource allocaton Platform hardware and software mplementaton System operaton and mantenance System upgrade Company A Busness partner (especally telecom platform operator) Partnershp Prcng scheme Revenue sharng Content qualty control Computaton capacty allocaton Soluton development Company A Jon or not? Soluton bug fxng Independent software Content qualty vendors Development staff allocaton All the actvtes n the servce provder category Retalers exstng B2B applcaton B2B platform developed by others who come later All the decsons n the servce provder category Company A, the soluton provder, but smlar technology may also be appled to analyze other busness enttes. Servce ecosystem modelng The roles and potental players and ther decsons for ths ecosystem are lsted n Table 2. The potental nteractons between the decson makers are shown n Fgure 6. Even n ths smple case, nteractons between decson makers quckly become complcated. In the next secton, we study how envronmental factors nfluence busness model selecton by usng a game-theoretc approach wthn the context of our proposed framework. Prcng-decson rghts allocaton We consder three decson varables mportant n a GTM strategy: servce prce to consumer p, nternal revenue sharng rate b, and sale volume Q, and nvestgate the mpact of prcng-decson rghts allocaton on these decson varables under dfferent scenaros. Assume that the assgnment of prcng rghts s cost free. The revenue of the platform operator R P can be denoted by R P ¼ð bþp Q: Denotng unt marketng cost as c, the revenue of the GTM party R G s gven as R G ¼ðb p cþq: The wllngness of consumers to subscrbe usually IBM SYSTEMS JOURNAL, VOL 47, NO, 2008 TIAN ET AL. 09

10 GTM Jon or not? Servce Consumer Customer targetng Marketng effort Prcng scheme Platform Operator Partnershp Subscrpton decson Delvery Prce scheme selecton Revenue-sharng mechansm Content Provder Jon or not? Prce scheme selecton Jon or not? Confgurablty Content qualty control Computatonal capacty allocaton Content qualty Staff allocaton Staff allocaton Support Jon or not? Servce level Resource allocaton Fgure 6 Interactons between decson makers decreases wth p, and unt marketng cost s closely related to the rato of sale volume Q to real demand. Here assume that the unt marketng cost c s gven as Q cðp; QÞ ¼a D o b o p ¼ Q D bp : Here, D o denotes the extreme subscrpton volume when the servce s free, b o s the consumer prce elastcty coeffcent, and a s the cost parameter. Here, D ¼ D o /a, b ¼ b o /a. We frst consder the scenaro n whch servce consumers are homogeneous and we then compare that scenaro to one n whch there are two types of servce consumers. The thrd scenaro examnes nteractons among the enttes of the GTM roles. Homogeneous consumer scenaro Ideal case (centralzed decson makng) If all the decsons are made by one entty, the optmal decsons are found to be p ¼ 2 3b D; Q ¼ 9b D2 : The total revenue s R ¼ D3 27b : 2 Case (prcng rght allocated to platform operator) Ths case follows a Stackelberg game, that s maxð bþp Q pb Q ¼ arg max Q b p Q Q: D bp The equlbrum decsons under ths case are p P ¼ 2 3b D; QP ¼ 8b D2 ; b P ¼ =2: The revenues of the platform operator R P P and GTM entty R P G ; are gven as 0 TIAN ET AL. IBM SYSTEMS JOURNAL, VOL 47, NO, 2008

11 R P P ¼ D3 54b ; 2 RP G ¼ D3 08b : 2 Case 2 (prcng rght allocated to GTM) For ths case, the GTM entty s responsble for the servce prcng. Thus the optmzaton problem s changed to max ð bþp Q b ðp ; Q Þ¼arg max p;q b p Q Q: D bp It can be shown that the equlbrum decsons under ths case are the same as for Case. The comparson between the two dfferent decson structures and the deal centralzed case shows: Double margnalzaton 9 from the decentralzed decson makng causes the neffcency of the servce ecosystem When consumers are homogeneous, allocaton of prcng rghts to the platform operaton or GTM gves equvalent results Heterogeneous consumer scenaro For smplfcaton, assume there are two types of consumers. Group 0 s an nsenstve group wth prce senstvty coeffcent as b 0 ¼ ( c)b (c 2 [0, 0.5]); Group s more senstve, wth prce senstvty coeffcent as b ¼ ( þ c)b. However, the prce senstvty heterogenety s nvsble to the platform operator. Assume the sze of consumers under ths scenaro s the same as that for the homogeneous scenaro and the sze of these two groups s equal when the servce s free. That s, the marketng cost for the two groups s gven as c ðp; QÞ ¼ 2Q ð ¼ 0; Þ: D b p Case (prcng rght allocated to platform operator) Because the platform operator has nformaton only on the average senstvty coeffcent b, the problem can be expressed as max p;b ð bþp ðq þ Q 2 Þ Q ¼ arg max b p 2Q Q ð ¼ 0; Þ: Q D b p The equlbrum decsons for ths case are p P ¼ 2 3b D; bp ¼ =2; Q P 0 ¼ þ 2c 36b D2 ; Q P 2c ¼ 36b D2 : The revenues of the platform operator and GTM are gven as R P P ¼ D3 54b ; 2 RP G ¼ D3 08b : 2 Case 2 (prcng rght allocated to GTM) In ths case, the GTM entty can gve dfferentated prces to dfferent consumer groups. Let max b X 2 ¼ ð bþp Q ðp ; Q Þ¼arg max b p 2Q Q ð ¼ 0; Þ: p ;Q D b p The equlbrum results for ths case are b G ¼ =2; p G ¼ 2 D; Q G ¼ D 2 ð ¼ 0; Þ: 3b 36b The revenues of the platform operator and GTM are gven as R G P ¼ þ c2 ð c 2 Þ 2 D 3 54b ; 2 RG G ¼ þ c2 ð c 2 Þ 2 D 3 08b : 2 Ths scenaro shows that under nformaton asymmetry and customer heterogenety, the prcngdecson rght should be gven to the role that has the customer nformaton. Heterogeneous consumer scenaro wth multple GTM enttes Based on the prevous scenaro, we assume there are two dentcal busness enttes takng a GTM role. Denote p k ð; k 2 f 0; gþ as the prcng to customer group by GTM entty k. Denote Q k ð; k 2 f 0; gþ as the sale volume to customer group by GTM entty k. The marketng cost to customer group by GTM entty k, c k, can be gven as c k ðpk ; Qk ; p k ; Q k Þ¼ 2 X Q m m¼0 X p m m¼0 D b 2 ð; k 2 f0; gþ: IBM SYSTEMS JOURNAL, VOL 47, NO, 2008 TIAN ET AL.

12 Case (prcng rght allocated to platform operator) The game form can be gven as X maxð bþp p;b Q 0 Q ¼ arg max Q 0 ¼ arg max Q ;k¼0; Q k b p 2Q0 þ 2Q! Q 0 D b p b p 2Q0 þ 2Q D b p! Q ð ¼ 0; Þ; where n addton to a Stackelberg game between roles (that s, the platform operator and GTM) there s also a Nash game between the enttes of the GTM role. The equlbrum decsons of ths case are p P ¼ 2 3b D; bp ¼ =2; Q mðgþ 0 ¼ þ 2c 54b D2 ; Q mðgþ 2c ¼ 54b D2 ðm ¼ 0; Þ: The revenues of the platform operator and GTM are gven as R P P ¼ 2D3 8b 2 ; RP G ¼ RP G2 ¼ D3 243b 2 : Case 2 (prcng rght allocated to GTM) In ths case, GTM can gve dfferent prces to dfferent consumer groups: max b X 2 ¼ ðp 0 ðp ð bþ X ;k¼0; ; Q 0 Þ¼arg max p 0 ;Q0 ; Q Þ¼arg max p ;Q p k Q k The equlbrum of ths case s b p 0 2Q0 þ 2Q! D b 2 ðp0 þ p Þ! b p 2Q0 þ 2Q D b 2 ðp0 þ p Þ b G ¼ =2; p mðgþ ¼ 3 4b D; Q mðgþ ¼ 64b D 2 Q 0 Q ð ¼ 0; Þ: ð; m ¼ 0; Þ: The revenues of the platform operator and GTM are gven as R G P ¼ þ c2 ð c 2 Þ 2 3D 3 28b ; 2 R G G ¼ RG G2 ¼ þ c2 ð c 2 Þ 2 D 3 256b 2 : When c, 0.02, the performance of case s better than that of case 2. Ths means that when there are multple GTM players and consumers are not so heterogeneous, t s better for the platform operator to do the servce prcng. When c. 0.02, the concluson s the same as shown n the heterogeneous consumer scenaro above. Compared wth the heterogeneous consumer scenaro, the exstence of multple GTM enttes mples that servce prcng and sales volume are a lttle hgher than for sngle GTM enttes because each GTM entty acts ndependently n decson makng. The platform operator benefts more from ths scenaro, whle the payoff for the GTM role declnes. Ths comparson of three smple scenaros shows that the performance of decson rghts allocaton s senstve to envronmental factors. Although results for these smple scenaros could be worked out analytcally, smulaton technology s needed to evaluate results for more complex scenaros encountered n practce. SUMMARY AND DIRECTIONS FOR FUTURE RESEARCH An mportant part of modelng and analyss of a servce ecosystem s to capture the dynamc nteractons among ecosystem busness enttes. Ths paper presents a comprehensve framework to ntegrate busness ecosystem modelng and analyss capabltes usng MAS as the computaton framework, game theory as the fundamental entty behavor model, and value network modelng as a systematc modelng method. A case study of a retal B2B servce platform demonstrated how actual system performance can dffer from ntuton, whch does not take nto account the effects of nteractons among busness enttes. The technques presented here are not lmted to servce ecosystem analyss, but may also be appled n other busness ecosystems, such as supply chan analyss. A few lmtatons reman n the proposed approach. At a modelng level, certan factors that dstngush servces from products are not fully consdered. For example, the smultanety of producton and 2 TIAN ET AL. IBM SYSTEMS JOURNAL, VOL 47, NO, 2008

13 consumpton 20 for a servce and ts potental mpact on the servce dynamcs s not nvestgated. In the smulaton, the decson-makng nteracton across herarchcal levels of a servce ecosystem should also be consdered. For example, what effect does the decson of an enterprse have on the decson makng of a department wthn the enterprse? At the analyss level, more work s requred to develop a holstc approach that can easly allow for dfferent perspectves and dfferent lfe-cycle stages of the model elements, as well as allow for a larger number of decson-nteracton combnatons. CITED REFERENCES. E. van Heck and P. Vervest, Smart Busness Networks: How the Network Wns, Communcatons of the ACM 50, No. 6, (2007). 2. J. Peppard and A. Rylander, From Value Chan to Value Network: Insghts for Moble Operators, European Management Journal 24, No. 2, 28 4 (2006). 3. F. Erhun and P. Kesknocak, Game Theory n Busness Applcatons, Workng Paper, Stanford Unversty, Stanford, CA (2003), GT.pdf. 4. N. R. Jennngs, K. Sycara, and M. Wooldrdge, A Roadmap of Agent Research and Development, Autonomous Agents and Mult-Agent Systems, No., 7 38 (998). 5. M. Ianst and R. Leven, The Keystone Advantage: What the New Dynamcs of Busness Ecosystems Mean for Strategy, Innovaton, and Sustanablty, Harvard Busness School Press, Boston (2004). 6. E. Bonabeau and C. Meyer, Swarm Intellgence: A Whole New Way to Thnk About Busness, Harvard Busness Revew, 07 4 (May 200), antoptma.com/admn/pdfrassegna2/pdf028.pdf. 7. D. W. van Lere, Network Horzon and the Dynamcs of Network Postons: A Mult-Method Mult-Level Longtudnal Study of Interfrm Networks, Ph.D. thess, RSM Erasmus Unversty, Rotterdam, Netherlands (May 2007). 8. J. Spohrer, P. P. Maglo, J. Baley, and D. Gruhl, Steps Toward a Scence of Servce Systems, IEEE Computer 40, No., 7 77 (2007). 9. V. Allee, Reconfgurng the Value Network, Journal of Busness Strategy 2, No. 4, (2000). 0. H. Chesbrough and R. S. Rosenbloom, The Role of the Busness Model n Capturng Value from Innovaton: Evdence from Xerox Corporaton s Technology Spn-off Companes, Industral and Corporate Change, No. 3, (2002).. G. Camponovo and Y. Pgneur, Busness Model Analyss Appled to Moble Busness, Proceedngs of the 5th Internatonal Conference on Enterprse Informaton Systems, Angers, France (2003), pp A. A. Tsay, S. Nahmas, and N. Agrawal, Modelng Supply Chan Contracts: A Revew, n Quanttatve Models for Supply Chan Management, S. Tayur, R. Ganeshan, and M. Magazne, Edtors, Kluwer Academc Publshers, Norwell, MA (999), pp G. P. Cachon and S. Netessne, Game Theory n Supply Chan Analyss, n Handbook of Quanttatve Supply Chan Analyss: Modelng n the E-Busness Era, D. Smch-Lev, S. D. Wu, and Z.-J. M. Shen, Edtors, Sprnger ScenceþBusness Meda, New York (2004), pp O. Baron, D. Beyer, and G. R. Btran, Prcng of Shared Computer Servces, Journal of Revenue and Prcng Management 4, No., (2005). 5. M. Wooldrdge and N. R. Jennngs, Intellgent Agents: Theory and Practce, Knowledge Engneerng Revew 0, No. 2, 5 52 (995). 6. E. H. Durfee, V. R. Lesser, and D. D. Corkll, Trends n Cooperatve Dstrbuted Problem Solvng, IEEE Transactons on Knowledge and Data Engneerng, No., (989). 7. S. Parsons and M. Wooldrdge, Game Theory and Decson Theory n Mult-Agent Systems, Autonomous Agents and Mult-Agent Systems 5, No. 3, (2002). 8. M. Kearns, M. L. Lttman, and S. Sngh, Graphcal Models for Game Theory, Proceedngs of the 7th Annual Conference on Uncertanty n Artfcal Intellgence, Seattle, WA (200), pp J. J. Spengler, Vertcal Restrants and Anttrust Polcy, Journal of Poltcal Economy 58, No., (950). 20. P. N. O Farrell and L. A. R. Moffat, An Interacton Model of Busness Servce Producton and Consumpton, Brtsh Journal of Management 2, No. 4, (99). Accepted for publcaton July 0, Publshed onlne February 2, Chun Hua Tan IBM Chna Research Laboratory, Buldng 9, Zhouguancun Software Park, 8 Dongbewang West Road, Hadan Dstrct, Bejng, P.R.C (chtan@cn.bm.com). Dr. Tan s an advsory research and development engneer. He holds a Ph.D. degree n ndustral engneerng from Tsnghua Unversty. Hs current research nterests nclude system dynamcs modelng and smulaton, logstcs and supply chan management, and rule-based optmzaton. Bonne K. Ray IBM Chna Research Laboratory, Buldng 9, Zhouguancun Software Park, 8 Dongbewang West Road, Hadan Dstrct, Bejng, P.R.C (bonner@cn.bm.com). Dr. Ray holds a Ph.D. degree n statstcs from Columba Unversty and has been a research staff member at IBM snce 200. She s currently program manager, data and analytcs, at IBM Chna Research Laboratory. Her area of expertse s appled statstcs, wth a partcular nterest n the use of statstcs for busness analytcs. Dr. Ray s a Fellow of the Amercan Statstcal Assocaton, and currently serves as an assocate edtor for the Journal of Computatonal and Graphcal Statstcs and for the publcaton Technometrcs. Juhnyoung Lee IBM Research Dvson, Thomas J. Watson Research Center, P.O. Box 28, Yorktown Heghts, NY 0598 (jyl@us.bm.com). Dr. Lee s a research staff member. He holds B.S. and M.S. degrees from Seoul Natonal Unversty, and a Ph.D. degree from the Unversty of Vrgna, all n computer scence. Hs current research nterests nclude cost and value estmaton n servce engneerng and management, busness and IT modelng, model-drven busness transformaton, and semantc Web. IBM SYSTEMS JOURNAL, VOL 47, NO, 2008 TIAN ET AL. 3

14 Rongzeng Cao IBM Chna Research Laboratory, Buldng 9, Zhouguancun Software Park, 8 Dongbewang West Road, Hadan Dstrct, Bejng, P.R.C (caorongz@cn.bm.com). Dr. Cao s a research staff member. He holds a B.S. degree n ndustral engneerng and computer scence, and M.S. and Ph.D. degrees n management scence, all from the Harbn Insttute of Technology. Hs current research nterests nclude busness process modelng and analyss and logstcs optmzaton and smulaton. We Dng IBM Chna Research Laboratory, Buldng 9, Zhouguancun Software Park, 8 Dongbewang West Road, Hadan Dstrct, Bejng, P.R.C (dngw@cn.bm.com). Dr. Dng s the manager of the Busness Optmzaton team at IBM Chna Research Laboratory. She receved her Ph.D. degree n management scence from Harbn Insttute of Technology. Her research nterests nclude optmzaton technology, busness value analyss, portfolo management, contnual busnesstransformaton technology, logstcs, and graph-theory-based model checkng. & 4 TIAN ET AL. IBM SYSTEMS JOURNAL, VOL 47, NO, 2008