704 Technological Challenges in E-Collaboration and E-Business ness processes poses enormous opportunity for value creation in the supply chain and enhances SCM practices (Horvath, 2001). PROCESS AND SYSTEM ALIGNMENT AND INTEGRATION: ISSUES AND OPTIONS Integration refers to collaborative planning and control, decision integration, information integration, and business process integration EHWZHHQ LQWHU¿UP SDUWQHUV XVLQJ LQIRUPDWLRQ technologies and systems. The technological side of the integration is crucial to e-collabora- tion. For example, the complexity of integration required by e-marketplaces is one of the big problems that have been attributed to the sharp decline in the number of e-marketplaces. Today, most companies have implemented enterprise resource planning (ERP) systems to automate their back-end planning and scheduling processes and to undertake internal IT integration to meet the needs of multiple vendors and customers for years. B2B software has allowed IT integration across companies with different IT platforms. But there is limited application of Web technologies to the rest of the procurement process. There is generally a lack of real-time supply and demand L QIR U PD W L R Q À RZ V D P R Q J V W V X S SO\ F K D L Q S D U W QH U V which results in inaccurate planning leading to either inventory shortages or excessive invento- ries. Therefore, system integration and alignment becomes paramount to e-collaboration, and thus affects directly bottom line results. E-business provides organizations with oppor- tunities to align their processes for e-collabora- tion to attain success. However, technology-wise, interoperability requires enhancement of existing V\VWHPVWRWUDQVIHUWKHPLQWRDFURVV¿UPPRGH Electronic supply chain requires integration of software platforms or open systems across the entire network. Integrating processes and systems is paramount to a seamless link with partnering companies. For example, successful implementa- tion of electronic data exchange (EDI) requires realignment of work processes and systems within the network of e-collaboration. However, accord- ing to Lowson and Burgess’ (2003) study, many organizations, particularly small to medium-sized enterprises (SMEs), have not taken on, or have a limited use of, EDI and other interorganizational systems (IOS) to integrate their supplier pro- cesses, operations processes, and sales processes, because they are often not able to undertake the cost of technologies and the management systems integration. E-collaboration often requires the reengineer- ing of business processes across companies, which is very expensive in terms of time, capital, and human resources. As one former supply chain executive explained, it took major collaboration efforts and 12-18 months to implement business process reengineering between just two trading partners (Davis & Spekman, 2004). In addition, system integration and alignment should take into account the diversity of e-partners. There is KDUGO\DRQHVL]H¿WVDOOVROXWLRQIRUDOOSDUW QHUV Take Sun Microsystems, for example. Sun has employed three main Web technologies in its e- network: connected ERP systems, B2B e-market- places, and Webstores. The company enables its large partners to directly place their orders in its ERP systems. Other partners have the options to choose the e-business application that suits them EHVW7KHÀH[LELOLW\RIHEXVLQHVVDSSOLFDWLRQVWKDW Sun provide facilitates system integration and alignment in an optimal way. In a recent study, de Man and der Zee (2002) suggested that there were a number of technological lessons learned in the process of starting and building Web ap- plications in e-collaboration. These included that systems should never be forced upon partners, DQGWKDWFKDQQHOFRQÀLFWVVKRXOGEHDYRLGHGE\ selecting the right e-business application for each partner and client group. Internal systems should be changed to cater for the requirements of e- QHWZRUNDQG¿QDOO\WKHRYHUDOOSURFHVVVKRXOG 705 Technological Challenges in E-Collaboration and E-Business be guided by the concepts of standardization, KDUPRQL]DWLRQDQGVLPSOL¿FDWLRQ Establishing e-business process standards is DQRWKHULVVXHIRULQWHU¿UP%%LQWHJUDWLRQZKRVH objective is to meet the needs of global supply chains. RosettaNet (2004) has been successful in providing a common language for B2B transac- tions and in building integrative e-business pro- cesses among partners within the global trading network. RosettaNet standards that have been used E\)RUWXQHFRPSDQLHVZRUOGZLGH³SUHVFULEHKRZ networked applications interoperate to execute collaborative business process.” There are numerous companies that specialize in providing business-to-business integration, synchronization, and collaboration solutions. Global eXchange Services (GXS) is one of lead- HUVLQWKH¿HOG*;6KDVGHVLJQHGDVHWRIVROX- WLRQVFDOOHGWKH³([WHQGHG9DOXH&KDLQ´WRKHOS streamline cross-enterprise business process. The Extended Value Chain consists of four key layers: transaction, monitoring, synchronization, and collaboration, enabling companies to: • Transact information with their trading partners by enabling the transmission of information regardless of protocol (e.g., TCP/IP, EDI, XML, etc.) • Monitor their operations by providing vis - ibility and analytics into the movement of information between enterprise • Synchronize business processes by enabling their integration, automation and optimiza- tion • Collaborate using solutions that leverage cross-enterprise business processes in real WLPH*UHHQ¿HOG Cisco System is often cited as a successful example of seamless integration throughout its supply chain operating systems with its partners (Davis & Spekman, 2004). The integration consists of three parts: (1) planning, control, and design integra- tion, (2) information integration, and (3) business process integration. Planning, control, and design integration mainly concerns making collabora- tive decisions regarding inventory replenishment, and collaborative product development. As the name suggests, information integration refers to the sharing of forecast data, inventory data, customer order, and status information, but it also includes system application integration with trading partners. Business process integration involves allowing partners to access ERP sys- tem and MRP processes, automation of routing of EDI data to supplier partners, automation of FURVV¿UPEXVLQHVVSURFHVVHVDQGUHDOWLPHÀRZ of customer orders to all partners. FUTURE TRENDS As e-commerce and e-business practices will continue to grow, e-collaboration will be more mature (rather than experimental) in nature, in terms of the scope, quality, and credibility of on- line customer services and products. Participating in e-collaboration will be part of every executive’s job in the near future. In terms of supply chain network integration, McCormack et al.’s study shows that most industry supply chains today have not reached the stage at which information and system integration is in place to build a supply chain network (McCormack, Johnson, & Walker, 2003). Full network integration—that is, all key business processes being online and being aligned within the network—will be the next step that organizations need to take to gain competitive advantage over other supply chain networks. E- collaboration in supply chains or virtual supply chains will become a critical part of the future supply chain landscape. Collaboration amongst virtual manufacturers, virtual distributors, vir- tual retailers, and virtual service providers will dominate the virtual supply chains. E-business infomediaries will leverage the Internet to perform 706 Technological Challenges in E-Collaboration and E-Business matching of products and buyers or coordinate marketing and transaction processes in e-col- laborations. E-collaboration is, and will continue to be, the key to sustained business success. An e-business strategy will be ineffective without an integrated e-collaboration strategy, because the ability to leverage collaborative relationships becomes es- sential in today’s competitive e-business world. Consumer/purchaser power will dominate the e-business world and propel smaller e-busi- nesses to collaborate to provide customers with an ever-widening array of products and services, real-time and rich information, and speedy and quality transactions. Moreover, e-collaboration helps streamline the product-to-market process through collaborative planning and design, im- SURYH HI¿FLHQF\ IURP WKH FKDQQHO QHWZRUN E\ reducing inventories, and ultimately generate SUR¿WDELOLW\=KDR CONCLUSION (FROODERUDWLRQ UHTXLUHV LQWHU¿UP EXVLQHVV DU- chitecture, including the reengineering of the processes that link companies to their channel trading partners and the development of a col- laborative community of trading partners. It also requires closely integrated databases and closely V\QFKURQL]HG LQIRUPDWLRQ ÀRZV WR HOLPLQDWH GLVWRUWLRQVDQGWKH³EXOOZKLS´HIIHFWLQWKHFRP- munication of information between supply chain partners. E-application architecture is imperative WRWKHFROODERUDWLRQDQGLQYROYHV³GHWHUPLQLQJ individual integration points between the applica- tion and data sources, the application and back-end installed software, and between multiple back-end systems” (Hoque, 2001, p.153). This article has demonstrated that information technologies have greatly expanded the way companies do business and partners interact with each other. The value and the prospects that e-collaboration strategy FDQJHQHUDWHIRUEXVLQHVVDUHFRPSHOOLQJ¿UPVWR adopt e-collaboration technologies and systems into their business processes. However, technol- ogy integration and interoperability issues can be complex. For example, data synchronization using XML can be a formidable task in the transforma- tion process because there are many different data and alert types, and the published XML-based standards do not cover all possible collaboration data. The article highlights many implementation issues regarding technology adoption. REFERENCES Damanpour, F. (2001). E-business e-commerce evolution: Perspectives and strategy. Managerial Finance, 27(7), 16-32. Davis, E. W., & Spekman, R. E. (2004). The extended enterprise: Gaining competitive advan- tage through collaborative supply chains. Upper Saddle River, NJ: Prentice Hall. De Man, A. P., & der Zee, H. V. (2002). Strate- gies for e-partnering: moving brick-and-mortar online. Groningen: Gopher Publishers. *UHHQ¿HOG*GXS: Enabling tomorrow’s solutions today. Retrieved July 10, 2004, from www.gxs.com. Hoque, F. (2001). E-enterprise: Business models, architecture, and components. Cambridge, MA: Cambridge University Press. Horvath, L. (2001). Collaboration: The key to value creation in supply chain management. Supply Chain Management: An International Journal, 6(5), 205-207. Interoperability best practices: The ongoing problems of sharing engineering data. (2004). Strategic Direction, 20(5), 31-33. 707 Technological Challenges in E-Collaboration and E-Business Kersten, W., Schroeder, A. K., & Schulte-Bisping, A. (2004). Internet-supported sourcing of complex material. Business Process Management Journal, 10(1), 101-114. Lee, H. L., & Whang, S. (2002). Supply chain integration over the Internet. In J. Genunes et al. (Eds.), Supply chain management: Models, applications, and research directions (pp. 3-18). Bordrecht: Kluwer Academic Publishers. Lowson, R. H., & Burgess, N. J. (2003). The build- ing blocks of an operation strategy of e-business. The TQM Magazine, 15(3), 152-163. McCormack, K. P., Johnson, W. C., & Walker, W. (2003). Supply chain networks and business process orientation: Advanced strategies and best practices. New York: St. Lucie Press. Neef, D. (2001). E-procurement: From strategy to implementation. Upper Saddle River, NJ: Prentice-Hall. Prawel, D. (2003). Interoperability best practices: Advice from the real world. Paper presented at the TCT 2003 Conference, NEC, UK. RosettaNet. (2004). Dynamic trading networks. 2SHUDWLRQDOHI¿FLHQF\1HZEXVLQHVVRSSRUWX- nities. Investment protection (p. 6). California: The Author. Ross, D. F. (2003). Introduction to e-supply chain management: Engaging technology to build mar- ket-wining business partnerships. Boca Raton, FL: St. Lucie Press. Zhao, F. (2004), E-partnerships and virtual or- ganizations: Issues and options. In M. Singh & D. Waddell (Eds.), E-business: Innovation and change management (pp.105-119). Hershey, PA: Idea Group Publishing. Zhao, F. (2006). Maximize business profits through e-partnerships, Hershey, PA: Idea Group Publishing. KEY TERMS E-Collaboration: E-collaboration refers to the use of the Internet and/or Internet- based tools among business partners beyond market transactions. The term is often used in the context of supply chain, in particular, in supply-buyer relationships. E-Partnership: Theoretically, e-partnership refers to a business partnership relying on elec- tronic (information) technologies to communicate and interact amongst partners. As e-business has become an integral part of most business practices where consumers, suppliers, buyers are connected by information technologies, the term e-partnership is mostly associated with electronic commerce partnerships, and in a broader sense, electronic business partnerships. E-SCM (E-Supply Chain Management): E-SCM as the latest advance of SCM has two pillars: the emerging strategic capabilities of SCM and the Web technologies that empower SCM. E-SCM aims to foster agile organizations and supplier-buyer partnerships. E-Supply Cchain Interoperability: The ability to be fully compatible and capable of being integrated with each other in e-business supply chain. Informediary: As the name suggests, info- mediaries specialize in information management, collecting and storing customer information and FRQWUROOLQJ WKH ÀRZRIFRPPHUFH RQWKH:HE Yahoo! is one of the most popular and powerful infomediaries in the world. Integration: Integration refers to collabora- tive planning and control, decision integration, information integration and business process LQWHJUDWLRQ EHWZHHQ LQWHU¿UP SDUWQHUV XVLQJ information technologies and systems. RosettaNet Standards: RosettaNet stan- dards prescribe how networked applications 708 Technological Challenges in E-Collaboration and E-Business interoperate to execute collaborative business process. They provide a common language for B2B transactions and assist in building integrative e-business processes among partners. RosettaNet standards consist of a three-level business process DUFKLWHFWXUHIRULQWHUDFWLRQEHWZHHQLQWHU¿UP e-partners: (i) partner interface processes, (ii) RosettaNet dictionaries, including the Master Dictionary which contains over 6000 common terms and processes, and grammar that describes how systems communicate, and (iii) RosettaNet implementation framework (RNIF). This work was previously published in Encyclopedia of E-Collaboration, edited by N. Kock, pp. 606-611, copyright 2008 by Information Science Reference (an imprint of IGI Global). 709 Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 3.3 Econometric Simulation for E-Business Strategy Evaluation Lidan Ha Coppin State University, USA Guisseppi Forgionne University of Maryland, Baltimore County, USA ABSTRACT (IIHFWLYH DQG HI¿FLHQW e-business strategy de- velopment is crucial to achieve a competitive advantage in the electronic marketplace. How- ever, e-business strategy evaluation constitutes complex and dynamic challenges for business management. This paper offers assistance for the evaluation process by applying a computer simu- lation that uses an econometric model delivered through a decision making support system. The major econometric simulation logic and method- ology introduced here covers multidisciplinary DUHDVDQGLVDPRQJWKH¿UVWDWWHPSWVWRLGHQWLI\ and establish a comprehensive, quantitative tool to support the strategy development processes of e-businesses. The authors hope to shed some lights on e-business strategy research through this paper. INTRODUCTION While bringing new opportunities (Ghosh, 1998; Huizingh, 2002; Lumpkin & Dess, 2004; Udo & Marquis, 2001/2002) to organizations, the devel- opment of Internet and Web-based technologies also presents challenges (Hoffman, 2000; Laudon & Laudon, 2004; Porter, 2001). To many compa- QLHVLWLVVWLOOQRWFOHDUKRZWREHQH¿WIURPWKH Web and other digital technologies (Lumpkin & Dess, 2004). Business strategy plays an increas- ingly important role in order for companies to achieve a competitive advantage in the Internet age (Porter, 2001; Raisinghani & Schkade, 2001). However, the new and dynamic nature of e- businesses makes strategy development a tough mission for companies. Comprehensive and ef- fective tools are needed to deal with this problem by supporting the strategy making processes of e-businesses. 710 Econometric Simulation for E-Business Strategy Evaluation E-business has attracted a lot of attention Z K H Q O R RN L Q J D W G L I IHU H Q W ¿H OG VH J P D QD J H P H QW science, economics, econometrics, management, and information science), The major related re- search on e-business decision-making support includes areas like strategy studies (Hambrick & Fredrickson, 2001; Kim, Nam, & Stimpert, 2004; Li & Chang, 2004; McGrath & Heiens, 2003; Podgainy, 2001; Rayport & Sviokla, 1996; Rohm & Sultan, 2004), e-business models (Afuah, 2004; Applegate, Austin, & McFarlan, 2003; Hayes & Finnegan, 2005; Lam & Harri- son-Walker, 2003; Lumpkin & Dess, 2004; Scott & Scott, 2004), business process reengineering (Greasley, 2003; Gunasekaran & Kobu, 2002; Hengst & De Vreede, 2004; Jang, 2003; Kim & Ramkaran, 2004; Vuksic, Stemberger, & Jaklic, 2001), supply chain management (Laudon & Laudon, 2004; Swaminathan & Tayur, 2003), distributional channel management (Anderson & Day, 1997; Chiang, Chhajed, & Hess, 2003; Dykstra, 2001; Kumar, 2000; Rohm & Sultan, 2004), and customer relationship management (Hellier, Geursen, Carr, & Rickard, 2003; Kohli, Devaraj, & Mahmood, 2004; Peppard, 2000; Wilson, Daniel, & McDonald, 2002). With business strategy becoming increasingly important (McGrath & Heiens, 2003), several researchers present their work on how to establish successful e-business strategies. However, these studies are mostly qualitative in nature. What they mainly provide is a high-level explanation of major decision factors and their relationships toward a company’s performance. The factors, relationships, and their organizational effects usually are not measured and evaluated quan- titatively. Further, research in the management ¿HOG%HVDQNR'UDQRYH6KDQOH\6FKDHIHU 2004; David, 2003; Hambrick & Fredrickson, 2001; Robbins & Coulter, 1999) points out the importance of support for top-level decision making (vs. tactical-level decision making) in an organization (Finlay, 1994). However, due to the complex nature of top-level decision making, information technology has not been employed fully as with the other lower-level activities (Finlay, 1994). Different e-business models are explored to help companies win on the Internet. However, e-business models do not stand alone. Through e- business models, organizations aim to implement their strategy and to realize their organizational goals. It is strategic objectives that determine the appropriate e-business model(s), and e-business models are among the ways to implement strategy (Lam & Harrison-Walker, 2003). Business process reengineering (BPR), ac- cording to David (2003), mainly addresses short- WHUPDQGEXVLQHVVIXQFWLRQVSHFL¿FLVVXHV WKDW are involved in tactical decisions. Furthermore, BPR is from a process perspective and aims to improve an organization’s performance through operational effectiveness. However, as pointed out by Porter (1996), the effectiveness of individual activities or functions within an organization is not enough for a company to stay competitive; sound strategy needs to be in place to guide, integrate, and optimize overall organizational functions. In other words, BPR practices also should be guided by the overall strategy of an organization. As put by researchers, process-oriented 1 sup- ply chain management (SCM) involves decisions that should be assessed based on an organization’s strategic positioning (Chopra & Van Mieghem, 2000); distributional channel management (DCM) activities should be in alignment with overall organizational strategic goals (Anderson & Day, 1997); a strategic perspective is indispensable for successful customer relationship management (CRM) initiatives (Peppard, 2000). The previous summarizes the e-business strat- egy studies and studies that focus on tactical issues (e.g., e-business models, BPR, SCM, DCM, and CRM) in businesses. Certain quantitative tools (e.g., simulation, econometrics, and analytical tools) are provided by the prior work, but such tools or methods are utilized mainly for lower- level, not strategic-level, decision-making issues, 711 Econometric Simulation for E-Business Strategy Evaluation and few quantitative tools or empirical methods are available to evaluate e-business strategy. This article presents the logic of using infor- PDWLRQV\VWHPVRUVSHFL¿FDOO\GHFLVLRQPDNLQJ support systems (DMSSs) to help to simulate and evaluate strategic plans of e-businesses quantita- tively and empirically. The core of this research involves using the simulation methodology sug- g es t e d b y m a n a g e m e n t s ci e n c e w it h g u i d a n c e f r o m economic theories. Such a simulation approach employs a previously developed econometric model through a DMSS and can enhance the quality and speed of strategic e-business deci- sion-making processes in a comprehensive and XVHUIULHQGO\ ZD\ 7KH UHVW RI WKLV DUWLFOH ¿UVW explains the major research methodology, then provides an example of implementing an actual VLPXODWLRQZLWKDSURSRVHGV\VWHPDQG¿QDOO\ presents discussions and draws conclusions. METHODOLOGY The major methodology of this research is the use of computer simulation in order to simulate the actual strategy development processes of e-businesses. Through this simulation, business managers can evaluate and test their policies and events and establish strategic plans that will im- prove their overall organizational performance in an effective and timely manner. Simulation is the selected methodology because of the unstructured and dynamic nature of e-business strategies. According to Obaidat and Papadimitriou VLPXODWLRQLV³WKHLPLWDWLRQRIWKHRSHUD- tion of real-world systems or processes over time. It is the process of experimenting with a model of the system under study and it measures a model of the system rather than the system itself” (p. 1). As pointed out by Forgionne (1999), simulation LVJRRGIRUFRPSOH[SUREOHPVDQG³>V@LPXODWLRQ also is relatively easy to understand, offers a con- trolled experiment, compresses time, and serves as a mode for training decision makers” (p. 856). The development of computer and communication WHFKQRORJLHVKDVIXUWKHUHQKDQFHGWKHHI¿FLHQF\ of using simulations (Obaidat & Papadimitriou, 2003). In the simulation process, there are mainly six steps (Naylor & Vernon, 1969): problem for- mulation, model establishment, computer program development, validation, experimentation, and data analysis. Problem Formulation The problem in this research is to establish an effective strategy for an e-business. Through simulation, the mechanism behind the strategy development processes can be represented in a model, and then the model can be used to help to evaluate and test different strategies and scenarios and their potential impacts on an organization, ZKLFKZLOOOHDGWRPRUHHIIHFWLYHHI¿FLHQWDQG rational strategies without actually implementing all those alternatives. Model Establishment Simulation, in general, is an experimental device. $PRGHOZKLFKLV³DVLPSOL¿HGUHSUHVHQWDWLRQ or abstraction of reality” (Turban & Aronson, 1998, p. 38) is the vehicle for simulation (Pidd, 1998). In practice, according to Forgionne (1999), different types of models (e.g., physical models, analog models, and quantitative models) can be XVHGIRUVLPXODWLRQSXUSRVHVDQG³>L@QLWVLQLWLDO IRUP WKH PRGHOLGHQWL¿HVWKH NH\ HOHPHQWV RI the problem and their interrelationships” (p. 859). The simulation outcome depends on the inputs DQGWKHZD\WKHLQSXWVDUHUHODWHGDQGVSHFL¿HG through a model. 1D\ORUSXUSRUWVWKDW³>Z@LWKFRPSXWHU simulation, there are virtually no limitations placed on the type of model structure that may be XWLOL]HG´DQGKHXVHV³VLPXOWDQHRXVVWRFKDVWLF difference equations” to illustrate the notion of simulation (p. 18). As pointed out by Pritsker (1998), equations can be used to construct simu- 712 Econometric Simulation for E-Business Strategy Evaluation lation models. Econometric models, as a type of quantitative model and represented by one equa- tion or a set of equations, can identify the major problem elements and their relationships empiri- cally and, thus, can represent the mechanisms of HEXVLQHVVVWUDWHJLHV6XFKDZD\RI³>P@RGHOLQJ the dynamics of very large business or economic V\VWHPV´ LV FODVVL¿HG DV V\VWHP VLPXODWLRQ E\ Forgionne (1999, p. 858), and econometric mod- els are among the simulation methods that can provide what-if analysis. For example, in a book about corporate simulation models, Ogunsola (1979) gives examples of econometric equations established for an oil company. In this article, the authors use an econometric model that contains a system of simultaneous equations (equations (1)-(10)). The econometric model (equations (1)-(10)) was established in the authors’ prior related papers (Ha, Forgionne & Wang, 2003a, 2003b) and the dissertation 2 of one of the authors. Usually, simulation models are differentiated as discrete-event and continuous models (Obaidat & Papadimitriou, 2003). If based on the time dimension, an econometric model can EHFODVVL¿HGDVDFRQWLQXRXVPRGHOKRZHYHULI based on product purchases, an econometric model can be treated as a discrete-event model. In this current and the prior related research, we do not consider the time effect in the model. Rather, we focus on product purchases for a particular time period; thus, we are doing discrete-event simulation. The authors’ previous papers (Ha et al., 2003a, 2003b) and research 2 establish the econometric model representing an e-business strategy for the problem formulated here based on general busi- ness economics through a deductive approach. There are reasons for using an economic-based approach. For one thing, e-business also follows certain basic business principles that have been existing for decades (Besanko et al., 2004). In addition, whether companies should set up their e-business strategy differently than the traditional business strategy has not been tested empirically DQGH[SOLFLWO\LQWKHUHODWHG¿HOGV0RUHRYHUWR follow a deductive approach, it requires that a gen- eral theory should be in place to derive a model of e-business strategy. Among the available theories (e.g., cognitive, management science, accounting, econometrics, and marketing), economic theory is at a high level and is more comprehensive. Finally, the basic economic motivation of e-busi- nesses also makes an economic-based approach more desirable. While the model is built on general business economics, it is operationalized from an e-business S H U V S H FW L Y H 0 R U H V S H F L ¿FD O O \ W K H R S H U D W LRQ D O L ]H G model incorporates measures that are unique to e-businesses. An e-business strategy model and a general business strategy model mainly differ by the different meanings or operationalizations of the model variables. Using econometrics enabled the authors to achieve the following capabilities: (1) simulate and evaluate e-business strategies empirically, explicitly, and precisely; (2) incorporate uncer- tainty (Palmer & Wiseman, 1999) into the analysis through the stochastic disturbance terms in the model’s equations; and (3) explicitly account for the likely interrelationships among the variables in WKHPRGHOLHUHVROYHWKHLGHQWL¿FDWLRQSUREOHP for the model’s parameters). The econometric model from the authors’ previous studies (Ha et al., 2003a, 2003b) 2 is re- produced in the simultaneous equations (1)-(10) to make the illustration in this article complete. This model containing variables and their relationships is established on theories with certain assump- tions or adaptations and represents the mechanism behind the e-business strategy formulation. For example, equations (1)-(3) are developed directly from economic theories (Hyman, 1986; Landsburg, 1999; Mankiw, 2001). They specify the GHWHUPLQDWLRQRIWKHSUR¿WUHYHQXHDQGTXDQWLW\ sold of an organization. The cost equations (8) and (9) are a direct adoption from economic (Hyman, 0DQNLZ0DQV¿HOG<RKH Stiglitz, 1997) and accounting theories (Weygandt, 713 Econometric Simulation for E-Business Strategy Evaluation Kieso, & Kell, 1996). The rest of the equations (equations (4)-(7) and (10)) identify the factors and relate them based on a wide range of prior work (e.g., economics, management, marketing, and research on customer satisfaction and cus- WRPHU OR\DOW\7KHVSHFL¿FDWLRQV DUH DFKLHYHG by exploring different mathematical functional forms presented by researchers (Goldberger, 1964; Johnson, Johnson, & Buse, 1987), and the result- ing functional forms can represent the desired theoretical implications. 3UR¿W 5HYHQXH±&RVW Revenue = Quantity * Price (2) Quantity = Min (QuantityDemanded, Quantity- Supplied) (3) Price = a 0 + a 1 (UnitCost) + a 2 ln(Product/Service) + a 3 ln(DistributionChannelManagement) + a 4 ln(Competition) + a 5 ln (Product/Service) * ln(DistributionChannelManagement) * ln(Competition) (4) QuantityDemanded = b 0 (Price) b1 * (Place) b2 * (Promotion) b3 * (Product/Service) b4 * (NetworkPerformance) b5 * (Availabilityo fOtherChannels) b6 &XVWRPHU3UR¿OH b7 * (Goodwill) b8 (5) QuantitySupplied = c 0 (SupplyChainManageme nt) c1 * (Capacity) c2 (PSOR\HH(I¿FLHQF\ c3 (6) Competition = d 0 + d 1 (Product/ServiceSubstitutes) + d 2 (Rivalry) + d 3 + d 4 + d 5 (Product/Servic- eSubstitutes) * (Rivalry) * (SupplyChain- Management) * (EntryBarriers) (7) Cost = FixedCost + VariableCost (8) VariableCost = UnitCost * QuantitySupplied (9) (PSOR\HH(I¿FLHQF\ e 0 + e 1 ln(EmployeeQuali ¿FDWLRQe 2 ln(Training) + e 3 ln(Salary) + e 4 ln(OtherIncentives) (10) For the detailed model development process and explanations, please refer to the authors’ previous studies (Ha et al., 2003a, 2003b) and one author’s dissertation. 2 The econometric model (equations (1)-(10)) is aimed for general e-business strategy development. Different e-business appli- FDWLRQVPD\UHTXLUHGLIIHUHQWVSHFL¿FDWLRQVHJ YDULDEOHGH¿QLWLRQVDQGIXQFWLRQDOIRUPVRIWKH general model during the model operationaliza- tion. Further empirical studies can be implemented LQRUGHUWRGHYHORSRSHUDWLRQDOPRGHOVIRUVSHFL¿F applications. (Ha & Forgionne, 2004) Computer Program Development 2QFHDPRGHOLVGHYHORSHGIRUDVSHFL¿FVLWX- ation, computer program(s) can be established to simulate the strategy development process with the model. For this purpose, we propose that a decision-making support system (DMSS) can be developed to implement the strategy simulation. A DMSS can integrate the data gathering, model operationalization, and strategy evaluation in an HI ¿F L H Q W D Q G G \ QD P LFZ D \ )R U J L R Q QH 6 X F K capabilities are not readily and directly available with other types of computer programs (e.g., spreadsheet implementation of the model). The same deductive logic for the model devel- opment applies here; the DMSS or the computer program to be developed may have variations for different applications, but the basic functions and the structures of the program will be the same across applications. In the following (Figure 1), a general DMSS architecture (Forgionne, 2000) . e-business world and propel smaller e-busi- nesses to collaborate to provide customers with an ever-widening array of products and services, real-time and rich information, and speedy and quality. processes can be represented in a model, and then the model can be used to help to evaluate and test different strategies and scenarios and their potential impacts on an organization, ZKLFKZLOOOHDGWRPRUHHIIHFWLYHHI¿FLHQWDQG rational. E-Collaboration and E-Business ness processes poses enormous opportunity for value creation in the supply chain and enhances SCM practices (Horvath, 2001). PROCESS AND SYSTEM ALIGNMENT AND INTEGRATION: ISSUES