The Role of ERP Implementation in Enabling Digital Options: A Theoretical and Empirical Analysis

International Journal of Electronic Commerce / Spring 2009, Vol. 13, No. 3, pp. 7–42.Copyright © 2009 M.E. Sharpe, Inc. All rights reserved.

1086-4415/2009 $9.50 + 0.00.DOI 10.2753/JEC1086-4415130301

The Role of ERP Implementation in Enabling Digital Options: A Theoretical and Empirical Analysis

Jahangir Karimi, Toni M. Somers, and Anol Bhattacherjee

ABSTRACT: Many fi rms are transforming themselves from vertically integrated organi-zations into digitally enabled organizations. As fi rms become more innovative in their technical infrastructures and more competitive in their respective industries/verticals, their extended enterprise models include using their previous enterprise resource planning (ERP) investments as foundations for prioritizing additions and for longer-term strategies. The key issue for many fi rms is how to leverage their ERP implementation to become better partners and collaborators by enabling digital options to exploit business opportunities. This paper ascertains the contextual conditions under which ERP system implementations have the greatest impact on intention to adopt digital options. Using empirical data, it fi nds that the impact of ERP implementation on digital-options adoption intention is moderated by a fi rm’s digital-resource readiness. For information systems (IS) practice, the study sug-gests that fi rms should view ERP divisibility as an option value generator for supporting new customers and revenue opportunities. For IS research, it relates digital-options theory to specifi c measurable constructs and to the fi rm’s digital-resource readiness. Theoretical and practical implications are discussed.

KEY WORDS AND PHRASES: Customer-relationship management, digitally enabled extended enterprise, extent of ERP implementation, knowledge reach/richness, process reach/richness, resource readiness, supply-chain management.

Many fi rms today are transforming themselves from vertically integrated or-ganizations into digitally enabled organizations to take advantage of business-to-consumer (B2C) and business-to-business (B2B) e-commerce opportunities [28, 42, 54, 80, 94]. Digitally enabled organizations are agile, multi-enterprise recombinant entities executing core-competency-based strategies [54, 91]. They coordinate their activities and interact with their stakeholders through the exchange of information over electronic networks and markets [28, 80]. The dominant business confi guration for digitally enabled organizations is a network, Web, or hub connected via IT wherein suppliers, customers, complementors, and alliance partners engage in collaboration via alliances and compete via coalitions [28, 54, 91]. According to this perspective, suc-cessful digitally enabled organizations operate within the equilibrium of ongoing interactions of technical competence and capabilities, and technology strategy-experience forces.

To improve revenue and profi ts in today’s fast-paced and competitive business environments, traditional fi rms must implement digitally enabled initiatives and develop competencies to form, maintain, and optimize partner-ships with other fi rms for long-term value creation [9]. While some fi rms have cultivated the digitally enabled extended-enterprise model for business inno-vations, others have found doing so a serious struggle [54, 91]. As fi rms become more innovative in their technical infrastructures and more competitive in their

8 KARIMI, SOMERS, AND BHATTACHERJEE

respective industries/verticals, their extended-enterprise model includes us-ing their previous ERP investments as a foundation for prioritizing additions and for longer-term strategies. Since the traditional ERP’s technical focus is internal on transaction processing, it is insuffi cient for sharing information and collaborating commerce. Consequently, the vast majority of today’s ERP applications are inadequate not only from an architectural standpoint, but also from a business-relevance perspective [2, 13, 44]. The key issue now fac-ing most fi rms is how to leverage their ERP implementation to become better partners and collaborators. The combination of evolving business drivers, changing customer demands, and the evolution of the business model for the extended enterprise have changed the way fi rms transact with partners, making it critical to optimize business processes across enterprises rather than just within an enterprise.

Digitally enabled organizations continually invoke organizational routines for IT adoption and reconfi gure their internal and external resources to enable digital networks (digital options) to exploit business opportunities [91]. How-ever, as suggested recently, “there has been a limited theoretical understanding as well as limited empirical grounding regarding how IT can be leveraged to design and govern the extended enterprise and how IT-enabled process capabilities across the extended enterprise enables [sic] fi rms to leverage resources, exploit competencies, manage partner relationships, and explore opportunities” [54, p. 233]. The present paper proposes a theoretical viewpoint, supported by empirical evidence, on how the extent of ERP implementation affects a fi rm’s digital-options adoption intentions. An investment in extend-ing the scope of ERP implementation can create growth options for future investments in customer-relationship management (CRM) and supply-chain management (SCM) systems that could not be exercised by fi rms that have a less modern integrated architecture [83]. Since each follow-on project enabled by the initial ERP implementation is viewed as an option, the vital question for many fi rms in dynamic and diversifi ed environments becomes: Should extending the scope of ERP implementation be viewed as enhancing rigidities or as generating option value for enabling digital options? The extent to which ERP implementation results in enhancing a fi rm’s knowledge and process reach and richness provides insights into digital-options adoption intentions. This is captured in the research model shown in Figure 1. The model suggests that extending the scope of a fi rm’s ERP implementation has a direct impact on the extent of its digital-options adoption intention and that such impact can be positively moderated by its digital-resource readiness.

Theoretical Framework

Digital options are a set of digitally enabled organizational processes (e.g., customer capture, order fulfi llment, supply chain, product innovation, manu-facturing, logistics and distribution networks) for automating, informing, and integrating the activities of an extended enterprise [30, 47]. In digital-option theory, IT adoption is also viewed as an option. Accordingly, an initial invest-ment in new IT creates “growth” options [10, 40, 86, 88], especially in the fi rst

INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 9

phase of a multiphase implementation [81, 82]. Growth options refer to the opportunity to grow the project’s scope beyond what was initially anticipated by creating one or more additional related assets through follow-on invest-ments [86]. Growth options are most likely to be present on more innovative projects and those that implement a platform for future applications [40]. Subsequently, a fi rm may apply its capabilities to emerging opportunities or let them remain unused. However, “for those things that a fi rm may do, value is created by actively structuring those elements as an option” [40, p. 78].

According to Fichman, the option value of an IT platform investment can be predicted based on four types of determinants: (1) strategic factors, such as the strategic importance of affected products or processes, radicalness of process improvements, and innovative capabilities/endowment, (2) organizational learning factors, such as learning-related endowments and exploitable absorp-tive capacity, (3) technology bandwagon factors, such as network dominance of technology class or technology instance, and (4) adaptation factors that yield increased payoffs over time, such as divisibility [38]. Although the quantita-tive value of a real option for determining the value of IT investments is not always intuitively obvious, nevertheless real options provide a new way of thinking (i.e., options thinking) about how projects can be structured and managed. The essence of options thinking is more a philosophy for project management than a science of precise quantifi cation [40]. “Signifi cantly, real option theory, in fact, does not dictate use of any particular pricing models. It is simply an approach that recognizes the value of management fl exibility in investment evaluation” [25, p. 16]. Therefore, IT managers do not necessarily need to acquire arcane option-quantifi cation skills or use option-pricing mod-els (e.g., Black-Scholes, Binomial) to put option thinking to work [38], since

Figure 1. Hypothesized Research Model


“the bigger win comes from using real options concepts to actively create and extract the value of embedded options that can otherwise be diffi cult to see” [40, p. 75]. When quantifi cation is not feasible, the above determinants could be used with qualitative methods, such as decision tree analysis, qualitative scoring models, or general project management heuristics, for valuing IT platform options [38]. Over time, fi rms learn and gain experience in strategic processes associated with capability building as they launch digital options to support competitive actions [74]. Organizational learning is seen as a purposive quest to retain and improve competitiveness and innovation in uncertain technological and market conditions [33]. It is, therefore, contingent upon path dependency and cannot occur without appropriate preconditions [35, 83]; that is, “where a fi rm can go is a function of its current position and the paths ahead” [84, p. 522].

Extent of ERP Implementation

The extent of ERP implementation defi nes the type of benefi ts that can be derived from ERP systems and specifi es the degree to which ERP systems will change process integration in the business units of the enterprise [57]. ERP implementation is divisible to the extent that it can be divided up for sequential or incremental implementation by functions, departments, the entire company, multiple companies, locations, or regions. Each incremental segment positions the fi rm for positive payoffs even if no further implementa-tion segments are pursued [38]. ERP divisibility, allows managers the fl exibil-ity to change the scope of the implementation, which in turn can reduce the potential for losses or increase the potential for gains in response to external and internal events.

In the present research, the extent of ERP implementation is defi ned as ERP functional scope (system range), organizational scope (system reach), and geographic scope [15, 51]. ERP functional scope refers to the range of business functions (accounting, manufacturing, sales, etc.) that share the ERP implementation. Greater ERP functional scope is achieved through the implementation of multiple or cross-functional ERP modules. It (1) provides data and process integration across functions and ensures more benefi ts than a single function implementation [17], (2) facilitates the exchange of data be-tween applications for monitoring and managing business activities across the extended enterprise, and (3) enables end-to-end automation of business processes, thereby allowing fi rms to react more quickly to changes in busi-ness conditions. ERP organizational scope describes the organizational locations that the ERP implementation can reach (link), such as departments, divisions, an entire company, and multiple companies. ERP geographic scope refers to the regional, national, and global reach of the ERP implementation. Recent research has shown that ERP projects with greater functional, organizational, or geographic scope result in positive, higher shareholder returns [71]. The largest increase in returns (3.29%) is found for ERP purchases with greater functional, organizational, and geographic scope, and negative returns are found for projects with lesser scope.


ERP Divisibility as Option-Value Generator

The full potential of an ERP system cannot be realized if its integration and coordination capability is confi ned within the walls of a fi rm [4, 5, 81]. ERP divisibility provides for stage options, where value is created in providing the opportunity to alter or terminate an implementation before each new stage of funding, based upon the receipt of updated information about costs and benefi ts. Although stage options overlap with other operational options, such as options to abandon, defer, change scale, and grow [10], the growth option is often exercised in the case of an ERP implementation [86]. Since ERP divisibility offers a variety of embedded growth opportunities (options), it tends to be more complex, more innovative, with high uncertainty and path dependencies, which can result in a longer time frame for an overall positive payoff [40].

A fi rm can derive value from its ERP implementation through a strategy of continuous business improvement both within its enterprise and beyond by (1) extending the scope of ERP implementation to create the capability to compete [75] and (2) integrating its ERP systems with those of its trading partners to collaborate effectively by reducing costs and improving business processes, data integrity, and customer service [67]. This can lead to a platform for real (growth) options, an escalation of a commitment to a full-scale ERP implementation, and a future investment in e-business applications [71, 86].

The most important categories of e-business applications are considered to be the sell-side (CRM) and buy-side (SCM) of e-business applications [9, 42, 56]. SCM and CRM systems support the external part of the extended enter-prise, while an ERP system comprises the internal portion [65]. Further invest-ments to extend the ERP system by linking it to CRM and SCM systems can signifi cantly improve business-process integration for the extended enterprise, and result in enterprise-applications integration [13, 60]. Enterprise-applications integration is often required to achieve business-process integration for the extended enterprise, since CRM and SCM systems will offer an ERP-based fi rm the opportunity to build interactive relationships with its business partners [4, 5, 56]. Others have argued that such enterprise applications integration might produce greater benefi ts than ERP, SCM, or CRM systems alone [8, 58, 63]. For example, when middleware and a standardized software infrastructure are used, the information generated by the ERP system can be shared via the Internet directly with the supplier’s ERP systems [94]. Inventory levels, production planning, and material purchasing can be exchanged between the focal fi rm and suppliers via ERP and SCM systems to support B2B integra-tion. The focal fi rm can also exchange valuable business data, such as order status, invoice, and on-line order fulfi llment, with its customers using CRM’s B2C applications.

Digital-Options Adoption Intentions

Prior research in invocation diffusion suggests that assimilation of innovation starts by a fi rm’s initial awareness and evaluation of innovation. Innovation


adoption decision occurs in the initiation stage. In this stage, the fi rm collects information, builds knowledge of the innovation, examines its relevance and appropriateness to the organization, and makes a decision whether to adopt the innovation. This initiation stage “amounts both to identifying and prioritizing needs and problems on one hand and to searching the organization’s environ-ment to locate innovations of potential usefulness to meet the organization’s problems” [72, p. 391]. According to Fichman, even though an organization may exhibit a high propensity to innovate, it can still lag in its intention to adopt innovations that do not fi t with its needs, strategies, resources, or capabilities [37]. Likewise, a less innovative organization may still choose to be an early adopter of innovation when it perceives it as a good fi t. The degree to which an innovation fi ts the problem to be solved and the potential of the innova-tion to enhance a fi rm’s performance in value chain activities are signifi cant motivators for the fi rm to adopt the innovation [3]. Therefore, the intention to adopt an innovation will be affected by its functional fi t to the fi rm’s needs and its requirement fi t to the fi rm’s current resource base.

In collaborative trading communities, ERP’s role in serving information across the supply chain becomes more critical, and erroneous data (e.g., about inventories) become more costly. Integrating ERP with SCM and CRM is com-plex. Business processes such as procurement and fulfi llment are inherently complex, and enabling these transactions over electronic networks is even more challenging [78]. Firms need to avoid large B2B or B2C projects until they have a consistent interpretation of the data that are shared with multiple users, applications, business units, and trading partners [92]. Serious data inconsistency problems occur in disparate and fragmented systems spread across organizational boundaries, such as the supply chain [70]. Semantic rec-onciliation of reference data (e.g., product, services, employees, assets, ledger accounts, customers, suppliers) becomes increasingly important as the estab-lished business applications become more integrated and business processes span entire companies. In this environment, adjusting ERP data and processes to serve the needs of one customer will negatively affect all trading partners as well as the enterprise’s ability to execute within the trading community [93]. Data consistency can enable both enterprise-application integration (systems integration) and business-process integration [56], since “having common data defi nition and data consistency not only enables connectivity across the supply chain, but also enables the exchange of complementary information between a fi rm and its supply chain partners” [70, p. 237]. The enterprise-applications integration depends on the fi t between data and applications supported by a fi rm’s internal ERP system and the data and applications that are supported by the external CRM and SCM systems [94]. This fi t is necessary for both enterprise-application integrations and business-process integration [56, 62]. A fi rm’s ERP data need to be combined with external sales forecasts and customer data to provide business intelligence that purchasing activities can use to react to rising demand and to obtain a better price from a single supplier. Any inconsistency, however, can result in a performance gap and a lower satisfaction level with the fi rm’s existing ERP systems.

Data consistency (quality, integrity, and standardization) is often a key issue in SCM implementation because SCM systems receive much of their data from


other applications and legacy systems with multiple data sources and vary-ing quality [55, 70, 76]. Data consistency in supply chains will be enabled by common data defi nitions for the reference data as well as automated systems for data capture. For organizations to benefi t from SCM systems, they must ensure that their ERP systems are implemented correctly beforehand. Without properly functioning ERP systems, SCM systems may do nothing more than create upstream and downstream problems at Internet speed [65]. Allowing all the partners in the supply chain to view and dynamically manage both demand and capacity data raises opportunities for simultaneous improvement in customer-service levels and reduction in both overall inventory levels and associated costs [47, 52].

Similarly, CRM-adoption intentions depend on the fi t between the data of ERP and CRM systems. CRM systems help fi rms to continually refi ne in-sights into customer needs, behaviors, and economics. ERP systems contain vast amounts of data about customers. CRM effi ciency can be maximized by integrating it with ERP systems to differentiate the way fi rms treat each and every customer. By developing cross-functional integration, supported by data warehousing and ERP systems, fi rms can create a customized view and a deeper understanding of each customer, which allows them to respond to sales opportunities or impending support issues [49]. This will result in greater customer value, improved retention, and lower support costs. Furthermore, it leads to the elimination of outdated processes and redesigned activities that in the past have failed to deliver customer value.

The more a fi rm’s resources meets its performance needs, the less likely it is to perceive a need to alter the status quo and the less likely it will try a new IT innovation, even though it may, in fact, be useful for its needs. Since strategic decisions are tied to an awareness of some real or anticipated per-formance gap, if a fi rm’s strategic needs can be fulfi lled by the current busi-ness practices, there will be little incentive for the fi rm to adopt innovations. However, a gap, in turn, will provide the fi rm the impetus to fi nd new ways to improve performance via adaptation and strategic actions (plans) [72]. As a fi rm perceives more performance gaps, it will be under urgency to take action to close those gaps [34]. Achieving strategic fi t requires alignment of organizational resources, capabilities, and competencies with environmental opportunities and threats, and an internal consistency (fi t) with regard to the fi rm’s overall activities, operations, and plans [14]. The managerial challenge is, therefore, to reconcile innovative aspects of strategy with existing resource endowments, capabilities, and organizational routines, all of which refl ect prior strategic choices. Barriers to innovation are likely to refl ect such strategic choices and plans [11].

Digital-Resource Readiness

A fi rm’s resource readiness is considered to be a major factor affecting its intention to adopt Internet business strategies [2, 48]. It also explains the ex-tent of e-business use in U.S. fi rms [48]. Digital options are created through enhancements to the reach and richness of fi rm’s knowledge and processes


[66]. Knowledge reach refers to the comprehensiveness and accessibility of codifi ed knowledge that is available to a fi rm, and knowledge richness relates to the quality of information. High digital process reach is the implementation of digitized processes that tie activity and information fl ows across depart-mental units, functional units, and geographical regions as well as between value network partners (including suppliers, customers, and vendors). High digital process richness is associated with the quality of the information collected about transactions in these processes. Process richness improves the quality of information available to process participants by making it timelier, accurate, relevant, and customized [66].

ERP remains at the center of an organization’s information-management strategy even as the business becomes more complex and distributed. ERP implementation can create digital options by extending the knowledge and process reach and richness of a fi rm so that it is integrated internally and digitally extended to external customers, suppliers, and partners. Traditional ERP applications are clustered in two major groups: enterprise-support modules (accounting and fi nance, human resources and payroll), and value-chain modules (operations, manufacturing and material management applications, distri-bution and logistics, etc). The better the quality of the information obtained from value-chain modules, the higher the quality of planning and forecasting, which in turn will reduce the frequency of demand-and-supply mismatches. Therefore, the fi rm’s digital knowledge and process richness (its digital-resource readiness) will be higher because of its capabilities to collect and access high-quality information about its internal processes. Recent research has suggested that single-site projects that support administrative functions (enterprise-support modules) rather than value-chain activities are much less likely to be able to exploit the growth options from future initiatives that le-verage an ERP platform [71]. Multi-site projects are likely to generate greater returns than single-site ERP projects involving the same modules. However, the greatest benefi ts accrue from ERP platform investments for multiple sites that also involve purchase of two or more value-chain modules. The higher a fi rm’s digital-resource readiness in terms of the value-chain modules, the higher is the fi rm’s digital knowledge and process reach and richness because of its capabilities to link processes and information fl ows across departmental units, functional units, and geographical regions. From an options-value per-spective, value-chain modules can generate greater options value because of (1) their potential for more radical improvements in strategically important processes, (2) innovation-related capabilities and endowments, and (3) the organizational learning associated with the implementation of cross-functional processes [38, 71]. The options value of an ERP implementation that involves higher functional, organizational, and geographic scope will, therefore, be higher because of the increased future options for digital knowledge and process reach and richness [74].

Further, SCM and CRM systems can extend the ERP digitized process reach and richness to suppliers, customers, and vendors by supporting cascading transactions. A new on-line sales order, for example, triggers factory work or-ders, claims inventory, reserves manufacturing capacity, schedules labor, and leads to automated generation of invoices and credit evaluations of customers


and purchase orders for suppliers [46]. Process reach and richness support the fi rm’s responding capabilities by improving the coordination internal and external to the fi rm in product development, supply chain, and production.

Hypothesis Development

As the extent of ERP implementation increases, the resources made available by the IT infrastructure and its ability to support a wide range of strategic initiatives increase as well [16]. Firms with greater process reach can co-opt more customers or business partners with their value-chain activities [89]. However, only fi rms that have acquired or developed the precursory resources can begin to build up a resource over time because of the interconnectedness of asset stocks [32]. For example, prior research on predicting intention to adopt Financial Electronic Data Interchange (FEDI) has suggested that the extent of EDI application implementation has a positive impact on FEDI adoption intentions because it provides complementary benefi ts and greater confi dence to the decision-makers to assimilate FEDI more effectively [84]. Similarly, the initial experience with digital options often results in additional IT investment in similar technologies, which are likely to enable learning-by-doing and thus shape management insight about IT complementarities and the fi rm’s IT capability [74]. Through the increasing investment and its as-sociative learning, fi rms can both expand their knowledge and skill base and improve their ability to assimilate external information and its application to commercial ends [27, 38]. This absorptive capacity is widely understood to enhance a fi rm’s innovative capabilities, and it is related to the fi rm’s ability to assimilate and adopt new IT—a low absorptive capacity can limit invest-ment in strategic options that have path dependencies [85]. Path dependence is important to organizational learning and often infl uences the fi rm’s locus of search for new technologies [73]. The knowledge to be gained during deploy-ment of extended ERP systems will contribute to technological development that occurs in the vicinity of the absorptive capacity being acquired today by enabling future digital options with long-lasting strategic relevance. Therefore, the ability to integrate SCM or CRM systems with ERP systems is predicated on the availability of precursory ERP and on the fi rm’s ability to learn from its ERP implementations and identify areas where additional system, process, and business training, as well as organizational realignment, are necessary, since implementation fundamentals (regarding product, process training, and change management) do not change [68]. Missed opportunities during ERP implementation become risks in implementing SCM/CRM and linking it to the ERP system. The option value for CRM/SCM adoption increases with the increased organizational learning gained from implementing process changes across distinct organizational units, which are likely to require technology adaptations to accommodate exogenous and endogenous “misfi ts” [77]. This exploitable absorptive capacity, will therefore (1) give the fi rm the capability to become a better partner and collaborator at a lower cost, more quickly, and with less inherent risk than competitors, (2) increase the managerial fl exibility to pursue currently unforeseen investments, (3) increase the option value of


positioning investments in IT platforms, and (4) have a positive impact on the fi rm’s intention to adopt SCM/CRM [38, 70, 74]. Hence the following hypothesis is proposed:

H1: The higher the extent of ERP implementation, the higher will be the extent of a fi rm’s digital-options adoption intentions.

Resource readiness is the necessary condition for a fi rm to adopt new IT innovations, since a lack of the necessary resources would limit its ability to fully use the innovation. Firms that possess innovation-related capabilities and endowments will have a greater ability to recognize and exploit a larger array of immediate follow-on projects and more managerial fl exibility in the structuring and executing of options—they can innovate more economically with higher expected value of returns and probability of success from the investment [38]. Digital options are enabled when existing resources and capabilities allow preferential access to future opportunities for growth. They can broaden the original value proposition of ERP by enhancing the reach and richness of a fi rm’s knowledge and processes [66]. Process-oriented IT is more directly supportive of a fi rm’s responding ability, whereas knowledge-oriented IT is more supportive of its sensing ability. Sensing and responding capabilities are both necessary to ensure a fi rm’s agility [66]. Enhancements of the breadth of resources (reach) and the quality of information (richness) available to the fi rm improve its ability to sense and respond to environmental changes, thereby making it more agile [66]. However, defi ciencies in either knowledge or process-oriented IT create an imbalance in the digital-options “platform,” making it an unsteady base from which to launch agile moves by adopting new IT innovations. Therefore, it is reasonable to suggest that the impact of the extent of ERP implementation on the extent of digital-options adoption intentions is positively moderated by a fi rm’s digital-resource readiness, that is, its digital knowledge and process reach and richness. The following hypothesis is proposed next:

H2: Greater extent of ERP implementation in conjunction with greater digital-resource readiness is positively associated with greater digital-option adoption intentions.

Research Methodology

Measurement of Constructs

In order to develop scales to measure the constructs, the underlying domain was identifi ed and items were created accordingly. As some measures were developed for the fi rst time, past literature was reviewed and constructs were carefully defi ned to specify the domain and ensure content and face validity [23, 62]. The model, shown in Figure 1, is represented by fi rst- and second-order formative and refl ective constructs. The guidelines of Jarvis, MacKenzie, and Podsakoff were followed in determining this choice because


they provide several decisive factors that cogently differentiate refl ective and formative constructs [50]:

1. Direction of causality. If the direction of causality is from the con-struct to the indicators, and changes in the underlying construct are hypothesized to cause changes in the indicators, the measures are referred to as refl ective [41]. In contrast, with formative indicators changes in the measures are “assumed to cause changes in the latent variable” [12, p. 65] and “assumed to be defi ned by, or to be a func-tion of its measurements” [6, p. 34].

2. Expectation of correlation. Refl ective indicators are expected to be correlated and have high internal consistency reliability. Conversely, with formative indicators, there is no expected a priori pattern of association among the dimensions. Formative indicators are not assumed to refl ect the same underlying construct, that is, they can be independent of one another and measure different factors. It is possible for formative indicators of the same construct to have a positive, negative, or no correlation with one another. Internal con-sistency measures do not apply to formative indicators.

3. Consequences of removing an indicator. For refl ective indicators, re-moval of an indicator from the measurement model does not change the meaning of the construct or its construct validity. The measures are assumed to be equally valid, reliable, and interchangeable. Consequently, the remaining indicators can adequately represent the construct. Since formative indicators defi ne the concept, there must be dimensions covering the domain of the construct for the construct to be properly defi ned. The removal of an indicator can be critical, as it may change the meaning of the construct.

4. Nomological net of the construct indicators. Since refl ective indicators refl ect the same construct and are interchangeable, they should have the same antecedents and consequences. Formative indictors are not expected to have the same antecedents and consequences because the measures do not necessarily capture the same aspects of the construct’s domain, nor are they interchangeable.

Based on these criteria, the constructs were modeled as follows: (1) extent of ERP implementation was modeled as a fi rst-order construct with formative indicators, and (2) digital-resource readiness and digital-options adoption intentions were both modeled as second-order formative constructs, each with two fi rst-order constructs. The scale items are listed in Appendix A and described next.

A minimum of three indicators were selected to measure each of the con-structs [12]. The extent of ERP implementation (ERPEX) is measured by three formative indicators, the functional, organizational, and geographic scope of the implementation. ERP functional scope (ERPFS) was measured as the range of the implementation project and as a summation of the business func-tions covered by the ERP implementation. ERP organizational scope (ERPOS) was measured by the number of locations (departments, divisions, entire


company, multiple companies, etc.) targeted for ERP implementation, and ERP geographic scope (ERPGS) was measured by the geographic reach of the implementation (i.e., single site, multiple sites, national, worldwide). Extent of digital-options adoption intentions (EDOAI) is a second-order construct formed by SCM- and CRM-adoption intentions. The SCM-adoption inten-tion (SCMAI) was measured with three items describing the extent of ERP/SCM fi t (SCMFT) and the four items indicating the extent to which a fi rm is actively planning to use SCM applications for improving information and workfl ows among suppliers, customers, and partners. The CRM-adoption intention (CRMAI) was measured with three items describing the extent of ERP/CRM fi t (CRMFT) and the four items that assessed the extent to which a fi rm is actively planning to implement CRM applications for improving customer interactions, sales force automation, and improving customer ser-vice. Digital-resource readiness (DRR) was assessed by ten items measuring the extent of a fi rm’s digital knowledge and process reach and richness. It is appropriately modeled as a formative second-order construct formed by two fi rst-order facets that measured digital-knowledge reach/richness (KRR) and digital-process reach/richness (PRR). The constructs, with the exception of ERPEX, were assessed using fi ve-point Likert scales anchored between “strongly disagree” and “strongly agree.”

To help provide a better explanation for the moderating effects of DRR on the relationship between ERPEX and EDOAI, a control was used for fi rm size (as a proxy for fi rm fi nancial resources) to partial out organizational size ef-fects. Firm size was measured by the total number of employees. Prior research suggests that fi rm size can have a positive infl uence on adoption behavior and on implementing radical innovations [59, 72, 87]. Larger fi rms, which may have larger operating budgets, technology base, and resources, are generally able to implement more extensive ERP systems. Conversely, large fi rms tend to be less fl exible than smaller fi rms because they need more communication, coordination, and support to effect radical innovations [64].

Data Collection

The survey design was supported by (1) semi-structured interviews with knowledgeable executives to assess content validity, and (2) a pilot random sample. The purpose of the interviews was to evaluate each measure for com-prehensibility, consistency of meaning, and respondents’ ability to respond accurately. The interviews helped to clarify questions and improve instructions and scale items. The study built on the use of interviews with experts. The individuals included were a chief information offi cer (CIO), an ERP project leader, a senior ERP consultant, and three business group managers in charge of overseeing ERP implementation in procurement, marketing, and planning/budgeting areas (in two manufacturing fi rms in, respectively, the generic drugs and disk drive industries).

After their review, the instrument was pilot-tested with a sample of Fortune 1000 fi rms to assess the face and content validity of the measures. The purpose of the pilot study was threefold: to assess the internal and external validity


of the scale items, to estimate potential participation rates for the subsequent study, and to provide insight into blind spots and oversights needed to plan the research study. The survey was sent to one of four key executives. Al-though only 123 surveys were returned after two rounds of follow-up requests, yielding a response rate of 12.3 percent, it was nonetheless possible to glean important information. As shown in Table 1, a large percentage (36%) were manufacturing fi rms, suggesting that it would be appropriate to focus on this industry to test the research model. The pilot study also made it possible to confi rm that the psychometric properties of the measures were robust across both samples.

Empirical data for hypothesis testing were collected via a fi eld survey of fi rms that had recently implemented ERP systems. The respondent popula-tion was chosen using a stratifi ed sample of U.S.-based manufacturing fi rms drawn from the Harris Nationwide Manufacturing database. Manufacturing fi rms were selected because ERP implementation is particularly prevalent among these fi rms, since the sales of ERP systems were originally targeted to large manufacturing companies. This was also observed from the pilot data of Fortune 1000 fi rms. Consequently, the survey was restricted to manufactur-ing fi rms to minimize the potential confounding effects of industry variations on fi rm digital-option adoption intentions and digital-resource readiness. The stratifi ed sampling technique helped in avoiding biases due to fi rm size. For this purpose, the total population of 22,700 fi rms in the Harris database were stratifi ed into four categories based on employee counts (3,000 and over, 1,000–2,999, 500–999, and 200–499), and the survey questionnaire was mailed to a random sample of fi rms within each strata (the fourth stratum was underweighted because ERP implementation is less prevalent among smaller fi rms).

The survey questionnaire was mailed to the senior-most IS executive in each fi rm (e.g., chief information offi cer, vice president in charge of IS), along with a letter outlining the purpose of the research and a pre-addressed stamped envelope for mailing back completed responses. Firms received no specifi c incentive for completing the survey, beyond a copy of the aggregated survey results. A total of 550 fi rms were contacted. Of these, 104 fi rms declined par-ticipation, citing reasons such as corporate policy and departure of the contact person from the fi rm. Two surveys were dropped (both from the 200–499 em-ployee range) because they were incomplete. After two follow-up mailings for nonrespondents, conducted two and four months after the original mailing, a total of 148 usable responses were obtained, for a reasonable response rate of 27 percent (148/550). Response rate by fi rm strata is reported in Table 2. Chi-square analysis indicated that the nonrespondents did not differ system-atically from the respondents on two criteria: fi rm revenue and number of employees.

As shown in Table 3, the manufacturing organizations in the survey typically were using systems that had been rolled out for 18 months. Their implementa-tion projects took an average of almost 15 months to complete, with an average of 55 internal employees devoted to the implementation, and an average of 25 fi rms hired external consultants. The number of ERP users ranged from 50 to 5,000, with an average of 350 users.


N

um

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and

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e

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stry

re

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g

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ract

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n M

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stem

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sines

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)Re

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Num

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f ful

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lent

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45.0

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42

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.4)

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ical

s 8

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.5)

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mac

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tiliti

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nt c

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s 30

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(6.5

) (e

xter

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r ERP

impl

emen

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ther

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(1

3.8)

Tab

le 1

. Des

crip

tive

Sta

tist

ics

for

Pilo

t Te

st D

ata

fro

m F

ort

une

10

00

Fir

ms

(N =

12

3).


Data Analysis and Results

Partial least squares (PLS) using PLS-Graph, v3.00 software was used to test the hypothesized relationships shown in the model of Figure 1 [21]. PLS is appropriate for studies that have formative constructs, a relatively small sample size, and an emphasis on theory development [22]. It is widely used in IS research [53, 90]. Further, it is more appropriate for analyzing moderating effects because traditional techniques cannot account for measurement error in exogenous constructs [19, 20]. The PLS model is evaluated in two steps: (1) assessment of the reliability and validity of the measurement model, and (2) assessment of the structural model [8].

Exploratory Factor Analysis

The measures for EDOAI were factor analyzed to determine the factors that would form the underlying dimensions of the construct. As seen in Table 4, two factors emerged that explained 77 percent of the variance. Factors were named based on the inherent construct identifi ed from the items that loaded onto each factor. Factor 1 contained items that measure CRM-adoption intentions

Employee Firms in Firms Surveyscount database contacted returned

3,000+ 395 200 441,000–2,999 1,684 100 36500–999 4,327 200 51200–499 16,301 50 17Total 22,707 550 148

Table 2. Stratifi ed Sample Response for Manufacturing Firms (N = 148).

Characteristic M Mdn Max Min SD

Number of months system 18.61 18.00 36 6 6.59 has been used since rolloutLength of time (in months) to 14.80 12.00 36 4 5.62 complete projectNumber of full-time-equivalent 55.09 45.00 225 12 38.80 employees (internal) devoted to ERP implementation Number of full-time-equivalent 25.12 20.00 125 3 30.64 consultants (external) hired for ERP implementation Approximate number of ERP 350.12 200.00 5,000 50 540.40 (business) users in company

Table 3. Descriptive Statistics for Manufacturing Data (N = 148).


CRM

-ad

op

tio

n

SCM

-ad

op

tio

n

in

tent

ions

in

tent

ions

Cod

e

(CR

MA

I)

(SCM

AI)

ERP/

CRM

fi t (

CRM

FT)

C

RMFT

1: Th

ere

is a

good

fi t b

etw

een

ERP

impl

emen

tatio

n an

d cu

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p m

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emen

t ini

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es.

0.9

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–0

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C

RMFT

2: D

ata

prov

ided

by

ERP

mat

ch w

ell w

ith d

ata

requ

ired

for c

usto

mer

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tions

hip

man

agem

ent.

0.9

27

–0

.058

C

RMFT

3: O

ur E

RP im

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will

hel

p us

take

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f our

cur

rent

/fut

ure

custo

mer

-rela

tions

hip

0

.92

9

–0.0

22

m

anag

emen

t pro

gram

s.C

RM-a

dopt

ion

plan

(CRM

AP)

C

RMA

P 1:

We

plan

to in

tegr

ate

our e

-mai

l, ph

one,

pap

er d

ocum

ents,

and

Web

inte

rface

s fo

r im

prov

ed

0.9

28

0.

061

custo

mer

inte

ract

ion.

C

RMA

P 2:

We

plan

to u

se s

ales

forc

e au

tom

atio

n to

impr

ove

and

track

our

sal

es p

roce

sses

. 0

.90

3

0.05

7

CRM

AP 3

: W

e pl

an to

use

the

Inte

rnet

for h

andl

ing

sale

s tra

nsac

tions

and

impr

ove

custo

mer

ser

vice

. 0

.92

3

–0.0

09

CRM

AP 4

: W

e pl

an to

aut

omat

e cu

stom

er re

spon

ses

and

profi

les,

trac

k m

arke

ting

cam

paig

ns a

cros

s di

ffere

nt

0.9

41

0.

030

med

ia a

nd c

hann

els,

and

man

age

quot

es a

nd p

ropo

sals

from

neg

otia

tions

to c

losin

g.ER

P/SC

M fi

t (SC

MFT

)

SCM

FT1:

Ther

e is

a go

od fi

t bet

wee

n ER

P im

plem

enta

tion

and

supp

ly-c

hain

man

agem

ent i

nitia

tives

. 0.

015

0.7

01

SC

MFT

2: D

ata

prov

ided

by

ERP

mat

ch w

ell w

ith d

ata

requ

ired

for s

uppl

y-ch

ain

man

agem

ent.

0.05

3 0

.75

2

SCM

FT3:

Our

ERP

impl

emen

tatio

n w

ill h

elp

us ta

ke a

dvan

tage

of o

ur c

urre

nt/f

utur

e su

pply

-cha

in

–0.0

51

0.7

51

man

agem

ent p

rogr

ams.


SCM

-ado

ptio

n pl

an (S

CM

AP)

SC

MA

P 1:

We

plan

to u

se th

e In

tern

et o

r ext

rane

ts to

impr

ove

info

rmat

ion

fl ow

am

ong

our s

uppl

iers

, cus

tom

ers,

0.

039

0.9

17

and

partn

ers.

SC

MA

P 2:

We

plan

to im

prov

e le

ad ti

me,

qua

lity,

and

cus

tom

izat

ion

by s

harin

g da

ta w

ith o

ur b

usin

ess

partn

ers.

–0

.028

0

.89

0

SCM

AP 3

: W

e pl

an to

use

our

ow

n da

taba

se in

form

atio

n an

d su

pplie

rs’ p

ipel

ine

proc

esse

s to

fore

cast

dem

and

–0

.007

0

.87

4

m

ore

accu

rate

ly a

nd c

reat

e vi

able

sch

edul

ing

appl

icat

ions

.

SCM

AP 4

: W

e pl

an to

redu

ce o

vera

ll pr

oduc

tion

cost

by s

tream

linin

g ou

r wor

kfl o

w a

nd in

form

atio

n fl o

w to

and

–0

.038

0

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6

fro

m b

usin

ess

partn

ers.

Eige

nval

ue

5.

985

4.84

2Pe

rcen

t of v

aria

nce

42.7

51

34.5

84C

ronb

ach’

s al

pha

coef

fi cie

nt

0.97

1 0.

922

Tab

le 4

. Ex

plo

rato

ry F

act

or

Ana

lysi

s R

esu

lts

for

SCM

- and

CR

M-A

do

pti

on

Inte

ntio

ns.

Not

es: E

xtra

ctio

n M

etho

d--P

rinci

pal A

xis

Fact

orin

g; R

otat

ion

Met

hod-

-Pro

max

with

Kai

ser N

orm

aliz

atio

n. K

aise

r-Mey

er-O

lkin

(KM

O) m

easu

re o

f sam

plin

g ad

equa

cy: 0

.873

; Bar

lett’

s te

st of

sph

eric

ity: c

hi-sq

uare

= 2

,552

.86;

df =

91;

p =

.000

. Bol

dfac

e fi g

ures

sho

w w

hich

fact

or it

ems

load

ont

o.


(CRMAI), and factor 2 consisted of measures of SCM-adoption intentions (SCMAI). For convergent validity, it was expected that items belonging to the same scale would have factor loadings exceeding 0.60 on a common factor and factor cross-loadings less than 0.30 for all other factors, demonstrating accept-able discriminant validity. Table 4 shows that items loaded high and uniformly on their expected factor and that cross-loadings were small. Reliability was assessed using Cronbach’s alpha. There was a high degree of reliability for each factor, with both exceeding 0.90.

Once an interpretive structure for EDOAI was established, confi rmatory factor analysis was applied to further assess the measurement properties of the constructs. Table 5 provides the mean values and standard deviation of all constructs.

Measurement Model Analysis

In order to focus on the psychometric properties of the measurement model, the study examined the composite reliability, convergent validity, and dis-criminant validity of the constructs. Formative indicators are not assumed to measure the same construct, nor are they expected to be correlated or to dem-onstrate internal consistency. Consequently, measures of internal consistency do not apply for ERPEX [19, 20, 43]. Moreover, there are no defi nitive criteria for the validation of formative variables. However, the general practice is to examine item weights, which will usually have smaller absolute values than item loadings [24].

Convergent validity can be assessed by an examination of the measure-ment model loadings (and weights). The loadings, once deemed consistent with the underlying construct, were used to assess internal consistency and average variance extracted (AVE). Convergent and discriminant validity is adequate for constructs modeled using two or more refl ective indicators when (1) the square root of the AVE by a construct from its indicators is at least 0.707 (AVE > 0.50), and (2) item loadings exceed 0.70 and load more highly on the constructs they are intended to measure [1].

Table 6 provides the measurement model results. All AVEs exceeded 0.50, providing evidence that measures in the constructs are more closely aligned with themselves than with other constructs [43]. Convergent validity is also evident from the high loadings of refl ective measure items on their associated constructs. Moreover, all item loadings were signifi cant at the 0.05 level, and were above the 0.70 suggested benchmark noted above [41]. An evaluation of the formative measurement model for ERPEX is based on the weights of the indicators. The weights for the formative construct (ERPEX) were signifi cant at the p < 0.05 level. The weights provide information about the make-up and relative importance for each indicator in the creation/formation of the compo-nent [20, p. 307]. For example, the ERPOS is more important in determining the construct than the ERPGS or ERPFS. Reliability measures are reported in Table 6. Since a measure may have unacceptable convergent validity and still be reliable, the reliability was assessed after the convergent validity was determined [79]. All composite reliability measures were above the suggested


minimum value of 0.70. Collectively, the measures provide strong evidence of item reliability and convergent and discriminant validity.

Second-Order Constructs

PLS can be used to approximate second-order factors using two procedures [22]. One approach is to use a direct item to construct approach, where the second-order construct is measured by the items of the fi rst-order constructs. Another approach, which we use here, models the path coeffi cients from the fi rst- to second-order constructs using the weights of the formative constructs.

Item ItemConstruct M SD

Extent of ERP implementation (ERPEX) ERPFS 3.57 1.29 ERPOS 3.27 1.10 ERPGS 2.96 1.37Extent of digital-options adoption intentions (EDOAI) ERP/CRM Fit (CRMFT) CRMFT1 2.86 1.39 CRMFT2 2.91 1.41 CRMFT3 3.04 1.40 CRM-adoption plan (CRMAP) CRMAP1 3.27 1.37 CRMAP2 3.31 1.40 CRMAP3 3.09 1.38 CRMAP4 3.07 1.36 ERP/SCM fi t (SCMFT) SCMFT1 3.97 1.21 SCMFT2 3.98 1.27 SCMFT3 3.99 1.24 SCM-adoption plan (SCMAP) SCMAP1 4.07 1.18 SCMAP2 4.04 1.19 SCMAP3 3.93 1.21 SCMAP4 3.95 1.22Digital-resource readiness (DRR) Knowledge reach/richness (KRR) KRR1 3.82 1.10 KRR2 3.66 1.06 KRR3 3.65 1.12 KRR4 3.50 1.08 KRR5 3.46 1.09 Process reach/richness (PRR) PRR1 3.82 1.19 PRR2 3.79 1.27 PRR3 3.46 1.11 PRR4 3.65 1.09 PRR5 3.58 1.07

Table 5. Descriptive Statistics for Model Constructs.


Thus, to estimate the proposed second-order constructs, DRR and EDOAI, the coeffi cients (γ i) of each fi rst-order factor were modeled to the latent second-order factor following the procedure of Diamantopoulos and Winklhofer [31, p. 270]:

DRR = γ 1 × KRR + γ 2 × PRR (1)

EDOAI = γ 1 × SCMAI + γ 2 × CRMAI (2)

Factor CriticalConstructs Loading Ratio

Extent of ERP implementation (ERPEX) ERPFS 0.486w 3.06** ERPOS 0.629w 4.13* ERPGS 0.423w 2.56*** CR; AVE = N/ACRM-adoption intentions (CRMAI) CRMFT1 0.915 45.24* CRMFT2 0.927 53.41* CRMFT3 0.929 42.82* CRMAP1 0.927 47.77* CRMAP2 0.902 36.56* CRMAP3 0.922 44.13* CRMAP4 0.941 47.33* CR = 0.976; AVE = 0.854SCM-adoption intentions (SCMAI) SCMFT1 0.704 10.03* SCMFT2 0.752 13.27* SCMFT3 0.753 13.44* SCMAP1 0.917 47.84* SCMAP2 0.890 26.62* SCMAP3 0.873 28.20* SCMAP4 0.896 39.50* CR = 0.939; AVE = 0.689Digital resource readiness (DRR) Knowledge reach/richness KRR1 0.729 7.91* KRR2 0.778 9.25* KRR3 0.830 16.61* KRR4 0.776 11.31* KRR5 0.700 6.72* CR = 0.873; AVE = 0.581 Process reach/richness PRR1 0.820 16.21* PRR2 0.848 23.08* PRR3 0.746 15.10* PRR4 0.781 16.02* PRR5 0.719 11.84* CR = 0.888; AVE= 0.615

Table 6. Measurement Model Results.

Notes: Standard errors for all indicators ranged from 0.01 to 0.10. w Weights; Composite Reliability, CR; Average Variance Extracted, AVE. *** p < 0.05; ** p < 0.01; * p < 0.001.


The results in Figure 2 indicate that the weights are signifi cant for both second-order factors.

Structural Model

PLS does not provide summary statistics to assess the overall “fi t” of the model. However, the variance explained by the path model (multiple R2 for the endogenous construct, EDOAI) and the sign and signifi cance of path coeffi cients are typically used to assess model fi t. A bootstrapping approach was used to generate 500 random samples of the original sample size from the data set by sampling through replacement. This was necessary to obtain estimates of standard errors to test the statistical signifi cance of the path coef-fi cients. Such an approach provides valid estimates of the signifi cance of the path coeffi cients in the PLS models [61].

The empirical testing and validation of the theorized causal links are shown in Figure 3. The estimated path effects, associated t-values, and standard errors are provided. As observed, all coeffi cients are in the appropriate direction and are statistically signifi cant (except for the control variable size). For the main effect, the results reveal that ERPEX has a direct and signifi cant relationship to EDOAI (β = 0.240; t = 3.33, p < 0.01), providing support for hypothesis H1.

Moreover, the standardized path coeffi cients exceed the suggested mini-mum standard of 0.20 [19, 20]. The predictive power of the model can be as-sessed by examining the explained variance. The model explained 42 percent of the variance in EDOAI.

As seen in Table 7, the correlations between ERPEX and EDOAI (0.43), ERPEX and DRR (0.26), and DRR and EDOAI (0.38) were all signifi cantly related.

Figure 2. Results for Second-Order Constructs: Digital Resource Readiness (DRR) and Extent of Digital Options Adoption Intentions (EDOAI).

*** p < 0.05; ** p < 0.01; * p < 0.001


Tests for Moderation

Chin, Marcolin, and Newsted note that it may be desirable to model formative moderator constructs differently from refl ective constructs [22] (see the on-line supplement to their article, www.informs.org/Pubs/Supplements/ISR/1526-5536-2003-02-SupplA.pdf). They suggest creating centralized or transformed composite scores using PLS weightings, then using these composites rather than product-item indicators [22]. All items were standardized or centered consistent with recommended strategies for assessing interactions [22]. Further, to ensure that the moderating effects are tested and interpreted correctly, the study followed the guidelines reported by Carte and Russell [18]. The moder-ating effects, if found to be signifi cant, will provide a strong indication of the lack of common method bias [36]. Although the respondents may anticipate the linear relationships and answer accordingly, they are not likely to predict the moderating relationships. Second, the correlation matrix (see Table 7) did not indicate any highly correlated variables, while evidence of common method bias usually results in extremely high correlations (r > 0.90) [7].

The strength of the relationship between ERPEX and EDOAI is signifi cantly dependent upon DRR (β = 0.512; t = 7.16; p < 0.001), confi rming hypothesis H2. Although a control was applied to EDOAI to partial out the effects of fi rm size, it was not signifi cant (β = 0.123; t = 1.87; p > 0.05). In addition, to examine whether the hypothesized interaction effect added any signifi cant explanatory power over and above the main effect of ERPEX on EDOAI, the R2 values of the dependent variable, EDOAI, were compared. Accordingly, the effect size1 (f 2) was computed to compare the R 2 values between the main and interaction effect models as a gauge for whether the interactions had a small (0.02), medium (0.15), or large effect (0.35) on EDOAI [26]. The model was examined with and without the signifi cant interaction effect, an approach consistent with the guidelines recommended by Carte and Russell [18]. The model without the interaction explained less variance (0.189) in EDOAI than the model that included DRR as a moderator (0.419). Based on the results of the two competing models, the formative effect size was considered large

Figure 3. Model Results


Cons

tru

cts

(1)

(2)

(3)

(4)

(5)

(6)

(7)

ERPE

X —

DRR

0.

26

—KR

R 0.

11

0.30

—

PRR

0.23

0.

41

0.28

—

EDO

AI

0.43

0.

38

0.19

0.

27

—SC

MA

I 0.

24

0.56

0.

16

0.29

0.

38

—C

RMA

I 0.

29

0.14

0.

12

0.07

0.

28

0.22

—

Tab

le 7

. Co

rrel

ati

ons

of

Stru

ctu

ral M

od

el C

ons

tru

cts.


(f 2 = 0.39), indicating that the models are different in terms of their ability to explain EDOAI. This analysis, consistent with the path signifi cances reported earlier, confi rmed that the hypothesized interaction effect of DRR provides a better explanation of the variance in EDOAI relative to the main effect and that DRR must be considered along with ERPEX.

Discussion and Conclusions

This study addresses a central question in the innovation fi eld in the context of complex organizational technologies: Under what circumstances should a fi rm take the lead as an innovator with emerging technologies [38]? It is based on the argument that positioning investments in a multiphase implementa-tion creates real (growth) options for the subsequent implementation and use of the platform [33]. The study provides theoretical arguments for the role of ERP implementations in enabling digital options and measures the impact of the extent of ERP implementation on the extent of digital-options adoption intentions. The study draws on unique data sets collected from two surveys. These provided evidence of the scales’ robustness and generalizability across two populations. The scales in this study exhibited good validity and should provide a useful tool for further inquiry into factors that can lead to differential digital-options adoption intentions as the result of extending ERP implemen-tation. By relating digital-option theory to specifi c measurable constructs, it enabled testing of the hypotheses. The results validated the expectations and provided strong support for the theoretical predictions, confi rming that the effects of extending ERP implementation on the extent of a fi rm’s digital-options adoption intentions are positively moderated by its digital-resource readiness. This research also suggests that the digital-adoption decisions of a fi rm are linked to its digital-resource readiness, and that a fi rm’s knowl-edge and process reach and richness can be powerful explanatory variables that account for important differences in digital-options adoption intentions among fi rms.

This study offers further empirical research into IT platform adoptions and factors that predict digital-options adoption intentions. It demonstrates that extending a fi rm’s ERP implementation can affect its digital-options intentions. Therefore, fi rms should view ERP divisibility and extending ERP implementation as an option-value generator and as having the potential to create growth options for future SCM/CRM adoptions rather than as enhancing rigidities. These growth options will be dependent on the fi rm’s digital-resource readiness (its knowledge and its process reach and richness). In addition, the increased extent of ERP implementation will result in ex-ploitable absorptive capacity needed for assimilating SCM/CRM. Although, SCM/CRM-adoption intentions represent a fi rm’s future plans, and confer the right, but not the obligation, to obtain benefi ts from future deployments of these technologies, there is a general link between the motivations of early adopters and the technology options [38, 39]. That is, those fi rms that defer the adoption of new IT platforms may (1) not have quite the same claim on future benefi ts because of time-compression diseconomies, (2) suffer a general


loss of innovative capabilities, (3) lose the ability to appreciate new technolo-gies, and (4) fi nd themselves “locked out” of future opportunities as well as current ones [32, 40]. Factors leading to high option value (e.g., extent of ERP implementation, organizational learning, innovation-related capabilities and endowment, resource readiness, absorptive capacity, divisibility) should be predictive of early adoption. A meta-analysis of innovation research found that the same variables that predicted adoption also predicted implementation [29]. An evaluation of these factors should help to direct managerial attention to the most promising options and could provide insights into the structuring of options [38].

Implications for Research

Recent research suggests that a fi rm’s ability to enhance its existing IT-dependent strategic initiatives and deploy new ones is dependent on its de-velopment of strong IT infrastructure resources and relationship assets [69]. Digital-options theory suggests that IT directly and indirectly supports agility by enabling fi rms’ digital options [74]. SCM/CRM adoption will enhance a fi rm’s knowledge and process reach and richness and, therefore, its agility [66]. Firms possessing higher levels of agility are better positioned to detect and exploit market opportunities in order to meet changing environmental conditions [66]. It is hoped that this research will trigger a series of related investigations. For example, future studies need to test whether ERP value-chain modules infl uence CRM- and SCM-adoption intention in different ways, and test for the direct and independent roles of SCM- or CRM-adoption on enterprise agility. Future studies need to test the research model longitudinally and test the sustainability of the effects of SCM/CRM adoption on enhanc-ing a fi rm’s sensing and responding abilities, and for the effects of building strong IT infrastructure and relationship assets using both competing and complementary IT platforms. Prior research has suggested that institutional pressures, such as mimetic, coercive, and normative pressures, have the po-tential to infl uence the intent to adopt interorganizational systems [85]. Future research needs to include these pressures along with the constructs in the present study to provide the theoretical linkages to institutional environment in which a fi rm is situated. This can provide greater insight into the factors that enable adoption of digital options.

Implications for Practice

A fi rm’s IT competence is based on the readiness of its stock of IT resources and capabilities and determines its capacity for IT-based innovation by con-verting its IT assets into strategic applications. A fi rm has the option to use its ERP capabilities as a dominant enterprise-application model to enable its Net-enabled business-innovation cycle for collaborate commerce [91]. This can be done by extending its internal business process and system integra-tion to enterprise-application integration and by including its suppliers and


customers. Digital-option creation via SCM and CRM systems allows for both integrated and complementary (e.g., best-of-breed) approaches. However, a fully integrated platform (e.g., Oracle e-business suite, www.oracle.com/applications/e-business-suite.ht) allows for better data consistency and se-mantic reconciliation of reference data and thereby provides better business information for effective decision-making and optimal responsiveness. Prior research has also suggested that by providing collaboration platforms based upon data and process standardization, B2B markets can foster a higher level of interorganizational integration [28]. A fully integrated platform can also increase the prospects for network dominance of the technology class, which is defi ned as the extent to which an IT innovation class is likely to achieve a dominant position relative to competing technology classes. Such an increase will subsequently increase the option value of positioning investments in the IT platform [38].

Firms should position their ERP investment to not only scale up internal operations, but also to trade electronically with customers, suppliers, and other business partners [71]. They should seek growth opportunities rather than cost cutting, turnaround, and sustaining strategies when it comes to ERP implemen-tation. Although the benefi ts from extending the scope of ERP implementation can be more indirect than basic cost-cutting measures, fi rms can still maximize the return on their investment in the long run. Firms that extend the scope of their ERP implementation today can leverage the economies of scale to support new customer and revenue opportunities that simply were not feasible before. However, overhauling business processes to operate horizontally across a fi rm will not be effective unless the installed applications are frequently improved and, when appropriate, retirement strategies are identifi ed based on changing business requirements and emerging technologies [66]. Without these a fi rm may not have fl exibility in adoption or implementation and may have to defer the decision to invest for some period.

Effective option thinking requires managers to (1) recognize and enhance opportunities to create options with IT, (2) value those options (in the same way), and (3) manage projects to fully extract this value [40]. Although the extent of ERP implementation can directly and signifi cantly infl uence a fi rm’s digital-options intentions, the study found that the effect can vary based on the readiness of the fi rm’s digital resources. This suggests that the impact of extending the scope of ERP implementation on digital-options intentions can be strengthened (or weakened) when (or under the conditions of) fi rms have a superior (vs. inferior) knowledge and process reach and richness. Firms need to evaluate the readiness of their digital resources and to fi nd out how they compare with the best-in-practice with respect to (1) the frequency and variety of digitally enabled competitive actions, and (2) the actions of industry leaders in the same industry or other industries. In collaborative environ-ments, fi rms need to optimize their internal processes for mutual benefi t and to exchange accurate and up-to-date information to share best practices. The lack of a perceived performance gap of current business practices, perceived potential benefi ts, or perceived resource readiness is a major reason for not enabling digital options.

Technology leaders have the capacity necessary for IT-based innovation to be fi rst movers. To be a leader, a fi rm must evaluate the strategic importance of


different capabilities and be willing to patiently and persistently build them, even though it may be less expensive or more effi cient in the short term to rely on outsiders for procurement. Prior research suggests that beside resource readiness, institutional pressures have a signifi cant infl uence on organizational intention to adopt [24, 85]. While CRM-adoption decisions may be viewed as one-sided organizational decisions, supply-chain integration involves the integration of information fl ows, physical fl ows, and fi nancial fl ows between a fi rm and its supply-chain partners [70]. Firms wishing to extend their internal processes, therefore, must develop trusting and collaborative relationships with their partners, and focus their efforts on organizational capabilities and strategic processes that contribute to the creation of customer value for their extended enterprises. Not only must they develop supply-chain process-integration capabilities, but they must also develop capabilities to quickly select similar digitally enabled partners to share information and create virtual organizations, and then to dissolve these partnerships just as quickly [45]. Therefore, they must develop new competencies for adding or removing partners, and for raising the level of partnerships to increase their capabilities for long-term value creation. They must be able to leverage the relationships with partners so that each can focus on its own core competencies.

Limitations

Like any empirical research effort, this study has a number of methodological strengths and limitations, and specifi c issues warrant caution in interpreting the results. The breadth of the sample included in the study suggests that the fi ndings are fairly generalizable to many manufacturing industries. However, the fi ndings are limited in some important ways. The study did not consider the institutional effect of focusing on a single industry. Compared to other industries, it is conceivable that SCM has a greater impact than CRM in manufacturing industries. Although the respondents possessed a high degree of relevant knowledge, all of the measures are self-reported and therefore subject to hindsight and other biases. Reliance on the report of a single infor-mant is also a limitation. The extent to which the fi ndings are consistent or dissimilar across different sectors represents an avenue for future research. Another limitation is the cross-sectional nature of the study and the fact that it captured only a snapshot of the fi rms’ digital resources. Finally, the fi rms in the survey had only recently implemented ERP systems, and it was assumed to be unlikely that they would have implemented SCM and CRM. Thus, the respondents were asked about their post-ERP expectations with a series of statements that asked about their fi rm’s future plans regarding SCM and CRM. Although it seems doubtful, nonetheless it is still possible that some of the fi rms had already implemented CRM and SCM.

Concluding Remarks

This study integrated theoretical perspectives and empirical fi ndings on when (or under what conditions) extending the scope of ERP implementation can be


leveraged for adopting digital options. It theoretically derived valid and reli-able scales for measuring the extent of ERP implementation, digital-resource readiness, and SCM-/CRM-adoption intentions. It shows that extending the scope of a fi rm’s ERP implementation can have a direct and signifi cant impact on the fi rm’s digital-options adoption intentions and that such impacts are moderated by the fi rm’s knowledge and process reach and richness.

NOTE

1. Computed as F = f 2 * ([n – k] – 1), with 1, (n – k) df, where n = sample size, k = number of constructs in the mode, and f 2 = (R2 interaction – R2 main)/(1 – R2 interaction).

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Appendix A

Extent of ERP Implementation (ERPEX)

ERPFS: Functional scope of implementation of your selected ERP (select all that apply): Accounting/Finance | Manufacturing | Planning/Scheduling | Human Resources | Sales/Distribution | Logistics/Inventory Control | Other (please specify): ___________

ERPOS: Scope of implementation of your selected ERP: Depart-ment | Division | Entire company | Multiple Companies | Other: ___________

ERPGS: Geographical extent of implementation: Single site | Multiple sites | National | Worldwide

Extent of Digital-Options Adoption Intention (EDOAI)

ERP/CRM fi t (CRMFT)

CRMFT1: There is a good fi t between ERP implementation and customer-relationship management initiatives.

CRMFT2: Data provided by ERP match well with the data required for customer-relationship management.

CRMFT3: Our ERP implementation will help us take advantage of our cur-rent/future customer-relationship management programs.

CRM-Adoption Plan (CRMAP)

CRMAP1: We plan to integrate our e-mail, phone, paper documents, and Web interfaces for improved customer interaction.

CRMAP2: We plan to use sales force automation to improve and track our sales processes.

CRMAP3: We plan to use the Internet for handling sales transactions and improving customer service.

CRMAP4: We plan to automate customer responses and profi les, track market-ing campaigns across different media and channels, and manage quotes and proposals from negotiations to closing.

ERP/SCM fi t (SCMFT)

SCMFT1: There is a good fi t between ERP implementation and supply-chain management initiatives.

SCMFT2: Data provided by ERP match well with the data required for supply-chain management.

SCMFT3: Our ERP implementation will help us take advantage of our cur-rent/future supply-chain management programs.


SCM-Adoption Plan (SCMAP)

SCMAP1: We plan to use the Internet or extranets to improve information fl ow among our suppliers, customers, and partners.

SCMAP2: We plan to improve lead time, quality, and customization by shar-ing data with our business partners.

SCMAP3: We plan to use our own database information and suppliers’ pipe-line processes to forecast demand more accurately and create viable scheduling applications.

SCMAP4: We plan to reduce overall production cost by streamlining our workfl ow and information fl ow to and from business partners.

Digital-Resource Readiness (DRR)

Knowledge Reach/Richness (KRR)

KRR1: ERP implementation has reduced error rates in our operational processes

KRR2: ERP implementation has signifi cantly improved our forecasting accuracy

KRR3: ERP implementation has improved the fl exibility of our decision-making

KRR4: ERP implementation has made us more adaptive to changing busi-ness environment

KRR5: ERP implementation has made our company more agile

Process Reach/Richness (PRR)

PRR1: ERP implementation has given us more ways to customize our processes

PRR2: ERP implementation has improved our effi ciency of operationsPRR3: ERP implementation has reduced the amount of rework needed

for data-entry errorsPRR4: ERP implementation has improved our quality of operationsPRR5: ERP implementation helps us complete more transactions in less

time

JAHANGIR KARIMI ([email protected]) is Accenture Term Professor of Information Systems and serves as the discipline director for information systems programs at the Business School of the University of Colorado at Denver and the Health Science Center. He has a Ph.D. in management information systems from the Univer-sity of Arizona. His research interests include information technology management in national and international environments, IT innovation diffusion, and IT-enabled e-business transformation. He has published in MIS Quarterly, Information Systems Research, Communications of the ACM, Journal of Management Information Systems, IEEE Transactions on Software Engineering, IEEE Transactions on Engineering Management, Decision Sciences, Journal of Systems and Software, Information and Software Technology, and Concurrency, Practice and Experience, as well as in several books and conference


proceedings. He is on the editorial board of International Journal of Electronic Commerce and IEEE Transactions on Engineering Management Journal. Dr. Karimi is a member of the Association for Information Systems.

TONI M. SOMERS ([email protected]) is a professor of information systems management in the School of Business Administration at Wayne State University. Her research interests focus on information technology adoption and implementation. She has published in Information Systems Research, Decision Sciences, Journal of Manage-ment Information Systems, Information and Management, and Production and Operations Management Journal.

ANOL BHATTACHERJEE ([email protected]) is a professor of information systems at the University of South Florida. He received his Ph.D. and M.B.A. degrees from the University of Houston, and M.S. and B.S. degrees from the Indian Institute of Technology. His research interests include IT adoption and post-adoptive behaviors, knowledge creation/transfer in social networks, and healthcare informatics. Dr. Bhat-tacherjee’s research has been published in MIS Quarterly, Information Systems Research, Journal of Management Information Systems, Decision Sciences, Decision Support Systems, IEEE Transactions, Data Base, and Information & Management. He serves on the editorial board of MIS Quarterly.

The Role of ERP Implementation in Enabling Digital Options: A Theoretical and Empirical Analysis

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