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Alignment and Outsourcing – Part I

How to align business and technology objectives when large-scale outsourcing exists

As a follow-up from the previous post on Fields of Alignment, here is the continuation of the subject for one of our fields – Alignment and Outsourcing Strategy. In this post, I explain the impact of the outsourcing strategy on an organization and the objective is to contribute to the research body of literature by relating two different conceptual models (SAM/mSAM and Decision cube for IT sourcing engagements) as an entry point into the development of theory.

There are five forms of IT sourcing: (a) insourcing, (b) selective sourcing, (c) strategic alliance sourcing, (d) outsourcing, and (e) off-shore outsourcing. While the insourcing relies on in-house resources, the other three types of outsourcing use resources external to the organization. Selective and strategic alliance sourcing are established on multiple suppliers and on joint venture partners respectively. While outsourcing predominantly uses resources external to the organization at a local or regional level, offshore outsourcing implies delegation of the selected business operations to an offshore location outside the country.

Information systems (IS) outsourcing (especially its offshoring aspect) as a special type of model began early in 1990 as a way to supplement in-house IT development activities. IS outsourcing became a growing economic phenomenon worldwide because of (a) the development of IT-related infrastructures in developing countries, (b) a surging demand for IT specialists in developed world, and (c) availability of a highly skilled pool of personnel in the developing world at a reasonable cost. Organizations that outsource their activities are expecting the following benefits: (a) cost savings; (b) increased rate of returns on investments, and, (c) improved access to best practices in IT design, implementation and operations.

The IS offshore outsourcing has some pitfalls if not well planned and implemented on both sides – client and vendor, and these pitfalls create client-vendor relationship problems. In addition to the problems mentioned, the three potential concerns related to privacy in information security raised by off-shoring data processing are the legal aspects, information security, and vendor reliability and dependability.

In September 2004, JP Morgan Chase, one of the world’s largest financial institutions (over $1.2 trillion in assets and the second largest U.S. bank) scrapped a 7-year, $5 billion IT outsourcing contract with IBM, as a result of a decision to bring back IT inhouse (insourcing). People who oppose outsourcing, especially offshoring, declared the “end of sourcing.” As a matter of fact, Adams, the CIO who led the process, said that his move was greatly misunderstood: “I am clearly an advocate of offshoring.” In the case of such a large bank, there was a reason for insourcing, which was mainly to get a better competitive advantage from IT, while in smaller organizations, Adams believed large-scale outsourcing is logical.

Here are some observations made by Adams:
• The work ethics, attitudes, and ambitions of the company’s employees in India are significantly higher than its U.S. employees.
• Outsourcing of major parts of mission-critical technologies is not a best solution for a large firm. Technology development should be in-house, while support services can be outsourced.
• Four criteria were used to determine what and how much to outsource: (a) the size of the company (should be large enough to attract good IS employees), (b) cost of outsourcing versus insourcing, (c) the interest level of top management to have and properly manage IT assets, and (d) financial arrangements of the outsourcing.
• It may be difficult to align business and technology objectives when large-scale outsourcing exists.
• The insourcing includes data centers, help desks, data processing networks, and systems development.
• Buying technology directly from vendors saved the bank a considerable amount of money (10 to 15 percent).
• Usually less than 5 percent of outsourcing contracts are canceled as in this case.
• The cancellation was driven mainly by the merger with Bank One, which made the combined bank very large.

There are advantages and disadvantages of outsourcing the integration of new technologies into the organization. Offshoring offers many advantages such as the focus on unique core competencies, exploitation of the intellect of another organization, and costs reduction. The main downsides of offshoring are security, lack of knowledge specific to the industry of outsource companies, languages and cultural barriers, government restrictions, and loss of control of IT assets.

Most companies enter outsourcing agreements without a good process discipline. This situation can lead to escalating costs, poor results, and difficulties managing the relationship. IS managers should know what, when, and how to outsource. They should be aware of the leadership decision-making regarding outsourcing and offshoring. In 1960, Simon published what must be one of the better-known models in the management literature, his model of decision-making. According to Simon, there are four different stages in decision-making: intelligence, design, choice, and implementation.

Whilst Simon’s model is general for decision making, some scholars and practitioners proposed adapted frameworks that parallel the decision-making process an organization supposedly goes through when evaluating its sourcing options and subsequent outcomes. These frameworks generally have two phases: (a) decision process (three phases of Simon model: why, what, which) and (b) implementation (how, outcomes).

In 2004, Dibber and his colleagues proposed a framework of IS offshoring decision-making process by identifying three variables that influence strategic offshoring decisions: (a) internal IS capacity, (b) IT services opportunity contemplated, and (c) potential strategic business values obtained from IT service. The internal IT capacity comprises commitment of top leadership, provision and deployment of infrastructure, pervasiveness and sophistication of use, technological and managerial skills. Discrete IT functions and end-to-end IT enterprise-wide solutions are the IT services opportunity being contemplated. The potential strategic business values to obtain from IT service are: service level, core competencies, alignment of goals, time to market, world class processes, industry on process knowledge, new business opportunities and overall competitiveness.

Discrete IT functions comprise (a) stand-alone functions (voice and data communication management), network monitoring and management, network services (LAN/WAN management); (b) desktop management (helpdesk, desktop support, asset management, imaging, and procurement); (c) data centers services (operation of mainframe, midrange, distributed systems, monitoring of systems performance, business continuity, backup and storage services, cloud computing and virtualization, information security and assurance); and (d) application service (application management, software maintenance and upgrades, systems operations management, problem tracking, etc.). End-to-end solutions include enterprise-wide solutions that add values to the organization.

Pati and Desai’s (2005) decision cube for IT sourcing engagements helps to determine whether or not an IT service should be outsourced. This model defines eight scenarios which guide strategic in-sourcing or outsourcing decisions. The two scenarios that guide strategic in-sourcing decisions are (a) a high level of internal capability for end-to-end solution engagement that has high strategic business values for the organization; and (b) a high level of internal capability for a discrete IT service engagement that has high strategic business values for the organization. Other scenarios favor outsourcing strategy.

I agree that most companies have outsourced some portion of their business to lower costs and over time, have achieved cost savings in the outsourced portion of the business. It is likely that this cost-saving mirage could not produce long-term benefits for a firm because it contradicts the purpose of alignment, information security, and knowledge management. The main economic driver for in-sourcing is the value associated with corporate knowledge. In the knowledge society, the value-creating strategies and long-term viability of a firm depend on sustaining its competitive advantage. Sustaining competitive advantage of the firm requires aligning IT to business in a secure environment by considering knowledge as a distinctively unique resource that should be managed.

Some References:

Dibbern J. et al. (2004). Information systems outsourcing: A survey and analysis of the literature. The DATA BASE for Advances in Information Systems, 35(4), 6-102.
Pati, N and Desai, M., S. (2005). Conceptualizing strategic issues in information technology outsourcing. Information Management & Computer Security, 13(4), 281-296.

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Measuring the Business Value of IT Investments

IT Investment projects are defined as jobs with different sizes generally subdivided into sequence of activities, tasks with clear timelines and expectations, or complex IT efforts of interconnected activities performed by various teams to achieve well-defined objectives, budget, and schedule. IT investments are unsuccessful for many reasons but the primary explanations for these failures are the lack of commitment from the management, organizational problems, lack of strategic vision and execution capabilities, implementation problems, and lack of projects planning.

The research on IT project planning process can be subdivided into strategic and operational perspectives. Some research on IT project planning has explored the strategic aspects and the identification of projects that match with corporate objectives. Some other studies have focused on the analysis and selection of a project from several capital expenditures alternatives (or capital budgeting of IT investments).

Existing Valuation Frameworks

The traditional capital budget methods are based on the calculation of the cash flows input and outputs. Seven traditional budgeting models are used to evaluate capital projects: (a) payback method, (b) return on investment, (c) cost-benefit ratio, (d) profitability index, (e) net present value, (f) economic value added, and (g) internal rate of return.

The payback method measures the number of years required to reimburse the initial outlay of a project, by dividing the original investment by the annual net cash inflow. The return on investment (ROI) is found by dividing the net benefit by the total initial investment. The net benefit is calculated by considering the total benefits minus the total cost and the depreciation and divide by the useful life. The cost-benefit ratio method calculates the returns from a capital investment using the ratio between the total benefits and the total costs. The net present value (NPV) method is the amount of money an investment is worth, taking in account the costs, the earnings and the time value of money. The profitability index is calculated by dividing the present value of cash inflows by the initial cost of the investment. The internal rate of return (IRR) is the discount rate of return that an investment is expected to earn such as equate the present value of the project expected cash flows to the initial investment. Finally, the economic value added (EVA) approach refers to the measurement of the excess value created by managers showing a created or destroyed value of the enterprise in the analyzed period. Similar to other value-based methods (like the economic profit, cash or market value added, or cash flow ROI), EVA promotes the maximization of the economic value of a company by allocating its resources to their best use.

Traditional capital budget methods are limited to valuate IT projects because of (a) their inability to cope with risk, uncertainty, and flexibility, (b) they overlook the cost to train users, the learning curve to adapt to new technologies, and the socials subsystems costs and benefits of the IT projects, and (c) their inability to quantify intangible benefits such as improving knowledge, customer service, or decision making.

These shortcomings are especially clear with IT investments done under conditions of uncertainty in today global economy, which requires dynamic capabilities and strategic flexibility. The real option approach has been proposed as an alternative to the deterministic capital budget methodologies and the extension of the financial option theory to the options on real (non-financial) assets. The concept of real options was originally developed in the financial industry by Black, Scholes, and Merton in 1973. Myers (1984) pioneered the concept of real options by applying it to managing capital budget investments of an organization.

Prior research used real options valuation (ROV) theory for evaluating IT investments. For example, some scholars and practitioners used real options for evaluating an IT telecommunications infrastructure project. Others used a Black-Scholes approximation for valuating an IT project for the implementation of a point-of-sale banking service. Some others proposed a valuation framework for IT investments drawing upon the ROV theory and game theories.

Measuring the Performance of IT in Organizations

Even though the overall performance of the information systems (IS) function seems to be difficult to conceptualize and measure, two approaches can be distinguished in research into the IT business value: variance and process approaches. The former focuses on the relationship IT investments-organizational performance by taking into consideration financial measures such as lower costs, higher revenues, and improved market share. The latter analyses combine the returns of IT investments with process and organizational changes.

The process approach analyzes the impact of IT on an organization in terms of efficiency, effectiveness, and strategic IT alignment. IT efficiency is the IS function that highlights the relationship between IT expenditures (IS capabilities) into IT assets (or IS function outputs such as systems performance, information effectiveness, and services performance). IS capabilities are inputs such as hardware, software, human skills, and management processes that serves to translate IT expenditures into IT assets. Various metrics are used to assess IT efficiency: availability of systems and applications, number of help desk tickets, mean time between failure or license usage. These metrics comment on efficiency of systems, applications, and networks; unlike other performance variables that focus on engineering performance.

Business-IT Strategic Alignment

The metrics used to assess the efficiency of IT do not inform effectiveness. In fact, IT effectiveness is measured against the business goals and objectives. The impact of IT on organizations is moving from an efficiency production factor to the maximization of the business value of IT investments (or IT effectiveness). Enterprises use IT for two main reasons: (a) capturing information to support corporate processes, and (b) enabling business change. For these purposes, the contribution of IT must be both specific (by supporting defined business processes) and generic (by enabling undefined business change). Such measurement models are closed to capability models and different from performance models.

A reliable measurement of capability metrics is the key to align the corporate business and IT. Strategic alignment refers to the proper use of IT in the elaboration and implementation of corporate strategies and goals. Alignment is defined as the degree of fit between business and IT strategic orientations, and in particular how the integration can be achieved.

Business-IT strategic alignment grows in importance as organizations strive to link business and technology in light of the internationalization of their businesses. Our recent research study (see www.nkoyock.net) used a field survey design to examine (a) the role of knowledge management processes in the relationship between contextual factors and alignment in a multinational corporation (MNC), and (b) the role of IT projects in the relationship between alignment and the performance and effectiveness of an MNC.

The results of our research study (http://blog.nkoyock.net/?p=77) had at least four implications to leaders in MNCs: (a) the effects of top managers’ knowledge of IT on strategic business-IT alignment, (b) the importance of business-IT alignment to organizational performance and effectiveness, (c) the importance of internal context and nature of the organization to knowledge integration, and (d) the role of senior management in knowledge management and strategic management of IT.

A theoretical and practical perspective of business-IT strategic alignment in MNC was provided. Our study drew upon the strategic alignment model and the typology of MNCs to propose and test an IT strategic alignment model for MNCs (mSAM). The business-IT strategic alignment implementation model for MNCs (mSAIM) was the model for application proposed as the critical recommendation of our research study.

Please feel free to join the Business-IT Strategic Alignment community and share your experience using LinkedIn. You can also send me an invitation to connect to and download the five-stage process of business-IT strategic alignment (http://blog.nkoyock.net/?p=82) from the Slideshare presentations on my profile.

Reference:

Myers, S. (1984). Finance theory and financial Strategy. Interfaces, 14, 126-137.

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MIS Research and Practice: Data Analysis with SEM Techniques – Part I

On August 21, 2010, one reader commented on structural equation modeling (SEM) techniques as follow: “I don’t really know enough about SEM, I know some, but I definitely think I need to know more. Where is the best place to start for someone who has a basic understanding, but wants to learn more?”

Dear reader, the purpose of this post is to provide relevant information on Structural Equation Modeling (SEM) to you. I also want to use this opportunity to emphasize that SEM and other second generation data analysis techniques are increasingly being applied in management information systems (MIS) research and practice. I will be posting additional materials on the use, usefulness, and ease of use of SEM in MIS research and practice on my weblog.

SEM techniques are important for MIS research and practice because they provide powerful ways to address key IT research and practice problems such as understanding IT usage, end-users satisfaction, IT strategic alignment, IT project planning and implementation, knowledge management projects, and so on. These techniques can be extremely useful to IT executives who have IT substantive knowledge for modeling, exploration, and interpretation of results.

Structural Equation Modeling

Structural equation modeling (SEM) techniques refer to a range of multivariate methods aimed at examining the underlying relationships (or structure) among multiple predictor and criterion variables in a model. A structural equation modeling process requires two steps: (a) building and testing a measurement model, and (b) building and testing a structural model. The measurement model serves to create a structural model including paths representing the hypothesized associations among the research constructs.

Various scholars compared and contrasted SEM techniques to other multivariate techniques such as multiple regression analysis, multivariate analysis of variance (MANOVA), and canonical correlation analysis (CCA). While multiple regression analysis assesses the relationship between several independent (or predictor) variables and a dependent (or criterion) variable, MANOVA assesses the relationship between two or more dependent variables and classificatory variables or factors. In contrast, SEM techniques represent only a single relationship between the dependent and independent variables.

Multiple regression and MANOVA do not consider the association effects between criterion and predictor variables that generally characterized human and behavioral problems in management. These techniques are unable to examine a series of dependence associations concurrently. In contrast, SEM techniques can expand the statistical efficiency and explanatory ability for model testing with a single comprehensive framework. According to various scholars, SEM techniques have the ability to (a) model relationships among multiple predictor and criterion variables, (b) represent latent variables (or unobserved concepts) in those relationships, and (c) account for measurement error in the estimation process.

Dow et al. (2008) compared three structural equation techniques: path analysis, item level, and parcel. Dow and his colleagues noted that these techniques produced similar structural results even though they differed in the degree they fit the data and the degree of explained variance. Path analysis is considered as an extension of the regression model with a regression performed for each variable in the model as endogenous variable on others indicated by the model as causal. Item level SEM uses individual items as measured indicators for latent variables and requires a large number of parameters to estimate the fit of the model to the data. Unlike path analysis, this method can test hypotheses regarding the structural (path) model and the measurement relations. Parcel SEM used item parcels to conduct analyses with latent variables. Partial sum of responses to individual items were derived from the parcels derived from partial sum of responses to individual items. Our research study, for example, used path analysis to build and test a business-IT strategic model for multinational corporations.

Various vendors propose different SEM software packages: Amos 5, EQS 6, LISREL 8, Mplus 3, PLS Graph 3, PROC CALIS, and Mx. For our study, LISREL 8 was used for statistical analyses. LISREL is a multivariate analytical software package intended for standard and multilevel structural equation modeling techniques. These methods accommodate different data types including complex survey data on continuous variables and simple random sample data on ordinal and continuous variables.

One can note an increased use of structural equation modeling (SEM) techniques in the social sciences and in information systems research. Prior research used these techniques to address business-IT strategic alignment. Kearns and Lederer (2003) used SEM to build and test a model that examined the relationship between business-IT strategic alignment and organizational strategies. Their research model contained 6 constructs, 26 items, general demographics, and 8 hypotheses. Kearns and Sabherwal (2007) used SEM to create a structural model exploring the linkage between IT strategic alignment and IT business value. Their research model contained 9 constructs, 44 items, and 10 hypotheses.

Earlier studies using SEM did not propose models of business-IT strategic alignment for multinational corporations (MNCs). Our research study (Nkoyock, Spiker, Schmidt, & Martin, 2010) used SEM to analyze data collected through the stratified random sampling of two groups of IT and business managers. Our study drew upon SEM techniques to build and test a business-IT alignment model for MNCs with 10 constructs, 53 items, and 11 hypotheses.

SEM Modeling Process

SEM modeling process is an incremental approach specifying the procedures for testing a model. SEM analyses require essentially a 2-step modeling process of building and testing measurement and structural models: (a) measurement model, and (b) structural model.

The measurement model serves to test the reliability and validity of the measures based on the research model. This model postulates the relationship between the measured items and the underlying constructs or factors. The achievement of “best fitting” measurement model consists of attaining a non-significant chi-square statistic and the recommended values of other goodness-of-fit indices. The second step of the SEM modeling process is the test of the structural model.
Our research study used the measurement model to create a structural model, including paths representing the hypothesized associations among research constructs.

The structural model is the path model defining the relationships among the latent or unobserved variables. The structural model specifies which latent variables cause changes in the values of other latent variables. Various scholars posited the achievement of “best fitting” structural model requires the incorporation of theory, substantive knowledge, previous experience, or other guidelines to discern which independent variables predict each dependent variable.

In our research study, the initial structural model included three latent exogenous variables (organizational emphasis on knowledge management, management of perceived environmental uncertainty, and management of transnational IS strategies), seven latent endogenous variables represented the remaining constructs, and the hypothesized direct paths. The main outcome of our study is a theoretical and practical perspective of business-IT strategic alignment for multinational corporations. For more details about the results of this research study, please visit www.nkoyock.net.

Some References:

Dow, K. E., Jackson, C., Wong, J., & Leitch, R. A. (2008). A comparison of structural equation modeling approaches: The case of user acceptance of information systems. Journal of Computer Information Systems, 48(4), 106-114.
Kearns, G. S., & Lederer, A. L. (2003). A resource-based view of strategic IT alignment: How knowledge sharing creates competitive advantage. Decision Sciences, 34(1), 1-29. doi: 10.1111/1540-5915.02289
Kearns, G. S., & Sabherwal, R. (2007). Strategic alignment between business and information technology: A knowledge-based view of behaviors, outcomes, and consequences. Journal of Management Information Systems, 23(3), 129-162.
Nkoyock, A., Spiker B. K., Schmidt T., & Martin, C. (2010). Business-IT strategic alignment for complex multinational corporations: The case of the U.N. Secretariat. MIS Quarterly, to be published soon.

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Redefining IT Performance and IT Effectiveness

Performance refers to the ability to acquire resources necessary for organizational survival.  Organizational performance results from a combination of industry or environmental conditions, the strategy that an organization’s decision makers choose, and the structure in place to support the strategy.  Performance is a proxy measure that indicates legitimacy by resource suppliers and perceived organizational effectiveness. Performance measurement consists of an assessment tool to measure effectiveness, provides information to managers for decision-making, and helps them to analyze organizational efficiency at the operative and strategic levels. 

 

Performance issues relate to the disciplines of general systems theory elaborated by Ludwig von Bertalanffy in the 1920s.  This theory included various disciplines such as behavioral science (sociology), economics (management accounting), information technology, mathematics (operations research), and organization theory.  These core disciplines for agency and management theories form a suitable umbrella for HR management, public administration, and management control systems. The literature on management philosophies provides an examination of these disciplines with an emphasis on corporate culture and power, Taylor’s scientific management, Mayo’s humanistic management, or quality management.

 

From a performance standpoint, three major components relate to management and agency theories: analysis, evaluation, and measures.  Several methods facilitate a performance analysis: expert systems, data mining, factor analysis, geographic information systems, ratio analysis, statistical regression, structural equation modeling, and productivity theory (data envelopment analysis, total factor productivity, and stochastic frontier analysis).  Whereas performance measures result from various frameworks such as the balanced scorecard and performance pyramid, performance evaluation includes strategic management issues that cover the alignment between incentive means of knowledge workers and corporate strategic goals and processes.

 

Early traditional frameworks of organizational performance such as Du Pont’s pyramid of financial measures (1920s) were a single all-encompassing approach. Du Pont’s framework for example, only focuses on financial performance. In contrast, emerging tools integrate the complexity and dynamic aspects of organizations by considering various dimensions of performance.  In this line of reasoning, performance measurement covers processes, system dynamics, and strategies that characterize business.   

 

Even though the overall performance of the information systems function seems to be difficult to conceptualize and measure, two approaches can be distinguished in research into the business value of IT investments: variance and process approaches. The variance approach focuses on the relationship between IT investments and organizational performance by considering financial measures such as lower costs, higher revenues, and improved market share.  The variance approach examines the “what” question: What is the relationship between IT investments and organizational performance? In contrast, the process approach focuses on the “how” question: How do IT investments improve organizational performance?

 

The process approach combines the returns on investments with process and organizational changes. The process approach analyzes the impact of IT on organizations from efficiency, effectiveness, and strategic IT alignment standpoints.  IT efficiency is the IS function highlighting the relationship between IT expenditures (or IS capabilities) and IT assets (or IS function outputs such as systems performance, information effectiveness, and services performance).  IS capabilities are inputs such as hardware, software, human skills, and management processes that serve to translate IT expenditures into IT assets.  Researchers use various metrics to assess IT efficiency: availability of systems and applications, number of help desk tickets, mean time between failure, and license usage.  These metrics comment on efficiency of systems, applications, and networks, unlike other performance variables that focus on engineering performance.

 

Prior scholars pointed out the limitation of IT efficiency measurements to assess IT effectiveness.  The influence of IT on organizations moves gradually from an efficiency production factor toward the maximization of the business value of IT investments (or IT effectiveness).  Enterprises generally invest on IT for two reasons: (a) to capture information to support corporate processes, and (b) to enable business change.  These scholars advised that the contribution of IT is to be specific (by supporting defined business processes) and generic (by enabling undefined business change).  They added that the measurement models of the IT business value should differ from performance models but close to capability models.

 

Within the context of strategic IT planning, some of the prior research attempted to investigate the linkages between IT investment projects and the associated business value using selected financial measures related to performance and productivity.  Some other studies attempted to measure the business impact of IT in organizations by market expansion, cost avoidance, customer value, efficiency, and profitability.  Some other research compared two analytical models (linear and nonlinear) and two conceptual (contingency-based and resource-centered) frameworks to assess the business value of IT using both financial objectives (expense and revenue) and perceived measures (firm’s perceived profitability).

 

Drawing upon the theoretical input-output model, Chang and King (2005) developed an instrument that explored the role of the IS function on business process effectiveness and organizational performance. Silvius (2006) proposed a multivariate value framework to assess the impact of IT on an organization. Yeniyurt (2003) proposed a performance measurement framework for global corporations drawing upon methods involving both financial and non-financial variables such as Skandia navigator, economic value added, and balanced scorecard. Yeniyurt’s non-financial variables for the organizational performance and effectiveness construct are customer satisfaction, innovation, internal processes, and organizational culture and climate.

 

The research on IT project planning process can be subdivided into strategic and operational perspectives. Some research on IT project planning explored the strategic aspects and the identification of projects that match with corporate objectives. Some other studies focused on the analysis and selection of a project from several capital expenditures’ alternatives (or capital budgeting of IT investments). The traditional capital budget methods are based on the calculation of the cash flows input and outputs. Seven traditional budgeting models are used to evaluate capital projects: (a) payback method, (b) return on investment, (c) cost-benefit ratio, (d) profitability index, (e) net present value, (f) economic value added, and (g) internal rate of return.

 

These traditional capital budget methods are limited to valuate IT projects because of (a) their inability to cope with risk, uncertainty, and flexibility, (b) they overlook the cost to train users, the learning curve to adapt to new technologies, and the socials subsystems costs and benefits of the IT projects, and (c) their inability to quantify intangible benefits such as improving knowledge, customer service, or decision making. These shortcomings are especially clear with IT investments done under conditions of uncertainty in today’s global economy which requires dynamic capabilities and strategic flexibility. The real option approach have been proposed as an alternative to the deterministic capital budget methodologies and the extension of the financial option theory to the options on real (non-financial) assets.

 

Some References:

 

Chang, J. C., & King, W. R. (2005). Measuring the performance of information systems: A functional scorecard. Journal of Management Information Systems, 22(1), 85-115.

Silvius, A. J. G. (2006). Does ROI Matter? Insights into the True Business Value of IT. Electronic Journal of Information Systems Evaluation, 9(2), 93-104.

Yeniyurt, S. (2003). A literature review and integrative performance measurement framework for multinational companies. Marketing Intelligence & Planning, 21(3), 134-142.