Developing measures to assess the extent of sustainable competitive advantage provided by business process reengineering

Developing measures to assess the extent of sustainable competitive advantage provided by business process reengineering

R. Srinivasan

ABSTRACT

The study proposes a set of measures that assess the extent of competitive advantage provided by BPR. Naturally measures have to be both reliable (must not vary unreasonably because of irrelevant factors) and valid (should measure what they are intended to measure). The development of measures that have been tested for reliability and validity is a critical requirement for the advancement of knowledge in management disciplines.

This study is a planned methodological research program based on (Churchill’s 1979) guidelines for developing measures that have the desirable reliability & validity properties. The eight-step process recommended by Churchill is adopted in this research. In the main study primary data was collected using structured questionnaire by eliciting response from 181 respondents. The construct was checked for measurement properties through statistical analysis.

1. INTRODUCTION

Research in Business Processing Reengineering (BPR) is at critical crossroads today, with increased emphasis on developing theoretical concepts and testing empirical relationships rooted in such concepts. Such a transformation critically depends on conceptualizing, measuring & analyzing the role of BPR and its derivative constructs in a business enterprise. This is motivated by the management’s desire to gain competitive advantage through process innovation techniques. It is well recognized that improvement in competitiveness means improvement in both efficiency & effectiveness of business operations. This paper is based on a research study, which aims to contribute to the measurement stream of BPR research with an explicit recognition that measure development cannot be divorced from broader theoretical network. The specific aim is to develop and validate a set of operational measures for a particular conceptualization of BPR. The expectation is to provide an initial set of operational measures with strong support in terms of their measurement properties that can be utilized by other researchers for theory-testing as well as further extensions/refinements.

This paper is divided into four sections. The first section provides background for this research through a brief historical trace of various approaches to measurement of management constructs. The second section provides a theoretical circumscription of the proposed BPR construct. The research method including data analytical approach and results are presented in the third section. The fourth section discusses the implications of the results.

2. BACKGROUND

2.1 Alternative approaches for developing constructs in Management

Narrative Approach: This reflects the case-based tradition of business policy predicted on a view that the complex characterization of competitive advantage should only be described in its holistic and contextual form. Since the distinctiveness lies in its uniqueness to a particular setting, the implication is that competitiveness can (and should) be best described verbally, and any attempt to develop a measurement system will be incomplete. While such an approach has its role for conceptual developments, it has limited use for testing theories pertaining to the effectiveness of situations under differing environmental, organizational and temporal conditions.

Classificatory Approach: The first movement away from narrative descriptions of competitiveness is reflected in the development of strategy classifications–either conceptual or empirical. The conceptual classifications are term as ‘typologies’. The distinguishing feature is that such typologies are rooted in a set of parsimonious classificatory variables or conceptual criteria. While topologists are best know for their conceptual elegance, they do suffer from an inherent weakness in that it is fairly easy to find a single variable on which a typology can be used.

The empirical classifications are termed as ‘Taxonomies”. These reflect empirical existence of internally consistent configurations, but it is important to recognize that their development is sensitive to the choice of underlying variables as well as the analytical method used to extract the taxonomies. Thus, while it serves to capture the comprehensiveness and integrative nature of competitive advantage, it does not reflect the ‘within-group’ differences along the underlying variables.

Comparative Approach: The aim of the third approach is to identify and measure the key traits (also termed as variables) of the competitive advantage construct. Consequently, the focus is less on categorization and more on measuring the differences along a set of characteristics that collectively describe the construct in question. The attractiveness of this approach lies in its ability to decompose the variation that is seen across different BPR factors. This requires that the traits have specific theoretical content as well as adequate operational measures.

However, the present extent of research in construct measurement in BPR is grossly inadequate. Researchers continue to propose and employ measures without corresponding tests for reliability, convergent, discriminant and predictive validity. In the absence of a systematic basis to evaluate the adequacy of measurements, confidence in research results is considerably eroded, which implies that the managerial implications derived from such results may be questionable.

2.2 Study Objectives

This study aims to develop asset of measures for the construct, Sustainable Competitive Advantage provided by Business Reengineering (SCORE). It is one of the first steps in a program of substantive and methodological research on the performance impacts of BPR. In order to attain this objective, the SCORE developed by this study must be empirically validated, compared with alternative measures, and if acceptable, subjected to careful replication. The process of developing a construct measure that is used here is based on (Churchill’s 1979) approach. The study adopts the comparative approach and aims at linking conceptual definition and empirical indicators SCORE.

The Process of Developing Measures–This study is a planned methodological research program based on (Churchill’s 1979) guidelines for developing measures that have the desirable reliability & validity properties. The eight-step process recommended by Churchill is,

* Specifying the domain of the construct

* Generating a sample of items

* Collecting initial data

* Purifying measures

* Collecting new data

* Assessing reliability

* Assessing validity

* Developing norms

2.3 The Domain of the SCORE construct

A construct, also called a theoretical concept, is defined as an abstract entity that represents the ‘true’ nature of a phenomenon. The first step in construct operationalizations is to delineate its domain. One major factor that largely circumscribes the domain of SCORE is the level of impact of BPR.

The impact of BPR is experienced at different levels in Business Enterprise. The impact of BPR can be at three levels (1) internal i.e. impacting the efficiency & effectiveness of organizational structures and processes so as to achieve goals and objectives, (2) competitive i.e. the effect on the ability to outmaneuver competition in the industry in which the organization does business and (3) business portfolio i.e. effect on which industries to complete in and how to position the organization in these industries.

SCORE has defined at the level of competitive strategy, the level of organizations competing in an industry, because competitive advantage, as commonly defined (Portor 1980) is most directly manifested at this level. Thus, the traits underlying SCORE pertain to the impact of BPR in the competitive position of the organization in the industry.

Within the above domain, the following conceptual definition was adopted: “SCORE” refers to benefits accruing to a Business Enterprise, in terms of changes in the Enterprise’s competitive position, that are caused by BPR program. As described above, the phrase “changes in competitive position” includes a variety of effects that enable the Enterprise to compete better.

Literature describes a number of different types of benefits that may be gained from a BPR program. A review of these yielded a comprehensive initial list of different potential benefits. However, many were judged to be components of the same underlying theme and thus grouped together; e.g., cost leadership (Portor 1980), internal and interorganizational efficiency Bakos & Treacy 1986), comparative efficiency (Bakos 1987) and productivity (Synnott 1987).

A detailed discussion of benefits, conceptualized as fourteen underlying dimensions follows:

Efficiency: This primarily refers to the extent to which a BPR program allows a firm to manufacture or produce outputs at a lower cost as compared to competition. It is conceptualized as an effect resulting from the organization’s efforts toward process innovations leading to a radical reduction in the input-output conversion ratio. Efficiency enables an organization to maintain and sustain its overall cost leadership. This is based on the following descriptions in the literature, use of BPR to reduce product design & development cycle, operations, marketing sales, distribution, order fulfillment, comparative efficiency and productivity.

Threat: This variable refers to the extent of impact on the bargaining power of customers and suppliers. This is related to switching and search related costs. Rise in threat increases the dependence of the organization on its primary customers and suppliers to sustain the operations, thereby impacting the very bottom line of the organization. Thus threat is a surrogate and indirect measure of the benefits to the organization due to its ability to exert and clout over its customers and suppliers. The individual dimensions here are Customer Threat & Supplier Threat.

Congruence: This refers to the integration of BPR program with overall corporate goals, strategies and environment. Top management needs a framework to understand the method to integrate BPR into corporate structure and generally an action plan to seize the opportunities. A proper integration and synergy between BPR planning and overall corporate planning ensures that the program enhances and leverages an intrinsic strength of the business making it difficult for competitors to copy the process. This variable thus gives the business a long-term competitive edge. This trait is therefore a surrogate measure of the benefits accruing to the organization because of the exploitation of a distinctive business competence by the BPR program.

Enabler Development: BPR being a radical change process needs an enabler that would help make the change happen. In other words, the enabler facilitates the change process in the organization. This variable primarily measures the extent of involvement of information technology (IT) in the organization’s change process. The degree of involvement of IT as an enabler is directly proportional to the level of IT development in the organization, which is also dependent on the development of the overall IT infrastructure. Enabler Development also measures the extent of IT diffusion & infusion in the organization.

Enabler Capability: This measures the capability of IT in bringing about the radical change in the organization. IT can contribute in transforming unstructured processes into routinized transactions, transfer information with rapidity ease across large distances, replace/reduce human labor, and capture & disseminate knowledge. This variable measures the above.

System Flexibility: This variable primarily refers to the firm’s ability to respond to uncertainties and unforeseen environmental factors. Here the firm is expected to learn to cope with uncertainty whether it is based in product markets or manufacturing processes and their inputs. The extent of learning is reflected in a company which attempts to defensively adjust to uncertainty or proactively control it, in order to seize3 the initiative and try to tweak the environment to its will. The variable flexibility referred here is associated to mix flexibility, volume flexibility, new product flexibility and delivery time flexibility.

Exception Handling: The second primary impact of a firm’s flexibility is in its ability to handle exceptions. The term exception covers issues like incomplete & erroneous formation in inputs & outputs and requests to deviate from standard procedures and situation the business systems was never designed to handle. Thus system flexibility is a measure of the benefits, which a firm receives by virtue of ability to handle exceptions and other uncertain conditions.

Proactiveness: This variable reflects the proactive behavior with respect to an early & successful preemption to market, participation in emerging industries, continuous search for market opportunities and experimentation with potential responses to changing environmental trends. It will be manifested in terms of seeking new opportunities that may or may not be related to the present line of operations, leadership technological strategy. Pro-activeness enables a firm to enjoy first mover advantages in introducing new products and brands ahead of competition, strategically eliminating operations that are in the mature or declining stages of the product life cycle (PLC). These advantages are the result of both generic lead-time as well as competitive asymmetries. Hence pro-activeness is a measure of the benefits which a firm receives by virtue of its being early to market in all aspects.

Comprehensiveness: This refers to the extent to which a business enterprise has the tendency to search deeper for all relevant information and to generate optimum solution alternatives based on this information. It is considered to be an important characteristic of organizational decision-making. It relates to the ability of an organization to thoroughly analyze business situations prior to major decisions, ability to keep track of technology trends, and ability to analyze and understand business environment issues that will influence BPR and strategic planning of the company.

Process Orientation: This dimension refers to the extent of understanding of business processes and ability to synchronize business operation. Usually a business enterprise having some kind of process orientation would have cross-functional processes, thereby increasing the ability to reduce checks & controls, would have large degree of concurrency among various processes and their activities, would allow personnel at the lower level to be involved in decision making by providing them with necessary information and having multiple versions of a business so that it can be implemented according to suitability.

Profitability: This is first amongst the output dimensions. This primarily indicates the efficiency of business processes in practice. Thus profitability refers to whether an organization is implementing the business in a proper manner that will impact the bottom line of the company. The individual measures in this dimension are Net Profit, Return on Sales, ROI and Financial Liquidity.

Growth: This is the second output dimension that basically refers to the effectiveness of the business processes in practice. The individual measures in this are Sales Growth, Market Share, Net Income and EPS. It is generally accepted at if a business enterprise achieves good results in these, it can be said that the right processes are in place.

3. OPERATIONALIZING THE CONSTRUCT

3.1 Overview

Figure 1 is the schematic representations of the research study that links conceptual development part with measure development and its validation. As shown in the figure, the conceptualization is based on specific positions taken in relation to the theoretical question and articulation in terms of mutually exclusive variables.

[FIGURE 1 OMITTED]

There 14 are different dimensions that have been listed in Table 1 along with their individual item measures. Of these, 12 are input dimensions and 2 are output dimensions which have been specifically defined for the construct in question. The empirical component of this study has eighth major steps that have been lineated earlier beginning with the selection of individual items and ending with the assessment of measurement properties.

The content validity of the proposed SCORE variables may be justified with other competitive advantage (CA) models available in the literature. For instance, according to (Portor1985), three themes characteristic CA: low cost, differentiation and sustainability. The SCORE dimension efficiency taps low cost, system flexibility & exception handling captures differentiation, threat enables both low cost and differentiation, while proactiveness and comprehensiveness enable sustainability.

3.2 Item Selection

An initial list of items related to the 14 SCORE dimensions was generated through an exhaustive review of research literature. The purpose was to ensure adequate coverage of the domain of each of these 14 dimensions. This list served to generate 56 items. Each of these items was converted to a question in the questionnaire. The preference for perceptual data reflects the choice to operationalize the SCORE construct in terms of managerial perceptions.

3.3 Data Collection Procedure

Empirical verification of the SCORE construct was undertaken using a structured questionnaire. The perceptions were collected both through personal interview and mail responses. Measures of each dimension were phrased as questions on five-point Likert scale. They were anchored at the ends either with “strongly agree” and “strongly disagree” or with “greatly increased” and “greatly decreased”.

The questionnaire asked respondents to describe the most direct impact of BPR in the competitiveness of the company. However, the danger with this approach was that such a solicitation could have invoked negative responses because many programs are not implemented with the intention of primarily achieving CA.

3.4 Pilot Testing

A preliminary version of the questionnaire was pilot-tested with 30 respondents in nine organizations. Each respondent completed the questionnaire in the presence of the researcher and provided feedback regarding the wording of the questions, their understandability and overall organization of the instrument. The respondents faced no apparent difficulty in responding. However, some of the questions were found to be trying as responding to them involved revealing confidential information of the organization. On most of the other items respondents did not recommend any changes.

3.5 Sample Characteristics

Companies, which had embarked on a BPR program, qualified to be samples for this study. A list of 72 such organizations were drawn up. Most of these companies were in the large cities of India with a few sprinkled in small centers. It was decided that the data would be collected by personally administering the questionnaire in most of these companies and questionnaires would be mailed to companies that were not in large cities.

Thus for 60 organizations 142 responses were collected through personal interview and for the remaining 12 organizations 250 questionnaires were mailed and of them 39 responses which could be used for analysis were received. Hence 81 responses were collected from 723 organizations.

To ensure that the respondent sample was to biased toward specific types of firms based on location, size, and industry, an on-way ANOVA was performed on the complete set of data and it showed no significant differences. This enhances the generalizability of the results to a larger population. Table 2 summarizes the characteristics of the study sample.

3.6 Measurement Properties

Following (Bagozzi 1980), (Bagozzi & Phillips 1982), following measurement properties are considered minimally important for assessing the measures developed here:

* Internal consistency (i.e. reliability & unidimensionality),

* Convergent validity,

* Discriminant validity,

* Predictive validity.

The following paragraph briefly discusses these measurement properties–

Internal consistency refers to two related issue–unidimensionality and reliability. Assessing unidimensionality ensures that all the items measure the underlying theoretical construct in consideration, whilst reliability is an indication of the degree to which measures are free from random errors, and therefore yield consistent results. Convergent validity is an assessment of the consistency in measurements across multiple operationalizations, while discriminant validity is demonstrated when a measure does not correlate very highly with another measure from which it should differ. When the empirical data reflects certain predefined theoretical framework, the construct is inferred to fulfill the condition of Predictive Validity.

4. DATA ANALYSIS

4.1 Assessment of SCORE model

The measurement properties of SCORE model were first assessed by testing the hypothesized SCORE model using confirmatory factor analysis (CFA) The choice here was between exploratory analysis (EFA) and (CFA). Since in research design the dimensions were defined a priori, CFA was chosen over EFA.

A multi-dimensional hypothesized SCORE model was examined using LISREL framework (Joreskog & Sorbom 1989) using the following model:

(1) X = [LAMBDA][xi] + [delta]

Where X is a vector of Q observed variables [xi] is a vector of n (n<q) common factors, [delta] is a vector of unique factors (error terms) and A is a q x n matrix of factor loadings. Under the usual assumptions, the variance-covariance matrix of X (d) can be written as:

(2) [SIGMA] = [LAMBDA] [PHI] [LAMBDA] + [phi]

where [PHI] is matrix of intercorrelations among the common factors and [phi] is a diagonal matrix of error variance ([[theta].sub.[delta]]) for the measures.

Maximum Likelihood (ML) parameter estimates for [LAMBDA], [PHI], [phi] and [chi square] GFI for the null model implied by equations (1) and (2) are obtained from the LISREL program. The probability level associated with the given [chi square] statistic indicates the probability (p) of attaining a large [chi square] value given that hypothesized model is supported. The higher the value of p, the better is the fit, and as a rule of thumb, value of p>0.10 are considered as an indication of satisfactory fit (Hawley and Maxwell 1971).

Researchers increasingly complement [chi square] statistic with Bentler & Bonnet’s (1980) incremental fit index–which is an indication of the practical significance of the model in explaining the data, since exclusive reliance on [chi square] statistic is criticized for many reasons (Fornell & Larker 1981). The representations of the data are,

(3) [DELTA] = ([F.sub.o] – [F.sub.K]/[F.sub.o]

where [F.sub.o] = [chi square] value obtained from a null model specifying mutual independence among the indicators, and [F.sub.K] = [chi square] value for the specific model. The general rule of thumb is that should be greater that 0.90 (Bentler & Bonnet 1980). Table 3 summarizes the results of assessments for unidimensionality for the 14 dimensions. It provides the following model statistics for the assessment of GFI: The [chi square] statistic, its associated degrees of freedom, p-level of significance and the Bentler & Bonnet index [DELTA]. From the results in Table 3, one can conclude that each of the 14 dimensions achieve unidimensionality and convergent validity at the monomethod level of analysis.

4.2 Internal Consistency of Operationalizations (Reliability)

The above result for unidimensionality is not a direct assessment of reliability of the SCORE construct. A typical approach to measure reliability is in terms of the Cronbach Alpha coefficient (Cronbach 1951), which ranges between 0 and 1 has a desirable property of being a lower bound of reliability (Lord & Novick 1968). However, since Cronbach Alpha gives equal importance to all indicators, its application is limited. An alternative approach to measuring reliability is that it represents the proportion of measure variance attribute to the underlying trait. Thus with this approach, reliability ([P.sub.c]) can be calculated as,

(4) [[rho].sub.c] = [([SIGMA][[gamma].sub.1]).sup.2] Variance (A) / [([SIGMA][[gamma].sub.1]).sup.2] (A) + [SIGMA][[theta].sub.[delta]])

where [[rho].sub.c] is the composite measure of reliability, n is the number of indicators, and [[gamma].sub.1] is the factor loading which relates item I to the underlying theoretical dimension (A). In other words, [[rho].sub.c] is the ratio of trait variance to the sum of trait and error variances. When it is greater than 50% it implies that the variance captured by the trait is greater than that of error components (Bagozzi 1981). Table 4 shows that all the [[rho].sub.c] indices are greater than 0.50, thus indicating existence of internal consistency or reliability.

4.3 Discriminant Validity

Discriminant Validity refers to the degree to which measures of different SCORE dimensions are unique from each other. This is achieved when measures of each dimension converge on their corresponding true scores (unique from other dimensions). Testing discriminant validity requires a comparison of a model with this correlation constrained to equal one with the unconstrained model. To satisfy the discriminant validity criteria, the fit of the model with the unconstrained correlation should be significantly better than the fit of the constrained model. A [chi square] difference value with an associated p-value less than 0.05 (Joreskog 1971) supports the existence of discriminant validity criterion. Table 5 reports the results of pair wise tests among the fourteen SCORE dimensions. The results indicate the fulfillment of discriminant validity criterion.

4.4 Predictive validity

Not only its definition and operationalizations determine the conceptual meaning of a construct but also its relationship to antecedents and consequents (Bagozzi and Fornell 1982). More specific ally, predictive validity seeks to evaluate if the measures behave in accordance with the theory. Predictive validity of SCORE in this study is assessed by examining between each SCORE dimension and two important dimensions of business economic performance growth and profitability.

(5) [eta] = [GAMMA][zeta] + [zeta]

where [eta] is the endogenous theoretical construct, [GAMMA] is the matrix of structural coefficients relating the exogenous theoretical construct to [eta], and [zeta] is the residual of [eta]. Table 6 shows the results of the 24 tests carried out to relate each of the 12 SCORE input dimensions to the two output (performance) dimensions, growth & profitability.

5. DISCUSSIONS

5.1 Relationships among SCORE Dimensions

The SCORE dimensions are positively correlated (Table 5) amongst themselves. This implies that all BPR programs that provide sustainable competitive advantage accrue multiple benefits to the business enterprise. Conversely, lack of any negative correlations among the dimensions indicates that a high value on one dimension does not preclude a high value on another dimension, thereby suggesting that these dimensions supplement each other. However, it is possible that relationships among the same dimensions are profoundly different for different types of BPR programs. But empirical evidence, though limited, suggests that the SCORE dimensions reinforce each other, however, the extent of relationship among these dimensions vary with the type of BPR programs implemented.

Several interesting and significant results were brought out in studying the relationships among and output dimensions. The results are summarized in Table 6. All the coefficients were found to be in expected directions as theorized by management literature. The results are interpreted below. For instance, theoretically improvement in primary activity efficiency is predicted to have a good positive impact on both profitability and growth of the business enterprise, this comes out very clearly from Table 6 also, thus the empirical data only serves to strengthen management theory. From the data it was found the improvement in secondary activity efficiency did not any positive impact either on profitability or growth. The only explanation that can be given is that secondary activities in an organization were only support activities and not core business activities of the organization. The third very interesting result that comes out is the fact that as the threat (i.e. both buyer & supplier threat) increases for an organization, it has a direct negative impact on both its profitability and growth. Thus the statistical findings were found to be in line with theoretical framework.

The remaining input dimensions, namely, comprehensiveness, process orientation, proactiveness, congruence, enabler development and enabler capability, all had predictable business impacts of the organizations financial performance. An interesting result that is worth mentioning here is that enabler development has negative impact on the short-term profitability. This can be explained by the fact that most organizations use technology as an enabler in BPR programs. Technology by the very nature are resource intensive i.e. they require substantial investments and efforts on part of the organizations, to the extent that some organizations occasionally divert resources to technology based investments. This may have negative impact on the profitability of the business enterprise. This result is clearly shown in Table 6.

5.2 Extensions and Refinements

It is important to recognize understand that a single study does not provide ‘valid measures’ in the true spirit. Through successive stages of analysis and refinement, this study has generated a list of operational indicators that satisfied important validity criteria. However such a list only be viewed as a ‘suggestive’ list that needs to be suitably modified and refined before being replicated in other contexts. Analytical sophistication, will, in no way, improve the confidence in such measures. Thus these should be used only as anchors in related studies.

There are numerous unresolved theoretical issues, such as whether the dimensions and their measures are compensatory in nature (Kerlinger 1964), and methodological issues, such as whether to use linear models or not.

The SCORE measures are at most ‘first cut’ and alternative measures must be formulated and compared with these results to clarify the foundations of this construct. Given the perceptual nature of data it is important to recognize the demerits of ‘key informants in each organization, provided they fulfill inter-informant consistency. This would give the degree of shared consensus among the informants. Such an extension would be able to eliminate errors due to organizational structure and hierarchy. Similarly, as an extension to multiple informants could be the use of multiple methods itself. This should be done under the condition that responses from informants are obtained independently. And then this can be followed by ‘triangulation’.

6. CONCLUSIONS

This study developed an initial set of statistically validated measures of an important competitive advantage construct in Business Process Reengineering and Strategic Management. Statistically the construct was checked for reliability, convergent validity, and discriminant validity and predictive validity. The operational indicators from this research study should serve as an initial set of competitive advantage measures for Business Reengineering researchers.

Table 1: Hypothesized Variables of SCORE and their Proposed Measures

Primary Activity * Cost of activities associated with procuring,

Efficiency warehousing & distributing the inputs required

* Cost of transforming inputs into final product

* Cost of marketing the final product

* Cost of providing services to maintain/enhance

the product value.

Support Activity * Cost of improving the present products

Efficiency * Cost of overall coordination of primary

activities

* Cost of general management activities.

* Cost of interacting & coordinating activities

with suppliers & customers.

Supplier Threat * Ability to locate alternate suppliers

* Ability to change to alternate suppliers

* Ability to evaluate various suppliers and

choose the most appropriate one

* Ability to threaten the backward integration

Buyer Threat * Ability to locate alternate customers

* Ability to change to alternate customers

* Ability to evaluate various customers and

choose the most appropriate one

* Ability to threaten the forward integration

System Flexibility * Flexibility in product mix

* Flexibility in product volume

* Flexibility in product development

* Flexibility in new product development

* Flexibility in delivery schedules.

Exception Handling * Ability to fulfill requests for priority

treatment to orders

* Ability to handle unacceptable administration

information

* Ability to handle unacceptable technical &

configuration information

* Ability to handle unacceptable sourcing

information

Comprehensiveness * Development of thorough analyses to make major

business decisions

* Ability to analyze technology trends

* Ability to analyze business environment issues

that influence BPR planning

* Ability to analyze and understand existing

business processes

Process Orientation * Extent of reduction in checks & control

* Degree of concurrency among various jobs and

activities.

* Degree of empowerment in workers for decision

making

* Degree of multiplicity of versions of business

processes

Proactiveness * Ability to identify new opportunities using

BPR related to present operations

* Ability to obtain ‘first-mover’ advantages

* Ability to create and maintain technological

leadership

* Ability to obtain unique know-how about the

market

Congruence * Extent of alignment of BPR strategies to

overall business strategies

* Extent of integration of BPR goals &

objectives with firm’s overall goals and

objectives

* Extent of top management involvement in BPR

project

* Extent of interaction between BPR planners &

corporate planners

Enabler Development * Degree of IT diffusion & infusion within the

company

* Extent of IT development

* Extent of top management involvement in IT

development

* Extent of user involvement in IT development

Enabler Capability * Ability in transforming unstructured processes

into routinized transactions

* Ability to transfer information across large

distances with ease & rapidity

* Ability to replace/reduce human labor.

* Ability to capture and disseminate are

knowledge, information and expertise for

management of business processes

Profitability * Net profit position

* Return on Investment

* Return on sales

* Financial liquidity

Growth * Sales growth

* Market share gains

* Net income growth

* Earnings per share

Table 2: Characteristics of the study sample (N = 181)

Title/Level of Head of the Unit (Vice President, Chief 64%

Informant Information Officer, Chief Technology

Officers

Second Level Managers (Strategic Planners, 36%

Business Planners, IT/EDP Managers)

Range of Less than Rs.500 Million 19%

Sales of the Between Rs. 501 – 1000 Million 27%

Responding Between Rs. 1001 – 2000 Million 10%

Organization Between Rs. 2001 – 5000 Million 25%

Above Rs. 5000 Million 19%

Business Manufacturing Sector 71%

Category Services Sector 29%

Table 3: Assessment of Unidimensionality & Convergent

Validity

CFA Results

Number of Chi-

Dimension Indicators square DF

Prima Activity Efficiency 4 0.45 3

Support Activity Efficiency 4 12.43 3

Supplier Threat 4 6.54 3

Buyer Threat 4 7.37 3

System Flexibility 4 2.74 3

Exception Handling 4 10.96 3

Comprehensiveness 4 9.04 3

Process Orientation 4 3.89 3

Proactiveness 4 8.57 3

Congruence 4 7.62 3

Enabler Development 4 0.90 3

Enabler Capability 4 1.25 3

Profitability 4 2.34 3

Growth 4 1.35 3

Bentler and

Dimension p-level Bonett Index

Prima Activity Efficiency 0.52 0.98

Support Activity Efficiency 0.12 0.97

Supplier Threat 0.24 0.92

Buyer Threat 0.56 0.89

System Flexibility 0.14 0.90

Exception Handling 0.28 0.97

Comprehensiveness 0.26 0.96

Process Orientation 0.29 0.92

Proactiveness 0.47 0.95

Congruence 0.46 0.94

Enabler Development 0.39 0.98

Enabler Capability 0.40 0.90

Profitability 0.45 0.91

Growth 0.51 0.93

Table 4: Assessment of Reliability (Internal Consistency)

Indices

Number of Reliability

Dimension Indicators Index

Primary Activity Efficiency 4 0.78

Support Activity Efficiency 4 0.79

Supplier Threat 4 0.56

Buyer Threat 4 0.58

System Flexibility 4 0.65

Exception Handling 4 0.67

Comprehensiveness 4 0.68

Process Orientation 4 0.53

Proactiveness 4 0.79

Congruence 4 0.70

Enabler Development 4 0.69

Enabler Capability 4 0.62

Profitability 4 0.57

Growth 4 0.54

Table 5: Assessment of Discriminant Validity

Chi Square Statistic

Variable Constrained Unconstrained Chi Square

Model Model Difference

Primary Activity Efficiency with

Support Activity Efficiency 122.98 38.06 84.92

Supplier Threat 77.85 34.25 43.60

Buyer Threat 122.36 29.75 92.61

System Flexibility 87.90 43.98 43.92

Exception Handling 60.14 14.78 45.36

Comprehensiveness 90.53 83.96 6.57

Process Orientation 60.14 59.72 0.42

Proactiveness 153.92 106.23 47.69

Congruence 79.26 61.16 18.10

Enabler Development 54.06 49.19 4.87

Enabler Capability 183.92 78.88 105.04

Profitability 137.15 81.13 56.02

Growth 122.14 68.95 53.19

Support Activity Efficiency with

Supplier Threat 122.12 65.90 56.22

Buyer Threat 90.45 56.39 34.06

System Flexibility 45.87 23.67 22.20

Exception Handling 120.23 53.32 66.91

Comprehensiveness 34.19 12.43 21.76

Process Orientation 89.53 54.68 34.85

Proactiveness 40.06 19.99 20.07

Congruence 67.49 23.84 43.65

Enabler Development 56.30 22.20 34.10

Enabler Capability 100.35 40.43 59.92

Profitability 120.33 49.09 71.24

Growth 92.01 42.10 49.91

Supplier Threat with

Buyer Threat 123.78 67.56 56.22

System Flexibility 92.68 19.02 73.66

Exception Handling 34.72 12.65 22.07

Comprehensiveness 56.39 13.35 43.04

Process Orientation 72.33 34.45 37.88

Proactiveness 45.55 28.37 17.18

Congruence 48.30 23.01 25.29

Enabler Development 64.47 29.44 35.03

Enabler Capability 110.32 73.22 37.10

Profitability 99.22 66.23 35.41

Growth

Buyer Threat with

System Flexibility 88.31 46.90 41.41

Exception Handling 45.03 27.11 17.92

Comprehensiveness 120.12 45.92 74.20

Process Orientation 23.99 20.90 3.09

Proactiveness 111.20 89.02 22.18

Congruence 102.34 45.53 56.81

Enabler Development 139.42 99.22 40.20

Enabler Capability 74.33 66.43 7.90

Profitability 35.30 12.22 23.08

Growth 90.36 47.21 43.15

System Flexibility with

Exception Handling 111.02 79.02 32.00

Comprehensiveness 100.34 55.53 44.81

Process Orientation 129.42 89.24 40.18

Proactiveness 70.33 63.44 6.89

Congruence 55.30 22.42 32.88

Enabler Development 70.36 37.21 33.15

Enabler Capability 45.57 38.97 6.60

Profitability 48.33 28.01 20.32

Growth 64.44 29.24 35.20

Exception Handling with

Comprehensiveness 65.76 24.45 41.31

Process Orientation 48.35 29.39 18.96

Proactiveness 40.30 23.10 17.20

Congruence 67.47 29.48 37.99

Enabler Development 75.57 45.87 29.70

Enabler Capability 76.87 43.54 33.33

Profitability 77.45 32.45 45.00

Growth 90.43 43.43 47.00

Comprehensiveness with

Process Orientation 123.78 67.56 56.22

Proactiveness 92.68 19.02 73.66

Congruence 34.72 12.65 22.07

Enabler Development 56.39 13.35 43.04

Enabler Capability 72.33 34.45 37.88

Profitability 87.64 35.67 51.97

Growth 53.47 42.56 10.91

Process Orientation with

Proactiveness 89.65 45.67 43.98

Congruence 76.65 44.31 32.55

Enabler Development 77.98 43.20 34.78

Enabler Capability 87.42 64.65 22.77

Profitability 32.68 12.43 20.25

Growth 102.46 67.78 34.68

Proactiveness with

Congruence 89.48 54.53 34.95

Enabler Development 45.98 33.55 12.43

Enabler Capability 65.90 21.87 44.03

Profitability 76.92 32.34 44.58

Growth 109.53 78.65 30.88

Congruence with

Enabler Development 65.36 24.39 40.97

Enabler Capability 73.55 12.45 61.10

Profitability 42.10 24.31 17.79

Growth 86.76 42.76 44.00

Enabler Development with

Enabler Capability 68.67 24.77 43.90

Profitability 97.99 45.11 52.88

Growth 87.66 56.63 31.03

Enabler Capability with

Profitability 79.89 43.53 36.36

Growth 45.64 12.13 33.51

Profitability with

Growth 65.77 44.43 21.34

P< 0.01

Table 6: Assessment of Predictive Validity

SCORE Input Dimensions SCORE Output (Performance) Dimensions

Profita-

[gamma] bility [gamma] Growth

t-value t-value

Primary Activity Efficiency 0.421 4.546 0.524 4.103

Support Activity Efficiency -0.124 -1.451 0.139 0.927

Supplier Threat -0.138 -1.783 -0.127 -1.002

Buyer Threat -0.127 -1.385 -0.098 -0.946

System Flexibility 0.249 2.237 0.387 3.938

Exception Handling -0.116 -1.346 0.394 4.654

Comprehensiveness 0.233 3.894 0.159 1.985

Process Orientation 0.335 3.129 0.417 4.236

Proactiveness 0.376 4.634 0.320 3.995

Congruence 0.298 2.591 0.224 2.194

Enabler Development -0.195 -1.837 0.397 3.538

Enabler Capability 0.129 1.787 0.435 4.129

Note: P< 0.01

7. REFERENCES

(1.) Anderson, J.C., “An approach for confirmatory measurement and structural equation modeling of organizational properties”, Management Science, 33 (4), pp. 525-541, 1987.

(2.) Bagozzi, R.P., Causal Model in Marketing, John Wiley & Sons, New York, NY, 1980.

(3.) Bagozzi, R.P., “An examination of the validity of two models of attitude”, Multivariate Behavioral Research, 16, pp. 323-359, 1981.

(4.) Bagozzi. R.P. and Fornell, C., “Theoretical Concepts, Measurements and Meaning”, A second Generation of Multivariate Analysis, Volume 2, Praeger, pp. 5-23, 1982.

(5.) Bagozzi. R.P. and Phillips, L.W., “Representing and testing organizational theories: A holistic construal”, Administrative Science Quarterly., 27, pp. 459-489, 1982.

(6.) Bailey, J.E., and Pearson, S.W., “Development of a tool for measuring and analyzing computer user satisfaction”, Management Science, 29 (5), 1983.

(7.) Bakos, Y.J., and Treacy, M.E., “Information technology and corporate strategy: A Research Perspective”, MIS Quarterly, pp. 107-119, June 1986.

(8.) Cronbach, LJ., “Coefficient alpha and Internal structure of tests”, Psychometrika, 16, pp. 297-334, 1951.

(9.) Ghemawat, P., “Sustainable advantage” Harvard Business Review, 64 (3), pp. 53-58, 1986.

(10.) Johnson, R.A., and Wichern D.W., “Applied Multivariate Analysis, Prentice-Hall, Inc., 1988.

(11.) Joreskog, K.G. and Sorbom D., Analysis of linear Structural Relationships by method of maximum likelihood, National Educational Resources, Chicago, IL, 1978.

(12.) Kerlinger, F.N., Foundations of behavioral research, Holt, Reinhart & Winston, INC., 1964.

(13.) Porter, M., Competitive Strategy, Free Press, New York, NY, 1980.

(14.) Porter, M., Competitive Advantage, Free Press, New York, NY 1985.

(15.) Sethi, V. and King, W.R., “Construct Measurement in information systems research”, Decision Sciences, 22(3), pp. 455, Jul-Aug 1991.

(16.) Sethi, V., and King, W.R., “Development of measures to assess the extent to which an Information Technology application provides competitive advantage”, Management Science, 40 (12), 1994.

(17.) Venkatraman, N., “Strategic “Strategic Orientation of business enterprise: The construct, dimensionality and measurement”, Management Science, 35 (8), 1989.

(18.) Venkatraman, N., and Grant, J.H., “Construct Measurement in Organizational Strategy Research: A critique and a proposal”, Academy of Management Review, 11 (1), pp 71-87, 1986.

(19.) Venkatraman, N., and Ramanujam, V., Planning system success: A conceptualization and an operational model”, Management Science, 33 (6), pp. 687-705, 1987.

Dr. R. Srinivasan is a Professor in the Department of Management Studies, Indian Institute of Science, Bangalore, India. He earned his Doctorate at the Indian Institute of Management, Bangalore (Fellow Programme) in 1981. He also has bachelor’s and master’s degrees in Engineering. He has more than 25 years experience in Industry and Academia.

Dr. Pallab Saha earned his Doctorate from the Department of Management Studies, Indian institute of Science, Bangalore, in 2001. He has an MBA in Information Systems. Prof. R. Srinivasan served as Chairman of his Doctoral Research Committee.

COPYRIGHT 2003 International Academy of Business and Economics

COPYRIGHT 2004 Gale Group