Strategic confluence of continuity and change for

0 downloads 0 Views 225KB Size Report
would not enable a firm in achieving sustained competitive advantage despite ...... Barney, J.B. (1995) 'Looking inside for competitive advantage', Academy of ...

Int. J. Engineering Management and Economics, Vol. 2, Nos. 2/3, 2011

Strategic confluence of continuity and change for improved innovation performance J.S.A. Bhat* Department of Scientific and Industrial Research, Ministry of Science and Technology, Technology Bhawan, New Mehrauli Road, New Delhi 110016, India E-mail: [email protected] *Corresponding author

Sushil and P.K. Jain Department of Management Studies, Indian Institute of Technology, Vishwakarma Bhawan, Shahid Jeet Singh Marg, New Delhi 110016, India E-mail: [email protected] E-mail: [email protected] Abstract: Innovation management has been a subject of immense concern to researchers. Both continuity and change aspects in this context have been scrutinised at length, but management of continuity together with change has only recently been gaining some attention. This paper contributes to the understanding of the confluence of continuity and change to manage innovation. Through empirical research based on Indian manufacturing firms, the key factors that enable a firm manage innovation in a successful manner resulting in enhanced innovation performance are distinguished. Among several considerations involved in charting a roadmap towards improved innovation performance in a firm, the vital need to maintain a strong linkage between the business and technology strategies, and also ensure top management commitment, have been explicitly revealed to be key issues. Keywords: innovation; continuity and change; innovation management capability; innovation performance. Reference to this paper should be made as follows: Bhat, J.S.A., Sushil and Jain, P.K. (2011) ‘Strategic confluence of continuity and change for improved innovation performance’, Int. J. Engineering Management and Economics, Vol. 2, Nos. 2/3, pp.175–194. Biographical notes: J.S.A. Bhat is a Scientist in the Department of Scientific and Industrial Research, Ministry of Science and Technology, India. In her professional work spanning research, industry and government experience, she has dealt with different facets of innovation management and technology transfer at the micro and macro levels. Sushil is currently a Professor of Strategic Management in the Department of Management Studies, IIT Delhi. He has several publications and a number of books to his credit. He specialises in flexible systems management, strategic Copyright © 2011 Inderscience Enterprises Ltd.

175

176

J.S.A. Bhat et al. change and flexibility, and technology management. He is the Founder President of Global Institute of Flexible Systems Management and a life member of several other national and international bodies. P.K. Jain is currently a Professor of Finance and Modi Foundation Chair Professor at Department of Management Studies, IIT Delhi. He has a long teaching experience in finance and accounting related subjects. He has been associated with many research and consultancy projects in these fields. He has authored/co-authored several text and research books and has published around 100 research papers in national and international journals.

“The ability to change constantly and effectively is made easier by high-level continuity.” (Porter, 1997)

1

Introduction

Managing innovation is gaining increasing importance among business enterprises. Charting a strategy to enhance innovation performance, while necessarily encountering different continuity and change situations in a dynamic environment, is challenging (Volberda, 1996). In order to progress, tensions between the conflicting requirements posed need to be continually resolved. Research on innovation management has been a compelling concern. While various continuity and change aspects that underlie a firm’s capability to manage innovation have been mentioned in these studies, there is a gap in terms of conceptualising or measuring innovation management capability. Although much work has been done to unearth how firms develop this capability, such knowledge does not help in informing which skills constitute it. Our attempt in this paper is to take a direct step toward understanding the constituent elements that actually comprise innovation management capability and which of these elements matter the most to innovation performance. Better performing firms have unique capabilities to manage resources, or internal processes, enabling them to have an edge over their competitors (Teece et al., 1997). A review of past research studies yields two distinctive groups; one focusing on different firm processes that must necessarily be strengthened continuously in order to retain competitiveness and market leadership, and the other advocating the need for adoption of new firm processes to guard against complacency. The subject of managing the confluence of continuity and change is gaining some attention in recent times (Volberda, 1996). By and large, continuity and change aspects relating to management of innovation have been dealt with separately, providing no real solutions to firms that in reality are faced with situations confronting both of these. Our paper addresses this gap in literature. The paper is structured as follows: Section 2, overviews the literature and develops an integrated approach to the analysis of innovation management capability in firms and presents the main hypotheses. Section 3, presents the approach used to collect data and study the causal interrelations. Section 4, presents the results of the empirical tests and discusses the outcomes. Section 5, concludes.

Strategic confluence of continuity and change for improved innovation

2

177

Theory

Firms face the need to adapt to a dynamic environment in their quest of growth. Simultaneously, they face the pressure of having to sustain stability and preserve their identity just to retain their existing firm performance and survive (Nelson, 1991). Some important concepts have emerged in recent times to deal with this conundrum. In the debate between adaptation versus selection, one strand of researchers accepts that firms may be unable to change in the face of environmental disruptions, and only a select few that stand to gain advantage continue to prosper, and the remaining succumb to inertia with age (Sørensen and Stuart, 2000). Another strand pursues the view that firms can adapt to change through successful strategy planning and implementation (O’Reilly and Tushman, 2004). In support of the adaptation theory, the resource-based approach views a firm as not only a function of the opportunities it confronts, but as a function of the capabilities it has to muster resources for dynamic needs (Learned et al., 1969). Based on this approach, firms are heterogeneous with respect to their capabilities or resources or processes, but homogeneous with respect to the industry sectors they belong to. Researchers have highlighted the need for concurrent exploitation of existing capabilities and exploration of new capabilities to acquire competitive advantage (Penrose, 1959; March, 1991). The capabilities associated with exploitation are concerned with efficiency, enhancing productivity, and control through structured procedures to ensure current viability. On the other hand, exploration is to do with search and discovery directed towards future viability (March, 1991). These paradoxical capabilities are referred to as ambidextrous capabilities (Tushman and O’Reilly, 1997). Helfat et al. (2007) emphasised that a static set of resources or static capabilities would not enable a firm in achieving sustained competitive advantage despite environmental turbulence, which is the reality organisations are faced with. Thus, dynamic capabilities are necessary for the creation, extension and modification of the resource base of a firm. Evidently, capabilities are more than mere organisational routines, as pointed out by Cohen et al. (1996). They extend the meaning of organisational routines by referring to them as capabilities that result in repeated performance when executed in response to selective needs in an organisation, thus providing scope for inclusion of tacit learning together with explicit learning. Kogut and Zander (1992) refers to combinative capabilities as the ability to handle change by transforming (‘recombining’ with other knowledge) existing capabilities (viz., continuity) into new ones (viz., change). Firms, therefore, need to possess internal processes equipping them with the requisite sets of capabilities to perform in existing markets; and concurrently adapt constantly and reconfigure resources and capabilities in response to technology and market changes. The first set of capabilities is referred to as operational capabilities, while the second as dynamic capabilities (Teece et al., 1997). While the concept of dynamic capabilities has inspired substantial research, it faces several criticisms. A universal definition of dynamic capabilities is yet to emerge. For instance, Collis (1994) refers to it as the capability to “develop the capability to innovate faster” and that which governs the rate of change of ordinary capabilities. It has also been defined as the ability to integrate, build and reconfigure competencies in response to change needs (Teece et al., 1997). Zollo and Winter (2002) refers to it as a stable pattern of collective activity through which the firm systematically generates and modifies its operative routines in pursuit of improved

178

J.S.A. Bhat et al.

effectiveness, while Eisenhardt and Martin (2000) refers to it as the organisational and strategic routines by which firms achieve new resource configurations as markets emerge, collide, split, evolve and die. To date, a confounding number of definitions of dynamic capabilities have been coined. The importance of understanding these capabilities in terms of the organisational structures and managerial processes which support productive activity has been given due emphasis (Teece et al., 1997). A firm’s balance sheet does not reflect its true innovation management capabilities. Rather, the argument is that these capabilities are comprised of a firm’s processes, its position (assets such as technology, intellectual property or customer relations), and the number of strategic alternatives or paths available. These capabilities have a static component relating to coordination or integration, a dynamic component that is concerned with learning, and a reconfiguration component which is transformational. What is important about these capabilities is that these have been built over time and cannot be easily replicated, and thus lead to competitive advantage. Another major concept proposed to deal with change is the concept of flexibility suggested by Volberda (1996). A typology of organisational forms addressing change and preservation requirements has been suggested by him, to enable firms cope with competition. Flexible firms are best able to handle change and preservation by balancing the conflicting requirements posed. Sushil (2005) has suggested the flowing stream strategy framework to address the opposing concerns posed by continuity and change. The attempt in this paper is to advance the literature pertaining to management of innovation in the above context, by integrating the concepts discussed. We have adapted the broad definition of innovation provided by the National Knowledge Commission (2007), Government of India. Innovation is a process by which introduction of new/improved goods/services, and/or implementing new/improved operational processes, and/or implementing new/improved organisational/managerial processes can be planned and achieved, resulting in value enhancement for generators and/or users. It is implied that innovation encompasses multiple activities involving several continuity and change dimensions. Organisations desirous of achieving significant competitive advantage need to address innovation concerns continually. Technology capabilities play a central role in furthering and sustaining innovation-led competitive advantage (Lewis et al., 2002). The firm’s organisation capabilities, embedded in the social relationships among individuals in the organisation, contribute importantly to the generation and implementation of innovations (Barney, 1995). Learning capabilities, an outcome of appropriate learning processes, are doubtless deemed important for enhanced innovation capabilities (Nonaka and Takeuchi, 1995; Leonard-Barton, 1995). The generation and strengthening of the skill-sets, knowledge-base and resources to implement an innovation is vital; suitable managerial processes ensure supply and maintenance of these essential assets (Lawson and Samson, 2001). Interactions of the functions with the external environment, or networking processes, are equally as important as the interactions of the internal functions for successful innovation management (Trott, 1998). This provides a broad notion of the processes that a firm is required to handle in order to manage innovation. It also emerges that innovation management is a set of interrelated processes acting in an integrated fashion (Myers and Marquis, 1969), for the most part, involving managing different continuity and change dimensions of its technology, organisation, learning, managerial and networking processes. Also, while a fairly rich understanding of the individual issues and skills relevant to managing

Strategic confluence of continuity and change for improved innovation

179

innovation emerges, an integrated understanding of a firm’s innovation management capability eludes us. Hence, apart from relying on prior research, fieldwork was undertaken to investigate these aspects. Formal discussions were held with 27 experts and practitioners drawn from industry, government, academia, consultancy firms and research organisations to understand the main elements of this capability. This exercise corroborated that capabilities covering both continuity and change dimensions of technology, organisation, learning, managerial and networking processes are among the most important that comprise innovation management capability in firms. We refer to continuity processes as those processes concerned with the planned business trajectory of the firm, for meeting the needs of current and envisaged future markets (replication, optimisation and planned adaptation). For example, technology continuity related new-product-development processes include improvements in existing products to satiate and expand current markets (exploitation), or development of new models to meet new market needs (exploitation), or even development of completely new products to meet a targeted future demand (exploration). We refer to change processes as those that the firm resorts to in times of non-routine change (improvisation and adaptation). For example, using an optional development route when an innovation project fails (exploration), or when technical or marketing expertise is to be invoked urgently (exploitation), or when a strategic partnership is to be formed based on an informal relationship (exploitation) to meet an urgent need. Thus, continuity and change processes, as used in this research, may have both exploration and exploitation capabilities. Hence, based on prior research as well as fieldwork, innovation-managementcapability is conceptualised in this paper as a multidimensional construct comprising ten distinct components, viz., continuity and change dimensions concerning technology, organisation, learning, managerial and networking processes. The impact of these dimensions on relevant innovation outcomes is also examined in this paper.

2.1 Dimensions of innovation-management-capability The various constituent elements of innovation-management-capability are detailed, each in terms of four vital attributes, by building on prior literature and the fieldwork undertaken. Technology-continuity is seen as an overall measure of the firm’s capabilities related to: customer-oriented new-product-development (Utterback, 1994), companywide innovation (Doz and Kosonen, 2007), idea sourcing and selection (Kates and Galbraith, 2007), and the flexibility in innovation-operations (Volberda, 1996). Technology-change is measured in terms of the change-response of the firm to provide for alternative innovation approaches (Camillus, 2008); effectiveness of prevalent technology acquisition/transfer/spin-off mechanisms (Izosimov, 2008); forging of strategic partnerships (MacCormack et al., 2007); and effectiveness of technology-alert mechanisms (Brown and Eisenhardt, 1997). Organisation-continuity assesses extent of: employee involvement (Amabile et al., 1996), support through organisational culture (Chen and Huang, 2009), structure/acceptance of innovation procedures/routines (Kogut and Zander, 1992), and effectiveness of its innovation-leadership (Hamel, 2007). Organisation-change is a composite reflection of the firm’s tolerant attitude (Rushkoff, 2005; Drucker, 1995),

180

J.S.A. Bhat et al.

creativity-inculcation steps (Gilson et al., 2005), entrepreneurship/peer-pressure use (Mintzberg and Waters, 1982), and innovation-rewards use (Frey and Osterloh, 2001). Factors comprising training (Leonard-Barton, 1995), intra-firm knowledge-sharing practices (Beazley et al., 2002), external-source learning (Zirger, 2000), as well as benchmarking and intra-firm/peer-learning practices (Zairi, 1999) are considered while measuring learning-continuity. Learning-change is an integrated measure of the effectiveness of a quick-recovery system (DeLotto, 2004), measures to enhance learning capabilities (Garcia-Lorenzo et al., 2003), use of learning tools (DeLong, 2004) and sustained learning efforts taken in response to change (Beer and Nohria, 2000). Managerial-continuity is assumed to be a reflection of the linkage between the firm’s business and technology strategies (Collier, 1985), its external-environment trend-tracking practices (Pettigrew et al., 2006), the extent of top-management commitment (Collins and Porras, 1997), and effectiveness of internal procedures (Becerra-Fernandez et al., 2004). Managerial-change has been measured in terms of the flexibility of the firm’s strategy (Sushil, 2005), its use of effective management tools (Houlder and Sull, 2006), its capability of putting together resources and planning for innovation (Sushil, 2008), as well as its communication practices (Brynjolfsson et al., 1997). Networking-continuity is an integrated measure of the firm’s external-interactioneffectiveness (Bhat et al., 2008), lead-users-use (von Hippel, 2005), partnerships (Ahuja, 2000), and external-expertise-use (Lindvall et al., 2003). The change dimension, learning-change, is measured by the firm’s capacity to draw from different sources for innovation ideas (DeDreu and West, 2001), the extent of dynamism in its customer-interface (Nonaka and Takeuchi, 1995), its use of new suppliers (Camillus, 2008), and the extensiveness of its informal linkages (Tether and Abdelouahid, 2008). Summarising the above, innovation-management-capability is seen as comprising continuity and change dimensions of technology, organisation, learning, managerial and networking processes. Each of these ten dimensions plays a somewhat distinct role in addressing specific issues relating to innovation-management-capability, leading us to hypothesise: Hypothesis 1

A firm’s innovation-management-capability entails having distinct capabilities related to both continuity and change aspects of its technology, organisation, learning, managerial, and networking processes.

2.2 Implications of innovation-management-capability on innovation outcomes The firm’s innovation-management-capability influences its overall innovation outcome represented by innovation-performance, leading to: Hypothesis 2

A firms’ innovation-management-capability will be positively related to its innovation-performance.

Innovation-performance can be assessed in several ways. Input-related measures such as R&D expenditure (Katila, 2000), R&D intensity and number of technical personnel (Adams, 1990; OECD, 2005) have been traditionally used. Number of new products developed is an example of an output measure (OECD, 2005). With realisation of the importance of intangible property in assessing innovation outcomes, patents/patent citations and scientific/technical publications are also used (Katila, 2000). These

Strategic confluence of continuity and change for improved innovation

181

measures are not applicable to industries which are either not very technology-intensive or where the knowledge generated is guarded in the form of trade secrets. Combinations of traditional measures together with process indicators are more easily applicable in these cases (Bhat et al., 2010). Such measures include improved production prowess resulting in lower costs, improved product/service quality or improved delivery, and management process improvements (or improved management prowess) (Griffin and Page, 1996; Little, 2005). In this paper, innovation-performance is measured in terms of four kinds of outcomes (Bhat et al., 2010), viz., innovativeness, new-product-service, improved-management-prowess and improved-production-prowess. Innovativeness reflects the organisation’s tendency to innovate, be original and introduce new ideas (Little, 2005). When a firm improves its product/service offerings (adapts to change requirements) or introduces new concepts (exploit emerging technology), it demonstrates that it is innovative. For instance, while DEC prospered in the minicomputer era, it did not demonstrate its innovativeness when the technology environment changed. While it had reasonable leadership in its continuity processes, it was not innovative enough to adapt to meet the needs of a dynamic environment and this is an outcome of its innovation-management-capability, which led to its lower innovation-performance. Hence, it is stated that: Hypothesis 3

A firms’ innovation-management-capability will be positively related to its innovativeness.

New-product-service refers to the firm’s successful generation of new products or services (Griffin and Page, 1996). When a firm develops new products and services for use by customers, it is by virtue of its innovation-management-capability, which enables the firm orchestrate its skills and internal processes to meet a particular demand, whether planned or ad-hoc. For example, IBM has been consistent in developing new products and services to meet diverse needs at different points of time leading to higher innovation-performance through successful architectural transformation and adaptation by virtue of its innovation-management-capability. Hence, it is stated that: Hypothesis 4

A firms’ innovation-management-capability will be positively related to its outputs in the form of new products or new services (new-product-service).

Management process improvement reflects the improvements brought about resulting in more efficient management response (Klein et al., 2001). Management responses are reflected in terms of speed and quality of the response of a firm to an environment induced need. For example, a crisis situation perpetrated by an unforeseen calamity can be averted through suitable interventions, inter alia by ensuring quick recovery and using a flexible strategy by virtue of the firm’s innovation-management-capability, which gets reflected in overall innovation-performance through its management response that gets conveyed to its stakeholders. Hence, it is postulated that: Hypothesis 5

A firms’ innovation-management-capability will be positively related to its improved-management-prowess.

Improved-production-prowess refer to improvements made in the production processes resulting in lower costs, improved quality through better features, or improved delivery (Little, 2005). A firm succeeds in improving its production processes by inter alia instituting appropriate learning mechanisms in response to change needs, putting together

182

J.S.A. Bhat et al.

necessary resources, honing internal efficiencies and assigning the task to identified teams, such that the growth trajectory of the firm is maintained. Such a coordinated set of actions leading to enhancement of innovation-performance is an outcome of its innovation-management-capability. Therefore, it is stated that: Hypothesis 6

A firms’ innovation-management-capability will be positively related to its improved-production-prowess.

The broad framework of the research used in this paper has been depicted in Figure 1, indicating the set of causal interrelationships being examined. Figure 1

Inter-relationships between factors

Continuity processes Technology Organisation Learning Managerial Networking

H3

Innovationmanagementcapability H1

Change processes

H2

Technology Organisation Learning Managerial Networking

H4

Innovation-performance

H5

3

Innovativeness New-product-service Improved-management-prowess Improved-production-prowess

H6

Methods

To test the above hypotheses, we investigate the responses of Indian manufacturing firms in respect of each firm’s strategies in place for managing innovation, comprising the different continuity and change related processes discussed, and compare these with the firm’s innovation-performance. Our attention is focused on the Indian manufacturing industry displaying specific characteristics in respect of financial performance. This has been done in order to create a reasonably controlled group from which our sample can be drawn. Our sample consists of 150 manufacturing firms selected out of a readily available database of 550 companies, comprising 500 private sector companies and 50 public sector companies, that had been identified as India’s most valuable companies using the Prowess database of the Centre for Monitoring of Indian Economy (CMIE) on the basis

Strategic confluence of continuity and change for improved innovation

183

of highest average market capitalisation (broadly representative of public opinion of company’s net worth) during 2007 to 2008.1 This database assured us that the companies selected in the sample were valuable to the shareholders. We further desired that the sample companies should have a record of profitability and must belong to the manufacturing industry. With these additional conditions, the list of 550 got pruned to 150; first, by filtering in only manufacturing companies, and second, by selecting only those that had the highest rate of return on total assets (ROTA) during two successive years, viz., 2006 to 2007 and 2007 to 2008. Thus, in brief, our sample consists of 150 Indian manufacturing companies with effective management that maximises returns from investments for its shareholders. In recent years, the innovation potential of Indian industry has been drawing considerable global attention. The kinds of innovation practices that are practiced defy conventional logic. Few studies have been undertaken to study the specificities in this context (Krishnan, 2010). Therefore, this study covers another important gap in this respect, by relying on the perceptions of India’s top performing manufacturing firms. Cross-sectional survey methodology has been used for data collection; this method has been used successfully for comparable purposes in the past (Carbonell and Rodriguez-Escudero, 2009). We have used purposive-sampling technique to capture the perceptions of the top-management (managing-directors/chief-executive-officers or executives designated by them) of the targeted sample of 150 Indian manufacturing firms. These firms have covered the following sectors: 1

auto/auto-components (private-sector)

2

chemicals/drugs/pharmaceuticals (private-sector)

3

general-engineering (private-sector)

4

general-engineering (public-sector).

A questionnaire, designed to draw responses in respect of the relationships to be examined, was used. To measure the various dimensions, a total of 110 items have been used. The continuity and change dimensions have been measured using 42 items each, and innovation-performance has been measured using seven items. The basis of these items has been explained in Section 2. In addition, 19 objective items to assess the firm performance and innovation-performance of the firms using traditional indices has been included, for use as control measures. The perceptive data had been collected using a seven-point Likert scale (1: strongly disagree to 7: strongly agree). Face validity and criterion validity of the questionnaire had both been established by seeking suggestions of seven experts, comprising three academicians and four from industry. Before systematically administering to the entire target sample of 150 firms, the questionnaire had been refined through pilot testing; four select firms, a subset of the 150 sample firms, had been approached in this regard. Discussions held with a total of 22 employees from these four firms led to reframing of some of the questions by imparting greater clarity and reducing ambiguity in the some of the questions. The multi-dimensional characteristics of innovation-management-capability have been established. The homogeneity of the sample has been tested using one way analysis of variance (ANOVA) tests. After ruling out noteworthy differences among the industry sectors, significant relationships have been examined in depth using stepwise regression

184

J.S.A. Bhat et al.

analysis, which is based on comparison of the link coefficients calculated through the least squares method (Hair et al., 1998).

4

Results and discussions

We received 69 complete responses out of the 150 firms contacted; a response rate of 46%. Non-response bias was ruled out by comparing the companies that responded with those that had not responded on the basis of firm-age and R&D intensity (average calculated over three recent consecutive years). There were no significant differences, thus ruling out any non-response bias. Results of this opinion survey validating the data and bringing out the relationships between the continuity and change processes have been reported (Bhat et al., 2010). The objective measures were first analysed. All the firms are more than ten years old, the vast majority have been in existence for more than 30 years, out of which five are more than hundred years old. An overwhelming majority of the firms have low scores for innovation indices such as R&D intensity, national/international patents filed/granted and national/international publications. On the other hand, scores on indicators such as introduction of new/improved products/processes are very high. The remaining objective measures confirmed the control criteria, viz., above-average and consistent financial performance of the firms in three consecutive recent years. After examining the data received in the raw form in these 69 responses for completeness, checks for outliers, normality and linearity to satisfy the underlying assumptions of the analysis techniques applied have been made. Principal component factor analysis conducted to test the internal validity of the constructs yielded that the cumulative percentage values of all the factor loadings were ranging between 63.491 and 89.829; both well above the recommended cut-off of 50%. Thus, all the constructs were validated (results in Table 1). Correlations between the first-order variables were all high and these variables showed high loadings on the second-order variables in all cases. The significance of the loadings also provides support for the convergent validity of the measures used. Hence, the results suggest that it is appropriate to view innovation-management-capability as a multi-dimensional construct. The cronbach alpha values from the reliability tests ranged from 0.7911 to 0.9603 (results in Table 1). Both these values being well above the accepted criterion for internal consistency of 0.70l, reliability of the constructs was established. Summary results of the ANOVA and descriptive analysis tests are presented in Table 1. The significance level of all the variables except for new-supplier-use (part of technology-change) are observed to be greater than 0.01 (99% significance) and the F value (observed) is lower than the F value (critical) for all the other variables studied. It thus establishes that there is no significant difference between the various sectors and the sample can be treated as a single homogenous whole, paving the way for testing the predictor relationships.

4.1 Impact of innovation-management-capability on innovation-performance Summary results of stepwise regression tests are presented in Table 2, highlighting the details of the significant relationships among the hypotheses being tested.

Strategic confluence of continuity and change for improved innovation Table 1

No.

185

Summary of principal component factor analysis, reliability, ANOVA and descriptive analysis results Variables

ANOVA F

Cumulative Cronbach’s % of alpha loadings

Innovation-performance 1

Innovativeness

.563

.642

2

New-product-service

1.746 .166

3

Improved-managementprowess

.282

4

Improved-production-prowess

Mean

Standard deviation

6.0091

.81239

One

One

5.9130

.93524

77.917

.8733

6.0797

.86442

One

One

6.0145 1.00722

2.178 .099

75.891

.8381

6.0290

.85890

2.248 .091

67.971

.8312

5.6938

.71824

Customer-oriented-NPD

1.531 .215

79.034

.8760

5.9203

.80268

Companywide-innovation

.982

.407

86.313

.8806

5.6957

.88513

Idea-sourcing-and-selection

1.106 .353

79.427

.7384

5.4493

.95926

Flexible-operations

3.222 .028

84.256

.7986

5.7101

.87197

Organisation-continuity

2.976 .038

81.548

.9233

5.8647

.78051

Employee-involvement

2.660 .055

85.212

.8214

5.9565

.83903

Supportive-organisationalculture

2.797 .047

79.753

.7460

5.9203

.82079

Adequate-proceduresroutines

1.576 .204

86.759

.8399

5.8333

83871

Effective-leadership

2.876 .043

82.790

.8940

5.7488

.95727

1.322 .275

82.764

.9237

5.4830

.98006

One

One

5.7391 1.05234

.838

Continuity processes 5

6

7

8

Technology-continuity

Learning-continuity Training

.538

Intra-firm knowledge sharing

1.892 .140

89.730

.8855

5.4130 1.24545

Learning-from-externalsources

1.842 .148

71.971

.9010

5.4899

Benchmarking and inter-firm/peer learning

1.149 .336

87.874

.8618

5.2899 1.12918

1.805 .155

95.487

.8884

5.7162

One

One

5.6522 1.07315

88.906

.8717

5.5797 1.07315

Managerial-continuity Business technology strategy link

.902

.658

.445

External-environment-trend- 2.212 .095 tracking

.88752

.91126

Top-managementcommitment

1.185 .322

One

One

6.0145 1.06402

Structured-trajectory

1.916 .136

76.631

.8433

5.6184

.98895

186

J.S.A. Bhat et al.

Table 1

Summary of principal component factor analysis, reliability, ANOVA and descriptive analysis results (continued)

No. 9

Variables Networking-continuity

ANOVA F

Cumulative Cronbach’s % of alpha loadings

Mean

Standard deviation

2.153 .102

63.491

.7911

5.3990

.85160

External-interactions

3.103 .033

72.078

.7811

5.7778

.92473

Use-of-lead-users

2.173 .100

91.170

.9000

5.5797

.97627

Collaboration-with-firms

2.100 .109

One

One

4.8971 1.38370

External expertise use

1.785 .159

One

One

5.3235 1.01395

Change processes 10

11

12

Technology-change

2.928 .040

81.251

.9201

5.3068 1.11332

Providing for options

2.873 .043

79.417

8693

5.5121 1.00746

Well-aligned-technologyacquisition-and-transfermechanisms

1.723 .171

69.370

.7762

5.1208 1.22870

Strategic-partnerships

2.243 .092

One

One

5.3623 1.35007

Technology-alertmechanisms

3.255 .027

One

One

5.2319 1.34104

Organisation-change

3.044 .035

71.076

.8611

5.4251

Tolerant-attitude

3.027 .036

83.281

.7858

5.2174 1.15516

Creativity-inculcation

2.596 .060

71.757

.7948

5.3961

Entrepreneurship-peerpressure

3.184 .030

84.056

.8097

5.4928 1.00181

Innovation-rewards

1.187 .322

One

One

5.5942

.98993

1.830 .150

81.013

.9218

5.4239

.92955

1.191 .320

86.325

.8410

5.5797

.95341

Enhance-capabilities

1.841 .148

90.202

.8901

5.3768 1.09614

Use-of-learning-tools

1.216 .311

84.083

.8100

5.3913 1.04625

Sustained-efforts

2.061 .114

82.114

.7814

5.3456 1.05185

Managerial-change

1.496 .224

86.186

.9439

5.4985

.93082 .89227

Learning-change Quick-recovery

13

.95533

Flexible-strategy

.742

.531

81.580

.7742

5.5725

Use-of-effectivemanagement-tools

2.222 .094

84.844

.9097

5.3527 1.09497

Resources-and-planning

1.420 .245

76.867

.8984

5.5326

Communication 14

.86401

Networking-change Ideation

.93139

1.409 .248

86.622

.8296

5.5362 1.08911

3.058 .034

75.143

.8827

5.4897

.794

82.161

.8901

5.2705 1.08356

One

One

5.6232 1.03044

.502

Dynamic-customer-interface 2.210 .095

.92984

New-supplier-use

5.725 .002

One

One

5.4058 1.26397

Extensive-formal-informallinkages

1.745 .167

88.532

.8701

5.6594

.91753

Strategic confluence of continuity and change for improved innovation Table 2

Summary of step-wise regression analysis

Dependent variable Innovation-performance

R square .723 (.680)

(.653)

1

2

Innovativeness

New-product-service

.512 (.497)

.592 (.531)

(.507)

(.278) 3

Improved-management-prowess

.601 (.548)

(.495)

4

187

Improved-production-prowess

.630 (.604)

(.524)

Predictors: (constant)

Beta

Managerial-continuity: 1 Business-technology-strategy-link 2 Top-management-commitment 3 Structured-trajectory Technology-continuity: 1 Companywide-innovation 2 Flexible-operations 3 Idea-sourcing-and-selection

.485 (.438) (.238) (.270) .407 (.387) (.285) (.303)

Managerial-continuity: 1 Business-technology-strategy-link 2 Top-management-commitment

.715 (.534) (.265)

Technology-continuity: 1 Companywide-innovation 2 Flexible-operations 3 Idea-sourcing-and-selection Managerial-continuity: 1 Business-technology-strategy-link 2 Idea-sourcing-and-selection top-management-commitment Learning-change: 1 Enhance-capabilities

.566 (.337) (.294) (.266) .486 (.522) (.288)

Managerial-continuity: 1 Business-technology-strategy-link 2 Top-management-commitment Networking-continuity: 1 Collaboration-with-firms 2 External-interactions 3 External-expertise-use

.481 (.402) (.390) .349 (.326) (.318) (.217)

Technology-continuity: 1 Companywide-innovation 2 Flexible-operations 3 Idea-sourcing-and-selection Managerial-continuity: 1 Business-technology-strategy-link 2 Top-management-commitment

.531 (.319) (.360) (.286) .299 (.486) (.345)

Note: Figures in parentheses, viz. (), refer to values related to micro variables.

–.297 (.527)

188

J.S.A. Bhat et al.

Managerial-continuity, and to a slightly lesser extent, technology-continuity, are observed to be the major predictors explaining innovation-performance to the extent of 72.3%. Further, examining interrelations of the construct of managerial-continuity, it seems that having a strong business-technology-strategy-link (Collier, 1985), a structured-innovation-trajectory (Becerra-Fernandez et al., 2004), and above all, top-management-commitment (Collins and Porras, 1997), are vital. Companywideinnovation, broad-based idea-sourcing-and-selection (Kates and Galbraith, 2007), and flexible-operations (Volberda, 1996) of the technology-continuity construct are also important. On the whole, a specific focus on the salient aspects of ongoing innovation programmes, supported by an agile and flexible management strategy (Volberda, 1996), are apparently vital for innovation-performance. Managerial-continuity is the major predictor of innovativeness, explaining its variance to the extent of 51.2%. The major predictors of new-product-service are technology-continuity, managerial-continuity and learning-change, listed in descending order of importance (impacting to the extent of 59.2%). Managerial-continuity and networking-continuity matter most to improved-management-prowess, influencing it to the extent of 60.1%. Technology-continuity and managerial-continuity impact improved-production-prowess to the fairly significant extent of 63%. One significant finding is that learning-change has a negative influence on new-product-service. The most predominant influencer of innovation-performance is managerialcontinuity. All the four components of innovation-performance; innovativeness, new-product-service, improved-management-prowess and improved-production-prowess; are impacted significantly by managerial-continuity. Technology-continuity significantly impacts new-product-service and improved-production-prowess. Based on the preceding analysis, a validated model for innovation-performance has been derived. This is depicted in Figure 2. The figure highlights the significant relationships being investigated. The depiction, together with the degree of impact brought out in Table 2, evidently reinforces that managerial-continuity is the most significant driver; it enhances not only innovativeness in an organisation but also all the other three variables of innovation-performance. In addition, new-product-service is driven by technology-continuity and learning-change. The former impacts improved-production-prowess too. Learning-change has an opposite impact on new-product-service. Further, improved-management-prowess is also influenced by networking-continuity. Examining the relations of the constructs, one key observation is that business-technology-strategy-link and top-management-commitment appear to be imperatives for innovation-performance, significantly influencing all its four components. Structured-trajectory, besides companywide-innovation and flexible-operations are observed to be other vital factors that impact innovation-performance. Idea-sourcing-andselection influences innovation-performance directly, and through new-productservice and improved-production-prowess as well. Collaboration-with-firms, externalinteractions and external-expertise-use influence improved-management-prowess in particular. Enhance-capabilities has a negative influence on new-product-service.

Strategic confluence of continuity and change for improved innovation

189

Figure 2 Validated model for innovation-performance Technologycontinuity

Companywide-innovationÎ Idea-sourcingÎ Flexible-operationsÎ

Managerialcontinuity

Business-technology-strategy-linkÎ IP, IP1, IP2, IP3, IP4 IP2-new-productManagement-commitmentÎ IP, IP1, IP2, IP3, IP4 service Structured-trajectoryÎ IP

Networkingcontinuity

External-interactionsÎ Collaboration-with-firmsÎ External-expertise-use Î

IP, IP2, IP4 IP, IP2, IP4 IP, IP2, IP

IP1-innovativeness

IP3 IP3 IP3 Innovationperformance (IP)

Learningchange

Enhance-capabilitiesÎ (-ve)

Organisationcontinuity

IP2

IP3-improvedmanagement-prowess

Learningcontinuity

IP4-Improvedproduction-prowess

Technologychange

Organisationchange

5

Managerialchange

Networkingchange

Conclusions

Innovation management has been a subject of evident interest to researchers in recent years. Practitioners have been equally concerned with the complex aspects involved in attempting to chart a suitable roadmap to achieve targeted innovation performance standards. This study has contributed to empirical research as well as theoretical research in the context of evolving strategic approaches to innovation management in organisations through balance of continuity and change to enhance innovation performance by conceptualising a multidimensional construct for innovation management capability. Besides, this research provides useful insights into innovation management practices followed in Indian manufacturing organisations that can have wider application. This research makes useful strategy suggestions that can be implemented by industry (refer to Table 3).

190

J.S.A. Bhat et al.

Table 3

Decalogue: towards improved innovation performance

1

Your technology strategy should be closely dovetailed to your business strategy.

2

Your top management should be committed to your innovation strategy and innovation goals.

3

All your stakeholders should be conversant with your innovation road map. Further, your core competence should be well defined and understood by your management, employees, and all your external stakeholders.

4

You should ensure that all relevant departments in your firm participate in your innovation projects right from the conception stage. Moreover, you should be always geared to implement process improvements and changes, whenever necessary.

5

You should regularly deploy systems to systematically search for innovation ideas. You should also use well-established processes for innovation project selection from among the sourced ideas.

6

Your operations should be sufficiently flexible.

7

You should use effective mechanisms to understand your customer needs in a dynamic mode. You should have regular interactions on innovation aspects with your customers, and also with external knowledge sources such as universities and research centres in areas concerning your business interests.

8

You should seek ways of collaborating with other firms on innovation projects.

9

You should use the services of consultants and experts wherever necessary.

10

You must exercise due caution while assessing innovation capabilities and initiating corrective measures in order to ensure that the requirements of targeted innovation projects are never compromised.

Specific focus on managerial dimensions of continuity, calling for a structured innovation road-map with top management support, is advocated. The technology dimension of continuity; comprising a companywide flexible and user-oriented approach to newproduct-development, seems to also have a dominating role. In the interest of parsimony and focus, the conceptualisation used in this paper focuses more on aspects relating to manufacturing industries. For instance, some critical skills required in the service sector may not have been addressed adequately. Further research can be taken up using the current research as foundation to expand the scope of innovation management capability. Yet, although the study has been based in a manufacturing environment related to specific sectors of industry, care has been taken to confirm that no overarching concern limits the findings, and hence, these can be generally applied to all sectors of industry. Further, the findings and approach can be adapted with ease to suit any organisation desirous of enhancing innovation performance.

Acknowledgements The authors express their gratitude to the editor and the anonymous reviewers for their very useful comments. Any remaining errors are our own.

Strategic confluence of continuity and change for improved innovation

191

References Adams, J.D. (1990) ‘Fundamental stocks of knowledge and productivity growth’, Journal of Political Economy, Vol. 98, No. 4, pp.673–703. Ahuja, G. (2000) ‘Collaboration networks, structural holes, and innovation: a longitudinal study’, Administrative Science Quarterly, Vol. 45, No. 3, pp.425–455. Amabile, T.M., Conti, R., Coon, H., Lazenby, J. and Herron, M. (1996) ‘Assessing the work environment for creativity’, Academy of Management Journal, Vol. 39, No. 5, pp.1154–1184. Barney, J.B. (1995) ‘Looking inside for competitive advantage’, Academy of Management Executive, Vol. 9, No. 40, pp.49–61. Beazley, H., Boenisch, J. and Harden, D. (2002) Preserving Corporate Knowledge and Productivity When Employees Leave, John Wiley & Sons, Hoboken, NJ. Becerra-Fernandez, I., Gonzalez, A. and Sabherwal, R. (2004) Knowledge Management: Challenges, Solutions, and Technologies, Prentice Hall, Education Inc., Pearson, New Jersey. Beer, M. and Nohria, N. (2000) ‘Cracking the code of change’, Harvard Business Review, Vol. 78, No. 30, pp.133–141. Bhat, J.S.A. (2010) ‘Managing innovation: understanding how continuity and change are interlinked’, Global Journal of Flexible Systems Management, Vol. 11, Nos. 1/2, pp.69–80. Bhat, J.S.A., Sushil and Jain, P.K. (2008) ‘Strategic management of change and continuity: case of technology and social capital in India’, Proceedings of Eighth Global Conference on Flexible Systems Management, 14–16 June 2008, pp.852–861, Stevens Institute of Technology, Hoboken, New Jersey. Bhat, J.S.A., Sushil and Jain, P.K. (2010) ‘Interrelations among innovation performance variables: results from empirical evidence of Indian manufacturing firms’, Tenth Global Conference on Flexible Systems Management: Designing for Flexibility and Security for the Future, 26–27 July 2010, Keio University, Tokyo, Japan. Brown, S.L. and Eisenhardt, K.M. (1997) ‘The art of continuous change: thinking complexity theory and time-paced evolution in relentlessly shifting organizations’, Administrative Science Quarterly, Vol. 42, No. 1, pp.1–34. Brynjolfsson, E., Renshawamy, A. and Marshall, W.A. (1997) ‘The matrix of change’, Sloan Management Review, Vol. 38, No. 2, pp.37–54. Camillus, J.C. (2008) ‘Strategy as a wicked problem’, Harvard Business Review, Vol. 86, No. 5, pp.98–106. Carbonell, P. and Rodriguez-Escudero, A.I. (2009) ‘Relationships among team’s organizational context, innovation speed, and technological uncertainty: an empirical analysis’, Journal of Engineering and Technology Management, Vol. 26, Nos. 1/2, pp.28–45. Chen, C. and Huang, J. (2009) ‘Strategic human resource practices and innovation performance – the mediating role of knowledge management capacity’, Journal of Business Research, Vol. 62, No. 1, pp.104–114. Cohen, M., Burkhart, R., Dosi, G., Egidi, M., Marengo, L., Warglien, M. and Winter, S. (1996) ‘Routines and other recurring action patterns of organizations: contemporary research issue’, Industrial and Corporate Change, Vol. 5, No. 3, pp.653–699. Collier, D.W. (1985) ‘Linking business and technology strategy’, Strategy and Leadership, Vol. 13, No. 5, pp.28–44. Collins, J.C. and Porras, J.I. (1997) Built to Last: Successful Habits of Visionary Companies, Harper Business, New York. Collis, D.J. (1994) ‘How valuable are organisational capabilities?’, Strategic Management Journal, Winter, Vol. 15, pp.143–152. DeDreu, C.K.W. and West, M.A. (2001) ‘Minority dissent and team innovation: the importance of participation in decision making’, Journal of Applied Psychology, Vol. 86, No. 6, pp.1191–1201.

192

J.S.A. Bhat et al.

DeLong, D.W. (2004) Lost Knowledge: Confronting the Threat of an Aging Workforce, Oxford University Press, New York. DeLotto, R. (2004) It’s Time for Banks to Re-examine Business Continuity Assumptions, Gartner ID: QA-0704-0045, pp.1–4. Doz, Y. and Kosonen, M. (2007) ‘The new deal at the top’, Harvard Business Review, Vol. 85, No. 6, pp.98–104. Drucker, P.F. (1995) Innovation and Entrepreneurship, HarperCollins, New York. Eisenhardt, K.M. and Martin, J. (2000) ‘Dynamic capabilities: what are they?’, Strategic Management Journal, Vol. 21, Nos. 10–11, pp.1105–1121. Frey, B.S. and Osterloh, M. (2001) Successful Management by Motivation: Balancing Intrinsic and Extrinsic Incentives: Organization and Management Innovation, Springer, Berlin. Garcia-Lorenzo, L., Mitleton-Kelly, E. and Galliers, R.D. (2003) ‘Organisational complexity: organizing through the generation and sharing of knowledge’, The International Journal of Knowledge, Culture and Change Management, Vol. 3, No. 1, pp.275–293. Gilson, L.L., Mathieu, J.E., Shalley, C.E. and Ruddy, T.M. (2005) ‘Creativity and standardization: complementary or conflicting drivers of team effectiveness?’, Academy of Management Journal, Vol. 48, No. 3, pp.521–531. Griffin, A. and Page, A.L. (1996) ‘PDMA success measurement project: recommended measures for product development success and failure’, Journal of Product Innovation Management, Vol. 13, No. 6, pp.478–496. Hair, J.F., Jr., Anderson, R.E., Tatham, R.I. and Black, W.C. (1998) Multivariate Data Analysis, Prentice Hall, New Jersey. Hamel, G. (2007) The Future of Management, Harvard Business School Press, Boston. Helfat, C.E., Finkelstein, S., Mitchell, W., Peteraf, M., Singh, H., Teece, D. and Winter S.G. (2007) Dynamic Capabilities: Understanding Strategic Change in Organizations, Blackwell Publishing, Malden MA. Houlder, D. and Sull, D.N. (2006) ‘How companies can avoid a midlife crisis’, Sloan Management Review, Vol. 48, No. 1, pp.26–34. Izosimov, A.V. (2008) ‘Managing hypergrowth’, Harvard Business Review, Vol. 86, No. 4, pp.121–128. Kates, A. and Galbraith, J.R. (2007) Designing your Organization, Wiley-VCH, Weinheim. Katila, R. (2000) ‘Using patent data to measure innovation performance’, International Journal of Business Performance Management, Vol. 2, Nos. 1–3, pp.180–193. Klein, K.J., Conn, A.B. and Sorra, J.S. (2001) ‘Implementing computerized technology: an organization analysis’, Journal of Applied Psychology, Vol. 86, No. 5, pp.811–824. Kogut, B. and Zander, U. (1992) ‘Knowledge of the firm, combinative capabilities, and the replication of technology’, Organization Science, Vol. 3, No. 3, pp.383–397. Krishnan, R.T. (2010) From Jugaad to Systematic Innovation: The Challenge for India, The Utpreraka Foundation, Bangalore, India. Lawson, B. and Samson, D. (2001) ‘Developing innovation capability in organizations: a dynamic capabilities approach’, International Journal of Innovation Management, Vol. 5, No. 3, pp.377–400. Learned, E., Christensen, C., Andrews, K. and Guth, W. (1969) Business Policy: Text and Cases, Irwin, Homewood, IL. Leonard-Barton, D. (1995) Well-Springs of Knowledge Building and Sustaining the Sources of Innovation, Harvard Business School Press, Boston. Lewis, W.M., Welsh, M.A., Dehler, E.G. and Green, G.S. (2002) ‘Product development tensions: exploring contrasting styles of project’, Academy of Management Journal, Vol. 45, No. 3, pp.546–564.

Strategic confluence of continuity and change for improved innovation

193

Lindvall, M., Rus, I. and Sinha, S.S. (2003) Technology Support for Knowledge Management: Lecture Notes in Computer Science, Springer, Berlin/Heidelberg. Little, A.D. (2005) ‘Innovation excellence survey: how companies use innovation to improve profitability and growth’, available at http://www.adl.com/impublications.html?&download=53&file=ADL_Global_Innovation_Excellence_Survey_2005.p df&anchor=set53 (accessed on 8 December 2009). MacCormack, A., Forbath, T., Brooks, P. and Kalaher, P. (2007) Innovation Through Global Collaboration: A New Source of Competitive Advantage, Harvard Business School Press, Boston. March, J.G. (1991) ‘Exploration and exploitation in organizational learning’, Organization Science, Vol. 2, No. 1, pp.71–87. Mintzberg, H. and Waters, J. (1982) ‘Tracking strategy in an entrepreneurial firm’, Academy of Management Journal, Vol. 25, No. 3, pp.465–499. Myers, S. and Marquis, D.G. (1969) Successful Industrial Innovation: A Study of Factors Understanding Innovation in Selected Firms, National Science Foundation Technology Report pp.69–17. National Knowledge Commission, (2007) Innovation in India, available at http://www.knowledgecommission.gov.in/downloads/documents/NKC_Innovation.pdf (accessed on 15 January 2010). Nelson, R.R. (1991) ‘Why do firms differ, and how does it matter?’, Strategic Management Journal, Winter, Vol. 12, pp.61–74. Nonaka, I. and Takeuchi, H. (1995) The Knowledge-creating Company, Oxford Press, New York. O’Reilly, C.A. and Tushman, M. (2004) ‘The ambidextrous organization’, Harvard Business Review, Vol. 82, No. 4, pp.74–82. OECD (2005) Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, OECD, Paris, available at http://www.oecd.org/dataoecd/35/61/2367580.pdf (accessed on 13 January 2010). Penrose, E. (1959) The Theory of the Growth of the Firm, Basil Blackwell, London. Pettigrew, A.M., Thomas, H. and Whittington, R. (2006) Handbook of Strategy and Management, Sage Publications, London. Porter, M.E. (1997) ‘What is strategy?’, Harvard Business Review, Vol. 74, No. 6, pp.61–78. Rushkoff, D. (2005) Get Back in the Box: Innovation from the Inside Out, Harper Collins, New York. Sørensen, J. and Stuart, T. (2000) ‘Aging, obsolescence, and organizational innovation’, Administrative Science Quarterly, Vol. 45, No. 1, pp.81–112. Sushil (2005) ‘A flexible strategy framework for managing continuity and change’, International Journal of Global Business and Competitiveness, Vol. 1, No. 1, pp.22–32. Sushil (2008) ‘The concept of a flexible enterprise’, Proceedings of Eighth Global Conference on Flexible Systems Management, 14–16 June 2008, Stevens Institute of Technology, Hoboken, New Jersey, pp.18–35. Teece, D.J., Pisano, G. and Shuen, A. (1997) ‘Dynamic capabilities and strategic management’, Strategic Management Journal, Vol. 18, No. 7, pp.509–533. Tether, B.S. and Abdelouahid, T. (2008) ‘Beyond industry-university links: sourcing knowledge for innovation from consultants, private research organisations and the public science-base’, Research Policy, Vol. 37, Nos. 6–7, pp.1079–1095. Trott, P. (1998) Innovation Management and New Product Development, Pearson Education, Harlow. Tushman, M.L. and O’Reilly, C.A. (1997) Winning Through Innovation: A Practical Guide to Leading Organizational Change and Renewal, Harvard University Press, Boston, MA. Utterback, J. (1994) Mastering the Dynamics of Innovation, Harvard Business School Press, Boston.

194

J.S.A. Bhat et al.

Volberda, H.W. (1996) ‘Toward the flexible form: how to remain vital in hypercompetitive environments’, Organization Science, Vol. 7, No. 4, pp.359–374. von Hippel, E. (2005) Democratizing Innovation, MIT Press, Cambridge. Zairi, M. (1999) Benchmarking for Best Practice, Butterworth-Heinemann, Wourn, MA. Zirger, B.J. (2000) ‘Titel’, Journal of Engineering and Technology Management, Vol. 17, No. 1, pp.113–115. Zollo, M. and Winter, S.G. (2002) ‘Deliberate learning and the evolution of dynamic capabilities’, Organization Science, Vol. 13, No. 3, pp.339–351.

Notes 1

Out of 4,916 companies listed on the Bombay Stock Exchange (BSE), 2,785 companies that were traded on a minimum of 20% of the trading days in the period between April 1 and September 30, 2007 were selected after separating public sector companies, banks and financial institutions. The market capitalisation values were calculated on each trading day in this period and the average market capitalisation values were then computed. Ranking of the 500 valuable private sector companies was done on that basis. Fifty valuable public sector companies had been ranked on a similar basis.