Vision~15-4-02-Jyoti & Kotwal.indd

Forster 315 Article

Impact of Knowledge Management Practices on Innovative Capacity: A Study of Telecommunication Sector

Vision 15(4) 315–330 © 2011 MDI SAGE Publications Los Angeles, London, New Delhi, Singapore, Washington DC DOI: 10.1177/097226291101500402 http://vision.sagepub.com

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Jeevan Jyoti Pooja Gupta Sindhu Kotwal

Abstract

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Knowledge management is a process that enables organizations to learn, create, develop and apply necessary knowledge which makes organizations more profitable and innovative. The purpose of this article is to investigate the impact of knowledge management on the innovative capacity of an organization. Questionnaire method has been used to collect data from employees working in private telecommunication organizations. Extensive review of literature has been done to frame the dimensions of knowledge management and innovative capacity. Both scales have been duly purified and validated before the data analysis. Structural equation modelling has been done to investigate the relationship between knowledge management and innovation. The results revealed a significant relationship between knowledge management and innovation. Further, knowledge approach, knowledge protection and knowledge utilization processes of knowledge management affect technical as well as non-technical innovation.

Key Words

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Knowledge Management, Innovation, Confirmatory Factor Analysis, Structural Equation Modelling

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Introduction

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Human resources are the greatest internal asset and are one of the top strategic priorities for businesses across the nation and the world. Today, when organizations are finding it increasingly difficult to grow, investing in talent to meet sustainability is imperative to achieve high performance (Lacy et al., 2009). Organizations are now recognizing the value in attracting and retaining the best and brightest employees in order to achieve higher than average market share and elevated profiles. In the era of the knowledge worker, ‘talent’, narrowly defined as a core group of leaders, technical experts and other key contributors, is quickly becoming an organization’s most important asset (Phillips and Roper, 2009). So, in the present scenario, it is essential to manage the talent by providing additional knowledge to the employees. The efficient management of talent in the organization increases the commitment level of the employees, which in turn enhances their knowledge level and places them according to their suitability, aptitude and interest (Julia and Rog, 2008). The knowledgeable and competent talent helps to increase the innovative capacity of an organization by putting forward new

methods of management as well as technically improving the production process of an organization. Knowledge management is an organizational method that utilizes the strategic resource knowledge more deliberately and more efficiently. Many organizations are launching knowledge management initiatives with a view to improve business processes, make financial savings, generate greater revenues, enhance user acceptance and increase the competitiveness (Chua and Lam, 2005). The objective of a firm applying knowledge management is simply to make the right knowledge available at the right time at the right place.

Knowledge Management Knowledge management authors have classified ‘knowledge’ in different ways. Some have differentiated it as technical and strategic knowledge (Liebeskind, 1996), but the most common types of knowledge are tacit, explicit and implicit knowledge (Cavusgil et al., 2003; Nonaka, 1994; Nonaka and Konno, 1998). Tacit knowledge resides in people’s brains and explicit knowledge resides in the organizational systems and documents, both electronic and

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Impact of Knowledge Management Practices

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results in increased firm performance through implementation of ideas for restructuring or saving of costs, improved communication, new technology for production processes, new organizational structures and new personnel plans or programmes (Robbins, 1996). West and Farr (1990) viewed innovation as the intentional introduction and application within a role, group or organization of ideas, processes, products or procedures, new to the relevant unit of adoption, designed to significantly benefit the individual, the group, organization or wider society. It is the embodiment, combination, or synthesis of knowledge in original, relevant, valued new products, processes or services (Luecke and Katz, 2003), whereby organizations transform ideas into new/improved products, service or processes, in order to advance, compete and differentiate themselves successfully in their marketplace (Baregheh et al., 2009). Further, innovation, like many business functions, is a management process that requires specific tools, rules and discipline. From this point of view, emphasis is moved from the introduction of specific novel and useful ideas to the general organizational processes and procedures for generating, considering and acting on such insights leading to significant organizational improvements in terms of improved or new business products, services or internal processes (Palacios et al., 2008). It appears that the context in which a new idea, product, service or activity is implemented determines whether it can be regarded as an innovation within that specific context or not.

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Innovation

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on paper, while implicit knowledge is embedded within an organization’s processes and procedures, products or service (Baptista et al., 2005; Kidwell et al., 2000). On the other hand, ‘management’ is the creation and maintenance of an internal environment in an enterprise where individuals, working in groups, can perform efficiently and effectively towards the attainment of group goals. The existing literature defines knowledge management as the integration and coordination of individual and organizational knowledge by managing current organizational knowledge and the creating new knowledge (Diakoulakis et al., 2004). It is a process which enables organizations to learn, create, develop and apply necessary knowledge (Bhatt, 2001). It encompasses any processes and practices concerned with the creation, acquisition, capture, sharing and use of knowledge, skills and expertise (Swan et al., 1999), wherever it resides, to enhance learning and performance in organizations (Quintas et al., 1997). Alavi and Leidner (2001) viewed knowledge management as the systematic process of acquiring, organizing and communicating the organizational knowledge in such a way so that others can make use of it more efficiently to positively contribute towards the organizational process, namely, innovation, competitive advantage and ultimately, organizational performance. The literature exhibits the existence of six basic knowledge management practices in the organizations that focus on knowledge management: (i) orientation towards development and transfer and protection of knowledge; (ii) continuous learning in the organization; (iii) an understanding of the organization as a global system; (iv) development of an innovative culture that encourages research and development projects; (v) approach based on people; and (vi) competencies development and management based on competencies (Davenport and Prusak, 2000; Nonaka, 1994; Nonaka and Takeuchi, 1995; Scarbrough, 1999).

Innovation is crucial to the success and survival of companies (Auernhammer and Leslie, 2001). It is a challenge for the company to be innovative and creative to bring to the market stream new, improved, added value products and services that enable the business to achieve higher margins, and thus profits, to reinvest in the business. The concept of innovation has recently emerged in the academic and policy debate as a meta-concept to denote the real and potential capabilities of a system to convert knowledge into innovation that is able to drive long-term economic growth and wealth creation (Freeman, 1995; Furman et al., 2002; Lundvall and Johnson, 1994; Nelson, 1993). It is the process of introducing new ideas to the firm which Vision, 15, 4 (2011): 315–330

Knowledge Management and Innovation Knowledge management has become a cornerstone in emerging business strategies. Post-industrial organizations are knowledge based, and their success and survival depends on creativity, innovation, discovery and inventiveness. An effective reaction to these demands leads not only to changes in individuals and their behaviour but also to innovative changes in organizations to ensure their existence (Read, 1996). It appears that the rate of change is accelerating rapidly as new knowledge, idea generation and global diffusion is increasing (Chan Kim and Mauborgne, 1999; Senge et al., 1999). The nature of global economic growth has been changed by the speed of innovation, which has been made possible by rapidly evolving technology, shorter product life cycles and a higher rate of new product development. The complexity of innovation has been increased by growth in the amount of knowledge available to organizations. Innovation is extremely dependent on the availability of knowledge and therefore, the complexity created by the explosion of richness and reach

Jeevan Jyoti, Pooja Gupta and Sindhu Kotwal

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On the basis of the literature presented, a conceptual model shows that knowledge management affects innovation as proposed by the present study. The framework consists of two latent constructs, that is, knowledge management and innovation. Knowledge management consists of seven manifest variables and innovation consists of two manifest variables as shown in Figure 1.

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Hypotheses

Knowledge management implantation can enable innovation by competences deployment (Cavusgil et al., 2003) and coordinating mechanism (Darroch and McNaughton, 2002). A firm with knowledge management capability will

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1. To explore the relationship of knowledge management with innovation.

Research Framework

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Objectives

2. To validate the knowledge management scale and innovation scale for good results.

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of knowledge has to be recognized and managed to ensure successful innovation (Plessis, 2007). Knowledge management has frequently been identified as an important antecedent of innovation (Darroch and McNaughton, 2002) because a firm with a knowledge management capability will use resources more efficiently and will be more innovative and perform better (Darroch, 2005). Introducing knowledge management programme in the organization affects generation of innovative, distinctive competencies by developing skills in investment and knowledge flow management; the acquisition of internal knowledge; transfer, dissemination and internal application of accumulated knowledge; and an increase in variety of the organizational memory (Gloet and Terziovski, 2004).

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Figure 1. Conceptual Framework

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Knowledge Management Scale (KMS)

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The second section comprised 48 statements regarding knowledge management under seven dimensions. The dimensions are: knowledge sharing (Yi, 2009); knowledge acquisition; knowledge conversion (Azari et al., 2008); knowledge utilization (Berce et al., 2008); knowledge creation (Nonaka, 1994); knowledge protection; and knowledge approach (Sher and Lee, 2003). Knowledge management has been measured on a five-point Likert scale.

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Knowledge is the most important organizational resource in the knowledge society and it is the knowledge management strategy of an organization which forms a context for acquiring, using and utilizing knowledge and information in research and innovation (Bouthillier and Shearer, 2002). Chang and Lee (2008) revealed that the capability to obtain knowledge positively and significantly affects administrative and technical innovation. Further, Kamasak and Bulutlar (2010) showed that knowledge acquisition significantly affects all types of innovation. Whereas Krogh and Venzin indicated that knowledge creation is the process which produces new knowledge and innovations. Fostering the process of knowledge creation is the first step to facilitating innovations in the company. Gloet and Terziovski (2004) indicated that knowledge management contributes to innovation performance when a simultaneous approach of ‘soft HRM [human resource management] practices’ and ‘hard IT [information technology] practices’ are implemented. Further, Plessis (2007) revealed that the nature of global economic growth has been changed by the speed of innovation, which has been made possible by rapidly evolving technology, shorter product life cycles and a higher rate of new product development.

The statements of the questionnaire were finalized after reviewing the existing literature and after detailed discussions with the experts and interaction with the local managers of the leading telecommunication organizations. The questionnaire comprised three sections. The first section was concerned about the demographic profile of the employees of telecom sector, where they were asked about the name of their organization, department, designation, qualification, age, gender and length of service. It was followed by two different scales, that is, knowledge management scale and innovation scale.

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Hypothesis 1: Higher the implementation of knowledge management practices, higher is the innovative capacity of an organization.

Generation of Scale Items

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use resources more efficiently and will be more innovative and perform better (Darroch, 2005). Different types of innovations, that is, technical and non-technical, in the form of product, process and organizational innovations, can be delivered efficiently by company (Palacios et al., 2008) by sound implementation of knowledge management practices (Edvardson, 2009), by providing a better manufacturing or services process and implementing new managerial regulations, systems, practices and methods that increase managerial efficiency (Beckman, 1997; Palacios et al., 2008). Hence, it affects not only administrative/non-technical but also technical innovation (Huang and Li, 2009). So, the hypothesis generated from the given literature is:

Hypothesis 2: Knowledge protection, knowledge utilization, knowledge creation and knowledge (IT) approach affect technical innovation as well as non-technical innovation.

Research Design and Methodology In order to make the study more accurate and objective, following steps have been taken. Vision, 15, 4 (2011): 315–330

Innovation Scale (INS) This scale has been used to measure the innovative capability of telecom sector. It contained 12 statements pertaining to two dimensions, that is, technical and nontechnical dimensions.

Sample and Response Rate The population for the study comprised 1,190 employees working in the telecommunication organizations in Jammu. To determine the sample size, a pilot survey of 50 respondents, selected conveniently from all the telecommunication organizations in Jammu, was conducted to work out the mean and standard deviation in the population with the help of the following formula (Mukhopadhya, 1998, pp. 21–31): 1.96∗S.D√ N-n/n∗N = 0.05∗mean where, S.D. = standard deviation; N = total population; n = sample population; and mean = sample mean. After determining the mean and standard deviation in the population of 1,190 the sample size was worked out at 57 which was too small for application of multivariate techniques. So, it was decided to find out the sample size according to number of items to be used for studying knowledge management. Every item requires 5–10 respondents (Hair et al., 2006). This research construct contained 48 items, so it was decided to take 480 as the sample size. The selection of employees was done on the basis of


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proportionate sampling by the mean of following formula (Malhotra, 2002, pp. 266–91):

were ignored. The purification of both scales have been carried out separately. The detailed analysis is as follows: Purification of Knowledge Management Scale

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Purification of Innovation Scale This construct initially contained 12 statements that got reduced to six under two factors, namely, technical and non-technical (Table 3). The KMO value (0.729), Bartlett’s test of sphericity (chi-square = 516.350, p < 0.001) revealed the adequacy of data for factor analysis. The total variance explained by these factors came to 66 per cent (Table 3).

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The multivariate data reduction technique of factor analysis has been used for the study. The primary purpose of factor analysis is to define the underlying structure in a data matrix. It involves examination of interrelationships (correlations) among a large number of variables and reduction of large number of variables into few manageable and meaningful sets. Factor analysis was carried out through the Statistical Package for Social Sciences (SPSS, 15.0 version) to simplify and reduce the data. It was carried out with principal component analysis method along with orthogonal rotation procedure of varimax for summarizing the original information with minimum factors and optimal coverage. The statements with factor loading less than 0.5 and Eigen value less than 1.0 were ignored for the subsequent analysis (Hair et al., 2005; Sharma and Jyoti, 2005). The data reduction was performed in three steps. First, in the anti-image correlation, the items with value less than 0.5 on the diagonal axis were deleted. In the second step, the extracted communalities were checked (amount of variance in each variable) and items with values less than 0.5 were ignored for the subsequent analysis. In the third step, in rotated component matrices, statements with multiple loadings and values less than 0.5

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Scale Purification—Exploratory Factor Analysis (EFA)

Factor analysis reduced the 48 statements to 22, which got compressed under seven factors, namely, KS (F1), KAP (F2), KCR (F3), KP (F4), KCO (F5), KU (F6) and KA (F7), respectively. The high Kaiser-Meyer-Olkin (KMO) value and chi-square value in Bartlett’s test of sphericity (0.756 and 1487.313, respectively) revealed the sample adequacy for factor analysis. The total variance explained by these factors has arrived at 71 per cent (Table 2). Eigen value of each factor is greater than one (Table 2). The ordering of factors shows their respective importance. Knowledge sharing, knowledge approach and knowledge creation are of great importance in this construct. Each is explaining about 12 per cent of the total variation, followed by knowledge protection, knowledge conversion, knowledge utilization and knowledge acquisition (Table 2).

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where, n = number of employees; and N = total population. Convenient sampling technique has been used for data collection. The data were collected from employees working in telecommunication organizations in Jammu. The list of employees in each company was not provided by the management. So, it was difficult to identify the number of employees in each category or group. Permission could not be obtained for personal contacts in the working hours. Therefore, respondents were contacted during lunch hours. Only 331 employees responded properly. Hence, the response rate came to 68 per cent (see Table 1).

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n/N∗Sample size

Scale Validation—Confirmatory Factor Analysis (CFA) Confirmatory factor analysis (CFA) is a tool that enables us to either confirm or reject our preconceived theory. It is used to provide a confirmatory test of our measurement. It is based on measurement theory. It specifies series of relationship that suggest how measured variables represent a latent construct that is not measured directly (Hair et al., 2005). In present study, before running CFA, EFA was carried out to restrict the number of indicators. After EFA, CFA was run and items with standard regression weight less than 0.5 were deleted (Hair et al., 2005).

Table 1. Total Number of Employees Contacted and the Number of Responses Received Name of the Company Reliance Aircel Airtel Vodafone Tata Indicom

Total Numbers of Employees

Number of Employees Contacted

Number of Questionnaires Received

Percentage (%)

210 250 180 300 250

84 101 73 121 101

84 86 45 85 31

100 85 62 70 31

Source: Head offices of the respective companies in Jammu.

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Table 2. Summary of Results of Overall Factor Analysis of Knowledge Management Data Com.

4.06 4.09 3.36

1.39 0.89 0.91

0.750 0.865 0.875

0.628 0.731 0.769

KAP (F2) Knowledge formalization Standard data Corporate data IT specialists

4.11 4.15 4.13 4.10

0.83 0.88 0.89 0.84

0.837 0.814 0.747 0.795

0.758 0.672 0.604 0.512

KCR (F3) Customer knowledge Social benefits According to problems

4.10 4.14 4.09

0.85 0.76 0.85

0.834 0.796 0.705

0.754 0.653 0.589

KP (F4) Protecting trademarks Protects knowledge Importance of protection

4.12 4.19 4.17

0.87 0.92 0.89

0.810 0.795 0.746

0.758 0.670 0.639

KCO (F5) Absorption of knowledge Organization knowledge Replacement knowledge

4.12 4.12 4.19

0.82 0.86 0.81

0.837 0.750 0.741

0.750 0.647 0.620

KU (F6) Improvement Better utilization Find out weakness

4.09 4.06 4.02

0.79 0.83 0.71

0.642 0.604 0.532

0.675 0.683 0.500

4.07 4.04 3.98

0.95 0.93 0.92

0.847 0.752 0.708

0.760 0.699 0.688

KMO

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0.7899 2.437

12.183 0.7641

2.407

12.036 0.7562

1.985

2.068

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Alpha Value

11.927 0.7800

0.756

1.527

10.342 0.7158

10.633 0.7010

1.334

10.671 0.7800

1.867

9.330 71.129

0.844

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KA (F7) Knowledge distribution Opportunities Competitors Total

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S.D.

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Mean

KS (F1) Brainstorming sessions Team meeting Share success stories

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Factors

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Notes: S.D. = standard deviation, F.L. = factor loading, Com. = communalities extracted, KMO = Kaiser-Meyer-Olkin, E.V. = Eigen value, V.E. = variance explained.

Dimensions

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Table 3. Showing the Mean, SD, Factor Loading, Communalities, VE, KMO, Eigen Value and Cronbach’s Alpha Value from Scale Purification of Innovation Data

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Technical Innovation (F1) Availability of broadband Improvement in broadband services Roaming Non-technical Innovation (F2) Management methods Change in strategy Management system Total

Mean

S.D.

4.16 4.19 4.18 4.12 4.21 4.20 4.18 4.27

0.88 0.84 0.90 0.92 0.85 0.86 0.87 0.84

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Com.

0.787 0.874 0.704

0.632 0.780 0.566

0.735 0.871 0.759

0.591 0.761 0.630

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KMO

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Alpha Value

33.091

0.729

1.985

0.7354

32.927

1.976

0.7350

66.019

3.961

0.763

Notes: S.D. = standard deviation, F.L. = factor loading, Com. = communalities extracted, KMO = Kaiser-Meyer-Olkin, E.V. = Eigen value, V.E. = variance explained.

Measurement Model Development for Knowledge Management The knowledge management construct comprised subscales, namely, knowledge sharing, IT approach, acquisition, creation, utilization, conversion and protection (Figure 2). The result of CFA on all sub-scales revealed Vision, 15, 4 (2011): 315–330

that all the manifest variables are highly loaded on their latent construct (Table 4). The fit indices of the specified measurement model have also yielded excellent results (x2/df = 1.93; p < 0.001; GFI = 0.910; AGFI = 0.884; CFI = 0.911; RMR = 0.045; and RMSEA = 0.054).


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Figure 2. Measurement Model of Knowledge Management

Notes: KS5–KA4 are the manifest variables of sub-scales; KS–KA are the sub-scales of knowledge management scale; e1–e20 are the error terms of manifest variables; er1–er7 are the error terms of sub-scales of knowledge management model; and KM is knowledge management.

Table 4 shows that all standardized regression weights (SRWs) are substantial and significant at p < 0.001. This measurement model did not contain any cross-loadings, either among the measured variables or among the error terms. These results supported the unidimensionality, convergent validity and discriminant validity of all constructs in the final measurement model (Hair et al., 2005).

Measurement Model Development for Innovation Innovation construct consisted of two sub-scales: technical innovation (TI) and non-technical innovation (NTI) (Figure 3). All the manifest variables of technical innovation have loading between average to good and significant standardized loadings (Table 5) which proves their unidimensionality and convergent validity. Non-technical Vision, 15, 4 (2011): 315–330

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SRW

CR

P-value

R2

KAp

KAp4 KAp3 KAp5 KAp6 KP3 KP4 KP5 KS5 KS6 KCr3 KCr4 KCr5 KCo3 KCo4 KCo5 KA2 KA3 KA4 KU4 KU3

0.742 0.684 0.723 0.546 0.665 0.807 0.681 0.660 0.748 0.599 0.838 0.639 0.660 0.681 0.590 0.599 0.874 0.562 0.602 0.600

10.342 Ref 10.160 8.158 Ref 10.206 9.629 Ref 7.744 Ref 8.501 8.420 Ref 8.494 7.835 Ref 8.249 7.934 Ref 5.225

0.001

0.450 0.554 0.517 0.295 0.450 0.647 0.459 0.568 0.429 0.353 0.729 0.395 0.456 0.451 0.338 0.359 0.765 0.315 0.363 0.304

KS KCr KCo KA KU

0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

Reliability The reliability of knowledge management scale and innovation scale was assessed through the Cronbach’s alpha, which assesses the internal consistency of the scale. The alpha reliabilities for each of the dimension of knowledge management (Table 2) and innovation (Table 3) are above 0.7, indicating internal consistency. The construct reliability (CR) of the two scales in CFA was tested with the help of following formula:

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KP

0.001 0.001

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Manifest Variables

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Latent Variables

good (chi-sq = 19.2; p = 0.001; CFI=0.783; GFI=0.79; RMSEA = 0.083). Standardized loading estimates below 0.5 and modification indices were employed to suggest item deletion while content validity of constructs were still satisfied (Anderson and Gerbing, 1988; Hair et al., 2006). After employing the suggested modifications, the fit of the model improved considerably (Goodness of Fit Index, GFI = 0.981; Adjusted Goodness of Fit Index, AGFI = 0.901; Root Mean Residual, RMR = 0.030; Root Mean Square Error Approximation, RMSEA = 0.046; Comparative Fit Index, CFI = 0.970; Normative Fit Index, NFI = 0.963).

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Table 4. Showing SRW, CR, P-value and R2 of Knowledge Management Model

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Figure 3. Innovation Measurement Model

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CR = (Sum of standardized loadings)2/(Sum of standardized loadings)2 + Sum of error terms

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Validity Face Validity/Content Validity

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Notes: IN6–IN10 are manifest variables; TI is technical innovation and NTI is non-technical innovation; and E1–E6 are error terms.

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Table 5. Showing the SRW, CR, P-value and R2 Values of Innovation Latent Variables TI NTI

Manifest Variables

SRW

CR

IN4 IN5 IN6 IN8 IN9 IN10

0.640 0.866 0.610 0.690 0.769 0.672

Ref 9.242 8.504 Ref 8.303 8.161

P-value 0.001 0.001 0.001 0.001

R2 0.437 0.713 0.378 0.410 0.591 0.451

Notes: SRW = standard regression weights, CR = critical ratios.

innovation consisted of three manifest variables, that is, IN8, IN9 and IN10. IN9 is highly loaded on its latent construct (NTI) followed by IN8 (Table 5). The initial fit of this overall measurement model was not particularly Vision, 15, 4 (2011): 315–330

The values of both the scales are greater than 0.9 (knowledge management = 0.966 and innovation = 0.974), thereby indicating strong construct reliability.

The content/face validity of the constructs, that is, knowledge management and innovation, was duly assessed through review of literature and discussions with the subject experts, managers and other employees of Telecom sector, that is, Airtel, Aircel, Vodafone, Tata Indicom and Reliance. Convergent Validity Convergent validity refers to the extent to which the measures correlate with other measures that were designed to measure the same thing. High correlations indicate that the scale is measuring the concept (Hair et al., 2005). A scale with Bentler–Bonett coefficient delta values of 0.90 or above implies strong convergent validity (Bentler and Bonett, 1980). Since the Bentler–Bonnet coefficient delta value for knowledge management scale (0.934) and innovation scale (0.916) is above 0.90, it indicates strong convergent validity. Further convergent validity can also be checked through variance extracted which should be 0.5 or


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Knowledge Utilization

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The overall degree of assessing knowledge management in telecommunication sector is very high (4.11) at five-point scale. Knowledge management, being a multifaceted phenomenon, was calculated on the basis of various dimensions. The detailed analysis of each dimension is given next.

The factorial mean of this dimension came to 4.15. The item ‘organization of knowledge’ highly reflects the construct (SRW = 0.681). The organizations store the organized knowledge (M = 4.12). It makes knowledge useful and promotes effective and efficient management. The item ‘integration of knowledge’ is significantly related with the construct (SRW = 0.660). The organizations integrate different source and types of knowledge (M = 4.12). Proper integration of knowledge increases the capabilities of the organization. The organizations also replace the irrelevant knowledge (M = 4.19) as it increases efficiency. The overall analysis of this dimension explains that integration, organization of useful knowledge and replacement of outdated knowledge are the important components of knowledge conversion.

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Measurement and Analysis of Knowledge Management

Knowledge Conversion

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Discriminant validity refers to the extent to which the measures differ from other similar measures designed to measure different concepts. It can be examined through the evaluation of the variance extracted (VE) (Fornell and Larcker, 1981). They suggested that the VE for each construct should be greater than squared correlation between constructs. Variance extracted for each construct— knowledge management (0.990) and innovation (0.982)—is greater than their squared correlations (0.400), proving the significant discriminant validity.

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Discriminant Validity

knowledge is always distributed throughout the organization (M = 4.07). The organizations inquire about the competitors (M = 3.98) to remain in the market (Table 2).

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higher. Variance extracted of knowledge management scale is 0.990 and innovation scale is 0.982.

Knowledge Sharing

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The total mean derived from different items of knowledge sharing came to 4.07. The CFA resulted into two items. The item ‘team meeting’ is highly related with knowledge sharing (SRW = 0.749). Most of the employees are involved in the team meeting (M = 4.09) of the organization, which gave solutions to their problems. The relation between the item ‘brainstorming’ and knowledge sharing is also high (SRW = 0.660). Regular brainstorming sessions are held which increases their knowledge and reduces their problems. The detailed analysis of this dimension revealed that brainstorming session and team meetings are necessary components of sharing knowledge among employees (Table 2).

Knowledge Acquisition The knowledge acquisition is the ability to seek new knowledge and enhance the knowledge management in the organization. Knowledge acquisition is an important source of new knowledge for a firm (M = 4.06). The CFA of knowledge acquisition resulted in three items. The item ‘knowledge about new opportunities’ is highly related with knowledge acquisition (SRW = 0.874). The knowledge is being acquired about new opportunities (M = 4.04) for the purpose of growth and diversification of the business. The

Knowledge utilization means the actual use of knowledge. The total mean of the dimension has come at 4.11. The item ‘knowledge utilization to change competitive advantage’ is highly related with the scale (SRW = 0.600). The organizations utilize knowledge for competitive advantage (M = 4.09) and for problem solving (M = 4.06). The detailed analysis revealed that effective utilization of knowledge can result in competitive advantage and help in solving problem in an organization (Table 2).

Knowledge Creation The overall mean of this dimension is figured out at 4.11. The CFA result showed that three items are highly related with latent construct. The item ‘creation of knowledge for social benefits’ is highly related with the construct (SRW = 0.838). Knowledge is created to provide social benefits (M = 4.14) and solve the problem (M = 4.09) faced by the organization. Knowledge created through customer feedback (M = 4.10) is also used for social benefits. The detailed analysis indicates that knowledge is created on the basis of customer feedback for the purpose of social benefits as well as to solve the problem (Table 2).

Knowledge Protection The total mean of this dimension comes out at 4.16. The item ‘protection of knowledge embedded in individuals’ highly reflects the construct (SRW = 0.807). The organizations are utilizing this source highly (M = 4.19) and frame Vision, 15, 4 (2011): 315–330

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Measurement and Analysis of Innovation

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The mean score of innovation obtained is 4.18, revealing high inclination towards innovation. Innovation has been studied on the basis of technical and non-technical innovation. The factorial mean of technical innovation comes to 4.16. The organizations are involved in technical innovation by providing broadband services (M = 4.18), and there is continuous improvement in broadband services (M = 4.19). The organizations also provide roaming facilities (M = 4.12). The overall analysis of technical innovation reveals that organizations are providing advanced techniques which increase their capabilities and performance, and encourage innovation. The total mean of non-technical innovation figured at 4.21 (SRW = 0.718). The organizations focus on nontechnical innovation as it is using advanced management methods (M = 4.20), which involves change in strategy and the ways of handling the business (M = 4.18) and change in organization structure and management system (M = 4.27). The overall analysis of non-technical innovation revealed that organization are using advanced management techniques and adopting renewed strategies, thereby bringing a change in the structure as well as system of management (Table 3).

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Knowledge approaches are the activities that make knowledge management successful in an organization. Modern age is the age of science. So, information technology or IT is an important approach in knowledge management. The total mean of this dimension has arrived at 4.13. The relationship of item ‘standardized data’ with the construct is quite high (0.742). Most of the employees (83 per cent) use IT system to enable knowledge formalization across the organization (4.11). These organizations have IT specialists who design the programme (M = 4.10) for corporate data to be shared among employees (4.13), which is identified and standardized across the organization (4.15). Information technology is being used by organizations to perform specific tasks as efficiently as possible (Table 2).

Structural equation modelling (SEM) is a tool for analyzing multivariate data that has been long known in marketing to be especially appropriate for theory testing (for example, Bagozzi, 1980). In fact, SEM goes beyond ordinary regression models to incorporate multiple independent and dependent variables as well as hypothetical latent constructs that clusters of observed variables might represent. It also provides a way to test the specified set of relationships among observed and latent variables as a whole (MacCallum and Austin, 2000). The results of SEM analyses are displayed in Figure 4, proving that the model satisfied an acceptable level of model fit (RMR = 0.049; RMSEA = 0.055; GFI = 0.990, AGFI = 0.889 and CFI = 0.921) and thus, was used to test the related hypothesis through the Standardized Regression Weights (SRWs), p-value and squared multiple correlations. Estimation from SEM (Figure 4) revealed that knowledge management has significant relationship with innovation. Knowledge management is the strong predictor of innovation (SRW = 0.862, significant at < 0.01), thus, Hypothesis 1 is supported, that is, higher the knowledge management, higher is innovative capacity of an organization. According to Hypothesis 2, knowledge approach (IT) (KAP), knowledge protection (KP), knowledge creation (KCR) and knowledge utilization (KU) processes of knowledge management (Figure 5) affect technical as well as non-technical innovation. The results revealed that knowledge creation has insignificant relationship with different types of innovation. So, in order to enhance the model fitness, this relationship was deleted to check the effect of other mentioned processes of knowledge management on technical and non-technical innovation. An examination of the effects of dimensions of knowledge management (Figure 6) on different types of innovation, that is, technical innovation and non-technical innovation, shows that all dimensions of knowledge management do not affect innovation. Knowledge approach (IT) (KAP), knowledge protection (KP) and knowledge utilization (KU) have a significant relationship with technical innovation as well as non-technical innovation, thus Hypothesis 2 is partially supported. Knowledge protection is the strongest predictor of technical innovation (SRW = 0.44, significant at 0.001), followed by knowledge approach (IT) (SRW = 0.241, p < 0.001). Knowledge utilization is also exercising significant influence on innovation (Figure 6). Knowledge approach (IT) and knowledge

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Relationship between Knowledge Management and Innovation: Structural Equation Modelling (SEM)

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extensive policies and procedures for protecting trade secrets (4.12). Further, the importance of protecting knowledge is also communicated to the employees (M = 4.17). The detailed analysis reveals that knowledge protection is the ability to secure knowledge from inappropriate uses, which is being highly practised in selected organizations (Table 2).


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Figure 4. Impact of Knowledge Management on Innovative Capacity of an Organization

Notes: Ks6–Ka4 are the manifest variables of knowledge management; in4–in10 are the manifest variables of innovation; e1–e20 are the error terms of manifest variables of KM; and e21–e26 are the error terms of manifest variables of innovation.

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Figure 5. Measurement Model of Knowledge Management Process on Technical and Non-technical Innovation

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Figure 6. Impact of Knowledge Approach (IT), Protection and Utilization on Different Types of Innovation

Notes: KAP – knowledge approach; KP – knowledge protection; KU – knowledge utilization; TI – technical innovation; and NTI – non-technical innovation.

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Thorough analysis and interpretation of data collection has led to following strategic implications to improve innovation through knowledge management. In terms of practical implications, this article attempts to provide telecommunication organizations a better understanding of knowledge management processes and their relative importance to create, develop and manage the innovation capabilities more effectively. In the current environment of telecommunication business, particular aspects of each process need to be emphasized: (i) acquisition process: generating new knowledge from existing knowledge and acquiring knowledge about customers, suppliers and new products/services within the business; (ii) application process: using knowledge to solve new problems, improve innovation and taking advantage of new knowledge; (iii) protection process: preventing knowledge from inappropriate use or theft by using variety of policies, rules, procedures, incentives and technologies, and clearly communicating the importance of protecting knowledge in the organization. In addition, practicing managers of these organizations should understand that these processes should not be considered in isolation but rather integrated and combined together. However, they need to be aware of the more critical role of application processes and thus, put more efforts in leverage and exploitation of the integrated knowledgebased resources to create and deliver products and services to their customers utilizing organizational capabilities. For organizations, it would be especially interesting to determine the knowledge flows that allow for the improvement of a particular type of competence, recombining their resources to innovate some new variables.

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The telecommunication employees are satisfied with the knowledge management in their organization. Knowledge sharing is the most important factor of knowledge management (Nonaka, 1994; Nonaka and Konno, 1998). Employees can share their knowledge through discussion (Swan et al., 1999) and team meetings, which increases their knowledge and helps to solve problems concerning attainment of goals (Baptista et al., 2005; Kidwell et al., 2000). Knowledge approach (IT) and knowledge creation are also valuable factors of knowledge management. Information technology specialists are required to maintain database which helps in the formation of new knowledge to perform special tasks efficiently (Darroch and McNaughton, 2002; Gloet and Terziovski, 2004). Further, knowledge conversion enhances the capabilities of the organization through proper integration of knowledge (Lacy et al., 2009) and managing the overall knowledge in the organization. Knowledge protection helps to protect the knowledge, utilized for various purposes, from inappropriate use by making policies and procedures. Knowledge acquisition helps in the growth and diversification of the business (Scarbrough, 1999). All the processes of knowledge management help in the creation, development and management of innovative capabilities. The results are consistent with Chang and Lee (2008), Kamasak and Bulutlar (2010), Nonaka (1994), Plessis (2007) and Trussler (1998). Further, in this investigation, the relationship between knowledge management and innovative capacity of an organization has been explored. The results suggest an association between knowledge management and innovative capacity. The theoretical model developed, and subsequently validated through the data, revealed that there is a positive relationship between knowledge management and innovative capacity of an organization. Introducing knowledge management processes in the organization has an effect on the generation of innovation competences. Knowledge management helps to develop skills through capability enhancement by the acquisition, transfer, dissemination and application of accumulated knowledge, and an increase in the variety of the

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Conclusion

organization memory. In this sense, knowledge management acts a mechanism to coordinate the explicit and tacit knowledge distributed in the organization. Further, the effect of knowledge management on technical and nontechnical innovation competences is due to the knowledge approach, knowledge protection and knowledge utilization processes. At last, knowledge management system improves functional activities and innovative capacities of an organization.

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protection are the strongest predictors of non-technical innovation (SRW= 0.41 and SRW= 0.40, significant at 0.001, respectively). These two dimensions are responsible for about 37 per cent variation in non-technical innovation and 29 per cent variations in technical innovation. The overall model fit summary is good (with chi-square = 3.261, df = 61, RMR = 0.05, RMSEA = 0.084, GFI = 0.913, AGFI = 0.870 and CFI = 0.982).

Theoretical Implications In this study, the knowledge management and innovation scales have been validated. Further, we established the relationship among knowledge management and innovation, dimensions of knowledge management on different types of innovation in the telecommunication organizations

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Limitations

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The two main limitations of this study should be acknowledged. First, in our study, the data were collected only from the private telecommunication sector. Second, the study has measured knowledge management and innovation on the basis of the employees’ responses which might have been depended on their own perceptions.

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1. A study of public organizations can be undertaken to examine the impact of knowledge management on innovative capacity. 2. Knowledge management and innovation should also be measured from different perspectives, like customer perspective.

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which can further be used. All this will not only enhance the level of knowledge management in telecommunication organizations, but it will also improve the innovative capacity and competition level of these organizations.

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Jeevan Jyoti ([email protected]) is an Assistant Professor in the Commerce department of University of Jammu, J&K, India. She teaches human resource management and entrepreneurship. Her areas of interest are HRM, OB and entrepreneurship. She has publications in international and national journals, namely, IIMB Management Review, Journal of Service Research, Annals of Innovation and Entrepreneurship, International Journal of Management Sciences, Nice—The Journal of Business, Arthanveshan, Business Vision, The Business Review, etc. She also has a paper published in an edited book, ‘Strategic Service Management and Strategic Service Marketing’. She is currently researching on women entrepreneurship, rural entrepreneurship and impact of mentoring functions and outsourcing on employee attitudes in J&K (India). Pooja Gupta ([email protected]) is a lecturer in senior secondary school based in Jammu, J&K. She teaches economics and accounts. Her areas of interest are HRM and OB. She has publications in journals and books, namely, Arthanveshan and

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Strategic Service Management. She is currently researching on reasons of attrition in IT Industry.

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Sindhu Kotwal ([email protected]) is a lecturer in Government Degree College for Women, Parade, J&K. She

teaches economics and communication. Her area of interest is strategic HRM. She is currently doing her research on ‘Impact of Knowledge Management on Financial Performance: Role of Value Disciplines’.

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