This paper aims to provide a study on knowledge management, facilitating new product innovation by intrapreneurial companies.
The methodology includes the empirical study which was conducted based on detailed questionnaire and data collection of 319 respondents from heavy engineering auto companies, such as Maruti, Honda and others. The data were analyzed to find the influence of knowledge management on new product development. Structural equation modeling method, critical path analysis and reliability were checked by Cronbach’s alpha.
The findings suggest that the innovation of a new product is critical for the companies. Also, it is very important for the companies to have knowledge management systems such as intelligence generation and dissemination process to facilitate information sharing among the various departments. Responsiveness to the market needs would be based on how authentic the customer data are and to what extent the company is able to share these data with research and product development departments to motivate new products for fulfilling these needs of the customers. This kind of process would enable the company to drive the next level of innovation within the company.
The present study has several implications for managers and researchers. The model proposed in the study suggests the adoption and implementation of knowledge management for product innovation. The study findings also suggest that developing better methods to share knowledge and intelligence among the employees about the customers’ data would be a very critical success factor for new product innovation.
This paper also suggests that the researchers can study this model with respect to inter-disciplinary and inter-country study to become competitive using new product innovation.
The study contributes toward development of theory on creating innovation facilitated by knowledge management for enhancing innovation.
Bhardwaj, B.R. (2019), "Influence of knowledge management on product innovation by intrapreneurial firms", Global Knowledge, Memory and Communication, Vol. 69 No. 1/2, pp. 38-57. https://doi.org/10.1108/GKMC-03-2019-0039Download as .RIS
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Copyright © 2019, Emerald Publishing Limited
The need for knowledge management in heavy engineering entrepreneurial firms is to find the scope for better product development for survival and competitiveness. In today’s highly competitive global environment, a company’s ability to introduce innovations is a key success factor for achieving sustaining competitive advantage (Clark and Fujimoto, 1991; Davila et al., 2007; Tidd and Bessant, 2009). The novelty of such a study adds to the body of knowledge by identifying best practices for knowledge sharing with respect to the manufacturing sector which is very scarce. Centobelli, Cerchione and Esposito (2017) stated the need to study knowledge management with respect to the innovation of new product development. The main focus of this paper is to find out how knowledge management strategies undertaken by the organization can facilitate the innovations within the organization. The paper also suggests how knowledge management can facilitate product innovations.
The purpose of this study is to understand the knowledge management process which can facilitate the knowledge generation leading to product innovation. Launching new products in the market is important, as product innovation is necessary for companies to adapt to changing conditions in markets, technologies and competitive forces (Dougherty and Hardy, 1996; Utterback, 1994; McDermott and O'Connor, 2002; Bessant et al., 2005; Pavitt, 2005). This paper defines product innovation as new product developed by the company for old market or modified product introduced in the markets. For example, Mahindra and Mahindra have introduced XUV developed in India in African markets. This was made possible with little modification made on tires to suit its local terrains.
A product innovation also includes the introduction of goods or services that are new or significantly improved with respect to their characteristics or intended uses. This includes significant improvements in technical specifications, components and materials, incorporated software, user-friendliness or other functional characteristics.
In general, innovation activities could be described as the efforts to create meaningful and focused change within a company’s financial and social potential (Drucker, 1997). Innovation was defined by Popadiuk and Choo (2006) as an idea that has been developed as a product, process or service and has been commercialized. They highlight that, in general, the concept of innovation is often related to the words including novelty, commercialization and/or implementation. McDermott and O’Connor (2002) defines innovation as new technology or a combination of technologies that offer worthwhile benefits, and they further note that the evaluation of a technology as innovative also needs to be related to existing technologies, both from an internal and an external perspective. This is in terms of the product usage.
According to the Oslo Manual (OECD, 2005, p. 46) the minimum requirement for an innovation is that the product, process or method of innovation must be new to the firm. This includes both innovations that the company is first to develop and those that are adopted and modified from other firms. Following from this argument, an innovation is considered to be new to the market if the firm is the first to introduce the innovation in its market (OECD, 2005, p. 58).
Thus, the range of innovations could go from the increased performance of an existing product, process or method to the development of entirely new products or processes. For one company, an innovation could be about an incremental product modification efforts resulting in increased product performance. For another company, innovation could be about major changes to their product portfolio, including a major element of novelty, both from an internal and a market perspective. According to Dewar and Dutton (1986), this range of innovation relates to the notion of radicalness, where incremental innovation could be seen as containing a low degree of new knowledge, as is the case with minor improvements or adjustments in current technology. Conversely, radical innovation is about revolutionary changes in technology, involving clear departures from existing practice and a high degree of new knowledge. Leifer et al. (2000) note that a radical innovation is based on new ideas or technologies which help to create new business lines or new product lines. Tushman and Nadler (1986) argue that incremental innovation contains changes in the form of added features and new versions or extensions to a product line, whereas a radical innovation includes the application of a new technology or a new combination of technologies to new market opportunities.
Christensen (2006) discusses the term sustaining innovation in contradiction to disruptive innovation. A sustaining innovation does not have a disruptive effect on existing markets but could include both evolutionary and revolutionary. This may include creating a new market by solving a problem in radically new ways. Commonly, sustaining innovations improve customer value by providing a higher degree of product performance. A disruptive innovation, on the other hand, brings an entirely different value proposition to the market that has not existed before.
During recent decades, increasing environmental concerns have become a strong incentive for innovation. For example, Mahindra and Mahindra have innovated Reva Electric cars. There are other followers in innovating less polluting cars. Maruti is phasing out its diesel cars recently to contribute toward more fuel efficient cars, which are being developed by the company.
Environmental regulations will exert immense pressure on manufacturing industries, enabling a more sustainable world for coming generations. The automotive industry is one of many industries causing environmental pollution, where cars have a significant impact on all including manufacturing, use, recycling and disposal (Orsato and Wells, 2007). The number of cars in global use will increase in the near future, particularly owing to growing demand in developing countries. For example, the number of cars sold in China has increased by over 25 per cent annually in the past decade, making China the world’s largest car market. In 2012, the global car fleet passed the one billion mark. As a consequence of the growing car market, the automotive industry accounts for 27 per cent of CO2 emissions in the world (WWF, 2013). Automakers have also shown an increasing awareness of the environmental impact of their products as environmental regulations and market demands for environmentally less destructive cars have increased. The focus on reducing CO2 has become a strong driver in the development of electric vehicles and hybrid electric vehicles. The weight of the car is one essential factor that has an effect on CO2 emissions in the case of conventional cars, electric vehicles and hybrid electric vehicles. A rough estimate suggests that a weight reduction of 100 kg results in decreased fuel consumption of 5 per cent (Swedish Association of Green Motorists). A rule of thumb is that a 10 per cent mass reduction results in a 46 per cent decrease in fuel consumption, indicating some of the potential in focusing on lightweight concepts in the automotive industry. Even though automakers understand and largely master technical difficulties with alternatives to the all-steel body, the mainstream industry has largely retained the all-steel body. This area also has a potential for innovation.
Given the environmental challenge facing the automotive industry, the industry is a mature industry characterized by mass-production, a dominant design and incremental innovation (Abernathy and Utterback, 1978; Clark and Fujimoto, 1991; Utterback, 1994; Orsato and Wells, 2007). The ﬁrst mass-produced cars entered the market at the beginning of the 20th century. The moving assembly line by Ford was a prerequisite for the mass-production of cars, but the mass-manufacture of cars was not complete until the introduction of Budd’s all-steel body in the 1920s (Nieuwenhuis and Wells, 2007). Already being assembled and painted when it arrived at the assembly line eliminated bottlenecks in production. Budd’s technology, to a large extent, shaped the automotive industry both from a process and a product perspective. Nieuwenhuis and Wells (2007, p. 207) even argue that the all-steel body constituted “a true revolution in car manufacturing, although the full long-term impact of this could not have been foreseen when it took place.”
The production of all-steel bodies became the primary activity of car plants, accounting for 75 per cent of the investments (Nieuwenhuis and Wells, 2007). While mass-production helped to create the automotive industry, the moving assembly line restricted the possibilities for change and introduction of product innovations (Abernathy and Utterback, 1978). The demand for new products has simultaneously shortened product lifecycles. This has led to alliances and take-overs to provide resources and platform for development (Clark and Fujimoto, 1991; Williams, 2007; Wells, 2010). The environmental challenge, particularly the need to reduce CO2 emissions exerted immense pressure on automakers. The new European targets for emissions of the entire average new car fleet of 130 g CO2 per km by 2015 and 95 g/km by 2020 (Transport and Environment, 2011). This demands major efforts and will force automakers to ﬁnd weight-reducing solutions, thus questioning Budd’s dominant design (Nieuwenhuis and Wells, 2003; Orsato and Wells, 2007). However, this kind of policy can also be a source of innovation.
Previous research on environmental innovation in the automotive industry predominantly focused on investigating the combustion engine and different alternatives to propulsion like EV, HEV and fuel cells (van den Hoed, 2007; Aggeri et al., 2009; Berggren et al., 2009; Zapata and Nieuwenhuis, 2010). Some research also indicates a lack of innovation toward developing more environmentally sound alternatives (Bhardwaj and Malhotra, 2013; Bhardwaj, 2016; Bhardwaj and Momaya, 2006).
Furthermore, an increasingly global world with rapidly growing populations implies a growing demand for transportation. To achieve our potential for a good life style and a sustainable society, our means of travel and consumption must change (Johan Rockström, Nobel talk 2013). This can be another source of product innovation. This kind of market intelligence information can be a great source of innovation for car manufacturers. The present study attempts to understand the correlation between managing and disseminating knowledge related to market intelligence and source of product innovation for the companies.
Product innovation and knowledge management
Some of the challenges of radical innovations include conflicting demands to explore new opportunities (March, 1990; Tushman and O’Reily, 1997). There is a necessity to plan for future growth when dealing with everyday engineering activities and competing with scarce resources. There is a need to understand how to achieve a harmony and balance between the environment, depleting resources and life’s existence. This can be a source of several radical innovation projects (Leifer et al., 2000; see also Dougherty and Hardy, 1996). However, successful adoption would depend on organizational, managerial and environmental factors (Lavie et al., 2010).
It is interesting to observe that the information received from the market never seems to reach the research and developmental department. There has to be a proper process for gathering customers’ data through feedback about the products. This information needs to be shared with the product developmental teams frequently. Mostly, it is seen that only sales figures are taken into consideration and customer’s feedback is neglected. Customer feedback kind of qualitative inputs can lead to greater source of innovation. The present study contributes by emphasizing the need for the companies to create this flowing stream strategy of knowledge that can facilitate and be a source of endless innovation and make products environmental-friendly and sustainable. This can be facilitated by designing fluid organization structures where there are continuous flow of knowledge and information among all departments. Therefore, too much formalization is deleterious for radical innovation (Benner and Tushman, 2002, Engwall, 2003). Furthermore, rigid organizational boundaries prohibit radical innovations (Wheelwright and Clark, 1992; Dougherty and Hardy, 1996; Christensen, 2006). Here, flexible organizational boundaries would be extremely helpful in enabling innovation (Bhardwaj and Momaya, 2006). A commitment to existing technologies and markets and their own investments result in a focus on incremental improvements of core technologies (Chandy and Tellis, 1998; Stringer, 2000; Bessant et al., 2005; Assink, 2006). There are serious difficulties to overcome to successfully achieve new environmental innovations (Chandy and Tellis, 2000; Hill and Rothaermel, 2003; Macher and Richman, 2004; Bergek et al., 2013). In the twenty-first century, an industrial paradigm shift is needed to find different value propositions for customers are necessary (Donada, 2013). This will be characterized by technology, market and business model diversity (Wells, 2010).
A company’s ability to introduce new products and services is a key success factor for sustaining competitive advantage (Davila et al., 2007). The last decade has been characterized by take-overs, mergers and discontinuances in this branch of business, in continuous attempts to gain economies of scale. Principles of lean production (Womack et al., 1990) have been largely influential in creating increasingly efficient development and manufacturing processes. However, large and small manufacturers are desperate to innovate their way out of the crisis for sustainable competitiveness.
Meaning and definition of product innovation
Innovation is one of the primary tools for strategic growth (Berry et al., 2010; Birkinshaw et al., 2008; Busenitz, 1999). Organizations are now realizing the essence of innovation in their day-to-day working as new and modern technology is adapted by competitors very quickly (Brown and Dant, 2014; Adams and Jeanrenaud, 2008). Therefore, tough competition gives encouragement for innovation.
Therefore, innovation becomes an important element of marketing strategies to improve the goodwill in the mind-set of the customers (Coombes and Nicholson, 2013; Buijs, 1993; Hamel, 1998; Hamel, 2006a, 2006b). It also provides opportunity to gain long-term survival in an aggressive world (Cronholm et al., 2013; Andrews et al., 2007; Hamel, 2007). Over the past 20 years, innovation has gained popularity amongst the researchers who tried to characterize the impact of innovation on performance (Dholakia, et al., 2010; Amabile, et al., 1996; Burns and Stalker, 1961). Innovation provides the platform to organizations to survive in the long term with profit-earning capacity. It is a tool that provides strategies to fight with competitors (Ericsson and Sundstrom, 2012; Drucker, 1985; Hitt et al., 2001; Kuratko et al., 2005; Hauser et al., 2006; Drucker, 2012; Drucker, 1985a).
The impact of innovation can be seen on sales growth, change in market share and profit (OECD Oslo Manual, 2005). McAdam and Keogh (2004) examined the correlation between firms’ concert and its awareness with modernism and further investigation. They established that the firms’ resources for innovations. Geroski (2005) observed special effects of innovations and copyrights on business performance including book-keeping, profit returns, stock prices and growth percentage. However, innovative firms appear less vulnerable to recurring changes and ecological demands than other firms (Biemans, 1992) (Birkinshaw, et al., 2007). Figure 1 shows the strategies of product innovation by organizations.
Figure 1 shows the experimental knowledge that evaluates the profits from adopting new strategies of product innovation. This study was performed in Canada. Peak performance of product innovation and technology was motivated by the business leaders in these businesses (Cooper et al., 2000).
Knowledge management includes intelligence generation, dissemination and responsiveness to market intelligence (Jwaroski, 1999).
Intelligence generation is conceptualized as a mental process which creates active communications among persons, firms and the surroundings (Nonaka and Toyama, 2002). Wiig (1997) defined intelligence generation as understanding, focusing and managing organized, unambiguous and intelligence. The term “intellectual capital” includes all types of firm and customer intelligence that can be transformed into income, procedures, copyrights and exclusive rights and which can serve as a source of revenue for the company. There source based view of the organization focuses on intelligence being the key resource of a firm’s innovation. The theory of intelligence-generation proposes that intelligence is critical for product innovation (Nonaka and Takeuchi, 1995). Further, the author also suggested how companies can organize the process of intelligence generation and dissemination and use it to design new products, services or systems (Barringer and Bluedorn, 1999; Covin, 1991). Moreover, commercial firms tend to connecting higher level of knowledge-scrutinizing behavior (Hambrick, 1982; Narver and Slater, 1990; Jawaroski and Kohli, 1993; Barret and Weinstein, 1998; Nonaka and Toyama, 2002; Ramachandran and Ray, 2006).
Maintaining good communication with external constituents, especially customers, facilitates the flow of information that are crucial for new business creation (Barringer and Bluedorn, 1999; Fiol, 1996; Hornsby et al., 1993; Kanter, 1982; Lumpkin and Dess, 1996; von Hipple, 1978; Zahra, 1991). Christensen (1997) pointed to the danger of allowing customers to dictate innovation. Given a closer attention into the shortage of intelligence processes, the knowledge and message expertise of the human resource management are required:
Intelligence generation influences product innovation significantly.
Firms act on the basis of their market intelligence. Appropriate infrastructure and processes are the instruments for improving intelligence dissemination (Ruggles, 1996). This may include the process of choosing, teaching and inspiring groups to distribute intelligence and a firm’s events. It has been found that a person’s interactions result in greater trust, self-disclosure and commitment between them (Frances and Sandberg, 2000). This kind of trust would lead to better product innovation. Awareness fosters a safe environment for facilitating effective decision-making (Nonaka and Takeuchi, 1995; Sheshadri and Tripathy, 2003):
Intelligence dissemination influences product innovation significantly.
These variables have not been studied with respect to the product innovation. The present study attempts to study how knowledge sharing with the research and development department facilitates more innovation.
Interestingly, this study attempts to quantitatively assess impact of knowledge management on product innovation of the select organizations in emerging countries such as India. Qualitative and quantitative methods have been used in knowledge management to document the case studies on “How.” This paper attempts to quantify the amount of influence of knowledge management on innovative processes. Some of the detailed case studies have been published (Bhardwaj and Momaya, 2011; Wahee et al., 2013). Thus, the utilization of a descriptive and causal research design was deemed appropriate. Descriptive causal studies have been widely used to assess the impact of product innovation on the financial performance of the selected organizations (Gill and Biger, 2009; Srivastava, 2007; Bennet et al., 2012; Dharmaja et al., 2012; Ting and Yu, 2007). However, studies of this type have never been related to knowledge management and innovation.
A descriptive and casual research design appropriate in the first attempt to quantitatively assess impact of knowledge management practices on product innovation is very important. This is because the present study intends to help the employees working in the product development department to understand how they can assimilate customer data to develop new products. It is important to understand how the company can collect customer data and how to disseminate this information to customize the products innovatively. Table I shows the constructs and measurement of variables that influences product innovation within the company.
Table I shows the identification of various constructs that are critical for knowledge management measures and their items for understanding the process of knowledge management. The validity of the design of questionnaire is based on identification of the items to measure the constructs from Kohli and Jwaroski (1993).
Reliability is the extent to which a list of scale items would produce consistent results (Malhotra, 2007). It is assessed by determining the Cronbach’s alpha coefficient of a scale for internal consistency (Pallant, 2007; Bhardwaj and Sushil, 2012). The ideal Cronbach alpha co-efficient should be over 0.7 (Hair et al., 2010). A value of below 0.7 is considered to indicate unsatisfactory internal consistency reliability (Malhotra, 2007). Table II summarizes the Cronbach’s alpha with 32 statements. Table II gives the overall reliability coefficient for the total questionnaires.
After the data were collected from 319 respondents, factor analysis was conducted to extract the common factors (DeCoster, 1998).
Table II shows the items that measure the construct such as intelligence generation. The study shows that it is also critical for the company to develop the capability to generate intelligence about customers which can lead to entrepreneurial pursuits through product innovations. Some of the important processes include information-integrity skills by the product developmental teams, constructive interaction among employees, availability of proper infrastructure such as email, whiteboards, intranets for sharing information, degree of familiarity for information exchanges and designing innovative meetings for marketing executives and R&D executives. Similar meetings used to be conducted every Wednesday at Sona Koyo Steering Ltd (Tables III and IV).
The factor analysis confirms the validity of these constructs. The components matrix is the output of the factor analysis process that includes the loadings of the scale items. Valid components have scale item loadings of 0.5 and above (Hair et al., 2010; Wixom and Todd, 2005).
Structural equation modeling
Structural equation modeling assesses the direct and indirect relationships among the variables (Anderson and Gerbing, 1988). Measurement model is represented by confirmatory factor analysis which studies the relationship between observed variables and their latent constructs (Byrne, 2010). On the other hand, structural model represents the relationship between latent constructs. The proposed hypotheses were tested statistically. The adequacy of goodness-of-fit proves the existence of relationships between variables. It is believed that it is highly unlikely to get a perfect-fit between the hypothesized model and the observed data which is known as residual (Byrne, 2010). The error associated with observed variables represents measurement error that reflects their adequacy in measuring the related underlying factors. Measurement errors are derived from two sources, namely, random measurement error in the psychometric sense and error uniqueness, which is a specific indicator variable. Residual item represent error in the prediction of endogenous factors from exogenous factors.
The analysis related to the items of intelligence dissemination shows that it is the latent construct having four measured variables. The degree to which each of these measured variables are related is represented by the variable’s loadings, as a measured variable does not explain the latent variable perfectly. The four unidirectional arrows leading from intelligence dissemination to each of the four observed variables including interaction among employees, availability of appropriate infrastructure and processes, familiarity with colleagues facilitating the generation of ideas and identifying and designing intelligence dissemination processes suggest that these score values are each influenced by the respective underlying factor. These path coefficients represent the magnitude of expected change in the observed variables for every change in the related latent variable.
The construct, namely, intelligence dissemination has an overall value of 0.42 and has been included in the validated model of product innovation. The items including interaction among employees (1.00), identifying and designing intelligence dissemination processes (1.18), availability of appropriate infrastructure and processes (1.01) and familiarity with colleagues facilitates the generation of ideas (1.00), which has been included in the validated model (Ruggles, 1996; Frances and Sandberg, 2000; Nonaka and Takeuchi, 1995; Sheshadri and Tripathy, 2003). The unstandardized regression coefficients represent the expected change in the dependent variable associated with a unit change in a given predictor while controlling for the correlated effects of other predictors. This result suggests that for every single unit of increase in intelligence dissemination, innovation is increased by 1.18 units. The study also shows the error variance which represents the amount of variance unexplained by the observed variable. For example, 0.56 shows that the model predicts 56 per cent of the variance by intelligence dissemination. The figure also shows the R2 value which is 0.42 and reflects the proportion of variance in the dependent variable is explained by all the predictors. The results show that 42 per cent of the variances in the dependability is explained by all the observed variables. The higher the R2, the better it is. Error associated with observed variables represents measurement error that reflects the adequacy in measuring the related underlying factor (Table V).
The standardized regression weights represent the amount of change in the dependent variable that is attributable to a single standard deviation unit change in the predictor variable. The standardization of the coefficients based on the standard deviations of the variables is the approach typically used to make coefficients comparable. In the table above, ID3 and ID4 have factor loadings of 0.674 and 0.676; this shows that they are best indicators of intelligence dissemination.
The analysis shows that intelligence generation is the latent construct having four measured variables. The degree to which each of these measured variables is related to latent construct is represented by the variable’s loadings or standardized estimates. As a measured variable does not explain the latent variable perfectly, an error is added. The four unidirectional arrows leading from IG to each of the four observed variables (IG1, IG2, IG3 and IG4) suggest that these score values are each influenced by the respective underlying factors. As such, these path coefficients represent the magnitude of expected change in the observed variables for every change in the related latent variable (or factor). Here the connotations for the following terms are:
The construct “intelligence generation” with an overall value of 0.86 is included in the validated model of product innovation. The items including process of intelligence generation is important to design new products and services (0.70), organize the process of intelligence generation and use it to design new products, services or systems (0.73), capability to generate intelligence and use it is the most important source of a firm’s sustainable competitive advantage (0.77), and people with entrepreneurial pursuits tend to engage in greater levels of information-integrity-scanning activities (1.00) has been included in the validated model of product innovation as suggested in conceptual model (Nonaka and Toyama, 2002; Wiig, 1997; Ramachandran and Ray, 2006). The unstandardized regression coefficients represent the expected change in the dependent variable associated with a unit change in a given predictor while controlling for the correlated effects of other predictors. This result suggests that for every single unit of increase in IG3, intelligence generation is increased by 0.77 units. The above figure also shows the error variance which represents the amount of variance unexplained by the observed variable as 0.73. This means 73 per cent of the variance is unexplained by IG3 in predicting product innovation. The lower the variance, the better it is. The figure also shows the R2 value which is 0.86 and reflects the proportion of variance in the dependent variable explained by all the predictors. The result here shows that 86 per cent of the variances in the dependability are explained by all the observed variables. Table VI shows the overall reliability, validity and model fit.
Results of the structural model
Intelligence generation is a component of product innovation that has a significant impact on financial performance – accepted.
Intelligence dissemination is a component of product innovation that has a significant impact on financial performance – accepted.
In conclusion, we can state that there is a huge need for the companies to adopt the knowledge management systems to enhance the product innovations in companies. The study findings suggest that knowledge management helps to generate intelligence about the customers. The various benefits of the present study show that the constructs that have been studied, such as intelligence generation and dissemination and responsiveness, have not been studied in terms of the customers’ behavior point of view. These have only been studied from an internal environmental perspective. However, this study adds to the existing literature on the utilization of knowledge management as a tool for gathering customer knowledge and makes it a source for furthering the innovation within the organization. Therefore, this study integrates the external environmental conditions using knowledge management systems to facilitate internal innovations, which can satisfy the market needs better. Using the step-wise structural equation modeling analysis, it emerged that intelligence generation acts as a driver of innovation. It is evident from the framework that intelligence generation has higher impact on the innovation in terms of good communication and periodical reviews of customers’ feedback. The study also shows that having good communication with customers and employees such as email feedback, scanning social media for consumer behavior and retail store feedback would greatly enhance the intelligence generation. The companies frequently talk about it but fail to facilitate or design a process which helps to do the same. Interestingly, the companies must also pick up the verbal as well as the non-verbal communication between the customers and product selection processes. For example, some companies record the consumer behavior through cameras installed in the stores. It is critical how the company uses this data for market analytics. Thus, it can be concluded that intelligence generation can be used as a major determinant to improve the financial performance in terms of innovativeness. It is critical for successful knowledge management that the information integrity shared is accurate, detailed about the innovation processes’ methods and courses. The results revitalize intrapreneurship by emphasizing the role of innovation within the large organization by the employees of the organization. In this process, the knowledge management is used in enhancing the continuous innovation within the organization for intrapreneurial survivability and sustainability. Results of the present study will revitalize intrapreneurship by enhancing the creativity and innovation driven by the customers’ feedback. This is because people with intrapreneurial pursuits tend to engage in greater levels of information-scanning activities. This feedback becomes the source of the continuous product innovation and services. The future directions of research may include the development of the capability to generate intelligence and use it for a firm’s sustainable competitive advantage. Moreover, the future areas of study may also include the development of skills through training and development in employees to collect customers’ data, developing capability, processes and systems. Designing appropriate organization structure and creating innovative culture and environment for facilitating innovation within the organization. These factors are critical not only for auto sectors but also for other service sectors for facilitating innovation. Therefore, there is a need to study these factors with respect to developing innovative DNA of the organization.
Implications include influence of knowledge management on marketing analytics and its applications for enhancing organizational competitiveness. The managerial implications include the design of such a system in organizations, so that innovations in products and services can sustain companies in hostile markets. The research implications include the need for studying the various process and detailed design of such knowledge management systems for other industries and sectors. Intelligence dissemination directly affects the financial outcomes. Intelligence dissemination has emerged as a major predictor of innovativeness. Implications for other sectors include the design of intelligence generation processes and use it to design new products and services.
Periodical reports circulation influences innovations significantly. These links were observed in research analysis. Thus, it can be concluded that intelligence dissemination can be used as a major determinant of product innovation to improve the financial outcomes.
The paper may also be used in teaching how the knowledge can be disseminated among all the employees and departments. The impact of the research on society concerns the design of innovation processes. The items used are the actions that would lead to better knowledge sharing.
These specific issues related to product innovation help the organization in intrapreneurial revitalization (Nwokah et al., 2009; Peters, 2008). These specific issues also help us to address the issue of organization’s lack of vitality which is one of the key aspects of the organization’s sustainability. The study leads to the conclusion that the organizations intending to practice of product innovation need to focus on intelligence dissemination, intelligence generation for enhancing product process innovation.
The predictors of innovations include intelligence dissemination, intelligence generation and responsiveness. This shows that for innovations to succeed, it is important to have proper processes for intelligence generation and dissemination. The study suggests that innovations require work discretion to succeed.
|Constructs||Measurement variables for investigation in the study||Author(s)|
|Intelligence generation||Process of intelligence generation||Nonaka and Toyama (2002), Wiig (1997), Ramachandran and Ray (2006)|
|Design of new products, services or systems|
|Capability to generate intelligence and use it|
|Capability to engage employees in innovative activities|
|Intelligence dissemination||Interaction among employees||Ruggles (1996), Frances and Sandberg (2000), Nonaka and Takeuchi (1995), Sheshadri et al.. (2003)|
|Availability of appropriate infrastructure and processes|
|Familiarity with colleagues|
|Identifying and designing intelligence dissemination processes|
Overall and individual constructs’ reliability analysis for the study
|Cronbach’s alpha||N of Items|
|IG (Intelligence generation)|
|IG1 In our organization, the process of intelligence generation is important to design new products and services|
|IG2 In our organization, it is critical for the companies to organize the process of intelligence generation and use it to design new products, services or systems|
|IG3 In our organization, the capability to generate intelligence and use it is the most important source of a firm’s sustainable competitive advantage|
|IG4 In our organization, the people with entrepreneurial pursuits tend to engage in greater levels of information–integrity-scanning activities|
|ID (Intelligence dissemination)|
|ID1 In our organization, interaction among employees is a critical success factor for product innovation|
|ID2 In our organization, the availability of appropriate infrastructure and processes are critical for improving intelligence dissemination within the organization|
|ID3 In our organization, familiarity with colleagues, which facilitates the generation of ideas, is critical to product innovation|
|ID4 In our organization, identifying and designing intelligence dissemination processes are important for product innovation|
|FN1||There is an increase in the ratio of return on sales (profit/total sales)||1||0.683|
|FN2||There is an increase in the ratio of return on assets (profit/total assets)||1||0.688|
|FN3||There is an increase in the general profitability of the firm||1||0.66|
|FN4||There is an increase in the cash flow of the firm excluding investments||1||0.875|
|ID1||In our organization, interaction among employees is a critical success factor for product innovation||1||0.666|
|ID2||In our organization, the availability of appropriate infrastructure and processes are critical for improving intelligence dissemination within the organization||1||0.693|
|ID3||In our organization, familiarity with colleagues facilitates the generation of ideas, is critical to product Innovation.||1||0.566|
|ID4||In our organization, identifying and designing intelligence dissemination processes are important for product innovation||1||0.693|
|IG1||In our organization, the process of intelligence generation is important to design new products and services||1||0.619|
|IG2||In our organization, it is critical for the companies to organize the process of intelligence generation and use it to design new products, services or systems||1||0.685|
|IG3||In our organization, the capability to generate intelligence and use it is the most important source of a firm’s sustainable competitive advantage||1||0.748|
|IG4||In our organization, the people with entrepreneurial pursuits tend to engage in greater levels of information-integrity-scanning activities||1||0.925|
Total variance explained
|Total variance explained|
|Initial eigenvalues||Extraction sums of squared loadings||Rotation sums of squared loadings|
|Factor||Total||% of variance||Cumulative %||Total||% of variance||Cumulative %||Total||% of variance||Cumulative %|
|Note: Extraction method: principal axis factoring|
Standardized regression weights: (group number 1 – default model)
Summary of overall reliability, validity and model fit
|Reliability||Convergent validity||Discriminant validity||Model fit indices|
|Constructs||Cronbach’s alpha||Average variance explained||Maximum shared variance||CFI||GFI||RMSEA||P-CLOSE||CMIN/df|
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About the author
Prof (Dr) Broto Rauth Bhardwaj is working as a Professor and a Head, Entrepreneurship and Incubation Cell at Bharati Vidyapeeth Institute of Management and Research, New Delhi since 2004. Prior to this, she has also taught at North Eastern Illinois University, Chicago, USA. She has worked with Apeejay School of Marketing, Dwarka, Evalueserve and Shaw Wallace Ltd. as a Business Research Associate and Quality Assurance Manager, respectively. She has launched entrepreneurial courses in BVIMR, ND. She has completed her Post-doctoral thesis from UCLA, USA and GLOGIFT, IIT Delhi, India. She has done PhD and MBA from IIT, Delhi and B.Tech in Biochemical Engineering. A gold medalist from IIT Delhi, she has more than 19 years of industry and teaching experience. She has presented papers at Academy of Management, Chicago, on August 10, 2018 on teaching pedagogy. She has been awarded AGBA Vice President for Indian Entrepreneurship and Fellowship award from University of Texas, USA. She has been awarded Emerald literary award for publication in the entrepreneurship area. She is a Visiting Professor at North Eastern Illinois University, Chicago, USA. Under her mentoring, more than 56 businesses have been launched. She has got the best paper award at an international conference organized by University of Robinson, USA at Solo, Indonesia. She has published more than 120 papers in national and international journals including Emerald and Inderscience Journal, such as Strategic Direction, International Journal of Economics Management and Engineering, Journal of Management Development, Journal of Chinese Entrepreneurship, Bench Marking, and Management Journal Review. She is the editor of BVIMR Management Edge, Greener Journal, UK. She is in the advisory and review board of several international journals including International Journal of Economics Management and Engineering, Academy of Management Conference, USA, IIM Bangalore Journal, International Journal of Global Business Competitiveness and other Inderscience, IEEE and Emerald journals. She has several publications in entrepreneurship with impact factor and indexed journals. She is the member of Advisory Board, Redefine Dimensions Consulting Pvt. Ltd., India and JKU University, Orissa. Her research areas include strategic management, sustainability, business policy and strategic management, business analytics, innovation and technology management, business ethics, corporate entrepreneurship, women entrepreneurship, international entrepreneurship and social entrepreneurship. She has received the best paper award from Malaysia and Bankok University in cyber entrepreneurship. She is a recognized guide for PhD programs and is a PhD examiner with several universities including Dr. D.Y. Patil University and Rajasthan University. She has completed seven PhD scholarsships in cyber entrepreneurship, women entrepreneurship and new product development and social media marketing. She has organized management development programs for Trimax, faculty development programs and motivational programs for the US Government and has undertaken such assignments with Hopkins County College, USA. She is also taught at Northeastern Illinois University, Chicago. She has organized MDPs and FDPs in various areas including entrepreneurship, women entrepreneurship, corporate entrepreneurship and innovation and technology management, mindfulness technique, motivation, leadership, women leadership, talent acquisition, talent retention and employee engagement, innovation and creativity in organizations and role of spirituality on enhancing management skills. She has mentored technical knowhow and helped students in building prototypes, innovation and design of new business ventures, inculcating solution oriented approach and inspiring young minds for businesses, leadership and self-motivation and leading teams in business and entrepreneurship by encouraging ideas and team building. She has been invited to deliver workshops in entrepreneurship at University of Eldoret, Kenya and King Mongkut’s Institute of Technology at Ladkrabang, Bangkok, Thailand on women entrepreneurship development program.