Knowledge creation processes in small inovative hi tech firms


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Knowledge creation processes in small inovative hi tech firms

  1. 1. Knowledge creation processes 879 Management Research News Vol. 31 No. 11, 2008 pp. 879-894 # Emerald Group Publishing Limited 0140-9174 DOI 10.1108/01409170810913060 Knowledge creation processes in small innovative hi-tech firms Martin Spraggon and Virginia Bodolica Department of Management, Marketing and Public Administration, School of Business and Management, American University of Sharjah, Sharjah, United Arab Emirates Abstract Purpose – The purpose of this paper is to explore knowledge creation processes in small innovative hi-tech firms operating in the software industry. Design/methodology/approach – The research framework examines specific action and interaction processes aiming at creating knowledge. This exploratory research is constituted by five case studies, each of them being represented by a small Canadian software firm. Analysis draws upon four sources of data. A total of 15 interviews (three per case) had been conducted and subsequently transcribed and coded using qualitative software – Nvivo 07. Findings – The results of the study reveal that interaction processes permitting the creation of knowledge in small hi-tech firms can take place via: formal meetings; informal communities; project teams; external interaction; and information technology-tools. Rapid prototyping represents the kernel activity of knowledge creation through action. Details of the results, implications of the findings, and conclusions are presented and discussed. Research limitations/implications – This paper is based on a limited number of case studies, therefore empirical results cannot be generalized. Future research on larger samples of small Canadian software firms is needed, using the same eligibility criteria and comparing the same knowledge creation processes as those explored in this study. Other promising avenues of inquiry include such questions as the way small knowledge-based firms operating in turbulent environments organize internally to create knowledge, the conditions enabling the generation of knowledge, and the particular ‘‘spaces’’ in which knowledge creation occurs in these firms. Practical implications – The systematic description and comparison of knowledge creation processes in each explored company contribute to the better understanding of specific ‘‘interaction’’ and ‘‘action’’ processes through which knowledge is generated, enabling practitioners in small innovative hi-tech firms to design appropriate policies and procedures for enhancing knowledge creation behaviors of their employees. Originality/value – This research is among the first and most exhaustive exploratory and comparative studies carried out in the Canadian context of small firms operating in the software industry. Keywords Knowledge management, Knowledge creation, Small enterprises, Computer software, Canada Paper type Case study 1. Introduction In order to survive and remain competitive, small hi-tech firms necessitate creating and rejuvenating knowledge endlessly (Brown and Eisenhardt, 1997; Valkokari and Helander, 2007). Knowledge has become an essential source of value generation and sustainable competitive advantage (Teece, 2005; Nonaka and Takeuchi, 1995). The ability of small hi-tech firms to create knowledge relentlessly and manage it strategically is viewed as critical to organizational success and survival (Inkpen and Dinur, 1998; Nonaka and Teece, 2001; Desouza and Awazu, 2006; Sa´enz et al., 2007). Firms that develop and leverage knowledge resources achieve greater success than firms who are more dependent on tangible resources (Autio et al., 2000). Knowledge management is increasingly becoming an integral business function for many The current issue and full text archive of this journal is available at
  2. 2. MRN 31,11 880 companies, as they realize that organizational competitiveness hinges on the effective management and creation of knowledge (Grover and Davenport, 2001; Randeree, 2006). Although knowledge creation is viewed by business scholars as fundamental for securing a sustainable competitive advantage (Nonaka, 1994; Nonaka and Takeuchi, 1995; Prahalad and Hamel, 1990; Teece, 2005) and has become a widespread concern for firms operating in turbulent and hypercompetitive environments, few studies have systematically investigated the specific knowledge creation processes put in place by small hi-tech firms to generate innovations (Desouza and Awazu, 2006). Understanding knowledge creation processes is critical for small innovative firms in their effort to make optimum use of both explicit and tacit knowledge flowing within the organization (Demers, 2003). In order to create value and develop a competitive advantage through the innovations’ generation, knowledge must be created and strategically managed by small innovative firms (Teece, 2005). In this paper, we focus on analyzing small hi-tech firms through a knowledge management perspective. More specifically, the aim of our research is to further the understanding of how and through what processes small firms operating in the Canadian software industry create knowledge to generate innovations. The main reasons for selecting a Canadian sample of small hi-tech companies are threefold. First, over the last decade this industry’s growth has been the fastest and most important of the information and communication technology (ICT) sector in Canada (Industry Canada, 2006). Whereas, the telecommunication industry’s output since 1997 has grown by 86 per cent that of the software and computer services has almost tripled (ICT Statistical Overview, 2006). Second, the software industry represents a vital source of employment in Canada, accounting for 240,283 new jobs since 1997, most of which were generated by small software firms (SSFs). Overall, employment in software and computer services has increased by almost 90 per cent over the last ten years, compared to 15 per cent in the telecommunications industry (Canadian ICT Sector Profile, 2005). Third, more than 98 per cent of companies operating in the Canadian software industry are small firms composed of less than 100 employees (Industry Canada, 2006), making this industry even more attractive for the purposes of our study. Even though the Canadian software industry is considered to be one of the most important ones for enhancing national competitiveness and economic development (OECD, 2004), empirical research is still embryonic in this fast-growing sector. Moreover, the few existing studies carried out on the software industry explored large rather than small firms and have been made more from a technical point of view than a knowledge management perspective (Dayasindhu, 2002; Jacob and Pariat, 2000). It is our belief that knowledge creation processes put in place to stimulate innovations’ generation significantly differ in small and large firms and in the software sector than in other industries. Therefore, we propose in this study a framework that views knowledge creation processes as an efficient means for small hi-tech companies to keep pace with technological change, particularly when an innovative firm is seeking to create and manage unique and pioneering resources to generate innovations in fast- moving environments. The next section provides a brief overview of the knowledge management perspective and its specific knowledge creation processes. We continue by explaining the research methodology adopted in this study. An in-depth analysis of the cross-case findings follows. We conclude the paper with a detailed discussion of our results in the light of extant literature and presentation of avenues for future research.
  3. 3. Knowledge creation processes 881 2. Literature review 2.1 Knowledge management perspective A knowledge-based view of the firm is a contemporary approach to strategic management that guides attention toward the understanding of the management of a firm’s core knowledge (Autio et al., 2004; Ling Ku et al., 2005). The extent to which organizational knowledge and related organizational learning processes, such as knowledge creation, represent the core elements of innovative firms (Tsoukas and Vladimirou, 2001; McEvily and Chakravarthy, 2002; Crossan and Bedrow, 2003; Inkpen and Tsang, 2005). Knowledge has become the most important strategic input and valuable asset for innovation activities, playing a prominent role in the development of small innovative firms (Nonaka and Teece, 2001; Pe´rez and Sanchez, 2003). Innovation generation demands that knowledge be continually renewed and replenished (Brown and Eisenhardt, 1997). Knowledge is dynamic, relational and based on human action (Davenport and Prusak, 1998; Nonaka, 1994; Nonaka and Takeuchi, 1995). Two types of knowledge exist, explicit and tacit (Polanyi, 1967; Nonaka and Takeuchi, 1995). Explicit knowledge refers to codified knowledge, which is easily transmitted in a formal, explicit and systematic language. Tacit knowledge refers to knowledge that remains much harder to transfer, formalize or codify, due to its personal quality. Tacit knowledge as opposed to explicit is deeply rooted in action, commitment and involvement in a specific situation or context (Nonaka 1994; Tsoukas and Vladimirou, 2001) and involves cognitive and technical components. 2.2 Knowledge creation The creation of new organizational knowledge is increasingly becoming a managerial priority, particularly for small hi-tech firms operating in fast-moving environments. New knowledge provides the basis for organizational renewal and sustainable competitive advantage (Prahalad and Hamel, 1990; Crossan and Berdrow, 2003). Although ideas are formed in the mind of individuals, interactions between individuals typically play a significant role in developing new ideas and creating new knowledge (Nonaka, 1994). Several advocates of the knowledge management perspective conceive an organization as an entity that creates knowledge by virtue of its actions and interactions with its environment (Nonaka and Teece, 2001; Levinthal and Myatt, 1994). A ‘‘spiral model’’ of knowledge creation, which explains the continual relationships between explicit and tacit knowledge has been proposed (Nonaka and Takeuchi, 1995). The interaction between these two types of knowledge is called ‘‘knowledge conversion’’. Through this ‘‘conversion’’ process, tacit and explicit knowledge increase in terms of quantity and quality (Nonaka and Teece, 2001). There are four types of knowledge conversion: . socialization, from tacit to tacit knowledge; . externalization, from tacit to explicit knowledge; . combination, from explicit to explicit knowledge; and . internalization, from explicit to tacit knowledge (Nonaka, 1994). Effective knowledge creation depends on an enabling context, called ‘‘Ba’’ (Nonaka and Teece, 2001; Sa´enz et al., 2007). ‘‘Ba’’ is a boundless context shared by those who interact with each other; through such interactions, participants and context (‘‘ba’’) evolve to create knowledge. ‘‘Ba’’ can possess a physical, virtual and mental dimension.
  4. 4. MRN 31,11 882 Knowledge creation contexts might also be favored by a shared identity (Kogut and Zander, 1996), dense social capital (Nahapiet and Ghoshal, 1998) and trust (Das and Teng, 2000). Knowledge may also be created in the ‘‘spiral’’ that goes through pairs of seemingly antithetical concepts, such as order and chaos, micro and macro, part and whole, mind and body, tacit and explicit and creativity and control (Nonaka and Teece, 2001). In a similar vein, successful innovative firms blend limited structure around responsibilities and priorities with extensive communication and design freedom in order to favor knowledge creation (Brown and Eisenhardt, 1997). This combination is neither so structured that change cannot occur, nor so unstructured that chaos develops. This seems to be the case of small hi-tech firms, where their ‘‘pendulous’’ knowledge creation processes amalgamate creative-chaotic and planned actions, explicit-formal-structured and tacit-informal-home-made procedures and knowledge. 3. Methodology This exploratory research is constituted by five case studies, each of the cases represented by a small Canadian software firm. Due to the complexity and multifaceted dimensions of knowledge, qualitative rather than quantitative analysis is required to explore knowledge management processes in SSFs. Explorative multiple-case studies (Yin, 2003; Eisenhardt, 1989; Sutton, 1997) constitute the research design with an inductive underlying logic. A case study allows the comprehension of complex social phenomena because it takes into consideration the contextual conditions that remain extremely pertinent to the phenomenon under investigation (Yin, 2003). The five cases were chosen in a purposeful fashion (Creswell, 2003) and for theoretical reasons (Eisenhardt, 1989). The rationale behind purposeful sampling resides in selecting ‘‘information-rich cases’’ that provide an in-depth understanding of the phenomena under study (Patton, 2002). The intensity sampling strategy was used in our research to select information-rich cases that manifest intensely the phenomena of interest – knowledge creation processes. We expect SSFs involved in innovation generation activities to manifest intensely specific knowledge creation processes and dynamics, since knowledge represents their most valuable resource (Heeks, 1999; Torrisi, 1998). To be eligible for inclusion in the sample, firms needed to meet some pre-established requirements (Table I). In order to avoid selection biases, we decided to retain only those SSFs which are perceived as highly innovative by software industry experts. We conducted informal interviews with 11 industry experts between October 2005 and January 2006. The final sample list was comprised of nine SSFs, all of which were named at least twice by different industry experts as those involved in innovation activities. Six of the nine firms were named at least three times; all of them were approached and five accepted to participate in our study. The informal interviews with Table I. Sample eligibility requirements Sample requirements for eligibility SSFs Canadian firm Software industry 100 Employees Main activity: conception, creation, development
  5. 5. Knowledge creation processes 883 the panel of experts also helped us to gather additional information about the selected firms before we approached the firms (Creswell and Symon, 2004). Our analysis draws upon four sources of data: (1) in-depth interviews; (2) public documentation; (3) archival records; and (4) direct observation. In each explored SSF, we gathered information on perspectives from two levels of the management hierarchy. The key informants included, among others, the Chief Executive Officer (CEO), Chief Technology Officer (CTO), marketing Vice-President (VP), product development VP and project manager. A total of fifteen interviews (three per case) had been conducted and subsequently transcribed and coded using qualitative software - Nvivo 07. Interviews typically lasted 90 min, although two of them ran as long as 2 h. To evaluate knowledge creation processes and facilitate comparisons across cases, we created a ‘‘continuous seven-level scale’’ (Brown, 2000). Such a scale allowed us to assign a level to each knowledge creation process along a continuum of seven levels: ‘‘low’’; ‘‘low/medium’’; ‘‘medium/low’’; ‘‘medium’’; ‘‘medium/high’’; ‘‘high/medium’’; and ‘‘high’’. ‘‘low’’ means that we found (almost) no evidence of a knowledge creation process and ‘‘high’’ means that the process occurred in a highly intensive and frequent fashion (compared to the rest of the sample). The seven levels are a function of comparison across cases. To categorize SSFs’ knowledge creation processes into seven levels, we considered and evaluated such factors as the intensity, the frequency, and the variance (among cases) of each explored knowledge creation process by juxtaposing different sources of data (i.e. interviews, observations and internal documents) related to each process (Inkpen and Dinur, 1998). Using qualitative software, we created nodes to help accurately assess the ‘‘density of citations’’, and compare patterns, contexts, and knowledge creation processes across cases with a high level of accuracy and transparency. Table II describes the firms included in our sample. 4. Findings What emerges from our data is that knowledge creation occurs at the individual, group, organizational and interorganizational levels via two main processes: ‘‘interaction’’ and ‘‘action’’. While ‘‘interaction’’ is related to exchange and communication, ‘‘action’’ is associated with the execution and implementation of knowledge. Table II. Description of sample SSFs Firms’ name Nature of technology Founded Total employees Alpha Web applications 1997 50 Beta Security 2004 50 Gamma Voice-over-IP 2004 15 Delta Wireless mesh system 2001 80 Epsilon Network 2003 100
  6. 6. MRN 31,11 884 4.1 Knowledge creation through interaction Knowledge is created by individuals: ‘‘It’ll often start with one developer having an idea or an approach’’, convey all SSFs’ interviewees. Although ideas are formed in the minds of individuals, our data indicate that interactions between individuals, groups and organizations play a significant role in developing new ideas. We found that continuous communication, exchange and interaction are the keystones of knowledge creation in all explored SSFs. According to our data, interaction promoting the creation of knowledge in SSFs can take place through: (1) Formal meetings; (2) Informal communities (i.e. communities of practice, communities of sharing, virtual communities or informal networks); (3) Project teams (i.e. ‘‘within’’ and ‘‘across’’ teams); (4) External interaction (i.e. customers and partners); and (5) information technology (IT)-tools (i.e. intranet). 4.1.1 Formal meetings. All five SSFs have put in place different kinds of formal meetings for creating and exchanging information and knowledge. Alpha, Beta and Gamma, for example, have ‘‘brainstorming sessions’’ which are scheduled in advance and where all employees are generally invited to participate. The main goal of these brainstorming encounters is to bring about new ideas freely, without judging them, in order to solve specific problems related to an innovation process or a new technology. The management teams from Alpha, Beta and Gamma believe that brainstorming sessions are vital for the generation of new ideas and knowledge. Delta and Epsilon have developed other kinds of formal meetings to create knowledge. According to Delta’s Engineering VP, the management team has put in place ‘‘short-intense teaching sessions’’ in order to exchange and create new knowledge ‘‘rapidly and intensely.’’ He explains, ‘‘. . .people come up with an idea, they put it on paper, we get together in a room, and the person that created the idea teaches to the rest of the group’’. In the case of Epsilon, Research and Development (RD) and Marketing employees have numerous formal reviews with their customers, such as product and specifications reviews, which permit them to exchange, receive, absorb, and create new knowledge. 4.1.2 Informal communities. According to all five SSFs’ management teams, knowledge creation also occurs via daily informal interaction and spontaneous communication and exchange between employees, teams and external partners. What emerges from our data is that informal communities and networks are continuously being formed and transformed within and across teams and departments. Based on our analysis and observation, we identified four types of informal communities: communities of practice, communities of sharing, virtual communities and informal networks. Communities of practice are informal and spontaneous communities of people that share common interests or goals and gather together in order to solve a given problem. Communities of sharing are formal or informal communities of people that meet to share and exchange ideas, information and knowledge. Virtual communities are formal or informal communities of people that share common interests, ideas and knowledge over the internet or other IT-tools. Informal networks refer to informal, voluntary and spontaneous relationships that are developed within an organization among members and are not found in any organizational chart. While Beta, Gamma and Epsilon present a ‘‘high’’ level of informal communities’ formation, Alpha and Delta exhibit a ‘‘medium/high’’ level. Gamma represents a good example of informal communities’ formation. In this firm, all employees collaborate and interact closely and intensely in order to provide their inputs to better understand
  7. 7. Knowledge creation processes 885 and solve a given problem, regardless its nature. From Gamma’s management team point of view, knowledge is created ‘‘informally, spontaneously and collaboratively’’ via communities of practice and sharing, where all employees participate. According to Gamma’s Business Development VP, ‘‘It’ll often start with one developer having an idea or an approach, and then a solution is sort of fielded by all of us’’. Our data suggest that extensive informal communities and interaction among employees make it possible to solve specific problems and create new knowledge and ideas. At Epsilon communities of practice and sharing are also common phenomena. The formation of these informal communities are explained by the fact that more than half of Epsilon’s employees have worked together in the past for other companies and know each other quite well. In the case of Beta, most communications are informal and internal informal networks represent the predominant interaction pattern. Beta’s CTO affirms, ‘‘The people that have a lot of knowledge, the couple of brains, they are consulted as need be. It’s informal like that. When you need, you go to see them. It’s just more of an informal network that way. That’s how we are running now that we are small’’. At Alpha, as at Gamma, there are virtual communities of sharing. ‘‘We have an intranet site that lets you publish everything, from a link to an article, and it lets other people either comment on that or even change that article itself’’, explains the CEO. Overall, our data suggest that informal communication and communities’ formation, regardless of their nature, are common phenomena in all five SSFs. We found that informal communities enable and prompt individual, group and organizational learning, knowledge exchange and knowledge creation. 4.1.3 Project teams. Our sample firms are formally structured in flexible and cross- functional project teams aiming at achieving knowledge creation via intense and frequent complementary resource exchange, communication and interaction. Organizing knowledge-workers into project teams seems to be a common pattern across the five cases. In all explored SSFs, project teams’ participants enjoy a high degree of empowerment, which permits them to interact, pool resources and take action freely within their teams. SSFs’ management teams agree on the extent to which employees create knowledge on a daily basis through interaction ‘‘within’’ and ‘‘across’’ project teams. In terms of interaction ‘‘across’’ project teams, Gamma, Delta and Epsilon present a ‘‘high’’ level of interaction. In the case of Gamma, for example, all 15 employees work together in order to resolve any emerging problem. ‘‘. . .We have fairly good discussions and just solve the problems all together’’ (Gamma’s Engineering VP); ‘‘. . .we work closely and we trade information back and forth within and across projects’’ (Gamma’s Business Development VP). Delta has implemented what its employees call ‘‘systems groups’’. These groups are formed by highly specialized programmers, ‘‘market thinkers,’’ and Doctorates in mathematics. At Delta and Epsilon, project teams are considered to be ‘‘permeable, flexible and interchangeable’’ and the resource reallocation across projects is formal, whereas at Gamma it occurs rather spontaneously. In all of these three companies, knowledge is endlessly and freely flowing, being ‘‘re-used’’ and ‘‘re-created’’ via interaction and new combinations within and across project teams. Alpha and Beta exhibited a ‘‘medium’’ level of interaction ‘‘across’’ project teams. The former firm tends to keep some limits between teams due to its intellectual property policy. Alpha’s CEO explains, ‘‘Each team can see the processes of other projects at the top, the templates are there, but the implementation or the filling of that template may have some intellectual property. So we have to keep China walls between
  8. 8. MRN 31,11 886 teams.’’ While at the latter company, teams are ‘‘pushed’’ to collaborate with each other by the management team because they do not do so spontaneously. According to Beta’s CTO, ‘‘Each function in the company kind of has its own style. Sales people are managed on quite a different way than developers are managed’’. All five SSFs exhibited a ‘‘high’’ level of interaction ‘‘within’’ project teams, which allowed participants to exchange, acquire, internalize and learn new knowledge and expand their base of expertise. Interaction ‘‘across’’ project teams enables an organization to create new knowledge by ‘‘cross-fertilizing’’ knowledge bases from a variety of employees and specialized teams. Our data suggest that previous relationships among employees may favor both ‘‘within’’ and ‘‘across’’ project teams’ interaction. However, interaction ‘‘across’’ project teams is not always natural and spontaneous. Factors such as teams’ cultural differences and organizational values might be an obstacle to knowledge flow and interaction ‘‘across’’ project teams. 4.1.4 External interaction. Since their very conception, Alpha, Gamma, Delta and Epsilon collaborate closely and intensely with their customers throughout their product development projects. These SSFs possess a ‘‘high’’ level of interaction with their customers, while Beta exhibits a ‘‘medium’’ level. From Alpha’s Marketing VP standpoint, ‘‘There’s a huge amount of collaboration that happens with clients’’. Delta’s CEO asserts, ‘‘Customer’s feedback loop is very important. That’s why it is important to have customers and interact closely with them’’. According to Gamma’s Business Development VP, ‘‘You have to get closer to customers. They can be quite useful in learning about issues that may be affecting the industry’’. From these four SSFs, customers represent a significant source of inspiration, new ideas and innovation. Lead customers, they explain, can reveal new uncharted product needs that might trigger new technology trajectories. Epsilon’s CEO, for example, affirms that co-developing products and tightly interacting with customers permits SSFs to learn and create new technical and market knowledge. Although Beta also interacts with its customers, its management team conceives customers’ relationships as a source of validation rather than a source of new ideas, knowledge creation and learning. Our sample firms present a ‘‘high’’ level of interaction with external partners. Interacting and collaborating with partners has been very important to the success of these SSFs. Interorganizational collaboration permits firms to learn from each other, exchange information and knowledge, pool complementary resources, prompt innovation and share costs and risks. As Gamma’s Business Development VP observes, ‘‘Partnerships are very important. . . Being able to work with other firms, basically when you’re a small company, is very important’’. Our data suggest that SSFs tend to develop partnerships, whether formal or informal, in order to share, exchange and create knowledge and enable organizational learning. Alpha’s CEO argues that one main advantage of collaborating with network partners is ‘‘that you get to do things that you wouldn’t be able to do by yourself’’. 4.1.5 IT-tools. Even though all five SSFs have an intranet in place, they use it for diverse motives and at different degrees. Alpha and Epsilon exhibit a ‘‘high’’ level of interaction and knowledge creation via intranet. For these two companies, intranet represents a valuable tool where individual, group and organizational knowledge is continuously codified, stored, diffused and renewed. The management teams of these two firms continually encourage their employees to consult, contribute and to nurture the content of intranet, which represents a significant source of organizational learning and knowledge creation.
  9. 9. Knowledge creation processes 887 Gamma and Delta present a ‘‘medium/low’’ and ‘‘medium/high’’ level of interaction and knowledge creation via intranet, respectively. At Gamma, intranet is basically utilized by designers and programmers to safely store and keep software codes. In the case of Delta, intranet is generally used by employees to store presentations, corporate documents, products’ information and innovation processes. Delta’s Engineering VP relates, ‘‘You need a good repository of documents available for everyone. Formalized documents can provide good guide and sources of ideas. Intranet is very useful for this’’. Beta presents a ‘‘low’’ level of interaction and knowledge creation via the intranet. ‘‘You know, we don’t spend any particular effort on creating knowledge management tools, or databases or so on’’. In this company, knowledge-workers will create knowledge mainly through informal communication and interaction rather than from IT-tools. ‘‘Very much of it [knowledge] is still in the heads of the various key people. So, a lot of it is done verbally’’ (Beta’s Marketing VP). Briefly, IT-tools can be used by SSFs to safely store, diffuse, share and create knowledge. Cases such as Alpha, Epsilon and Delta demonstrate that the intranet, for example, enables organizations to efficiently diffuse exchanges and create knowledge at the individual, group and organizational levels. Moreover, the intranet can become a valuable repository for safekeeping organizational memory. Table III summarizes and juxtaposes our research findings. Overall, our data suggest that Epsilon exhibits the highest levels of knowledge creation through interaction, followed by Delta and Alpha. In Alpha’s case, interaction ‘‘across’’ project teams aiming at creating knowledge is not very frequent due the firms’ internal intellectual property policy. Compared to the rest of the sample, Gamma presents the highest levels of informal communities ‘‘within’’ and ‘‘across’’ project teams’ and customers’ and partners’ interactions. Beta, as opposed to the other four SSFs, displays the lowest level of knowledge creation through interaction. Beta’s lowest level can be explained by its highly informal and tacit culture that influences employees to seldom use intranet as a means of interaction for creating knowledge. 4.2 Knowledge creation through action Knowledge can be also created by individual or group action. ‘‘Action’’ refers to the implementation and execution of existing knowledge aiming to create new knowledge. ‘‘Learning-by-doing’’ is central to knowledge creation. Rapid prototyping represents an interesting example of knowledge creation through action or ‘‘learning-by-doing’’. Rapid prototyping is defined as a group of techniques used to quickly fabricate a scale model of a part or assembly using three-dimensional computer-aided design. Rapid prototyping permits SSFs to rapidly experiment and test new ideas, reduce their development time in a cost-effective fashion, and allows customers to literally ‘‘visualize’’ the future product and provide feedback on it. All five companies do rapid prototyping. However, only Alpha, Beta, Delta and Epsilon possess a rapid prototyping lab and the latter three companies are intensely and frequently engaged in rapid prototyping activities. ‘‘Good new ideas are prototyped all the time’’ (Beta’s CTO); ‘‘We try to prototype as soon as possible, even a very small portion of the concept, and we do that all the time’’ (Delta’s Engineering VP); ‘‘So we do prototyping to get out new concepts. . . we do countless projects’’ (Epsilon’s Marketing VP). Alpha and Gamma engage in rapid prototyping activities from time to time or seldom. Alpha has put in place a ‘‘stage gate process’’ though which new ideas are filtered and funneled. This formal innovation funnel process may explain why only
  10. 10. MRN 31,11 888 Table III. Data summary: knowledge creation processes through interaction in small hi- tech firms Interaction Case Formal meetings Informal communities (CoP/CoS/VC/IN) Projectteams (within/across) External (customers/partners) IT-tools (intranet) AlphaHighMedium/high(‘‘Within’’)¼high (‘‘Across’’)¼medium (customers)¼high (partners)¼high High BetaMedium/highHigh(‘‘Within’’)¼high/ medium (‘‘Across’’)¼medium/ High (customers)¼medium/ high partners)¼high Low GammaMediumHigh(‘‘Within’’)¼high (‘‘Across’’)¼high (customers)¼high (partners)¼high Medium DeltaHighHigh/medium(‘‘Within’’)¼high (‘‘Across’’)¼high (customers)¼high (partners)¼high High/medium EpsilonHighHigh(‘‘Within’’)¼high (‘‘Across’’)¼high (customers)=high (partners)¼high High Notes:CoP,communitiesofpractice;CoS,communitiesofsharing;VC,virtualcommunities;IN,informalnetworks
  11. 11. Knowledge creation processes 889 very few ideas are prototyped. In the case of Gamma, prototypes are seldom performed due to its lack of financial resources. Alpha, Beta and Gamma exhibit a ‘‘medium-high’’ level of customer interaction when performing rapid prototyping activities, whereas Delta and Epsilon present a ‘‘high’’ level of interaction. According to Epsilon’s CEO, ‘‘The rapid prototyping lab, which we have since we moved into a marketing developing role, is huge for us. It allows us to really have solid conversations with our customer base’’. SSFs can get immediate and valuable feedback from their customers when they tightly interact with them when performing rapid prototyping activities. Customers are considered to be a significant source of new ideas and innovation. For our sample firms, the rapid prototyping lab represents an important source of knowledge creation via experimentation and exploration. Beta, Delta and Epsilon exhibit a ‘‘high’’ level of exploration and experimentation when involving in rapid prototyping activities, while Alpha and Gamma present a ‘‘medium-high’’ level. ‘‘They’ve [employees] got some idea they want to explore, let them have time to explore it. Let them experiment’’ (Beta’s CTO). In fact, Beta enjoys strong financial support from its venture capitalist partners, allowing it to engage fairly often in both exploration and experimentation, and radical innovation generation. Alpha and Gamma prefer to focus just on exploitation activities and less radical innovation creation. Rapid prototyping also permits SSFs to engage in ‘‘trial-and-error’’ activities, carrying out regular planned operations, observing outcomes and then revising future action. All sample firms show a ‘‘high’’ level of ‘‘trial-and-error’’ activities when performing rapid prototyping, since they imply less risk and lower technological, market, organizational and resources’ uncertainties. At Alpha, Gamma, Delta and Epsilon RD and Marketing employees collaborate closely in order to test existing ideas and explore and experiment uncharted technologies and new ideas emanating from both internal and external sources. These SSFs believe that interaction and iterations between RD, Marketing, and customers are extremely fruitful generating new knowledge. As Epsilon’s Marketing VP states, ‘‘. . . the rapid prototyping lab actually reports into marketing. . . that really helped to become more experimental’’. Gamma does not have a rapid prototyping lab. Due to its small size and scarce resources, this SSF tends to rationalize and focus its innovation efforts to respond to specific clients’ demands. What emerges from our data is that rapid prototyping activities permit SSFs to create new knowledge by exploring, experimenting, developing and testing new ideas and concepts. Close and intense interaction with customers during rapid prototyping activities appears to be extremely important for SSFs. Delta and Epsilon exhibit ‘‘high’’ levels of customer interaction when performing rapid prototyping activities, followed by Alpha, Beta and Gamma which display a ‘‘medium/high’’ level. This result might be explained by the fact that larger firms, such as Epsilon and Delta, tend to deal with larger customers that need to collaborate more intensely throughout the innovation process in order to reduce technological and needs’ uncertainties. Management teams in all the case SSFs believe customers can provide lots of information, new ideas and knowledge to enable and prompt innovation. We also found that larger firms (in terms of the number of employees) experiment, explore, and test new ideas, technologies and concepts much more frequently than smaller ones. This higher frequency in larger companies, such as Epsilon and Delta, can be explained by the fact that they possess more financial and human resources than smaller ones. Table IV synthesizes and compares our findings.
  12. 12. MRN 31,11 890 5. Discussion As argued by Nonaka and Teece (2001), we found that SSFs create knowledge by virtue of their actions and interactions. According to our results, interaction allowing the creation of knowledge in SSFs can take place via: (1) Formal meetings; (2) Informal communities; (3) Project teams; (4) External interaction; and (5) IT-Tools. Our data are in line with Grant’s (1996) and Nonaka and Takeuchi’s (1995) findings, which suggest that knowledge is created by individuals. Although ideas are formed in the minds of individuals, interactions among people typically play a significant role in developing new ideas, as evidenced in all explored SSFs. Knowledge creating interaction through formal meetings, informal communities, project teams and external interaction generally takes place through ‘‘socialization.’’ Nonaka and Takeuchi (1995) affirm that ‘‘socialization’’, a process through which tacit knowledge and experiences are shared, enables the generation of new knowledge. Von Hippel (1988) stresses the importance of developing tight interactions with customers to get new ideas. Even though our results support the findings of these researchers, we also argue that ‘‘intense teaching sessions’’ and ‘‘communities of practice’’ are processes through which employees share tacit knowledge and create new knowledge. Similar to Nonaka and Takeuchi (1995), we found that knowledge creation through IT-tools, another interaction process, occurs via ‘‘externalization’’ and ‘‘combination’’. However, in our study, we go beyond existing evidence, observing that knowledge creation using IT-tools can also take place via what we call ‘‘virtual-socialization’’. Virtual-socialization is a process through which individuals or groups interact using diverse IT-tools. Virtual communities of interaction appeared to be a frequent and formal knowledge creation practice in two SSFs we explored. This finding differs from Nunes et al. (2006), who state that ‘‘knowledge management activities within knowledge-based small medium enterprises tend to happen in an informal way and rarely supported by purposely designed ICT systems’’. Contrary to Nunes et al. (2006), we show that some SSFs, such as Alpha and Epsilon, not only implemented knowledge management processes exhibiting ‘‘high’’ levels of formalization, but also purposely designed some IT-tools (i.e. ‘‘knowledge libraries’’) to create knowledge. Knowledge can also be generated by individual or group action. We found that some SSFs prefer to create knowledge principally through ‘‘learning-by-interacting’’ (i.e. Gamma), while others prefer ‘‘learning-by-doing’’ (i.e. Beta). We focused on ‘‘rapid prototyping’’ activities in SSFs to investigate knowledge creation through action. Rapid prototyping allows firms to exchange market and technological knowledge with customers and, as a result, create new knowledge. Again, this finding strongly supports Von Hippel’s (1988) results regarding the role of external players Table IV. Data summary: knowledge creation processes through action in small hi-tech firms Action: rapid prototyping Case Lab Frequency Customers’ interaction Exploration/ experimentation Trial-and-error Alpha Yes From time to time/often Medium/high Medium/high High Beta Yes Often Medium/high High High Gamma No From time to time Medium/high Medium/high High Delta Yes Often High High High Epsilon Yes Often High High High
  13. 13. Knowledge creation processes 891 (i.e. customers) as significant sources of new ideas and knowledge. Moreover, we identified three specific knowledge generation practices – experimentation, exploration and trial-and-error – that can be performed separately or jointly by SSFs engaged in rapid prototyping activities. In the case of experimental practices, knowledge-workers deploy controlled situations and variables in order to create systematic experiences and new knowledge. This finding strengthens Cook and Campbell’s (1979) and Miner et al.’s (2001) results. According to their results, organizations deliberately vary activities and conditions via experimental learning to create new knowledge. In contrast to experimentation, exploration activities can be defined as ones in which knowledge-workers attempt to discover something new, have no control over the situation, and have no a priori, predefined plan of action. March (1991) associates exploration with behaviors such as research, discovery, and incurring into new courses of action. Going a step further, we suggest that exploration practices are likely to be performed by firms engaged in technology-oriented rather than market-oriented activities, and that they tend to tolerate higher levels of technological and market uncertainties. ‘‘Trial-and-error’’ processes refer to those through which a firm repeats activities that appear to produce successful outcomes and avoids those that appear to be disappointing (Cheng and Van de Ven, 1996). In this study, we not only corroborate Miner et al.’s (2001) results, finding that ‘‘trial-and-error’’ activities permit firms to create knowledge involving in lower levels of risk but also provide additional evidence that ‘‘trial-and-errors’’ are likely to be performed by those SSFs that are more market- oriented and exhibit a higher level of process formalization. 6. Conclusion Nonaka and Teece (2001) and Levinthal and Myatt (1994) conceive an organization as an entity that creates knowledge though actions and interactions with its environment. Confirming the results of these authors, we attempt in this study to go a step further by exploring the particular ways through which interactions and actions enable a SSF to create knowledge to generate innovations. To our knowledge, this investigation is among the first and most exhaustive comparative studies carried out in the Canadian context of small firms operating in the software industry. The systematic description and comparison of knowledge creation processes in each explored company contribute to the better understanding of specific interaction and action processes through which knowledge is generated, enabling practitioners in small innovative hi-tech firms to design appropriate policies and procedures for enhancing knowledge creation behaviors of their employees. Despite these contributions, our research presents some limitations. Given that our analysis and insights are based on only five case studies, we can not be completely sure about the degree of external validity or generalization. Nevertheless, in order to improve the external validity of our study, we invited a panel of eleven industry experts to make a list of SSFs they consider to be highly innovative. By setting up a panel of experts and approaching the most named companies, we believe our sample is highly representative of the population under investigation. In addition, theoretical or analytical generalizations are possible if the same ‘‘behavioral patterns’’ are found across cases. Since only five SSFs were included in the sample, results could possibly predict that specific organizational behaviors in a given context favor breakthrough innovation generation.
  14. 14. MRN 31,11 892 Another limitation is related to the fact that we have gathered information on the perspectives of such SSFs’ key informants as CEOs, VPs and project managers. The employees working in these SSFs were not available and did not have the time to formally participate in this study. During our onsite visits, however, we succeeded in involving in some informal exchanges of ideas and perceptions with employees and we took that information into consideration during the analysis phase. It is our belief that it is necessary to continue exploring knowledge management processes and dynamics in the particular context of small knowledge-based firms. For instance, the limitations of this study raise the need to validate and refine our findings regarding the way small hi-tech firms create knowledge. Future research on larger samples of small Canadian software firms is needed, using the same eligibility criteria and comparing the same knowledge creation processes as those explored in our investigation. Other promising avenues of inquiry include such questions as the way small knowledge-based firms operating in turbulent environments organize internally to create knowledge, the conditions enabling the generation of knowledge and the particular ‘‘spaces’’ or ‘‘ba’’ in which knowledge creation occurs in these firms. References Autio, E., Hameri, A-P. and Vuola, O. (2004), ‘‘A framework of industrial knowledge spillovers in big-science collaborations’’, Research Policy, Vol. 33 No. 1, pp. 107-26. Autio, E., Sapienza, H.J. and Almeida, J. (2000), ‘‘Effects of age at entry, knowledge intensity and imitability on international growth’’, Academy of Management Journal, Vol. 43 No. 2, pp. 909-24. Brown, J.D. (2000), Using Surveys in Language Programs, Cambridge University Press, Cambridge. Brown, J.D. and Eisenhardt, K. (1997), ‘‘The art of continuous change: linking complexity theory and time pace-evolution in relentlessly shifting organizations’’, Administrative Science Quarterly, Vol. 42 No. 1, pp. 1-34. Canadian ICT Sector Profile (2005), Annual Report on Information and Communication Technology Sector in Canada, Statistics Canada, Ottawa. Cheng, Y. and Van de Ven, A. (1996), ‘‘Learning the innovation journey: order out of Chaos’’, Organization Science, Vol. 7 No. 6, pp. 593-641. Cook, T.D. and Campbell, D.T. (1979), Quasi-Experimentation: Design and Analysis Issues for Field Setting, Houghton Mifflin, Boston, MA. Creswell, J.W. (2003), Qualitative Inquiry and Research Design: Choosing Among Five Traditions, Sage Publications, London. Creswell, J.W. and Symon, G. (2004), Essential Guide to Qualitative Methods in Organizational Research, Sage Publications, London. Crossan, M.M. and Berdrow, I. (2003), ‘‘Organizational learning and strategic renewal’’, Strategic Management Journal, Vol. 24 No. 11, pp. 1087-105. Das, T.K. and Teng, B.S. (2000), ‘‘Instabilities of strategic alliances: an internal tension perspective’’, Organization Science, Vol. 11 No. 1, pp. 77-101. Davenport, T.H. and Prusak, L. (1998), Working Knowledge: How Organizations Manage What They Know, Harvard Business School Press, Boston, MA. Dayasindhu, N. (2002), ‘‘Embeddedness, knowledge transfer, industry clusters and global competitiveness: a case study of the Indian software industry’’, Technovation, Vol. 22 No. 1, pp. 551-60. Demers, J. (2003), ‘‘Networked Knowledge’’, CMA Management, Vol. 43, February.
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