Concept of entrepreneurship development

entrepreneurial pioneer of international venturing and also define entrepreneur and entrepreneurship
OliverFinch Profile Pic
OliverFinch,France,Teacher
Published Date:15-07-2017
Your Website URL(Optional)
Comment
SOCIO-COGNITIVE FOUNDATIONS OF ENTREPRENEURIAL VENTURING by ROBERT M. GEMMELL Submitted in partial fulfillment of the requirements For the Degree of Doctor of Philosophy Dissertation Committee: David A. Kolb, Case Western Reserve University (chair) Richard J. Boland, Case Western Reserve University Ronald Fry, Case Western Reserve University Antoinette M. Somers, Wayne State University Weatherhead School of Management Designing Sustainable Systems CASE WESTERN RESERVE UNIVERSITY January, 2013 ACKNOWLEDGEMENTS This dissertation is the culmination of an interesting journey that would not have been possible without the support of a number of individuals. First of all, I want to express my thanks to my family—my wife Angela and children Robert and Catherine—for their patience while I immersed myself in this research over the last three years. The late nights, deadlines and frequent travel to Cleveland were at times an undeniable strain on our family. I appreciate the understanding and loving support from all of you, without which I could not have completed this major undertaking. Many of the best things in life seem to be the result of extraordinary good fortune or some kind of wonderful fate. This is certainly the case with my PhD program – I came to Case Western with a wide range of interests but no clear research focus. I began searching for a core topic and advisers with whom I could share interests which led to a series of fortuitous events that were turning points in my doctoral experience. Everyone in my cohort wrote a paper for Richard Boyatzis during our first semester about our personal vision and goals for pursuing a PhD. Richard’s comments on my paper pointed out (several times) that I would be crazy not to incorporate my interest in creativity and the arts into my research program. This was a major shift in my thinking; away from trying to do something properly “academic” that might have a valuable impact on knowledge to doing something fun and interesting. The best advice I received early on was to make sure the research topic fascinated me sufficiently to sustain interest through the trials and tribulations of doctoral research. I would like to thank Richard for his early xv guidance and support during this critical early phase of defining and focusing my research. The second important event came while surveying literature related to creativity and innovation. I was surprised at how little had been published about the creative process within entrepreneurship but I did see a few papers linking David Kolb’s experiential learning theory to entrepreneurial innovation and was intrigued with what I read. Meeting Dave was a huge turning point in my search for a research focus and my relationship with him has been a major high point for me, not just within this program, but for my life in general. Working with Dave as an advisor and committee chair has been an incredible stroke of good fortune and I cannot imagine a more supportive, professional and engaging research advisor. I will be forever grateful for Dave’s help and guidance. Dick Boland is clearly one of the most brilliant and interesting people I have ever met and I would have struggled to feel comfortable with my first qualitative study topic without his support and encouragement. Coming from such a staunch practitioner background, I was uneasy about studying something as fuzzy and intangible as “where do ideas come from” but Dick was very encouraging and instrumental in helping me blend my natural orientation toward business practice with interesting new philosophical perspectives. The research review sessions with Dave and Dick were among the most interesting and productive meetings of my entire career and the combined influence of these two geniuses on this research is immeasurable. I would like to also thank Toni Somers and Ron Fry for their staunch support and contributions to our committee and this research. Toni is a brilliant and compassionate xvi teacher whose expertise in quantitative methods is unsurpassed. I could always count on Toni for a balanced perspective, never panicking (even if I was) and always there to help me through whatever technical or conceptual problem we were facing at the time. Ron brought very valuable new perspectives to the research, in particular, introducing me to the high quality connections research thread. Thanks Ron for your help and positive energy. Sheri Perelli was a key driving force behind my qualitative study, always pushing me to stay on track and to do one more revision to clarify my thoughts. I would like to also thank Kalle Lyytinen for his strong program leadership and positive influence on my research and career. I appreciate the help from Sue Nartker and Marilyn Chorman who do a terrific job managing the program. I have benefited greatly from many other influential advisors during this journey – thanks in particular to Bo Carlsson, Gene Pierce, James Gaskin and Paul Salipante for their helpful guidance and support along the way. x vii Socio-Cognitive Foundations of Entrepreneurial Venturing Abstract by ROBERT M. GEMMELL This dissertation employs a mixed methods approach to explore cognitive and social dimensions of entrepreneurial creativity and innovation. I interviewed 32 technology entrepreneurs to generate a grounded theory about how technology entrepreneurs use social behaviors, techniques and cognitive processes to attain, develop, refine, validate and filter (for usefulness) creative ideas for successful new products, processes or services. The results reveal a complex, cyclical and recursive multi-level social process with emphasis on iterative active and social experimentation. Successful entrepreneurs use experimentation to facilitate and accelerate learning, preferring to succeed or fail quickly. Greatest ideational productivity occurs when strong social ties interactively solve problems in an environment of trust – in particular, when “Trusted Partners” exchange and refine ideas through a form of shared cognition. In the second study, I surveyed 172 technology entrepreneurs to determine the effects of learning style and learning flexibility on iterative decision methods and innovation decision speed, behavioral mediators hypothesized to produce entrepreneurial x viii innovation and success. The Kolb learning style preference for active experimentation predicted the entrepreneur’s use of iterative methods to innovate and achieve success. The anticipated positive indirect influence of learning flexibility on innovation surprisingly occurred via a chain of two consecutive negative effects. Entrepreneurs with high learning flexibility move less swiftly to make key strategic innovation decisions; however, in doing so they are more innovative. The final study explores the traits and interactions of “Trusted Partners” and their impact upon entrepreneurial learning capacity, innovativeness and firm performance. I surveyed 153 technology entrepreneurs, all of whom report having a Trusted Partner, and discovered that effective partnerships more likely develop between two individuals with broad combined expertise (high Partner Functional Breadth). However, partner expertise diversity negatively affected the ability of partners to engage in constructive learning interactions and exploratory learning. I conclude that cofounder/partners ideally need both breadth and significant expertise overlap to facilitate the shared language and vision necessary for productive collaborative learning interactions. These findings show that broad but overlapping partner/co-founder expertise, when combined with a strong sense of personal trust, leads to elevated absorptive capacity, innovation and performance within entrepreneurial firms. Key words: Absorptive capacity; collaboration; cognition; creativity; entrepreneurship; experimentation; expertise; innovation; learning; partners; product development. xix CHAPTER I: INTRODUCTION There has been surprisingly little written about creativity and entrepreneurship in spite of the fact that entrepreneurship was described early on as a process of “creative destruction” (Schumpeter, 1947). Studies of strategic innovation are commonplace but usually focus on abstract firm level processes and environmental factors, dismissing the individuals who compose organizations (Lane, Koka, & Pathak, 2006). Conversely, entrepreneurial behavior studies focus too much on the individual, framing the entrepreneur as a lone operator while losing sight of the social dimensions of creativity. The mischaracterization of entrepreneurship as a solitary practice remains prevalent in literature in spite of data showing that many new ventures would never have formed without the contributions of at least one other key supporting actor/co-founder (West, 2007). I selected the technology industry as a compelling and appropriate setting for our study of creative cognition and behavior. Domain knowledge is a fundamental component of creativity and technology innovation requires a rare blend of expert knowledge and freedom from the biases of such knowledge in order to create new paradigms (Amabile, 1983; Frensch & Sternberg, 1989). There is strong evidence that the analytical proclivity of an expert engineer or scientist is cognitively contrary to the more open and free thinking approach of a creative person in the arts or advertising. Kolb and Kolb’s (2005a) paper comparing learning styles of Case Western Reserve undergraduates (most of whom were majoring in engineering or business) to Case Western Reserve graduate business students and students at Cleveland Institute of Art revealed the prevalence of “northern” learning 1 styles among creative arts students who prefer a hands-on approach to learning with emphasis on divergent thinking versus the “southern” predominantly convergent and analytical styles of business and engineering students. Creativity leading to innovative new products is a messy, risky and non-linear process that is antithetical to the well- developed analytical capabilities of technical domain experts (Pinard & Allio, 2005). I had, over the course of a 30 year career as a senior executive in the technology industry, observed first-hand the plight of visionary domain experts struggling to become successful and innovative entrepreneurs. This phenomenon is so prevalent within the technology industry that brilliant engineers are commonly presumed a priori to be good company founders but rarely good CEOs. The difference between the inventor and the successful leader entrepreneur is often attributed to introversion or underdeveloped social skills—which might be the case—but I suspected the gap might also originate from cognitive traits common to domain experts i.e. relatively inflexible schemas, biases and proclivity to over-analyze (Dane, 2010). The inventor who launches a technology start-up company is usually replaced at some point by a CEO deemed by investors to possess a more balanced set of cognitive and social traits – however, this new CEO probably has advanced training in engineering and business and is almost certainly an expert in the relevant technologies and markets. The expertise that qualifies someone to manage such an enterprise also makes them vulnerable to biases and cognitive entrenchment exemplified by an inability to flexibly engage in the highly dynamic learning process of creativity and innovation (Dane, 2010). In spite of the challenges posed by high level expertise, some entrepreneurs seem to 2 easily navigate the ever changing minefield, abandoning old paradigms and creating entirely new innovative solutions. The goal of this research has been to gain a better understanding of the unique cognitive and social traits and behaviors of entrepreneurial and innovative domain experts in the technology industry. The research program maintained fluid continuity throughout the three studies by virtue of solid research design and by building upon the rich findings of the first qualitative study. The research focus remained steady and unwavering throughout while each of the studies unveiled new insights, refinement and clarity to the problem and phenomenon being examined. Research Design and Dissertation Structure This research was designed from the outset as a mixed methods program blending qualitative and quantitative empirical methods. The first study is a grounded theory exploration of how innovative technology entrepreneurs attain and develop creative ideas for new products. The findings of this first qualitative study provided the framework and research model for two follow-on quantitative empirical studies examining different facets of the model (Morse & Neihaus, 2009; Nastasi et al., 2007). This mixed methods approach blends the advantages of qualitative research (rich, multi-dimensional and colorful insight) with the granularity and statistical precision of structural equation modeling. The three studies are situated within chapters 3, 4 and 5 and readers can, if they wish, examine each chapter individually as a stand-alone exposition of that particular study. Chapters 3, 4 and 5 include literature reviews relevant to the particular study and we include a broad review of the literature within our domain of study as a prologue in 3 Chapter 2. Chapter 6 serves as an integrative discussion section, reflecting upon the full breadth of all three studies while looking forward to potential future follow-on research. 4 CHAPTER II: BACKGROUND LITERATURE REVIEW: ENTREPRENUERIAL LEARNING AND INNOVATION Introduction Crossan and Apaydin (2010) point out in their recent review of organizational innovation that only a small percentage of articles have been written on the individual or team level (11%) and suggest that studies of entrepreneurial innovation provide a useful context for examining the leadership and managerial levers portion of their model, i.e. the segments that depend most on individual or team agency. Crossan’s leadership and management levers components reflect the “upper echelon theory” perspective whereby the innovation of a firm is driven by traits and actions of the CEO or top management or founding team who use strategies, structures, resource allocations, organizational learning processes and organizational cultures to support and facilitate firm level innovation. Organizational learning, defined as “the change in the organization that occurs as the organization acquires experience” (Argote & Miron-Spektor, 2011: 1124) through processes of exploratory and exploitative innovation (March, 1991; Van de Ven, 1999), has played an especially prominent role in studies of innovation. Learning is multi-level knowledge acquisition process situated within the environmental context of the organization (Hutchins 1991; Lave & Wenger, 1990). Argote’s learning cycle (see Figure 1 below) approaches organizational learning from a strongly task oriented operational perspective and includes the sub-processes of knowledge creation from direct experience, knowledge transfer from others and knowledge retention by virtue of knowledge flowing into active context (Argote & 5 Miron-Spektor, 2011). These sub-processes function within the organizations learning context while interacting with the extra-firm environment. FIGURE 1: Theoretical Framework for OL (Argote & Miron-Spektor, 2011) Knowledge is complex, multi-dimensional and can be either explicit (easily communicated) or tacit which is less tangible and more difficult to transfer (Nonaka, 1994). Other knowledge dimensions include content (tasks, interactions), spatial (geographic by nature), temporal (frequency, pace, timing and rarity) and mindfulness. Heterogeneity of experiences (experience variety across dimensions) has been shown to enhance learning (Schilling, Vidal, Ployhart, & Marangoni, 2003), a finding that contradicts the intuitive advantages of specialization. Some (especially exploratory) knowledge creation processes such as analogical reasoning are more mindful and therefore demand greater attention (Weick & Sutcliffe, 6 2006) while other learning processes are more routinized and therefore require less attention (Levinthal & Rerup, 2006). Organizations that successfully balance both mindful and routinized learning processes achieve an ambidexterity that saves cognitive capacity for high demand activities. Knowledge retention can also be more or less mindful – some routines are retained and recalled by rote while others involve more reflection and potential for adaptation (Williams, 2007). Researchers struggle to consolidate various theories of innovation and organizational learning into a unified theory that transcends context, purpose, methods and disciplinary perspective (Crossan, Maurer, & White, 2011; Easterby-Smith, Snell, & Gherardi, 1998). There are four primary dimensions of divergence among organizational learning thought leaders: (1) purpose of organizational learning (Teleology), (2) definition of organizational learning (Ontology), (3) preference for different research methods (Epistemology) and (4) operationalization and interventional techniques (Easterby-Smith et al., 1998). Not surprisingly, researchers from different disciplines approach organizational learning differently – psychologists are most interested in the cognitive human development aspects of learning while strategists focus on the influence of interactions between organizations or the organization and its environment on competitiveness. Information scientists view learning through a data processing lens and operations specialists focus on learning as a means to improved productivity. Organizational theory development stems from widely varying theoretical orientations such as behaviorism (stimuli-response mechanisms), cognitivism (cognition as a separate process from behavior), humanism (human values) and social pedagogy 7 (learning through interaction with peers). While some theories straddle or combine elements of these various categories, there is no unified theory of organizational learning. The individual has been lost in much of this innovation and learning research, a condition rooted in the false assumption that individuals are innately homogeneous by nature and therefore exhibit collective behaviors and actions that can be modeled accurately with firm-level constructs (Felin & Zenger, 2009). Felin points out that in spite of a recent organizational capabilities trend in strategic innovation research, theorists have struggled to define even basic concepts such as “routines” and “capabilities” and little has been done to link these concepts to action. Felin and Crossan suggest that entrepreneurial ventures provide a useful setting to examine the micro- foundations of innovation and organizational learning through the actions and behaviors of company founders. Experiential Learning A core theory utilized in our research is the Kolb Experiential Learning Theory (ELT) (Kolb, 1984). The Kolb experiential learning theory has been applied to the real world issues of problem solving, entrepreneurial innovation and organizational learning in a variety of domains including entrepreneurship. The principles of experiential learning permeate other similar theories of learning, demonstrating the vast impact of experiential learning on scholars. According to Kolb, learners have a preference for certain learning modes of grasping and transforming experience into understanding which he defines as “learning style.” Learning style can be correlated to career choices, i.e. learners with a diverging style are often interested in the arts while convergent learners tend to be specialists in 8 technical fields. Assimilative learners are usually interested in theory and abstract problem solving while accommodative learners gravitate toward action oriented careers such as marketing and sales. Learners may also have a balanced or flexible style that allows them to adapt their learning on a situational basis (Kolb & Kolb, 2005a; Sharma & Kolb, 2009). Team Learning Researchers have struggled to define the “entrepreneurial team” – some have chosen a strict definition that includes only the founders who are also major shareholders while others have taken a somewhat broader view of the team to include non-founding senior managers (Cooney, 2005). Most firms start with a small team organized based upon interpersonal relationships (familiarity and homophily) (Ruef, Aldrich, & Carter, 2003) and later expand for pragmatic reasons, i.e. to add needed expertise to the team (Forbes, Borchert, Zellmer-Bruhn, & Sapienza, 2006). Start-ups with narrowly focused top management teams (TMT) struggle later to fill out the team as needed to support company growth (Beckman & Burton, 2008). University start-ups particularly struggle with starting team homogeneity and commonly suffer from a lack of diversity and constructive conflict necessary for higher performance (Ensley & Hmieleski, 2005). According to upper echelon theory, the performance of the TMT is intertwined with the firm performance. Firms with stronger teams are more likely to perform well and the performance of the firm reflects on the quality of the TMT (Hambrick & Mason, 1984). TMTs struggle to balance the cohesion necessary for convergent decision making with the conflict that is a natural by-product of team diversity to attain innovation (Ensley, Pearson, & Amason, 2002). Top management team cohesion leads to improved 9 firm performance through two mediators: cognitive conflict and affective conflict. Cohesive teams are more likely to engage predominantly in cognitive conflict, thus resulting in improved firm performance both directly from cognitive debate and indirectly by avoiding the negative effects of affective conflict (Ensley et al., 2002). Entrepreneurs are often characterized as optimistic by nature; however, a recent study has shown that entrepreneurial optimism is negatively related to firm performance, particularly in highly dynamic industries (Hmieleski & Baron, 2009). Team cohesion, cognitive conflict and realism are key elements of entrepreneurial innovation and performance. Entrepreneurial firms struggle to align limited resources to either exploit “old certainties” (March, 1991: 71) or explore new possibilities, knowing they lack the resources to do both and realizing that exploration could offer higher growth but exploitation lower risk (March, 1991). Top management team (TMT) composition in a start-up firm has been shown to influence the firm’s pursuit of exploitation or exploration as a successful strategy (Beckman, 2006). Founding teams with a diverse work history (coming from different companies) are more likely to pursue an exploratory strategy because they bring different ideas and network ties into the firm. Conversely, teams formed by individuals who all worked together at their previous company are more likely to pursue an exploitative strategy because they have already established shared mental models and more likely bring mature organizational routines and procedures from their previous company that allows them to quickly exploit known opportunities. Kolb experiential learning theory has been applied to team level learning (Kayes, Kayes, & Kolb, 2005), a natural extension of Kurt Lewin’s early concepts of 10 conversational space for teams to reflect on shared experience (Lewin, 1948). Kayes et al. utilized Mills’ (1967) team development theory, a five stage progression toward increasingly more sophisticated team goals and purpose: immediate gratification, sustained gratification through greater learning efforts, identification and pursuit of a collective goal, self-determination through conscious directed effort to achieve collective goals and growth to achieve multiple increasingly complex goals requiring higher levels of innovation. According to Kayes et al., shared purpose is the defining moment when the team begins to operate as a learning unit rather than simply a collection of individuals. Optimal team size is a balance between sufficient size to be effective without being too large to function and communicate and coordinate activities. Trust and a sense of safety, especially when expressing ideas to the group, are important to team performance (Edmondson, 1999; Kayes et al., 2005). Teams composed of individuals with learning style preferences covering the complete learning cycle will more easily function through the complete learning role taxonomy (Kayes et al., 2005). A balanced team can be difficult to attain, especially since individuals are often attracted to certain career fields based upon their learning style (Kolb & Kolb, 2005a). It is desirable, although sometimes difficult, to allocate team work by matching each project stage with someone whose style matches the demands of that stage (Kayes et al., 2005). Kolb’s experiential learning theory has been used to study the processes of team innovation within an R&D setting (Carlsson, Keane, & Martin, 1976). The research team in this study analyzed bi-weekly reports written by members of the corporate R&D teams 11 in order to map activities into the Kolb experiential learning space. Research activities generally followed the clockwise sequence of stages within the experiential learning space with minimal deviation (see Figure 2). Managers “looked ahead” one or two stages in order to anticipate upcoming challenges and addressed issues of entrenchment (Dane, 2010) by becoming more directly involved when a team became “stuck” at a particular stage. The researchers found that project team work could be improved by allocating work by matching learning style of the individual with the learning stage orientation of the particular task (see Figure 3 below). Effective managers resisted the temptation to jump across stages to accelerate projects and employed interventional techniques to address problems based upon the stage of the project. FIGURE 2: R&D Activities Mapped Into the Experiential Learning Space (Carlsson, Keane, & Martin, 1976) 12 FIGURE 3: Task Orientation Mapped Into Experiential Learning Space (Carlsson, Keane, & Martin, 1976) A meta-review of team learning models by Knapp (2010) compared the Kayes, Kolb team learning model to three other models by Edmonson (1999), McCarthy and Garavan (2008) and Van den Bossche, Gijselaers, Segers, and Kirschner (2006) as summarized in the Table 1 below. 13 TABLE 1: Summary of Team Learning Models (Knapp, 2010) Each model is based on either the “input-process-output” (IPO) or “input- mediator-output-input” (IMOI) process structure. Each model has certain unique elements, i.e. Edmondson focuses on the need for team members to feel safe expressing ideas to the team (analogous to Kolb’s vision of a learning space), McCarthy emphasizes the meta-cognitive aspects of team learning (another perspective) while Van den Bossche perceives team learning as a process of shared cognition. 14

Advise: Why You Wasting Money in Costly SEO Tools, Use World's Best Free SEO Tool Ubersuggest.