Learning competence within social technographic profiles
and new product development
Jan Pindus, 6207928
Thesis Master Information Science Program Business Information Systems
University of Amsterdam Faculty of Science
Faculty of Economics and Business
Final version: 17-08-2013 Supervisor: Toon Abcouwer
Signature supervisor:
1 Introduction...4 2 Problem statement...5 2.1 Objective...5 3 Theoretical Background...6 3.1 Introduction...6 3.1.1 Process of NPD...6
3.1.2 Stage Gate System of NPD...7
3.1.3 Process of innovation and NPD...10
3.2 Learning styles...12
3.2.1 Learning Styles and NPD...14
3.2.2 Customers and NPD...17
3.3 Social Technographic Profiles...18
4 Conceptual framework...20
5 Research Methodology...22
5.1 Introduction...22
5.2.1 Survey research...22
5.2.2 Population and sample...22
5.3 Questionnaire Design...23
5.3.1 Overview...23
5.3.2 Screening Questions...23
5.3.3 Research Questions...23
5.3.4 Demographic Questions...23
5.3.5 The Questionnaire Design...23
5.3.6 Mandatory Requirements...23
5.4 Data collection...24
5.4.1 Online questionnaire...24
6 Results and Analysis...25
6.1 Introduction...25
6.2 Qualified respondents...25
6.3 Demographic profile of respondents...26
7 Conclusion...44 8 Recommendations...46 9 References...47 Appendix A:...50 Questionnaire:...50 Appendix B:...57
Table setup for data analysis:...57
Appendix C:...61
1 Introduction
The importance of new product development (NPD) has grown dramatically over the last few decades, and is now the dominant driver of competition in many industries (Schilling, Hill 1998). Many companies like Apple continuous improve their products and launch new versions to boost their sales income. Companies often depend on products introduced within the last five years for more than 50 percent of their annual sales, however new product failure rates are still very high (Schilling, Hill 1998). For a new product to achieve significant and rapid market penetration, it must match customer requirements as new features, superior quality, and attractive pricing. Despite the obvious importance of this imperative, numerous studies have documented the lack of fit between new product attributes and customer requirements as a major cause of new product failure (Booz, Allen, & Hamilton 1982).
Social networking sites, social media technology, virtual communities and electronic networks of practice are some of the terms used to describe individuals connecting with others online to exchange information (Boyd, Ellison 2008). According to Boyd and Ellison (2008) , the first social networking site, Six Degrees. com was launched in 1997. However, it was the introduction of MySpace in 2003 when social media technology became mainstream. To understand Social Media adoption, Li & Bernhoff benchmark consumers by their level of participation in Social Computing behaviors. Li & Bernhoff call this method Social Technographic Profiles, since it extends the idea of Technographics — analysis of consumers’ approach to technology — to the Social Computing world. Today companies are increasingly focusing their efforts on Social Networking Sites (SNS), many companies use social technographic profiles to benchmark their customers. The demands today in terms of marketing, customer service, product development and advertising seem to have evolved as a consequence of the new Social Networking age (Schilling, Hill 1998). Because this phenomenon is growing and not going away, this study researches on how companies could use social technographic profiles in the process of new product development, and if this could positively influence the fit between new products and customer requirements .
For the Information Sciences (IS) domain it is particularly interesting to see whether companies can use social technographic profiles in the process of new product development (NPD). Schilling and Hill (1998) stated that for many industries, new product development is now the single most important factor driving firm success or failure. Companies are trying to alleviate the risk of lacking user-acceptance through opening their innovation processes to external actors, particularly customers (Bilgram, Brem, Voigt, 2008). Customers play an important role in new product development, if corporations can use their customers input via social technographic profiles into the process of product development this could lead to efficiency and better products. The main objective of this study is:
2 Problem statement
Today companies are increasingly focusing their efforts on social media where the main focus is to get in contact with their customers. Companies use social media as an advertising, marketing or
customer service tool, but social media can be much more than this. For the development of new products social media could be used to fill in certain (customer) needs that innovating companies a striving for. Because the phenomenon of social media is growing and not going away, this invites research if companies can use social media in the process of new product development (NPD).
2.1
Objective
The objective of this study is to find evidence if social media can be used in the process of new product development (NPD). To distinquish between different users of social media this study focuses on the level of participation. The method to benchmark users by their level of participation in social media is called social technographic profiles (Li & Bernhoff). I’m particularly interested about the specific human learning competences that are needed in the process of new product development (NPD) and if social technographic profiles could be used.
This results in the main objective:
Can social technographic profiles be used in developing new products?
Because I’m particularly interested in specific human learning competences and the process of new product development the following underlying sub-questions can be asked:
1. Which phases of new product development can be identified 2. Which human learning competences can be identified
3. Which human learning competences are important for new product development 4. Which social technographic profiles can be identified
3 Theoretical Background
The aim of this chapter is to review related research and literature and to justify a theoretical framework for analysis.
3.1
Introduction
The importance of new product development (NPD) has grown dramatically over the last few decades, and is now the dominant driver of competition in many industries (Schilling, Hill 1998). For many decades NPD has impacted many organisations. The new product development process has changed significantly from that of the intra-firm “activity-stage” model proposed by Booz, Allen and Hamilton (1968). A number of studies have pointed to the variable initiation and functional locus of product development activities in terms of “manufacture-active” vis-à-vis “customer-active”
paradigms (Von Hippel 1978; Foxal 1984, 1988). It is not surprising then that researchers are finding more cases where the complete NPD process comprises activities and initiatives on the part of several organizations with, for example, collaborative R&D, contracted-out research, customer and/or supplier involvement in development and testing (Saren 1994). In the next sections of this chapter I want to elaborate on the process of NPD and the changes that have been made by different
researcher to eventually come up with a theoretical framework. In this chapter the sub-questions defined in chapter 2.1 will be answered.
3.1.1 Process of NPD
In business and technology, new product development (NPD) is known as the process of bringing new products or services to commercialization (Sperry, Jetter 2009). This process is often conceptualized as a funnel which narrows down a large number of product ideas, so that in the end, only winners come out (fig 3.1).
Sperry & Jetter states that the initial activities of ideation, initial assessment, concept development, and business case analysis are commonly referred to as the fuzzy front-end (FFE) of new product development. In these stages, ideas and product concepts are shaped, and justified before they receive approval to move to full scale development, commonly known as NPD execution. There is an underlying assumption for separating the front-end from NPD execution and managing both phases with distinctly different processes, is they each encounter different levels of uncertainty. Since only 33% of all ideas made it to development, the front-end activities strongly impact overall product development success (Sperry, Jetter 2009). It can also be said that new product success is influenced by uncertainties, especially in the early stages of innovation.
3.1.2 Stage Gate System of NPD
R.G. Cooper developed the stage-gate system as a framework for NPD, in this framework several stages are defined (Fig 3.2). The Stage-gate system recognizes that product innovation is a process. And like other processes, innovation can be managed. Stage-gate systems simply apply process-management methodologies to the innovation process. A good analogy is the production process to manufacture a physical product. The way to improve the quality of output from the process, of course, is to focus on the process itself- to remove variances in the process (Cooper 1990).
Fig 3.2. Overview Stage-Gate system framework (Cooper 1990).
R.G. Cooper describes the sateges and gates from the stage-gate systems as follows:
Gate 1 initial screen, this is the first decision moment where the project is born. If the decision is a go, the project moves to the next stage.
Stage 1 preliminary assesment, this involves a variety of activities; a library search, contacts with key users, focus groups, and even a quick concept test with a handful of potential users. The purpose is to determine market size, market potential, and likely market acceptance.
Gate 2 second screen, this gate is essentially a repeat of Gate 1: The project is reevaluated, but in the light of the new information obtained in Stage 1. If the decision is Go at this point, the project moves into a heavier spending stage
Stage 2 detailed investigation, This is the final stage prior to product development. It is the stage that must verify the attractiveness of the project prior to heavy spending. And it is the stage where the project must be clearly defined. Here, research studies are undertaken to determine the customer's needs, wants and preferences that is, to help define the new product. Another market activity is concept testing, where the likely customer acceptance of the new product is determined.
Gate 3 decision on business case, This is the final gate prior to the Development Stage. At Gate 3, agreement must be reached on a number of key items before the project proceeds into the Development Stage. These items include target market definition; definition of the product concept, specification of a product positioning strategy, and delineation of the product benefits to be
delivered; and agreement on essential and desired product features, attributes, and specifications. Stage 3 development, involves the development of the product and (concurrently) of detailed test, marketing, and operations plans. An updated financial analysis is prepared, and copyright issues are resolved.
Gate 4 post-development review, check on the progress and the continued attractiveness of the product and project. Development work is reviewed and checked, ensuring that the work has been completed in a quality fashion.
Stage 4 testing and validation, This stage tests the entire viability of the project: the product itself; the production process; customer acceptance; and the economics of the project. A number of activities are undertaken at Stage 4:
1. In-house product tests: to check on product quality and product performance;
2. User or field trials of the product: to verify that the product functions under actual use conditions, and also to gauge potential customers' reaction to the product;
3. Trial or pilot production: to test and debug the production process, and to determine more precise production costs and rates;
4. Pretest market, test market, or trial sell: to gauge customer reaction, measure the effectiveness of the launch plan, and determine expected market share and revenues; 5. Revised financial analysis: to check on the continued economic viability of the project, based
on new and more accurate revenue and cost data.
Gate 5 pre-commercialization, This final gate opens the door to full commercialization. This gate focuses on the quality of the activities at the Validation Stage and their results.
Stage 5 production and market launch, this final stage involves implementation of both the marketing launch plan and the operations plan.
All stages can be divided into 5 different phases, according to Bilgram, Brem et al. (fig 3.3).
Fig 3.3: Overview phases of NPD (Bilgram, Brem et al. 2008).
Phase 1 can be identified with the generation of an idea, phase 2 can be identified with stage 1 and stage 2, phase 3 can be identified with stage 3, phase 4 can be identified with stage 4 and finally phase 5 can be identified with stage 5.
There are also critics to the stage-gate system framework, the normative model suggests that the product development process is linear and sequential, whereas in practice it is more likely to be iterative and recursive (Saren 1984). Espacially for the Fuzzy Front End (FFE) of NPD, It can also be said that new product success is influenced by uncertainties, specific in the early stages of innovation. As portrayed in Figure 3.4, the front-end has higher levels of uncertainty and tend to be more
freewheeling; therefore managing these activities requires a process that leaves room for iteration. At the point where the concept crosses into the NPD Execution the plans are defined and the process is more stable; therefore, a linear flow is typically used (Sperry, Jetter 2009).
Fig 3.4: NPD framework (Sperry, Jetter 2009).
The way in which an idea emerges from a vague, incomplete notion in the mind of one person to a well defined, formal organizational objective, to which considerable resources and activities are committed, is an exceedingly complex phenomenon (Kanter 1983; Burgelman and Sayles 1986; Conway and McGuinness 1986). For this study I’m particularly interested in NPD execution side where the plans have been defined and the process is more stable. The NPD execution side refers to the Stages 3, 4 and 5. It is interesting to research wheter Social Technographic Profiles (STP) can be used for these stages.
3.1.3 Process of innovation and NPD
Innovation refers to the creation of a product, service, or process (Birkinshaw, Hamel, Mol, 2008). In this study the main focus is on product innovation i.e. new product development. Innovations can be classified as:
1. continuous innovations,
2. dynamically continuous innovations, 3. discontinuous innovations.
According to Veryzer, a continuous innovation has the least disrupting influence on established patterns. Alteration of a product is involved, rather than the establishment of a new product. Examples: fluo-ride toothpaste; new-model automobile change-overs; menthol cigarettes. A dynamically continuous innovation has more disrupting effects than a continuous innovation, although it still does not generally alter established patterns. It may involve the creation of a new product or the alteration of an existingproduct. Examples: electric toothbrushes; the Mustang automobile; Touch-Tone telephones. A discontinuous innovation involves the establishment of a new product and the establishment of new behavior patterns. Examples: television; computers (Veryzer 1998). See for an overview of product innovations fig 3.5.
Fig 3.5: Types of product innovation (Veryzer 1998).
Robert Veryzer states that the NPD process as Cooper (stage systems) describes focusses mainly on incremental innovation i.e. continous or dynamically continous innovation. Here there is a high focus on customer needs, where as discontinous innovation follows a different patern. The process used for
above. If you look to innovation as a process then there are different phases involved from getting an idea to the implementation or production of a new product (fig 3.6).
Fig 3.6: Innovation process (Lazzarotti, Manzini 2009)
Apart from the partner variety, relevant conceptual and empirical contributions have focused on the so called “innovation funnel”. The steps in this “innovation funnel” are similar to the innovating thinking process of Basadur. In the framework of Basadur (1979, 1982, 1992), Basadur portrayed individual, team and organizational creativity as a dynamic, circular four stage process of continuously finding good problems, defining them, solving them and putting good solutions into practice.
Research also shows that skills in such a process can be deliberately developed (Basadur, 1979, 1994). To make the process work, skills in sequential diverging and converging thinking are necessary within and between the stages. In practice, the process is represented as eight diverging-converging steps (fig 3.7). Within the process four stages are defined as follows:
1. Generating: problem and fact finding
2. Conceptualizing: problem definition and idea finding 3. Optimizing: idea evaluation and action planning
Figure 3.7: – Simplex innovative thinking process
The innovating thinking proces can be identified with the phases of NPD; i) generating (phase idea), ii) conceptualizing (phase concept), iii) optimizing (phase development and concept), and implementing (phase production). These phases can be related to learning styles (section 3.2).
3.2
Learning styles
There is a long history of research on learning, and in particular on the role of experience in learning . Some argued that experience is all that is needed for learning to occur; others, such as Dewey, proposed that learning is an ongoing “reconstruction of experience” that reconciles new experiences with old ones in a continuous learning process (Dewey, 1997). David Kolb (1984), building on earlier work by Dewey and Lewin, provides a Comprehensive theory which offers the foundation for an approach to education and learning as a lifelong process and which is soundly based in intellectual traditions of philosophy and cognitive and social psychology” (Zuber- Skerritt 1992, 98). Kolb builds what he called “experiential learning theory” in which he defined learning as “the process whereby knowledge is created (fig 3.8).
Fig 3.8: Learning styles (Kolb)
Kolb states that the experiential learning theory model juxtaposes two approaches to grasping experience (concrete experience and abstract conceptualization) and two approaches to transforming experience (reflective observation and active experimentation). Placed on a two-by-two matrix (Figure 3.8), these dichotomies define four learning styles: diverging, assimilating, converging, and accommodating. Individuals with a preference for a diverging style are good in idea generation activities, while individuals with a preference for a converging style prefer technical tasks over tasks dealing with social or interpersonal issues. Individuals with the assimilating style are good at taking in a lot of information and logically ordering it, while individuals with the accommodating style prefer hands-on experience and action-oriented learning (Beckman).
The core of Kolb’s four-stage model is a simple description of a learning cycle that shows how experience is translated through reflection into concepts, which in turn are used as guides for active experimentation and the choice of new experiences. Kolb refers to these four stages as concrete experience (CE), reflective observation (RO), abstract conceptualization (AC), and active
Fig 3.9: Kolb’s experiential learning cycle (based on Jenkins 1998,431).
3.2.1 Learning Styles and NPD
The primary output of a product development process is knowledge about the new product in terms of drawings, specifications and procedures for manufacturing. In order to increase knowledge on individual level as well as on company level, learning has to occur (Carlsson, Keane & Bruce Martin, 1976). The four abilities that are named by Kolb are a prerequisite for effective learning, meaning that if a company goes through a learning process like the NPD process, all four abilities must be somehow in place to get the best results (Carlsson, Keane & Bruce Martin, 1976). Buijs (1984, 2003) has mapped the new business development process, the process from strategy to market, over the four stages of the Kolb model. According to Buijs (1984, 2003) the stage of reflective Observation
resembles the strategic process at the start of the innovation process. The formulation of the design brief resembles the abstract conceptualisation and the active experimentation resembles the actual product development. Product launch and use will provide the company with concrete experience regarding the new product, i.e. experiences that can be reflected on during a next strategic process. In this way product innovation processes of companies consist of successive learning cycles. Carlsson, Keane and Bruce Martin (1976) mapped the respective activities of the R&D process at a more detailed level over the model of Kolb. In doing so they mapped a comparable process on a much lower level of aggregation than Buijs. They describe the R&D process as successive learning circles on task level, e.g. ‘generation of nine alternatives’ (CE-RO) followed by ‘establishment of criteria for selection’ (RO-AC) and ‘evaluation of nine alternatives’ (AC-AE). Smulders mapped the successive development phases by placing them in between the former mentioned abilities of theKolb model (see Figure 3.10).
Fig 3.10: Successive phases of NPD mapped over stages (Kolb).
According to Smulders the diverger is a specialist in generating new perspectives by reflecting on former experiences. These new perspectives are necessary for the concept phase, i.e. creativity to get new and innovative conceptual ideas regarding the new product. In the phase of product
development, after the concept has been frozen, detailed engineering must be done. The product must be specified down to the last detail. Apart from some prototypes this is all done on a non-material level and it ends with the bill of non-materials (BOM) regarding the new product. The bill of materials is an abstract representation of the real product. In doing the engineering and creating the BOM this phase of product development forms the transition from reflective observation (RO) to abstract conceptualization (AC). After that, within the constraints of the design of the new product, the production process needs to be developed and prepared. Figure 3.10 also shows that the outcome of the first two phases, concept development and product development, represent the cognitive changes of the learning cycle. The outcomes of the last two phases represent the behavioural changes, i.e. the changes in behaviour that are necessary to produce the newly developed product. These two outcomes together form the ‘integrated learning’ as described by Inkpen and Crossan (1995). Apart from the above-mentioned distinction between the learning in the first two and in the last two phases, i.e. cognitive learning and behavioural learning, there is also learning in each of the four phases. In each phase there will be knowledge developed and therefore part of the work done in these phases can be regarded as learning processes. However, as indicated above, the emphasis regarding the learning in each phase will be different (fig 3.11).
Fig 3.11: Successive emphases in learning within NPD
Figure 3.11 shows four Kolb cycles. Each of these cycles emphasises the learning process of the successive product innovation phases relative to each other and seen from the perspective of an outside observer (Smulders 2004). From this you can conclude that the product innovation process equals the learning process of people described by Kolb. According to Kolb the individual learning style is, among other things like hereditary equipment and life experiences, depending on the demands of the working environment. This implies that the dominant learning styles of participants in a process resembles the learning style that is requested by the type of work in that process. Nevertheless to have succesive emphases in learning it is imperative to have a team of different people with different (dominant) learning style capabilities. For companies that are involved with NPD or product innovation processes involving other people into the process e.g. customers could lead to better products.
3.2.2 Customers and NPD
There is empirical evidence about companies that seek support in a specific phase of the innovation funnel or that integrate partners into their entire innovation process in an articulated network of relationships (Gassmann and Henkel, forthcoming). Companies are trying to alleviate the lack of user-acceptance through opening their innovation processes to external actors, particularly customers (Brem, 2008). Such customer-centric innovations not only harness the voice-of-the-customer but also take a further step beyond the traditional market research by integrating users as problem solvers in various phases of the individual innovation process. Hence, efficient processes and methods for a sustainable identification and integration of customers into the corporate innovation process are crucial to the success of new product development (NPD) (Herstatt,1991; Olsonand Bakke, 2001; Prügl, 2006; Bremand Voigt, 2007). In the literature, many contributions have studied collaborations with specific partners: universities, TSS, customers, suppliers, competitors, public governmental institutions, private research centres (Chiesa et al., 2004; Hoegl andWagner, 2005). It seems that collaborating with different subjects gives rise to different problems and advantages and requires specific organizational and managerial approaches. Collaborating with a single partner, such as customers in NPD, would presumably be quite easy (Lazzarotti, Manzini 2009). For a new product to achieve significant and rapid market penetration, it must match such customer requirements as new features, superior quality, and attractive pricing. Despite the obvious importance of this imperative, numerous studies have documented the lack of fit between new product attributes and customer requirements as a major cause of new product failure (Booz, Allen, & Hamilton 1982). In practice, the past twenty years have seen a codification and formalization of the innovation process—particularly in new product development, where the creation of “stage-gate”(Cooper)processes and their execution by cross-disciplinary teams has become well-entrenched in many organizations.However, companies today are struggling with increasingly broad and complex innovation challenges as they seek to provide complete solutions—not just discrete features or products—to their customers in a rapidly changing technological environment. This is causing many firms to seek understanding of the more fundamental principles underlying innovation (Beckman, Barry 2007). As described in chapter 3.2.1 these fundamental principles can be related to different learning styles to produce innovating new products. The rapidly changing technological environment can also be seen as a opportunity to get closer with your customers and hence involve them into the process.
3.3
Social Technographic Profiles
Combining the theory of Basadur’s innovative thinking process with Kolb’s experiential learning cycle you can conclude that an innovative team consist of people with different learning competences. Social networking sites, social media technology, virtual communities and electronic networks of practice are some of the terms used to describe individuals connecting with others online to exchange information(Boyd, Ellison 2008). According to Boyd and Ellison (2008) , the first social networking site, Six Degrees. com was launched in 1997. However, it was the introduction of MySpace in 2003 when social media technology became mainstream. To understand Social Computing adoption, (Li, Bernhoff,) benchmark consumers by their level of participation in Social Computing behaviors. Li and Bernhoff call this method Social Technographic Profiles, since it extends the idea of Technographics — analysis of consumers’ approach to technology — to the Social Computing world. This benchmarking method takes into account current Social Computing
technologies like blogs and social networks but is also flexible enough to incorporate new
technologies as well. The key: understanding how consumers approach these technologies, not just which ones they use. Their analysis examines a ladder of seven increasing levels of a participation in social technologies (see Figure 3.12). Participation at one level may or may not overlap with
participation at other levels. Starting from the top with the most sophisticated category, the seven rungs on the Social Technographics ladder are:
Li and Bernhoff describes the seven categories as follows:
Creators, at the top of the ladder, are online consumers who at least once a month publish a blog or article online, maintain a Web page, or upload videos or audio to sites like YouTube. Based on a 2010 survey, in the United States, Creators represent 23 percent of the online adult population; in Europe, they’re only 14 percent. South Korea, which has an extremely active blogging population, includes an amazing 68 percent Creators.
Conversationalists participate in the frequent back-and-forth dialogue that’s characteristics of status updates on Facebook and Twitter. Unlike the other groups, people in the Conversationalists group must do these updates at least weekly, not just monthly.
Critics react to other content online, posting comments on blogs or online forums, posting ratings or reviews, or editing wikis. Since it’s easier to react than to create, it’s no surprise that there are more Critics than Creators. One in three online American adults is a Critic, as are one in five online
Europeans and 42 percent of Japan’s online population.
Collectors save URLs and tags on a social bookmarking service like Delicious, vote for sites on a service like Digg, or use RSS feeds. This act of collecting and aggregating information plays a vital role in organizing the tremendous amount of content being produced by Creators and Critics. Collectors are an elite group, including only around 19 percent of online Americans and 10 percent of online Europeans, but should grow as more sites build in diverse types of Collector-type-activities. Japanese and Australians participate in Collector activity at similar rates to their American counterparts. Joiners, who participate in or maintain profiles on a social networking site like Facebook, are one of the fastest growing groups in this classification. In the United States, Joiners grew from 25 percent to 59 percent of the online population between 2007 and 2010. While social network growth in Europe is a little slower than in the United States, joiners already account for 41 percent of everybody online in large European countries.
Spectators consume what the rest produce-blogs, online video’s, podcasts, forums, and reviews. Since being a Spectator requires so much less effort than the other activities in the groundswell, it’s no surprise that this is the largest group.
Inactives-nonparticipants-still remain, although fewer people every year are completely untouched by social technologies.
4 Conceptual framework
As shown in the existing literature above NPD processes are individual as well as organizational learning processes. For an efficient and effective innovation process this requires different learning attitudes from the people involved in the respective phases. In this study I’m particullarly interested in the development phase of NPD. From the framework of Sperry this is the phase where the concept crosses into the NPD Execution, the plans are defined and the process is more stable; therefore, a linear flow is typically used (Sperry, Jetter 2009).
Fig 3. NPD framework (Sperry, Jetter 2009)
Innovation refers to the creation of a product, service, or process (Birkinshaw, Hamel, Mol, 2008). In this study the main focus is on product innovation i.e. new product development. Innovations can be classified as:
1. continuous innovations,
2. dynamically continuous innovations, 3. discontinuous innovations.
Because the main focus is on NPD where the processes are more stable this study focusses mainly on continuous innovations. Robert Veryzer states that the NPD process as Cooper (stage systems) describes, focusses mainly on incremental innovation i.e. continous or dynamically continous innovation. From Bassadurs innovating thinking process the converging style is refered to the optimalization phase. Where concepts are evaluated and validated. From Cooper stage gate systems framework this is refered to stage 4. This stage tests the entire viability of the project: the product itself; the production process; customer acceptance; and the economics of the project. A number of activities are undertaken at Stage 4:
1. In-house product tests: to check on product quality and product performance;
2. User or field trials of the product: to verify that the product functions under actual use conditions, and also to gauge potential customers' reaction to the product;
3. Trial or pilot production: to test and debug the production process, and to determine more precise production costs and rates;
4. Pretest market, test market, or trial sell: to gauge customer reaction, measure the effectiveness of the launch plan, and determine expected market share and revenues;
For this study I’m particullarly interested in activity 2 and 3. If you look to innovation as a process then there are different phases involved from getting an idea to the implementation or production of a new product. In practice, the process is represented as eight diverging-converging steps. Within the process four stages are defined as follows:
1. Generating: problem and fact finding
2. Conceptualizing: problem definition and idea finding 3. Optimizing: idea evaluation and action planning
4. Implementing: gaining acceptance and implementation
The experiential learning theory model juxtaposes two approaches to grasping experience (concrete experience and abstract conceptualization) and two approaches to transforming experience
(reflective observation and active experimentation). Placed on a two-by-two matrix (see Figure 3.8, section 3.2), these dichotomies define four learning styles: diverging, assimilating, converging, and accommodating. Individuals with a preference for a diverging style are good in idea generation activities, while individuals with a preference for a converging style prefer technical tasks over tasks dealing with social or interpersonal issues. Individuals with the assimilating style are good at taking in a lot of information and logically ordering it, while individuals with the accommodating style prefer hands-on experience and action-oriented learning (Beckman).
The primary output of a product development process is knowledge about the new product in terms of drawings, specifications and procedures for manufacturing. In order to increase knowledge on individual level as well as on company level, learning has to occur (Carlsson, Keane & Bruce Martin, 1976). The four abilities that are named by Kolb are a prerequisite for effective learning, meaning that if a company goes through a learning process like the NPD process, all four abilities must be somehow in place to get the best results (Carlsson, Keane & Bruce Martin, 1976). Buijs (1984, 2003) has mapped the new business development process, the process from strategy to market, over the four stages of the Kolb model. According to Buijs (1984, 2003) the stage of reflective Observation
resembles the strategic process at the start of the innovation process. For the phase where concepts are developed and the product development starts, assimilating and converging thinking
charecteristics are necessary.
There is empirical evidence about companies that seek support in a specific phase of the innovation funnel or that integrate partners into their entire innovation process in an articulated network of relationships (Gassmann and Henkel, forthcoming). Companies are trying to alleviate the lack of user-acceptance through opening their innovation processes to external actors, particularly customers (Brem, 2008). Such customer-centric innovations not only harness the voice-of-the-customer but also take a further step beyond the traditional market research by integrating users as problem solvers in various phases of the individual innovation process. Hence, efficient processes and methods for a sustainable identification and integration of customers into the corporate innovation process are crucial to the success of new product development (NPD) (Herstatt,1991; Olsonand Bakke, 2001; Prügl, 2006; Bremand Voigt, 2007). Therfor it is very interested to research wheter Social
Technographic Profiles (STP) could be used as an identification for learning charecteristics. If
companies can use STP to help them in developing new products, this could lead to better products. From theory you can conclude that people have a dominant learning style, I expect that this could be seen in the behavior that people have in social media technologies.
5 Research Methodology
5.1
Introduction
The research method for this study is of an exploratory nature. The specific research questions have been investigated from both literature review (secondary data) and an online questionnaire (primary data), to answer the research question in a qualitative and descriptive manner.
5.2.1 Survey research
A research method is a strategy of inquiry that incorporates certain fundamental philosophical assumptions to research design and data collection (Myers & Avision, 1997). As a research method, surveys are one of the most commonly used research methods, across all fields of research. Surveys are frequently used to describe populations, to explain behaviors, and to explore uncharted waters (Babbie, 1990). In short a survey is a well-defined and well-written set of questions to which an individual is asked to respond. The strength of the survey is the ability to get a large number of responses quickly from a population of users that is geographically dispersed. However surveys are typically self-administered by an individual, with no reseacher present; because of this, the data collected is not as deep and in-depth as with other research methods. There are many research projects in which a survey is the ideal method; in which the survey is well-designed, strict controls are used, and the resulting data has a high level of validity. Since surveys primarly rely on users to self-administer, remember data from a previous point in time, and return the survey, without a researcher being physically present, there are a lot of background details that must receive attention for the data collected to be valid and useful (Lazar, Feng, Hochheiser, 2010).
5.2.2 Population and sample
The population of interest is also known as the ‘target population’(Couper, 2000). For this study the population of interest consist out everyone who uses social media or has a social media account. Because this group is too large to investigate I will limit the population to the business students of the UVA and people from my network (colleques, friends and family). The main objective is to find evidence if there is an relation between social media behaviour (defined within STP) and human learning styles (defined by Kolb). The target population should mainly consist out of people (above 18), the use of social media is very high by young adults (18 – 30) nearly 80% uses them (Li, 2000). The aim is to analyse a small, nonprobability homogeneous sample of 100 or more responses from people who have at least one or many social media account(s).
5.3
Questionnaire Design
5.3.1 OverviewThe online questionnaire comprised of 66 continous questions, which are devided and clustered into three main groups: (1) screening questions, (2) research questions and (3) demographic questions.
5.3.2 Screening Questions
The online questionnaire has been designed in a way with certain logic built in, the first two questions are to ensure that respondents are screened on age and having a social media account registered. Respondents whit a social media account registered are qualified for this research.
5.3.3 Research Questions
The research questions are based on the conceptual framework. Respondents are asked to fill in questions that refer to their behaviour with social media based on the Social Technographic Profiles (Li, Bernhoff,) and to their learning styles based on Peter Honey and Alan Mumford learning style questionnaire (LSQ).
5.3.4 Demographic Questions
Two questions from the questionnaire are to serve against demographic profiles such as gender and eduction.
5.3.5 The Questionnaire Design
Questionnaire
Cluster Question number(s) Objective of question(s)
1. Screening
questions 1 and 3
To ensure that only responses qualify according to the criteria of this study
2. Research
questions 4 – 24
To determine the user behaviour on SNS's based on the Social Technographic Profiles
26 – 65 To determine the learning style charecteristics according to theory of Kolb’s (ELC).
3. Demographic
questions 2 and 25 To profile the respondents to gender and education.
5.3.6 Mandatory Requirements
All questions have been made mandatory, submission only takes place when respondents answer all questions.
5.4
Data collection
5.4.1 Online questionnaire
The online questionnaire was hosted via SurveyMonkey (nl.surveymonkey.net), primary data was collected from the responses of the online questionnaire. I created four different url’s to collect data from different groups. The first group consists out of colleaques of the Central Bank of the
Netherlands (DNB). The second group consists out of colleaques from my wife at ABN Amro bank (ABN Amro N.V.). The third group consists out of mainly business students from the Faculty of Economics and Business at the University of Amsterdam (UvA). The last group consists out of family and friends. The different url’s were distributed by e-mail. I send around 80 e-mails to DNB
colleaques, my wife had send around 50 emails to ABN colleaques, my thesis promotor had send around 250 emails to UVA students and finally there were around 20 emails send to family and friends. In total there were 400 emails send. All questions were made mandatory so that submissions were only accepted once all questions had been answered.
The initial questionnaire questions were checked by my thesis promotor to verify that the structure was appropriate. Before distributing the questionnaire I created a pilot that was held with 10 different candidates to gain feedback. In total I have held 3 different pilots, the first pilot gained feedback that it was to long. I shortend the questionnaire from 120 questions to 66 questions. The second pilot gained feedback about some ambiguous questions and the structure of certain
questions. The third pilot gained feedback about some spelling and grammar. The final questionnaire (questionnaire in Appendix A: questions 1-66) was distributed from 18 June 2013 for a period of one month to ensure sufficient time for these responses to be received. In total 140 people responded of which 125 really finished all the questions.
6 Results and Analysis
6.1
Introduction
In this chapter the results provide a detailed analysis of the primary data that were gained via the online questionnaire (refer to Appendix A). The results will be presented in three main parts, the first part consists out of the demographic profile of the respondents, the second part consists out of the social technographic profiles of the respondents, the third part consistst out of the learning profile of the respondents. At the end of this chapter there will be an analysis made consisting out of the three main parts as described above.
6.2
Qualified respondents
From the online questionnaire a total number of 140 responses were received, however 126 of these filled in all questions and thus completed the questionnaire. From these 126 responses a total of 105 respondents answered yes to the question; “Are you registered on a social networking site such as Facebook, Twitter, Linkedin or other?”, these 105 respondents are qualified for this research.
Fig 6.1 illustrates the total number of completed responses versus the total number of responses that qualified.
6.3
Demographic profile of respondents
From the 105 responses that qualified 71 of these are “male” and 34 are “female”. In percentages 67,6% are “male” and 32,4% are “female”. Within the DNB (De Nederlandsche Bank) these percentages represent the ratio of man and woman that work there.
Figure 6.2: Percentages of male versus female.
Figure 6.3 illustrates the age distribution within the genders
Male Female
Age group Number of
respondents Percentages of total Age group Number of respondents Percentages of total
under 18 0 0% under 18 0 0% 18-24 8 8% 18-24 3 3% 25-34 18 17% 25-34 14 13% 35-44 24 23% 35-44 11 10% 45-54 10 10% 45-54 6 6% above 55 11 10% above 55 0 0% Total: 71 68% Total: 34 32%
female respondents the majority 14 responses (41% of all female responses) were aged 25 to 34 years.
Fig 6.4 illustrates the total of responses distributed over age
From all the respondents the majority 67 responses (64% of the total) were aged 25 to 44 years. Because most of the responses given to the online questionnaire were from DNB (De Nederlandsche Bank) and ABN (ABN Amro bank N.V.) the age 25 to 44 years is also representative to the majority of people that work there. In figure 6.4 you can see the level of education that the respondents have completed.
Figure 6.4: level of education.
According to figure 6.4, the 105 qualified respondents were predominantly highly educated. A total of 32 (30.4% of the total) have a master degree, and 50 (47.6% of the total) have a bachelor degree. Only 5 (4.7% of the total) have a MBO (middle education), and 18 (17.1% of the total) have finished high school. From the high school responses most of them finished VWO (high school diploma that allows you to go directly to university). A majority of these responses where students on the University of Amsterdam.
Figure 6.5: Male education distribution
Male
Age group Master
degree Bachelordegree MBO degree High school degree under 18 0 0 0 0 18-24 1 2 1 4 25-34 8 8 1 1 35-44 7 16 0 1 45-54 3 4 1 2 above 55 4 4 0 3 Total: 23 34 3 11 Percentages: 32% 48% 4% 16%
Fig 6.6: Female education distribution
Female
Age group Master
degree Bachelordegree MBO degree High school degree under 18 0 0 0 0 18-24 0 0 1 2 25-34 3 9 1 1 35-44 3 4 0 4 45-54 3 3 0 0 above 55 0 0 0 0 Total: 9 16 2 7 Percentages: 26% 47% 6% 21%
According to figure 6.5 and 6.6 the level of Master degrees slightly differs between male and female responses. This can be clarified due to the mail distibution of the online questionnaire, I also mailed to groups of secrataries who are mainly female and the majority isn’t highly educated. For other functions within the DNB, a bachelor or master degree is minimal required. The distribution of highly educated females could be sligthly undervalued compared to the male responses. In figure 6.7 and 6.8 you can see the different responses received from the four different groups that responded on the online questionnaire.
DNB ABN
Age group Master degre e
Bachelo
r degree MBO degre e
High school degree
Age group Master degre e
Bachelo
r degree MBO degre e High school degree under 18 0 0 0 0 under 18 0 0 0 0 18-24 0 0 1 0 18-24 0 0 0 0 25-34 3 4 0 0 25-34 3 1 1 0 35-44 6 13 0 2 35-44 5 3 0 1 45-54 4 6 1 0 45-54 0 1 0 2 above 55 2 3 0 3 above 55 0 1 0 0 Total: 15 26 2 5 Total: 8 6 1 3 Percentages : 31% 54% 4% 10% Percentages: 44% 33% 6% 17%
Fig 6.7: education distribution DNB and ABN based on the responenses of the online questionnaire
Student Family/Friends
Age group Master degre e
Bachelo
r degree MBO degre e
High school degree
Age group Master degre e
Bachelo
r degree MBO degre e High school degree under 18 0 0 0 0 under 18 0 0 0 0 18-24 1 2 0 6 18-24 0 0 1 0 25-34 4 5 0 1 25-34 1 7 1 1 35-44 0 1 0 0 35-44 0 2 0 2 45-54 0 0 0 0 45-54 2 0 0 0 above 55 2 0 0 0 above 55 0 0 0 0 Total: 7 8 0 7 Total: 3 9 2 3 Percentages : 32% 36% 0% 32% Percentages: 18% 53% 12% 18%
Fig 6.8: education distribution student and family/friends based on the responses of the online questionnare.
6.4
Social technographic profile
The questions 3 to 24 of the online questionnaire (refer to appendix A) are setup in a way to determine the social technographic profiles of the respondents. The questions are based on the behaviour that respondents have on social media and refer to the social technographic ladder described in chapter 3.3. Figure 7.0 below represents the responses of people who visit social network sites at least on a monthly basis, from all the respondents that uses social media 89.5% regularly or at least on a monthly basis visit social media.
Fig 7.0: percentages of responses that uses social media on a monthly basis.
Figure 7.1 illustrates the time spend on a weekly basis, the majority 46.7% that regularly visit social media spends less than 2 hours time per week. Then there is a group of 23.8% that spends between 2 to 5 hours per week. 11.4% spends between 5 to 10 hours per week and there is a small group of respondents 7.6% that spends more than 10 hours per week on social media. Roughly you can state that from all the people that visit social media on a monthly basis (89.5%) 50% of these spends less than 2 hours and the other 50% spends more than 2 hours per week.
Figure 7.2 illustrates the average time spend per session, the majority 41.9% spends between 5 to 10 minutes on average per session. The second main group 35.2% spends less than 5 minutes on
Fig 7.1: time spend on social media per week.
Fig 7.5: types of usage of social media.
Fig 7.5 illustrates the the types of usage of social media of the respondents, the questions refer to the numbers (5,6,7,20,21,22,23,24) of the online questionnaire (Appendix A). The figure shows a clear distinction between behaviour. The questions were respondents are asked if they only read or review on social media are clearly stated “yes” (see first two questions of fig 7.5). The questions were respondents are asked if they forward or write on social media are clearly stated “no” (see last two questions of fig 7.5). From the social technographic profile for European people there is a higher percentage of “spectators” than there is of “creators”.
Figure 7.6 illustrates the details of the types of usage of socal media of the respondents, the questions refer to the numbers (10,11,12,13,14,15,16,17,18,19) of the online questionnaire
(Appendix A). Here the respondents are asked to answer how many times they do certain behaviour on social media. The types of questions are also more detailed, this helps in determine the social technograpgic profile that matches certain behaviour.
Fig 7.7: Labelling the questions to the social technographic profiles
Question number: Question: Label:
5 Do you write articles or stories and post them on social
network sites? Creator
6 Do you forward articles or stories and post them on social
network sites? Creator,Critic
7 Do you comment on articles or stories that are posted on
social network sites? Critic
10 How often have you updated your status on a social network
site in the last month? Conversationalists
11 How often have you uploaded one or more of the following
content items: audio, text or video in the last month? Creator, Critics 12
How often have you downloaded one or more of the following content items: audio, text or video in the last
month? Spectator
13 How often have you shared photos and / or videos with
friends in the last month? Creator,Critics,Joiner
14 How often have you commented on photos and / or videos
from friends in the last month? Critics
15 How often did you registered, submitted personal details, or
made new friends in the last month? Joiner
16 How often did you view or follow friends on Social Network
Sites (eg Facebook) in the last month? Joiner,Spectator 17 How often did you read news, viewed pictures or video's on
social network sites in the last month? Spectator 18 How often did you vote for websites online or added "tags"
to webpages/photos in the last month? Collector 19 How often did you comment or contributed to someone
else's blog or articles in a wiki in the last month? Critics
20 Did you use RSS feeds in the last month? Collector
21 Did you post updates on Twitter in the last month? Conversationalists 22 Did you listen to podcasts in the last month? Spectator
23 Did you use or read online forums in the last month? Spectator 24 Did you use or read ratings/reviews in the last month? Spectator
Fig 7.7 illustrates the labelling of the question numbers (refer to Appendix A) that were asked in the online questionnaire. The labels refer to chapter 3.3 (social technographic profile ladder), through this it is possible to create an overview of the percentages (level of participation) that respondents have in social media. The next step is to analyse the percentages of the responses given to the questions, e.g. how many respondents answered “yes” on creating own content and post them (profile creator). In the figures 7.8 and 7.9 below there is an overview of the responses given to the questions.
Fig 7.8: percentages answers to questions (10,11,12,13,14,15,16,17,18,19)
Fig 8.0: Social technographic profiles based on online questionnaire
Fig 8.0 illustrates the social technographic profiles of the respondents that filled in the online
questionnaire (refer to Appendix A). The percentages are based on the figures 7.8 and 7.9 above and clustered by profile. The creators are on the top of the ladder (refer to social technographic profile ladder chapter 3.3), these are the ones that write articles or stories (e.g. blogs) and upload content. The conversationalists constantly (at least weekly) update their status on social media. The critics comment on the content that the creators make, since it is easier to react than to create this group is much larger than the creators. The collecters consume all the available information. The Joiners are maintaining their profiles and participate in social networking sites. The spectators read and watch on social network sites, because this requires much less effort than the other activities it’s no surprise that this is the largest group. The inactives don’t have a social media account and are completely untouched by it.
Fig 8.1: Comparison between online users and respondents of online questionnaire
Fig 8.1 illustrates the comparison between online users (United States, Canada, Europe) held by North American Technographics in 2011, and the results of the online questionnaire (refer to Appendix A).
6.5
Learning profile
The questions 26 to 65 of the online questionnaire (refer to appendix A) are based on Peter Honey and Alan Mumford’s model. Honey and Mumford developed a modified version of Kolb’s learning style inventory, which turns the Do’er, Reflector, Theoretical, and Processor preferences into learning styles called Activists, Reflectors, Theorists, and Pragmatists (Arp, Woodard et al. 2006). Based on this they developed a learning styles questionnaire were the styles were directly aligned to the stages of Kolb’s experiential learning theory (chapter 3.2). This questionnaire contains statements that you have to agree with or disagree with, there are no wrong answers. The statements are based on the charecteristics of the four different learning styles and divided by these styles. At the end you count all the answers that you agreed on, a total score per learning style will appear.
Fig 8.3: Labelling questions into learning styles
Question number
Statement Label
26 I often take reasonable risks if I feel it is justified Activist 29
I often find that actions based on feelings are as sound as those on careful
thoughts and analysis Activist
37 I am attracted more to new unusual ideas than to practical ones Activist 43
I prefer to respond to events spontaneously, rather than plan things out in
advance Activist
47
The present is much more important than thinking about the past or future
Activist 49 In meetings I enjoy contributing ideas to the group Activist 50
On balance I tend to talk more than I should and I need to develop my
listening skills Activist
52 I enjoy communicating my ideas and opinions to others Activist 61 I quickly get bored with methodical, detailed work Activist 65 I enjoy the drama and excitement of a crisis Activist
Question number
Statement Label
28 I have a reputation of having a no-nonsense direct style Pragmatist 30
The key factor in judging a proposed idea or solution is whether or not it
works in practice Pragmatist
31
When I hear about a new idea or approach I like to start working out how
to apply it in practice Pragmatist
40
In meetings I have a reputation for going straight to the point, no matter
what others feel Pragmatist
45 I usually judge other people's ideas on their practical merits Pragmatist 51 In meetings I get very impatient with people who lose sight of the objective Pragmatist 53
People in meetings should be realistic, keep to the point and avoid in
indulging in fanciful ideas Pragmatist
58 Most times I believe the end justifies the mean Pragmatist Reaching the group's objectives should take precedence over individual
Question number
Statement Label
33 I take pride in doing a methodical job Reflector
35
I take care over the interpretation of data and avoid jumping to conclusions
Reflector 36 I like to reach a decision after considering my alternatives Reflector 41
I prefer to have as many sources of information as possible, the more the
better Reflector
44
I dislike having to present my conclusions under time pressure or rigid deadlines, when I could have spent more time thinking about the problem
Reflector 46 I often get irritated by people who want to rush headlong into things Reflector 48
I think that decisions based on the analysis of the information are sounder
than those based on intuition Reflector
54 I like to consider many alternatives before making up my mind Reflector 56
In meetings I am more likely to keep in the background than to take the
lead and do most of the talking Reflector
57 I prefer to do the listening than talking Reflector
Question number
Statement Label
27
I tend to solve problems using a step by step approach and avoid any
fanciful ideas Theorist
32
I like to follow a self disciplined approach, establish clear processes and
logical thinking patterns Theorist
34
I get on best with logical, analytical people and less with spontaneous
irrational people Theorist
38 I dislike situations that cannot fit into a coherent pattern Theorist 39 I like to relate my actions to general principles Theorist 42 Flippant people who cannot take things seriously usually irritate me Theorist 55
Considering the way my colleagues react in meetings I believe I am more
objective and unemotional Theorist
62
I am keen on exploring the basic principles and theories underpinning
events Theorist
63 I like meetings to be run on methodical lines, sticking to the agreed agenda Theorist 64 I steer clear of subjective or ambiguous topics Theorist
Fig 8.3 illustrates the grouping of the questions into the learning styles (according to Honey and Mumford). The question numbers refer to the online questionnaire (Appendix A), the questions had to be answered by “agreed”or “disagreed”. Only the questions that were answered with “agreed” are counted to determine the preferred learning style of the respondents. Fig 8.4 shows the outcome of the questions that were answered with “agreed”.
Fig 8.4: overview of the score on learning styles respondents
Fig 8.4 shows the total outcome per learning styles of the respondents. The total score for the learning style activist seems to be underperformed compared with the other learning styles while the learning style pragmatist seems to be overperformed compared with the others.
6.6
Analysis
In this chapter the results will be measured with statistical analysis to determine wheter differences in the data could be due to chance or meaningfull to conclude or exclude certain assumptions. The analysis will be executed by SPSS (IBM SPSS Statistics version 21.0). In the previous chapters an overview has been given about the social technographic profiles (chapter 6.4) of the respondents and their score on the learning style questions (chapter 6.5). In this chapter I want to analyse if there is a relation between these two variables. To analyse this I have setup an table with the data per
resondent see figure 8.0. below (full table is in Appendix B). Fig 8.0: table setup for data analysis (partial)
R e sp o n d en tI D A ge Gend e r A cti vi st P ra gm ati st R efl ec to r Th e o ri st C re at o r C o n ve rs ati o n al is t C ri ti cs C o lle ct o rs Jo in e rs Sp ec ta to rs In ac ti ve s 272022069 7 5 1 6 5 8 8 2 2 2 2 2 1 2 271214950 7 2 2 5 7 7 5 2 2 1 1 2 1 2 271212010 1 1 1 6 9 8 4 2 2 1 2 1 1 2 271201273 8 2 1 9 5 4 6 2 1 2 1 2 1 2 271196420 4 1 2 1 9 8 5 2 1 2 2 1 1 2 271150953 9 1 1 4 6 9 8 2 2 2 2 2 2 1 271106671 9 1 2 9 10 10 10 1 1 1 1 1 1 2 271103122 9 1 1 6 8 8 9 1 1 1 2 2 1 2 271079695 3 2 2 5 7 9 9 2 2 2 2 2 1 2 271071356 5 1 1 5 6 3 4 2 1 1 2 1 1 2 271061577 0 2 1 6 4 9 6 2 2 2 2 2 1 2 271053463 9 2 1 7 3 7 7 2 1 2 2 1 1 2 271050746 7 2 2 3 7 8 2 2 2 2 2 1 2 2 271039880 2 2 1 9 10 6 7 2 1 1 2 1 1 2
Figure 8.0. shows the table setup used for data analysis, the respondentID’s are unique numbers given by the survey tool (SurveyMonkey.nl). The variable “age” is divided into 5 groups, where
number 1 represents age group 18-24, number 2 represents age group 25-34, number 3 represents age group 35-44, number 4 represents age group 45-54 and finally number 5 represents age group above 55. The variable “gender” is divided into two numbers where number 1 refers to “male” respondents and number 2 to “female” respondents.
For the learning style “variables”; “activist”, “pragmatist”, “reflector” and “theorist”. The total score that each respondent had is setup. For each learning styles there were 10 questions so a respondent could score maximum 10 for each learning style and minimum of 0.
For the social technographic profiles variables only two numbers are setup, where number 1 refers to “yes” and number 2 refers to “no”. Based on the questions (refer to fig 7.8 and fig7.9) to define the social technographic profiles a respondent could answered “yes” or “no” to these questions.
To calculate if the probability that differences among the observed means of the data could simply be due to change or are statistical significant to make conlusions about, I executed an one-way ANOVA with SPSS.
Fig 8.1. Overview setup ANOVA
Fig 8.1. shows the setup, in the dependent list the variables; respondentID, activist, pragmatist, reflector, and theorist are defined. As an factor I defined the social technograpic profiles variable, this way the one-way ANOVA calculates wheter there are differences in the learning style scores for each social technographic profile variable. In the Appendix all output files from SPSS are included.
Fig 8.2: Outcome of the ANOVA test
Creator Conversationalist
Critics
N Mean Sig. N Mean Sig. N Mean Sig.
Activist Yes 26 6,31 0,02 4 49 6,10 0,009 52 6,21 0,001 No 100 5,33 77 5,17 74 5,05 Tota l 126 5,53 126 5,53 126 5,53 Pragmatis t Yes 26 7,42 0,434 49 7,06 0,688 52 7,29 0,516 No 100 7,08 77 7,21 74 7,05 Tota l 126 7,15 126 7,15 126 7,15 Reflector Yes 26 6,54 0,78 2 49 6,49 0,82 1 52 6,48 0,84 3 No 100 6,41 77 6,40 74 6,41 Tota l 126 6,44 126 6,44 126 6,44 Theorist Yes 26 6,23 0,92 1 49 6,14 0,85 4 52 6,17 0,94 4 No 100 6,18 77 6,22 74 6,20 Tota l 126 6,19 126 6,19 126 6,19 Collect
or Joiner Spectator Inactive
N Mean Sig. N Mean Sig. N Mean Sig. N Mean Sig.
Activist Yes 36 6,22 0,01 3 65 5,72 0,26 4 90 5,64 0,31 4 21 5,14 0,32 6 No 90 5,26 61 5,33 36 5,25 105 5,61 Tota l 126 5,53 126 5,53 126 5,53 126 5,53 Pragmatis t Yes 36 7,19 0,87 7 65 7,45 0,08 5 90 6,98 0,12 2 21 7,57 0,28 9 No 90 7,13 61 6,84 36 7,58 105 7,07 Tota l 126 7,15 126 7,15 126 7,15 126 7,15
Reflector Yes 36 6,72 0,33 4 65 6,46 0,890 90 6,53 0,414 21 6,76 0,437 No 90 6,32 61 6,41 36 6,19 105 6,37 Tota l 126 6,44 126 6,44 126 6,44 126 6,44 Theorist Yes 36 6,56 0,26 3 65 6,22 0,901 90 6,36 0,205 21 6,24 0,918 No 90 6,04 61 6,16 36 5,78 105 6,18 Tota l 126 6,19 126 6,19 126 6,19 126 6,19
Fig 8.2 shows the outcome of the 7 different ANOVA tests that have been executed, for each social technographic profile you can see the mean of the four different learning styles; activist, pragmatist, reflector and theorist compared with all individual respondents.
7 Conclusion
The main objective of this study is to find evidence if social technographic profiles can be identified with human learning styles (chapter 2.1). This study has been setup in a way that findings from both literature review (secondary data) and a online questionnaire (primary data) are used to answer the research questions. The underlying sub-questions have been answered in the literature review (secondary data), the main research question; “Can social technographic profiles be used in developing new products?”, will be answered by the findings of the results (chapter 6).
From the literature review you can conduct that the learning process equals to the new product development process (fig 3.11). Therefor it is relevant to research if human learning styles can be connected to the behaviour on social network sites. To find evidence an online questionnaire has been setup and distributed to two different companies (DNB, ABN), students of the business faculty (University of Amsterdam) and people from my own network (friends, family). The dataset has been analysed and the results are set in chapter 6.
The conclusion based on the analyzed dataset is that people with a high(er) preference for the learning style “activist” (definition Honey and Mumford) derived from the learning style
“accommodating” (definition Kolb), are the most active persons on social media compared to the other learning styles. Based on the results of the one-way ANOVA (fig 8.2) the learning style “activist” had a 95% confidence interval level for the social technographic profiles; creator and collector. And a 99% confidence interval level for the social technographic profiles; conversationalist and critics. However social technographic profiles have been setup in such a way that you can’t isolate persons
