The influence of network characteristics on the innovative behaviour of an entrepreneur

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1 Have you ever heard of this product ?

The influence of network characteristics on the

innovative behaviour of an entrepreneur

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2 The influence of network characteristics on the innovative

behaviour of an entrepreneur

Master Thesis Business Administration

Business Development

Rijksuniversiteit

Groningen

Author:

Jeroen Munk

Student number:

1506528

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3 Index

1. Introduction to the network characteristics ... 1

2. Network characteristics ... 4

2.1 Nodes ... 4

2.1.1 Number of peers ... 4

2.1.2 Supplier information... 5

2.1.3 Mass media influence ... 7

2.2 Ties ... 9

2.3 Opinion leadership and being a lead user regarding innovative behaviour ... 10

2.4 Innovative behaviour and the performance of a business ... 10

3. Research design ... 11

3.1 Research method ... 11

3.2 Hairdressers outside Groningen ... 12

3.3 Population and sample ... 13

3.4 Questionnaire ... 13

3.5 Data collection ... 14

3.6 Representation ... 15

3.6.1 Age of the hairdressers ... 15

3.6.2 Type of hairdresser business ... 15

3.6.3 Size of the hairdresser business ... 16

3.6.4 Number of participating hairdressers ... 16

4. Operationalizations ... 17

4.1 Measuring Peers ... 17

4.2 Measuring tie strength ... 17

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4.4 Measuring lead user ... 18

4.5 Measuring the influence of mass media ... 19

4.6 Measuring innovative behaviour ... 20

4.7 Measuring received supplier information ... 21

4.8 Measuring performance ... 22

5. Analysis ... 23

5.1 The reliability of the scales ... 23

5.2 Reliability analysis... 23

5.3 Correlation analysis ... 24

5.4 Multiple regression analysis ... 26

6. Discussion of the results

... 31

6.1 Main conclusions of the paper ... 31

6.2 Network characteristics ... 32

6.3 Managerial implications ... 35

Limitations and suggestions for further research ... 35

References ... 36

Appendix 1 ... 43

Appendix 2 ... 44

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1 Introduction to the network characteristics

Notions of networks and networking have, over the past twenty years, received significant research attention in the business and management literature (Shaw, 2006). Particular to small firm research, a review of the literature identifies that despite the considerable attention that networks have received, significant gaps exist in knowledge and understanding of the contents, processes and dynamics of small business networks (Shaw, 2006). This is remarkable, because networking has found to be important to entrepreneurial firms (Lechner, 2007). It may even be the most important strategic resource for an entrepreneur, because entrepreneurs consistently use networks to get ideas and gather information and advice (Pittaway, 2004). The strategic benefits are also noticeable in the performance of the business, because network size is related to growth (Lechner, 2007).

Social networks are defined by a set of actors (individuals or organizations) and a set of ties. Nodes are the actors in a network. The nodes in this network research consist of peers, mass media and suppliers. The nodes are connected through ties. A tie or link is directional in nature and represents a flow of intermediate product or information from a given node (Bontis, 1998). There are different kinds of ties, namely weak ties and strong ties. Weak ties consist of nodes who have little interaction. Strong ties are ties between nodes that have frequent interaction.

The information from peers (persons in the network) is gathered through WOM (Word of mouth), which is the interpersonal exchange of information (Derbaix & Vanhamme, 2003). WOM is seen as an important influence on the innovative behaviour. Innovative behaviour is the production or adoption of useful ideas and idea implementation (Scott, 1994).

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important market trend (von Hippel, 1986; Lilien et al, 2002). Keeping this in mind, an interesting question would be how innovative an opinion leader and lead user is.

Opinion leaders and lead users are influential actors in a network and it is interesting to study which network characteristics characterize these actors. As mentioned earlier, a couple of network characteristics are important for the innovative behaviour: peers, mass media influence and received supplier information. What kind of influence do peers have on being an opinion leader or a lead user? Moldovan & Goldenberg (2004) stated that opinion leaders have more contacts than ordinary users. For lead users the contact with peers is needed to get information which contributes to the possibility to be innovative.

According to Damanpour (2006) an innovation is a means of transforming or changing the adopting organization to achieve improved performance. Based on this relationship, another question arises, namely does innovative behaviour contribute to the performance of the small businesses?

Networks are important aspects for entrepreneurs. But as mentioned in the preceding text (Shaw, 2006) significant gaps exist in knowledge and understanding of the contents, processes and dynamics of the small business network. Research has been done on the different network influences and the different relations, but it is interesting to study the network characteristics of the small business. Namely, testing the different network features’ influences on the opinion leaders and lead users in a small business network and to test their relation on the innovative behaviour. Furthermore, an important question is if there is a positive relationship between innovative behaviour and the performance of the business.

In summary this paper is guided by the following questions:

1. How do network characteristics characterize opinion leaders?

2. How do network characteristics characterize being a lead user?

3. What is the relationship between opinion leadership and innovative behaviour?

4. What is the relationship between being a lead user and innovative behaviour?

5. What is the relationship between innovative behaviour and the performance of the business?

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Groningen. We decided to focus on the hairdresser market because of the accessibility of the hairdresser market and the high innovative character of the products used by hairdressers. Our empirical study is conducted under 60 hairdressers in Groningen.

To answer the questions which guide the paper, a literature study has been performed in chapter 2, to collect information about the different relationships between the variables. Based on this literature, the hypotheses were stated. Chapter two discusses the research design. How the different variables are measured will be discussed in chapter 4. The analyses is reported in chapter 5. The results will be discussed in chapter 6.

Based on the preceding text the following research model is proposed:

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4 1. Network characteristics

1.1 Nodes

When studying the hairdresser’s network, three types of network characteristics can be distinguished. In the following paragraphs hypotheses are conducted for the opinion leader, lead user role and the innovative behaviour. The following network characteristics will be discussed:

• Number of peers, meaning the hairdressers they have contact with.

• Mass media influence, the influence of television, magazines, newspaper articles.

• Supplier information, i.e. the information they receive from their suppliers.

• Tie strength (contact intensity), which is an important variable regarding peers.

1.1.1 Number of peers

1.1.1.1 Opinion leader

Opinion leaders are often found to have a network with many direct contacts (Rogers & Cartano, 1962). The information shared by opinion leaders, according to several studies is bilateral: from the opinion leaders to the followers and vice versa. But opinion leaders have more contacts with the ones they share their information with (Moldovan & Goldenberg 2004). Based on this we formulate the following hypothesis:

Hypothesis 1a: There is a positive relationship between the number of peers an entrepreneur has contact with, and being an opinion leader.

1.1.1.2 Lead-user

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the number of ties. It refers to the extent to which an actor (hairdresser) facilitates the flow of

information being positioned on many informational paths. In other words, it refers to the probability that a ‘communication’ from actor j to actor k takes a particular route (Kratzer & Lettl, 2008). This is one way to measure if a certain actor is active. Another way to measure if an actor is active is the degree centrality. In this study we assume that a lead-user has a central position in a network, a high degree of centrality. Degree centrality is based on the number of units directly connected to the unit, in this case the

hairdresser (Kratzer & Lettl, 2008). Because this actor has the largest number of direct ties to other actors in the network (hairdressers) it must be the most active (Freeman, 1979).

Hypothesis 1b: There is a positive relationship between the number of peers an entrepreneur has contact with, and being a lead-user.

1.1.1.3 Innovative behaviour

Most innovations spread primarily through interpersonal influence. The channels through which this influence flows are the social networks that link individual members of a social group (Greenhalgh et al., 2004; Wejnert, 2002; Rogers, 2003). Pittaway (2004) was one of the researchers who already noticed the critical role of networks for entrepreneurs. Social networks are defined by a set of actors (individuals or organizations) and a set of ties. These ties are important to entrepreneurs and a number of studies document that they consistently use networks to get ideas and gather information to recognize entrepreneurial opportunities (Hoang & Antoncic, 2003). The reliance of the network is not constrained to the start-up stage. Entrepreneurs continue to rely on networks for business information, advice and problem solving, with some contacts providing multiple resources (Hoang & Antoncic, 2003).

Hypothesis 1c: There is a positive relationship between the number of peers an entrepreneur has contact with, and the innovative behaviour of the entrepreneur.

1.1.2 Supplier information

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As mentioned in the section on supplier- innovative behaviour, the supplier plays an important role in providing information about innovations. Because an opinion leader is, given the two step flow theory (Katz, 1957), a person who reacts more on information, we assume that:

Hypothesis 2a: There is a positive relationship between the amount of received information from the supplier and being an opinion leader.

1.1.2.2 Lead-user

As mentioned in section 2.1.2.1, the supplier plays an important role in providing information on new products and market information. Because a lead user has a need for information we expect that:

Hypothesis 2b: There is a positive relationship between the amount of information received from the supplier and being a lead user.

According to Verhees (2004) technological change initiated through R&D is considered to be the key technology push source of innovation. Except in small enterprises where there is often a different approach, namely through external contacts (Verhees, 2004). Small businesses use the supplier intelligence to detect new technologies and other types of input necessary for innovation (Verhees, 2004, Tidd, 2005). This is an important source of information because in reality, small firms rarely scan for new technological opportunities or articulate their needs (Verhees, 2004). Suppliers may take a more active role in stimulating innovation by trying to influence the small firm’s innovation decision (Verhees, 2004).

For example providing information regarding an innovation (new product) is an important aspect of supplier information. Innovation adoption is largely an information processing activity (Rogers, 1983; Frambach, 2002). Thus, the adopters of an innovation can be influenced by the extent to which they have processed information on the innovation. This is highly dependent on the degree to which the suppliers are involved (Frambach, 1998). According to Tidd (2005) most small businesses fall into the supplier-dominated category because the small businesses use the information as input for their main source of new technology. Based on this we assume that:

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1.1.3 Mass media influence

The literature regarding mass media and opinion leadership, being a lead user and innovative behaviour is discussed in the next sections.

Besides the word-of-mouth in a network, the opinion leader is influenced by mass media. This influence is explained by the two-step flow of communication of Katz (1957) used by Lee, 2002, which states that a small group of individuals is influenced by mass media and supplier information. People in this group are called the opinion leaders. All other people do not act on the information from the mass media, but start adopting the product when they hear about the experiences of the opinion leaders. This role is confirmed by other authors (Procter & Richards, 2002; Berelson and Steiner, 1964). Other authors also write about the relationship between media and opinion leaders. For example, Cheny (2001) points out that the information sources television and magazines are important to opinion leaders. This is also found by Deacon (2003) and Weimann (2007)

Hypothesis 3a: There is a positive relationship between the influence of mass media and being an opinion leader.

1.1.3.2 Lead-user

As mentioned in section 2.1.3.1, mass media creates the awareness and the network will do the rest (Procter & Richards, 2002; Berelson and Steiner, 1964). Thereby we expect that mass media also have an influence on opinion leaders.

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Since media are often the trigger of the adoption process, we should not neglect the influence of mass media. Greenhalgh et al (2004) and Gatignon (1985) found that the mass media create the awareness and the network will do the rest.

In other words mass media are important influences in the initiation of innovative behaviour, in sense of creating awareness. Mass media create the awareness and the interpersonal channels are more influential in promoting adoption of innovations (Greenhalgh et al, 2004). Based on the literature we could expect that:

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1.2 Ties

1.2.1.1 Weak & strong ties

Ties are very important for the diffusion of innovations. A tie is said to exist between communicators whenever they exchange or share resources such as goods, services, social support or information (Haythornthwaite, 2002). The tie determines the ways, means, and expression of communications, and it determines the motivation, needs, and desires for communication. According to Granovetter (1973) the strength of ties within a network defines the strength and quality of relations. He differentiates two types of ties; strong ties and weak ties. Compared to weak ties, strong ties consist of frequent interaction.

Information and support gained through strong ties offers multiple benefits: it is cheap; it is more trustworthy because it is richer, more detailed and accurate; it is usually from a continuing relationship and so in economic terms it is more reliable (Granovetter, 1973). Nevertheless, strong ties are perceived as being less beneficial than weak ties because they are likely to provide redundant information since they can be anticipated to move in similar, if not the same, social circles (Jack, 2005; Burt, 1992). So an

innovation is diffused to a larger number of individuals and traverses a greater social distance when passed through weak ties rather than strong ones (Granovetter, 1973 in Rogers 1976).

Burt (1992) extended and reformulated the “weak tie” argument. According to Burt it is not the quality of any particular tie but rather the way different parts of networks are bridged. The balance between weak and strong ties (embeddedness). In this study the hypotheses will be about the weak ties. Even though it gives no information about the balance between weak and strong ties, it does give a good idea about the change that they receive new ideas and information. Given that new ideas and information are more efficiently diffused through weak ties (Granovetter, 2005). Based on this we expect that:

Hypothesis 4a: There is a positive relationship between weak ties and being an opinion leader.

Hypothesis 4b: There is a positive relationship between weak ties and being a lead user.

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1.3 Opinion leadership and being a lead user regarding

innovative behaviour

According to Myers (1972) there is a positive relationship between opinion leadership and innovative behaviour. According to Greenhalgh (2004), opinion leaders are - generally speaking – not the enthusiasts behind an innovation. Opinion leaders can generally be categorized as being in the ‘late majority’ of adopters (Greenhalgh, 2004). Empirical lead user studies tend to find that lead users are early adopters and thus also based on our research a higher innovative behaviour value (Morrison, Roberts & Midgley, 2003). Based on this we could expect that there is a higher positive correlation between being a lead user and innovative behaviour than the relationship between opinion leadership and innovative behaviour. Therefore we state the following hypotheses:

Hypothesis 5a: There is a weak positive relationship between opinion leadership and innovative behaviour.

Hypothesis 5b: There is a strong positive relationship between being a lead user and innovative behaviour.

1.4 Innovative behaviour and the performance of a business

Han (1998) found a positive relationship between the innovative behaviour and the performance of the business. Also according to Neely (1998) there is a positive relationship between innovation and performance. Therefore we expect a positive relationship between innovative behaviour of the hairdresser and the performance of the business.

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11 2. Research design

2.1 Research method

To investigate the hypotheses we conducted a field research. The research method for a field research, can according to Cooper and Schindler (2003) be classified based on the way the data is collected. There are two alternatives: First, information can be collected through observation and data can be collected through communication with people. To answer our research questions we have chosen for the communication method, because this method is the most suitable for the purposes of our research.

The communication method means surveying people and registering their reactions for analyzing purposes. The benefit of the survey method is the versatility of this method. The survey method is most suitable to get insight in the opinions, attitude, motivation, intentions and expectations of people. The observation method only gives insight in the circumstances, behavior and events. Another benefit of the survey method is that it is more efficient and less expensive than the observation method (Cooper en Schindler, 2003).

There are different forms of a survey. One of them is the personal interview, in which a trained interviewer interviews the respondent personally. The disadvantage of this method is, that is it is very time consuming and the cooperation is low. In another survey respondents are being contacted by telephone. This kind of interview has lower costs than the personal interview and a larger geographically area can be covered. A disadvantage is that there is a low cooperation, and the respondents must be easy to reach by phone. The third and last survey method is the self-administered survey. In this method a questionnaire is used to collect the data from the respondents. This will be collected through posting, faxing or e-mailing the questionnaire. Another option is by delivering the questionnaire personally or by using the internet. The advantage of this method is that it is less time consuming, anonymously, easier for the respondents (they can fill in the questionnaire when they have the time for it), the respondent has more time to think about the given questions, et cetera. A disadvantage is that the length and complexity of the questionnaire is limited. There could also be a lower response because there is no interviewer present to explain the questions (Cooper & Schindler, 2003).

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We personally brought the questionnaires to the respondents. This way we could explain the hairdressers what the purpose of our research was and we could ask for their cooperation (To persuade them we made clear that we needed their response for our master thesis). This way we could personally convince them to cooperate which contributes to the number of respondents who completed the questionnaire. To collect the addresses of the hairdressers the online telephone guide was used. Based on these names and addresses a list was conducted. This list was used to visit the hairdressers personally. During the visit the research goal was explained and it was made clear that the questionnaires were anonymously and the research had no commercial purposes. This way, a high response rate could be accomplished and we received additional information from the respondents. This additional information were remarks from the hairdressers concerning the research (appendix 1 for a summary of these remarks). During the first visit an appointment was made for collecting the completed questionnaire. There was also a follow up, when the questionnaire was not completed at the given date we made another appointment to collect the questionnaire.

To get the network as complete as possible we compared the names given by the hairdressers to that of the names which we already had already listed from the telephone guide. The time pressure resulted in a limitation of our research; only hairdressers in the city Groningen were contacted, together with a couple of contacts outside Groningen. The hairdressers who where not already listed, but given by other hairdressers, where also visited personally.

2.2 Hairdressers outside Groningen

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2.3 Population and sample

Two factors are important for this research: getting the hairdressers network as complete as possible and getting a high response rate. This resulted in the choice to bring and collect the questionnaires personally. To accomplish these goals the choice was made to conduct the research in the city Groningen. This because the city of Groningen has establishments of different hairdresser businesses. Ranging from small hairdresser businesses (no personnel) to hairdresser businesses of large hairdresser business chains. Based on this aspect and because of the time limitation we have chosen to conduct the research in Groningen.

2.4 Questionnaire

For this field research a questionnaire is conducted based on, if possible, existing scales (For more information on the used scales see chapter 3).

Before the research was conducted in Groningen, the questionnaire was tested in another city. This pre-test was conducted in Meppel, where 5 hairdressers cooperated. The goal was check if the hairdressers understood the questions, the time needed to fill in the questionnaire and to get additional information from the hairdressers about the questions.

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Table 1: Test questionnaire / Final questionnaire

Test questionnaire Final questionnaire

Omcirkel het nummer van de uitspraak die het best bij u past Omcirkel het nummer van de uitspraak die het best bij u past 1 Ik gebruik nieuwe producten in mijn kapperszaak voordat

andere kappers in mijn omgeving op de hoogte zijn van deze producten of deze producten gebruiken in hun salon.

1 Ik ben meestal één van de eersten in mijn omgeving die nieuwe producten in de kapperszaak gebruikt.

2 Ik ben meestal één van de eersten in mijn omgeving die nieuwe producten in de kapperszaak gebruikt.

3 Ik ben meestal niet één van de eerste kappers in mijn omgeving die nieuwe producten gebruikt, maar ik ben wel eerder dan de meeste kappers.

2 Ik ben meestal niet één van de eerste kappers in mijn omgeving die nieuwe producten gebruikt, maar ik ben wel eerder dan de meeste kappers.

4 Ik ben meestal later dan de meeste kappers in mijn omgeving in het gebruiken van nieuwe producten.

3 Ik ben meestal later dan de meeste kappers in mijn omgeving in het gebruiken van nieuwe producten. 5 Ik behoor meestal tot de laatste kappers in mijn omgeving

die nieuwe producten in de kapperszaak gebruikt. 6 Ik gebruik geen nieuwe producten of alleen indien huidige

producten niet meer verkrijgbaar zijn.

4 Ik gebruik geen nieuwe producten of alleen indien huidige producten niet meer verkrijgbaar zijn.

Furthermore, an important aspect during the pre-test was to measure the time needed for the hairdresser to complete the questionnaire. We estimated that they needed 20 minutes to complete the questionnaire. During the pre-test it became clear that the time needed was shorter, namely 15 minutes. Based on this, the stated time needed in the introduction was changed from 20 to 15 minutes.

2.5 Data collection

The original list of hairdressers in Groningen was based on the data from the online telephone guide (www.detelefoongids.nl) and listed 99 hairdressers. Of the 99 hairdressers, 9 could not be used because the establishment was closed, did not have a salon or were hairdressers who worked at the home of the customer. To the 90 hairdressers who remained, the hairdressers who were not in the telephone guide list were added (15). These hairdressers in Groningen were named by other hairdressers or were discovered during the field research.

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2.6 Representation

To determine if the response is representative, we have to determine the sample distribution. According to Baarda & De Goede (2000) the representation of the sample depends on the heterogeneity of the sample. We want to determine the representation of our research to the population of hairdressers in Groningen.

There are a couple of factors on which we assume that the sample is heterogeneous and representative for the hairdressers in Groningen. Firstly, the age of the hairdressers. Secondly, the type of hairdresser businesses. Thirdly, the size of the hairdresser businesses and fourth the number of hairdressers that participated in this research.

A remark regarding the representation of this network research is that a network is dynamic. Furthermore, the number of hairdressers in the Netherlands grows extensively and a recommendation for further research would be to perform a longitudinal research.

2.6.1 Age of the hairdressers

One of the variables determining the representativeness is the age of the hairdresser. A study performed in 2004 on the hairdresser market, shows that the average age of the hairdresser is 41 in 2003 (ANKO, 2004). This is the average age of hairdressers in the Netherlands. We expect that this result also is the average age of hairdressers in Groningen. This assumption is made, based on the figures for the Netherlands and our experience in the hairdresser market in Groningen. The average age of the hairdressers who participated in our research is 30 to 49 year (table number 2). Thus the average age of our sample fits with the average age of the hairdressers population.

Tabel 2 Descriptive Statistics 'Age' (1=younger than 20, 2=20t/m29, 3=30t/m39, 4=40t/m49, 5=50t/m59, 6=60 and older)

N Minimum Maximum Mean Std. Deviation

Age 60 1 6 3,53 1,228

2.6.2 Type of hairdresser business

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businesses. Because we visited the hairdressers personally, we got an impression of the hairdressers in Groningen. Based on subjective findings we can assume that all the different types of businesses participated in our research. There is no difference between hairdressers that participated in our research and those who did not participate (regarding the type of hairdresser business).

2.6.3 Size of the hairdresser business

We also expect that the size of the hairdresser business has an influence on the findings of the research. The size of the hairdresser business in this research is measured based on the total number of labor hours per week. Because we only measured the labor hours of the hairdresser businesses that participated in our research we cannot make an objective comparison between the sample and the population. Another element on which we can assume the representativeness of the study is the size of the business, based on the number of seats. Based on our experience (visiting the hairdressers in Groningen) we can make a subjective assumption that there is no difference between the size of the hairdresser business and the participation to our research. There was no relation between small businesses or large businesses, regarding the cooperation with the research.

2.6.4 Number of participating hairdressers

As mentioned earlier in section 3.5 the response rate is 53%, this is a relatively high response. More than half of the hairdressers have participated in our research. Thus based on the preceding variables (age, type and size) and the number of hairdressers that participated we can assume that our research is representative for the hairdressers in Groningen.

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17 3. Operationalizations

3.1 Measuring Peers

(Question 8; peers in Groningen, and 12; peers outside Groningen)

To identify the number of peers that a hairdresser has, we asked all hairdressers how many other hairdressers they keep in touch with. This is a so-called “free-call” question asked in questionnaires or interviews (Wasserman & Faust, 1994). With this particular type of question the analyst can ask respondents to name the number of hairdressers they with whom one has (a particular frequency of) contact.

An additional question referred to the name and location of the hairdresser they communicated this to.

3.2 Measuring tie strength

(Inside Groningen question 9a-e & outside Groningen 13a-e)

To measure the tie strength (contact intensity) we asked the respondents for the number of contacts (other hairdressers) and for the total number of contacts per year (for each hairdresser). This way we could measure the contact intensity (by dividing the total number of contacts with the peers by the number of peers they have contact with).

Naam kapperszaak………. aantal keer contact per jaar…….

1. Met hoeveel kappers (buiten uw eigen kapperszaak/vestiging) binnen de stad Groningen heeft u contact (zowel werk als sociaal)? (in cijfers, aantallen)

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3.3 Measuring opinion leadership

(Question 15 - 21)

To measure the variable opinion leadership we used a seven-item scale, measured on a 1-to-5 Likert-type scale. This can be found the article of Flynn (1994). The article of Flynn discusses a revision of the scale from King and Summers (1970) which is a modification of Rogers and Cartands (1962). We adjusted the scale for our research by changing terms as friends and neighbours in the question in hairdressers (kappers).

3.4 Measuring lead user

(Question 22a-e)

The measure for being a lead user consists of five indicators, measured on a 1-to-5 Likert-type scale. The indicators refer to the two characteristics of a lead-user suggested by von Hippel (1986). In addition, one indicator suggested by Lüthje and Herstatt (2004) was included, which refers to the dissatisfaction of a user with current market offerings (question 1). Translated into the world of hairdressers these indicators were:

1. Over het algemeen praat u met andere kappers over nieuwe producten. 2. Wanneer u met collega’s over nieuwe producten praat geeft u

3. Hoeveel kappers heeft u over nieuwe producten verteld gedurende de afgelopen 6 maanden? 4. Vergeleken met andere kappers, hoe groot is de kans dat uw advies wordt gevraagd 5. In een discussie over nieuwe producten is de kans groot dat u

6. Tijdens een discussie over nieuwe producten, wat is de grootste kans 7. Over het algemeen bent u tijdens discussies met andere kappers

De volgende vragen hebben betrekking op uw mening over de producten in het algemeen. 1. Ik denk dat de producten beter kunnen en makkelijker in gebruik

2. Ik mix zelf producten

3. Ik denk dat ik beter producten kan mengen dan andere kappers

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3.5 Measuring the influence of mass media

(Question 23a-f)

The measuring of mass media consists of six questions, measured on a 1-to-5 Likert-type scale. The scale is based on the scale used by Ross (1974). For each question the respondents were asked if the mass media item has a low influence or a high influence on their decisions (1-to-5 Likert-type scale).

Bij de volgende vragen kunt u aangeven in hoeverre bladen, televisie en vakbeurzen effect hebben op uw keuze voor nieuwe producten en/of merken

a) Professionele kappersbladen

b) Televisie uitzendingen over kappersgerelateerde zaken c) Beurzen

d) Mijn discussies met andere kappers tijdens beurzen e) Artikelen uit kranten

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3.6 Measuring innovative behaviour

(Question 25)

To measure the effect of the network on innovative behaviour we must know the phase of adoption. To specify the phase of adoption the adoption process can be divided in five categories, the so called adoption categories (innovators, early adopters, early majority, late majority and laggards) (Rogers, 1981). This classification has been used as dependent variable in several studies (Coleman, Katz & Menzel (1966); Herzog et al., 1968; Rogers & Kincaid, 1981; Waarts et al., 2002; Beatty et al., 2001; Martinez, 1998). Some researchers make use of five categories and others distinguish only two or three categories. Waarts et al. (2002) used two categories, early adoption and later adoption. Beatty et al. (2001) first used all the categories, but distinguished later between three other classifications based on those that adopted first, those in the middle and those that adopted last. The first classification consists of innovators and early adopters as first adopters, early majority as middle adopters, and late majority and laggards as late adopters. In the second classification the first adopters consists of the innovators, the middle adopters are the early adopters and early majority, and the late adopters consists of the late majority and laggards.

So there are differences in which classification can be used. In this research we will distinguish between four categories. This is based on the outcome of pre-testing the questionnaire. The early adopters; hairdressers who are among the first users of a new product (innovators and early adopters). Middle adopters; hairdressers who are not the first one to use a new product, but adopt the product earlier than the rest. The late majority; hairdressers who are among the last ones to adopt a product. And the laggards, the hairdressers who only adopt when the old product is not available anymore.

Omcirkel het nummer van de uitspraak die het best bij u past. (slechts 1 antwoord mogelijk)

1. Ik ben meestal één van de eersten in mijn omgeving die nieuwe producten in de kapperszaak gebruikt.

2. Ik ben meestal niet één van de eerste kappers in mijn omgeving die nieuwe producten gebruikt, maar ik ben wel eerder dan de meeste kappers.

3. Ik ben meestal later dan de meeste kappers in mijn omgeving in het gebruiken van nieuwe producten.

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3.7 Measuring received supplier information

(Question 26a-c)

Verhees & Meulenberg (2004) developed a scale to measure supplier intelligence. They asked rose growers during interviews about supplier intelligence. They were asked which information they would like to receive from the suppliers and which they already received. One item per category was generated and was included in their questionnaire. We have measured these items on a 1-to-5 Likert-type scale ranging from totally disagrees to totally agree.

De volgende vragen gaan over uw contact met uw belangrijkste leverancier

1. Ik ontvang veel informatie van mijn leverancier over de verschillende soorten producten. 2. Ik ontvang veel informatie van mijn leverancier over specifieke kenmerken van producten.

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22

3.8 Measuring performance

(Question 28)

To measure the performance of the business, a 1-to-5 Likert-type scale question was used. The question: ‘how is your business performing, in comparison to other hairdressers?’ was used. For this question a 1-to-5 Likert-type scale is used ranging from “worse” to “better”. The decision to use this self reported performance question was made because this way we could measure how the business is performing.

Table 3 Descriptive statistics about variables in this study

Items Scale

N Minimum Maximum Mean Std. Deviation Number of peers in Groningen 1 “free-call” question 57 0 7 1.70 1.783 Number of peers outside Groningen 1 “free-call” question 45 0 56 3.73 10.907

Supplier information 3 1-to-5 Likert-type scale ₅₅ _2.00 _5.00 _4.17 _.848 Mass media 6 1-to-5 Likert-type scale ₅₉ _1.00 _4.20 _2.78 _.603 Contactintensity inside work 1 “free-call” question 59 0 360.00 13.58 49.024 Contactintensity inside social 1 “free-call” question 60 0 180.50 5.80 24.840 Contactintensity outside Groningen 1 “free-call” question 60 0 31.00 2.97 6.915

Dependent variable Items Scale N Minimum Maximum Mean Std. Deviation Lead user 5 1-to-5 Likert-type scale 57 1.00 5.00 2.91 .952 Opinionleadership 7 1-to-5 Likert-type scale 60 1.00 4.60 2.77 .968 Dependent variable Items Scale N Minimum Maximum Mean Std. Deviation Innovative behaviour 1 1-to-5 Likert-type scale ₅₈ _1.00 _4.00 _2.78 _1.109

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23 4. Analysis

The hypotheses were tested by conducting regression analyses. Before the results of the regression analyses will be presented, the reliability of the scales used in the research will first be tested (because of the multi item constructs). After testing the scales correlation analysis and a multiple regression analyses are performed to test the various hypotheses.

The scales are tested on their reliability in section 5.1. In section 5.2 the results of the correlation analysis is discussed. Section 5.3 discusses the multiple regression analysis regarding the hypotheses.

4.1 The reliability of the scales

To test the reliability of the scales a Cronbach’s Alpha and a factor analysis was performed. The mass media scale was excluded from the analysis because this scale is based on formative indicators (Diamantopoulos, 2001).

4.2 Reliability analysis

To verify the reliability of the used scales, a Cronbach’s alpha and a factor analysis was performed for the suppliers, opinion leader, and the lead user scale (table 4 and for more information see appendix 3).

Table 4 Reliability analysis

Scale Number of items Cronbach’s alpha Scale statistics variance Factor analysis (# factor) Initial eigenvalues % of variance Opinion leader 7 .808 34.393 2 Lead user 5 .792 22.643 1 56.011 Supplier information 3 .841 6.477 1 76.497

Because of the 2 #factor outcome the opinion leader scale was adjusted by excluding item 15 and 19 from the scale (see table 5)

Table 5 Adjusted opinion leader scale

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4.3 Correlation analysis

The hypotheses in this research are tested by conducting a regression analyses. But before the results of these regression analyses will be presented, first the bivariate correlations are presented in table 6.

P e e rs i n s id e G ro n in g e n P e e rs o u ts id e G ro n in g e n M a s s m e d ia S u p p li e r in fo rm a ti o n C o n ta c t in te n s it y (w o rk ) C o n ta c t in te n s it y (s o c ia l) C o n ta c t in te n s it y o u ts id e G ro n in g e n O p in io n le a d e rs h ip L e a d u s e r In n o v a ti v e b e h a v io u r P e rf o rm a n c e Peers Peers inside Groningen P 1 Sig. N ₆₀ Peers outside Groningen P _-.063 ₁ Sig. _.316 N ₆₀ ₆₀ Mass media Mass media P .275** -.230** 1 Sig. _.018 _.040 N ₅₉ ₅₉ ₅₉ Supplier info Supplier information P .113 -.089 .215* 1 Sig. _.205 _.260 _.058 N ₅₅ ₅₅ ₅₅ ₅₅ Ties Contact intensity (work) P _.297** _-.053 _.187* _.055 ₁ Sig. _.011 _.346 _.080 _.346 N ₅₉ ₅₉ ₅₈ ₅₄ ₅₉ Contact intensity (social) P _.215** _-.035 _.310*** _.145 _.235** ₁ Sig. _.050 _.396 _.008 _.145 _.037 N ₆₀ ₆₀ ₅₉ ₅₅ ₅₉ ₆₀ Contact intensity outside Groningen P _.277** _.272** _.036 _.121 _.275** _-.055 ₁ Sig. _.016 _.018 _.393 _.190 _.018 _.338 N ₆₀ ₆₀ ₅₉ ₅₅ ₅₉ ₆₀ ₆₀ Dependent Opinion leadership P .258** .295** .174* .128 .005 .229** .219** 1 Sig. _.023 _.011 _.093 _.175 _.484 _.039 _.047 N ₆₀ ₆₀ ₅₉ ₅₅ ₅₉ ₆₀ ₆₀ ₆₀ Lead user P _.210 _.226** _.057 _.039 _.127 _.086 _.177* _.341*** ₁ Sig. _.056 _.042 _.334 _.388 _.171 _.258 _.089 _.004 N ₅₉ ₅₉ ₅₉ ₅₅ ₅₈ ₅₉ ₅₉ ₅₉ ₅₉ Innovative behaviour P _.156 _.170 _-.032 _.199* _.019 _-.057 _.116 _.286** _.454*** ₁ Sig. _.121 _.102 _.406 _.075 _.444 _.334 _.193 _.015 _.000 N ₅₈ ₅₈ ₅₈ ₅₄ ₅₇ ₅₈ ₅₈ ₅₈ ₅₈ ₅₈ Performance P _-.043 _.309*** _-.010 _.029 _-.141 _-.006 _-.093 _.079 _.327*** _.345*** ₁ Sig. _.376 _.009 _.471 _.416 _.148 _.483 _.243 _.277 _.007 _.005 N ₅₈ ₅₈ ₅₇ ₅₅ ₅₇ ₅₈ ₅₈ ₅₈ ₅₇ ₅₆ ₅₈

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As the correlations coefficients indicate in table 6, there are a number of correlations which positively and significantly correlates with the dependent variables.

Opinion leadership correlates positively and significantly with the number of peers inside (.258) and outside Groningen (.295), mass media influence (.229), contact intensity (social) (.229), contact intensity outside Groningen (.219) and between opinion leadership and innovative behaviour (.286). Having contacts outside Groningen has the strongest correlation with opinion leadership. This implies that there is a strong relation between the number of peers outside Groningen and being an opinion leader. In other words, we can assume that opinion leaders have many contacts outside Groningen.

Likewise being a lead user is positively related to the number of peers outside Groningen (.226) and with opinion leadership (.341). Opinion leadership has a slightly stronger correlation with the number of peers outside Groningen (.295) than the lead users (.226). This implies that there is a stronger relation between the opinion leadership and the number of peers outside Groningen. In other words, the chance that a hairdresser with many contacts outside Groningen is an opinion leader is more likely than being a lead user.

There is a positive significant relation between mass media influence and opinion leadership (.229). There is no relation between mass media influence and being a lead user or the innovative behaviour. This implies that there is a difference between being an opinion leader and being a lead user, concerning the mass media influence.

Regarding the tie strength there is a significant positive relation between the contact intensity, inside Groningen social (.229) and opinion leadership. And between the contact intensity outside Groningen, (.219) and opinion leadership. There is no significant relation between the contact intensity (inside Groningen) and being a lead user or the innovative behaviour of the hairdresser. This implies that having high contact intensity (social) has a relation with being an opinion leader, but it has no relation on being a lead user.

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In section 4.5 a multiple regression is performed for the explained variance of the network characteristics on opinion leadership (table 9), the explained variance of the network characteristics on being a lead user (table 10), and the explained variance of opinion leadership and being a lead user on the innovative behaviour (table 11).

4.4 Multiple regression analysis

Tables 9, 10 and 11 shows the regression analysis (method enter). The suitability of the regression analysis was examined testing for multicollinarity by checking the variable inflation factor (VIF). These examinations did not reveal any violation for conducting a multiple regression. In addition a distribution and Kolmogorov-Smirnov test was performed. Table 7 and 8 illustrate that the variables are normally distributed. Since the Kolmogorov-Smirnov test resulted in a value higher than .05, we can conclude that the distribution is normal.

Table 7 Skewness & Kurtosis

N Minimum Maximum Mean Std. Deviation

Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error Opinion leadership 60 1.00 4.60 2.77 0.968 -.029 .309 -.804 .608 Lead user 59 1.00 5.00 2.95 0.956 -.328 .311 -.588 .613 Mass media 60 1.00 4.20 2.78 0.603 -1.089 .309 3.691 .608 Supplier information 55 2.00 5.00 4.17 0.848 -1.018 .322 .597 .634

Table 8 One-Sample Kolmogorov-Smirnov Test

Opinionleadership Lead user Mass media supplier information

N 60 59 59 55

Normal Parameters(a,b) Mean 2.773 2.949 2.781 4.170

Std. Deviation 0.968 0.956 0.603 0.848

Most Extreme Differences Absolute .091 .122 .104 .176

Positive .071 .061 .104 .164

Negative -.091 -.122 -.094 -.176

Kolmogorov-Smirnov Z .707 .938 .797 1.302

Asymp. Sig. (2-tailed) .700 .343 .548 .067

a Test distribution is Normal. b Calculated from data.

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27 variables. The same multiple regressions are performed on being a lead user (table 10) and on

the innovative behaviour (table 11).

Table 9 regression analysis for opinion leadership

Opinion leader

Model 1 2 3

Beta T P Beta t P Beta T p

1 Peers inside Groningen .309 2.392 .011** .281 1.993 .026**

2 Peers outside Groningen .368 2.916 .003*** .334 2.456 .009***

3 Mass media influence .113 .839 .203 .087 .606 .274

4 Supplier information .102 .808 .212 .095 .734 .234

5 Contact intensity (work) -.132 -.964 .170 -.147 -1.067 .146

6 Contact intensity (social) .273 2.079 .021* .184 1.337 .094*

7 Contact intensity outside .264 1.989 .026* .088 .612 .272

R2 .248 .118 .284

Adjusted R2 _.187 _.069 _.174

F 4.113 2.443 2.600

P .003*** .037** .012**

* Correlation is significant at the .10 level (1-tailed). ** Correlation is significant at the .05 level (1-tailed). *** Correlation is significant at the .01 level (1-tailed).

In table 9 the results with respect to opinion leadership are presented. Model 1 (the network influences) has a significant impact on opinion leadership (Adj R2 .187). Regarding the Beta values, there is a significant relation between having peers inside Groningen and opinion leadership (Beta .309, p .011). There is even a stronger significant relation between the number of peers outside Groningen and opinion leadership (Beta .368, p .003). These results are in line with the results of the correlation analysis regarding the number of peers inside and outside Groningen, and opinion leadership. The variables mass media and supplier information have no significant relation with opinion leadership. Furthermore model 2 (the intensity) has a positive significant impact when the impact of the tie strength on opinion leadership is measured. The explained variance of model 2 (the intensity) is 6.9%. Model 3 (All network influences) presents a positive impact of the variables on opinion leadership, with an explained variance of 17.4%. Based on this outcome we can conclude that the proposed network characteristics have a positive impact on opinion leadership. With peers inside and outside Groningen and social contact intensity (Beta .184, p .094) having a significant positive impact on opinion leadership.

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Table 10 regression analysis for being a lead user

Lead user

Model 1 2 3

Beta t p Beta T P Beta T p

Peers inside Groningen .245 1.751 .043** ,200 1,280 ,104 Peers outside Groningen .255 1.868 .034** ,245 1,637 ,054* Mass media influence .035 .239 .406 ,015 ,097 ,462 Supplier information .027 .196 .423 ,029 ,205 ,419 Contact intensity (work) ,065 ,453 ,326 ,079 ,516 ,304 Contact intensity (social) ,078 ,562 ,289 ,052 ,344 ,366 Contact intensity outside ,159 1,142 ,130 ,025 ,154 ,439

R2 .121 .042 .126

Adjusted R2 .050 -.011 -.007

F 1.715 .794 .946

P .080 .251 .240

* Correlation is significant at the ,10 level (1-tailed). ** Correlation is significant at the ,05 level (1-tailed). *** Correlation is significant at the ,01 level (1-tailed).

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To test the hypotheses regarding the innovative behaviour another regression analysis was conducted. This analysis has the same models as the regression analysis of opinion leadership and being a lead user. Only the variables opinion leadership (model 4) and being a lead user are added (model 5).

Table 11 regression analysis for innovative behaviour

Innovative behaviour

Model 1 2 3

Beta T P Beta t P Beta T P

Peers inside Groningen .159 1.123 .134 .207 1.324 .096* Peers outside Groningen .239 1.643 .054* .266 1.619 .056* Mass media influence -.056 -.375 .355 -.029 -.180 .429 Supplier information .258 1.826 .037** .255 1.692 .049** Contact intensity (work) -.005 -.034 0,487 .005 .035 .486 Contact intensity (social) -.045 -.320 0,376 -.124 -.799 .215 Contact intensity outside .124 .871 0.194 -.036 -.220 .414 Opinion leader Lead user R2 .122 .018 .138 Adjusted R2 _.050 _-.038 _.003 F 1.696 .323 1.025 P .083* .404 .213

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Model 4 5 6

Beta T p Beta t P Beta T P

Peers inside Groningen Peers outside Groningen Mass media

Supplier information Contact intensity (work) Contact intensity (social) Contact intensity outside

Opinion leader .286 2.234 .029** .159 1.273 .208 Lead user .454 3.814 .000*** .404 3.239 .002*** R2 _.082 _.206 _.229 Adjusted R2 .065 .192 .201 F 4.991 14.546 8.164 P .029** .000*** .001***

As illustrated in table 11 model 1 (network influences variables) has a significant impact on the innovative behaviour. Models 2 and 3 had no statistically significant impact on the innovative behaviour. Model 4 (opinion leader variable) has a positive significant impact on the innovative behaviour 6.5%. The explained variance of model 5 (lead user variable) is 19.2%. Model 6 shows the influence of the variables opinion leadership and lead userness to innovative behaviour. The explained variance is 20.1%. The final regression analysis analyses the impact of innovative behaviour on the performance of the business (table12).

Table 12 regression analysis for performance

Performance B T P Innovative behaviour .345 2.700 .009 R2 _.119 Adjusted R2 .103 F 7.288 P .009***

* Correlation is significant at the ,10 level (1-tailed). ** Correlation is significant at the ,05 level (1-tailed). *** Correlation is significant at the ,01 level (1-tailed).

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31 5. Discussion of the results

The aim of this paper was to get insight in the network characteristics influencing the MKB businesses in the Netherlands. The network of a person is an important aspect in the behaviour of entrepreneurs. Network influence is not only interesting for researchers, but also an increasing number of companies get interested in the role of the network in the decisions of the consumer. To achieve this, first a literature study was performed based on which a research was conducted. The results of the research were analyzed.

The network characteristics were investigated in the network of the hairdressers in Groningen. The reason we investigated the characteristics of the network is because networking is an important aspect of the innovativeness of the entrepreneurs in the MKB.

This research confirms the statement that the network is an important influence on the innovativeness of entrepreneurial firms. But it also shows that there are some contradictions with the existing literature. By merging the literature part with the research outcomes, a conclusion has emerged. The main conclusions, which appear from this research, will be discussed in paragraph 6.1.

Paragraph 6.2 will answer the questions which have arisen in the beginning in the paper. In paragraph 6.3 the managerial implications will be discussed.

5.1 Main conclusions of the paper

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The following figure shows an overview of the confirmed hypothesises in the paper:

5.2 Network characteristics

This section answers the questions from the beginning of the paper.

1. How do the network characteristics characterize opinion leaders?

There is no relation between the opinion leader hairdressers and mass media nor with the received supplier information. This is an interesting outcome because it implies that an opinion leader in the hairdresser network is largely influenced by their contacts with hairdressers inside and especially outside Groningen.

The opinion leaders among the hairdressers can be characterized by the many peers they have. Especially the number of peers outside Groningen has an important influence. Not only the number, but also the contact intensity has an influence. The opinion leaders in the hairdresser network has a high contact intensity, which implies that there is a relation between the frequency of contact hairdressers have with other hairdressers and being an opinion leader. In other words, opinion leader hairdressers have frequent contact with many other hairdressers. Interesting, is that the social contact intensity and the contact intensity with hairdressers outside Groningen have a big influence on being an opinion leader. This implies that social contacts are also important for the opinion leader hairdressers. On the other side, frequent work related contact does not contribute to being an opinion leader. The opinion leader hairdressers in a network have many contacts with other hairdressers but no frequent contact with them. This is in line

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with the theory, that not having frequent contact with other people is an important input for the innovative behaviour of the opinion leader hairdressers.

2. How do the network characteristics characterize being a lead user?

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3&4 what is the relationship between opinion leadership & lead users and innovative behaviour?

Why are some hairdressers more innovative than others? There are a couple of network influences that has an influence on innovative behaviour. An innovative hairdresser has contact with many hairdressers outside inside and outside Groningen. They also rate the supplier information they receive higher. The hairdressers who are lead users are more innovative than the opinion leaders. This is in line with the recent literature that opinion leaders are generally speaking not the enthusiasts behind an innovation.

Since media are often the trigger of the adoption process, it is interesting to see that this research shows that mass media has no effect on being an opinion leader or a lead user hairdressers in the network.

In contrast with the influence on the opinion leaders and lead user hairdressers, the information received from the supplier has an influence on the innovative behaviour. In other words, the supplier doesn’t influence being an opinion leader or a lead user hairdresser, but the supplier does contribute to the innovative behaviour. So, opinion leaders and lead user hairdressers are not characterized by the supplier information but it does contribute to the innovative behaviour (the path of relations, network characteristics _{opinion leader/ lead user innovative behaviour). This could mean that an opinion} leader and lead user hairdresser do not consider themselves as people being influenced by the supplier information but that it does have an influence on them because of the relation with the innovative behaviour.

5. What is the relationship between innovative behaviour and the performance of the business?

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5.3 Managerial implications

The managerial implications of these findings are manifold. One of the implications is that the results of this study suggest that, in contrast with the more traditional marketing strategies, more emphasis should lie on the network (peers) of the hairdressers. Based on the outcome of this research we can conclude that the contacts outside Groningen are important in the network of the hairdressers in Groningen. This implies that if you influence the hairdressers outside a city, these hairdressers influence the hairdressers inside the city. And that according to the research this has an influence on the behavior of the

hairdressers inside the city.

Another important implication is that the hairdressers are segmented into hairdressers who are lead user, which has the most impact on the innovative behaviour. Involving the lead user hairdressers in idea generation or in smoothing ideas has two advantages. A company can use their innovative behaviour for product creation and the contacts of the lead user to influence the other hairdressers to use the product. Using the lead users raises another question, what kind of hairdressers are appropriate? There are different methods for selecting the particular lead users in network. One of them is using self reports (questionnaires as used in this study). Another way is to further investigate the personal and business characteristics of the hairdressers. Our study implies that there is a relation between the innovative behaviour and the performance, so a selection could be made on the performance of the hairdressers. Other characterizations could be the age of the hairdresser, for how long he/ she works as a hairdresser et cetera.

Limitations and suggestions for further research

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