3D printing: Analyzing the main drivers of its adoption and diffusion

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3D printing:

Analyzing the main drivers of its

adoption and diffusion

by

Ivan Mauri

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3-D printing:

Analyzing the main drivers of its adoption and diffusion

Author: Ivan Mauri1

First Supervisor: Dr. Prof. Peter Verhoef2

Second Supervisor: Frank Beke3

Date: 22nd of June

1_{Ivan Mauri is a Marketing MSc student at the Faculty of Economics and Business, University of Groningen,}

The Netherlands. The research forms part of the Marketing Management, study program. Address for correspondence: Ivan Mauri, via Ornato 15, 20162, Milan, Italy. Tel.: +393287527095; E-mail: ivan.mauri.sanchez@gmail.com; Student number: s2765349

2_{Dr. Peter Verhoef is a Professor in the fields of Business and Marketing, Faculty of Economics and Business,}

University of Groningen, The Netherlands

3_{Frank Beke is a Phd Candidate in the fields of Business and Marketing, Faculty of Economics and Business,}

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MANAGEMENT SUMMARY

The aim of this research is to analyze the main drivers that influence consumers’ intention to adopt a 3D printer. Indeed, it was found that 3D printing is already successful in the B2B environment, while it is having problems to penetrate the mass market. As a result, this study investigated the previous literature about adoption and diffusion of innovations, in order to individuate hypotheses of the factors influencing 3D printer adoption intention.

Based on the existing literature, the hypotheses are formulated and the conceptual model is developed.The framework expects that consumers' intentions to adopt 3D printers are determined by their perception of the five product attributes theorized by Rogers: relative advantage, compatibility, complexity, trialability and observability. In addition, it is

investigated the moderation effect of two consumer characteristics: product knowledge and innovativeness. An explanatory research examined these relationships on a sample of 150 respondents, gathered through an online survey. Consequently, the data were subject to different analysesand finally regression analyses presentinteresting results.

The outcomes show that relative advantage and compatibility are the main positive drivers of 3D printer adoption intention. In addition, it is found a negative effect of trialability on adoption intentions, while complexity and observability result to be not significant. Furthermore, the moderation effects of prior product knowledge and innovativeness were also tested, resulting to not be significant in the overall model. Finally, it is demonstrated that sociodemographics characteristics, such as age, gender and level of education, do not affect 3D printer adoption intentions.

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PREFACE

I am proud to say that this master thesis represents the last hurdle of my student career. I came a long way to be here and it was not easy, especially in the first semester of this master, when I had to adapt myself to satisfy the tasks and requirements of an high quality institute, such as University of Groningen. I am proud to complete my studies in this university and I am sure that it will be the best springboard for my future career.

I would like to thank all the persons that helped me in my academic career. Thanks to my family, for their support and for allowing me to pursue this fantastic master degree. Thanks to all my friends and collagues, for sharing with me the joys and pains of this adventure. Thanks to my supervisor Peter Verhoef, for his ispirational advices and positive support.

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A new revolution is coming that will change the way we make things. During the last few years indeed, the attention of the media about this topic deeply intensified. (Ponfoort et al. 2014). The main reason is due to the fact that the capabilities of 3D printers are improving day after day. Initially, they were supposed to produce just basic plastic prototypes, while now they can create large objects in short time and for low costs, with precision and resolution that are becoming almost perfect (Cohen et al., 2014).

3D printing, or additive manufacturing, is the processthat creates products by printing layer on layer, by using powder of different kind of materials such as plastic polymers or metals, instead of the usual inks. The use of 3D CAD software provides digital files of the exact 3-Dimensions of the object, drawn by designers or created through 3D scanner, in order to communicate to the printer exactly how each layer has to be build to complete the object (PWC 2014; Berman 2012). Through this process, it is possible to create a wide range of products, including household articles (e.g lamps), personal items as jewellery, medical appliances (e.g. prothesis) and industrial products as airplane buckles (Ponfoort et al. 2014).

According to Christopher Barnatt, author of the book “3D Printing: The Next Industrial Revolution” in 2013, the opportunities of this invention in the future are enormous, since transforming traditional productions and retail, to improving human health and saving the planet, passing through open design and direct digital manufacturing, storage and transportation (Barnatt 2013). Forbes, in an article of 2014 by Bill Conerly, added other opportunities for the economics of 3D printing, as rapid Prototyping, jigs and fixtures, mass customization, non-standard parts, production surprises and finally digital inventory (Forbes, 2014).

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(Crompton 2014). “The economic implications of 3D printing are significant: McKinsey Global Institute research suggests that it should be able to have an impact of up to $550 billion a year by 2025” (Cohen et al. 2014).

Nevertheless, “3D printing represents just a fraction of the $70 billion traditional machine-tool market worldwide” and experts evaluate market penetration of 3D printers still at levels lower than 10% of its capabilities, given the amount of opportunities and application that this invention should have (Cohen et al. 2014). Thus, the new challenge for 3D printers is to penetrate the entire market, indeed if early systems were thought and sold in a selective B2B perspective, more recently the focus has moved to the mass market, with a wide offer of cheaper machines, suitable for small-business, schools and even individual customers (De Jong & De Brujin 2013). The technology is in a tipping point, it appears ready to penetrate the entire market but when?

During these months, few experts stated that having a 3D printer at home will never become common, thus its market penetration will remain very limited (Barnatt, 2013). Generally, the opinion of the majority of the experts is that the mass adoption of 3D printers will require years (Cohen et al. 2014). "Consumer 3D printing is around five to ten years away from mainstream adoption" said Pete Basiliere, research vice president at Gartner (Gartner, 2014). According to Desjardijn (2014), “By 2040, we’d have nearly full adoption”. “Revolutions are a hard thing to predict” (Desjardins 2014), especially when referring to adoption of a really new product, as 3D printers. However, as marketing management student, it would be important to identify the innovation characteristics that affect the relative speed of adoption and diffusion of a new product. In other words, what are the attributes that have an effect on adoption intentions and, consequently, on the rate at which 3D printers would diffuse and become widely used?

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compatibility, complexity, trialability, and observability.” and the individuals’ perceptions of these attributes affect the intentions and the rate on innovations’ adoption (Rogers, 2003).

In the previous literature, no studies specifically examined the drivers of the mainstream adoption of 3D printing, therefore the antecedent general researches about diffusion of innovation will be implemented. In particular, Rogers’ (1983) study will be used as starting point to find the main drivers of the 3D printers’ adoption intentions. Based on that the following research question are formulated:

• What are the main drivers of 3D printers’ adoption intention?

In order to examine more in depth the main research question, the consecutive queries are derived:

• What are the customer characteristics that moderate 3D printers’ adoption intention?? • Do potential adopters consider 3D printers a relative advantage for them?

• Do potential adopters consider 3D printers compatible with their values, previous ideas and\or needs?

• Do potential adopters consider 3D printers difficult to use and understand?

• Do potential adopters consider the possibility to trying out 3D printer important in order to buy it?

• Do potential adopters consider the results 3D printers visible and communicable to others?

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attributes on customers’ adoption intention. Finally, the findings will enable 3D companies to better understand how to develop their products and marketing strategies, in order to meet the requirements of the potential adopters.

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2 THEORETICAL FRAMEWORK 2.1 Literature Review

As was mentioned above, no previous studies investigated the drivers of 3D printers adoption, however diffusion of innovation is one of the main topics in the marketing literature. A huge number of theories and models have been developed since the 1960s (Meade and Islam, 2006) and across a wide number of disciplines (Gatignon and Robertson, 1985). Gatignon and Robertson (1985) have wisely divided the diffusion of innovation literature in different type of research: the adoption process, personal influence and opinion leadership, the social system, the diffusion process, personal characteristics of the innovators and perceived innovation characteristics. In general, the previous studies could be divided in two different perspectives of analysis: the consumer characteristics and the product characteristics (Frambach, 1993; Nabih et al., 1997).

Since the main focus of this research is to investigate the diffusion process and, in particular, the perceived innovation characteristics that affect the adoption of 3D printing, the literature review will be based on the previous studies regarding product characteristics and these 2 topics. Neverthless, customer characteristics will also be investigated, in order to find moderators of the perception of the product attributes. Consequently, the principle reference will be the book Diffusion of Innovations, written by Rogers in 2003, however it will be provided a comparative explanation with the TAM model of Davis (1989) and other references will be implemented to provide a better explanation of the arguments (e.g. Robertson 1967; Shaw 1963).

2.1.1 Rogers’ diffusion of innovations

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The innovation-decision process is the evaluation of a new idea by an individual or a social group and the decision to accept it or not, through a series of choice and actions. This theory considers the steps since the first knowledge of a new technology by an individual to the formation of an attitude about the innovation, in order to make the decision to reject or adopt it, and consequently to implement the new idea and confirm his previous decision (Rogers, 2003). In this study, the first two steps of the process will be examined: knowledge and persuasion, which in turn is supposed to affect the decision on the adoption or rejection of 3D printing.

Table 1.1 Innovation-Decision Process

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knowledge, comprehension of how to use the innovation, and principles-knowledge, understanding the functioning principle of the innovation.

On the other hand, persuasion occurs when the individual or social group creates a positive or negative attitude, referred to the innovation. At this stage, the potential adopters are involved psychologically with the new technology. Crucial, on this step, are the perceptions that individuals hold regarding the product. In particular, Rogers (2003) suggested the importance of 5 perceived innovation’s attributes which are supposed to mainly explain the adoption intentions of the possible customers: relative advantage, compatibility, complexity, trialability and observability (Rogers 2003).

Finally, these two steps and their characteristics have a direct effect on the third step, the decision stage, where the individual decides to adopt or reject the innovation. Consequently, there is a main effect also on the rate of adoption that “is the relative speed with which an innovation is adopted by members of a social system” (Rogers, 2003). Specifically, the rate of adoption is largely explained by the perceived attributes, indeed from 49% to 87% of its variance is due to them. According to the model of Rogers (2003), the investigated drivers will be the 5 perceived product attributes.

2.1.2 TAM model

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Table 1.2 TAM Conceptual Model

Thus, TAM describes attitude and intention towards an innovation as result of 2 predictors: perceived ease of use and perceived usefulness. Perceived ease of use represents the degree to which the adopters do not have to employ huge efforts to use the product, while perceived usefulness refers to the degree of user’s subjective probability that using the innovation will increase his or her performance (Davis et al. 1989). These two drivers are Davis’ translation of “Relative advantage” and “Complexity”, also present in the model provided by Rogers, due to the fact that the construct of these attributes is highly similar (Venkatesh et al.; 2003). Although the TAM model is supposed to explain a high variance of the adoption intentions, this research will take into account only the attributes of Rogers because his model provides a more complete and comprehensive set of beliefs and it is more related to our research question.

2.2 Hypotheses

2.2.1 Product characteristics

2.2.1.1 Relative advantage

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their needs (Im and Workman, 2004). Therefore, product usefulness (or relative advantage) has a significant positive effect on product success (Szymanski et al., 2007).

H1: There is a positive relationship between relative advantage and adoption’s intention of

3D printers.

2.2.1.2 Compatibility

Compatibility represents the degree of perceived consistency of the new technology with the already existing values, experience and needs of potential costumers (Rogers 2003). When an innovation is not compatible with the potential groups of adopters, it will need much more time to diffuse compared to a compatible innovation. (Shaw 1963). Furthermore, Rogers (2003) mentioned 3 types of compatibility: compatibility with sociocultural values and believes, compatibility with previous introduced ideas and/or compatibility with clients’ needs for the innovation. It is expected that 3D printing needs each of these 3 types of compatibility, because it needs to be accepted by the existing values and beliefs of people, then it needs to be compatible to previous inventions as computer and software to make it easy and handy to use. Moreover, people have to recognize the need to buy 3D printer, which is not highly predictable for such a new innovation (Rogers 2003). Finally, the more an innovation is recognized as compatible with the system, the more it would be adopted (Kwon and Zmud, 1987), thus:

H2: There is a positive relationship between compatibility and adoption’s intention of 3D

printers.

2.2.1.3 Complexity

Complexity represents the degree of perceived difficulty to understand and to use an innovation (Rogers 2003). Indeed, there are innovations that can easily be understood by all the members of the social system and innovations that required time or are never understood by the potential adopters (Shaw 1963). 3D printing is supposed to have a high relation with this factor because at the moment it requires an high capacity to use computer and software as for instance, CAD. Therefore, the following hypothesis could be generated:

H3: There is a negative relationship between complexity and adoption’s intention of 3D

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2.2.1.4 Trialability

Trialability represents the degree to which a new technology may be experimented for a limited period of time. It is clear that the possibility of an innovation to be tried out personally, could give meaning to it, finding out how it works under the condition of the possible adopters (Rogers 2003). Trialability is perceived important especially for the earlier adopters, compared to later adopters (Ryan 1948). In the case of 3D printers, which are considered as a “really new product”, trialability could be of high importance, in order to convince the adopters of the capability, ease of use and usefulness of the product.

H4: There is a positive relationship between trialability and adoption’s intention of 3D

printers.

2.2.1.5 Observability

Observability represents the degree of visibility of the actual results and benefits of an innovation for the other members of a social group. Indeed, for some new technologies benefits could be easily observed and communicated to others, while for other innovations could be more complex (Rogers 2003). In the case of 3D printers, it can be hypothesized that the results, which are the objects created by this technology, could be easily observed. On the other hand, it could be more difficult to communicate them, due to the high novelty of the concept. Finally, it is hypothezed that:

H5: There is a positive relationship between observability and adoption’s intention of 3D

printers.

2.2.2 Consumer characteristics

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when companies have knowledge of customer characteristics and use it to design new products, they will be more likely to have success in the market (Di Benedetto, 2012) In the next part, the specific consumer characteristics will be described further.

2.2.2.1 Moderators: Knowledge and Innovativeness

Prior Product Knowledge

According to Rogers, knowledge is the first and necessary step in the innovation decision process, because the individual needs to be exposed to the existence of the innovation and understand how it functions (Rogers, 2003). Particularly, prior product knowledge has been defined in two ways: the consumer perception of knowledge about a product or the knowledge people have stored in memory about a product (Rao and Monroe 1988). Furthermore, radical innovations require a large degree of new knowledge (Dewar and Dutton 1986). Thus, specifically in the case of 3D printers, the awareness of the existence and of the functions of the product cannot be assumed. However, the previous exposure to the benefits and use of this system should significantly change the perception of the possible adopter (Suyan 1985). Consumers with substantial prior knowledge in a product domain can easily resolve the cognitive distance between the radically new product and existing products (Reinders 2010; Peracchio and Tybout, 1996). Finally, it is confirmed that consumers interpret product information based on the knowledge present at the time of comprehension (Lee & Olshavsky, 1994), for instance Graeff (1997) argued that people with high prior knowledge will comprehend product information more easily. Similarly, Wood and Moreau (2006) stated that prior knowledge leads to a higher capability of dealing with the “complexity” of a new product. Hence, it can be suggested that the level of product knowledge has significant moderating effects on the relative importance of several product attributes on the customers’ intention, resulting in the following hypotheses:

H6a: Prior Product Knowledge has a positive effect on the importance of “relative

advantage” on customers’ intention to adopt 3D printer.

H6b: Prior Product Knowledge has a negative effect on the importance of “ compatibility”

on customers’ intention to adopt 3D printer.

H6c: Prior Product Knowledge has a positive effect on the importance of “complexity” on

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Innovativeness

Innovativeness is one of the most relevant concepts that explaines consumer behavior and, for this reason, has been deeply investigated by consumer researchers (Hirschman, 1980). Innovativeness was defined by Rogers (2003) as “the degree to which an individual or other unit of adoption is relatively earlier in adopting new ideas than the other members of a system”. However, lately this conceptualization was contested by Midgley and Dowling (1978), who defined innovativeness as "the degree to which an individual is receptive to new ideas and makes innovation decisions independently of the communicated experience of others”. Innovative consumers are more prone to show purchase intentions towards highly original products, such as a 3D printer. According to previous studies (e.g. Hauser, Tellis, and Griffin, 2006), consumer innovativeness is considered a primary driver of the adoption and diffusion of new products and it has been demonstrated to explain a relatively high percentage of variance in adoption intention (Arts et al. 2011), however, the effect that innovativeness has on specific product attributes remains unclear (Li et al. 2014). Vandecasteele and Geuens (2010) provided a multidimensional consumer innovativeness scale, which identify four motivations behind innovative consumer: hedonic motivated innovative consumers are sensible to the uniqueness and newness of the product; a functionally motivated innovative customer has the goal to improve his performance or productivity; cognitive innovativeness consumers want to explore and understand in order to expand cognitive limits; and finally, socially innovative customers want to feel unique and superior by acquiring new products (Li et al. 2014). Hence, following hypotheses were developed based on the links between innovativeness, product attributes and adoption intention found in the existing literature:

H7a: Innovativeness has a positive effect on the importance of “relative advantage” on

customers’ intention to adopt 3D printer.

H7b: Innovativeness has a negative effect on the importance of “ compatibility” on

customers’ intention to adopt 3D printer.

H7c: Innovativeness has a positive effect on the importance of “complexity” on customers’

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2.2.2.2 Control variables: Age, Gender and Level of Education

Socio-demographics characteristics such as age, gender and level of education have been widely used throughout innovation literature, especially to profile consumer innovators (Im et al. 2003). According to Rogers (2003), gender and age do not have a clear effect on the attitude towards innovation, however Rogers stated that “earlier adopters have more years of education than later adopters have”. Conversely, other researches (e.g. Venkatesh et al. 2000; San et al., 2014) stated that males and younger consumers are more incline to adopt technologies to reach their goals. Therefore, these characteristics could be used in order to create segments and to find out potential differences on the importance of the innovation characteristics for each target segment. Subsequently, this could help managers to target segments with the highest chance of adoption and consequently adapt their products and/or their marketing communication strategies, depending on the target segment they want to reach.

2.3 Conceptual Model

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PRODUCT ATTRIBUTES

CONTROL VARIABLES

Table 2.1 Conceptual Model Relative advantage

Observability Trialability Complexity Compatibility

Prior Product Knowledge Innovativeness

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3. METHODOLOGY

3.1 Research Design, Sample And Data Collection

This study can be considered an exploratory research since we want to explore through the current situation regarding 3D printing, in order to provide insights and understanding about the possible drivers of its adoption, thus the findings of this study are supposed to be used as input into managerial decision making (Malhotra, 2007).

In the first place, an online survey was conducted during the month of May, 2015. The questionnaire has been proposed on an online platform, which permitted a complete autonomy, anonymity and user-friendliness on answering the questions and the involvement of people with a sufficient knowledge of technology (Malhotra, 2007). Due to limited time and budget of the research, the respondents have been provided through the use of social network, such as Facebook and Linkedln. Considering the typology and the number of hypotheses, 150 people were required to complete the questionnaire. At the time of the study, the adoption of 3D printers is still in its preliminary phase in the countries investigated, which are especially Netherlands and Italy, thus the emphasis is put on the potential adopters of 3D printing and on their perception of the product attributes.

3.2 Operationalization

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Langerak, 2001). An overview of the different items and scales used to measure the variables of the model is presented in table 3.1.

Concept Item Scale

Dependent Variable:

3D printer adoption intention

(Verhoef and Langerak, 2001)

Please indicate on the response scale from 0 to 10 to which extent you intent

to buy a 3D printer in the future

11-point scale anchored by 1 (absolutely not) and 11

(absolutely yes)

Independent Variable:

Relative advantage

Gounaris & Koritos (2008)

1) 3D printer would provide me with benefits compared to previous products

2) the concept of 3D printing is innovative

3) 3D printer would be useful for me

7 point Likert scale anchored by: Strongly agree- Strongly Disagree

Compatibility

Verhoef and Langerak, (2001)

1) 3D printer would suit my person well

2) 3D printer requires few

adaptations in my personal life 3) 3D printer yields little problems

for me

7 point Likert scale anchored by: Strongly agree- Strongly Disagree Independent Variable: Complexity Wu and Wu (2005)

1) 3D printer is difficult to use 2) Learning to operate the 3D

printer would be easy for me

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Trialability

Moore & Benbasat (1991)

1) Before deciding on whether or not to adopt the 3D printer, I would need to properly try it out

2) I would be permitted to use the 3D printer on a trial basis long enough to see what it can do

7 point Likert scale anchored by: Strongly agree- Strongly Disagree Independent Variable: Observability Wu and Wu (2005)

1) I have had a lot of opportunity to see the 3D printer being used 2) owning a 3D printer will make

me more considered among your neighbours and/or community

Moderator:

Prior Product Knowledge

1) I know what is a 3D printer 2) I understand the various

features of a 3D printer 3) I already came in contact with

3D printing

7 point Likert scale anchored by: Strongly agree- Strongly Disagree Moderator: Innovativeness Bruner (2009)

1) I like to buy new and different things

2) I am usually among the first to try new products

3) I like to go window shopping and find out about the latest

products

7 point Likert scale anchored by: Strongly agree- Strongly Disagree Control variable: Gender Gender

What is your gender?

Control variable:

Age

What is your age?

Control variable:

Level of education Level of education

What is your level of education?

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3.3 Plan Of Analysis

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4. RESULTS

The fourth chapter presents the results of the analyses. Firstly, it will be provided a general description of the survey, its respondents and the data gathered. Secondly, the results of the reliability analysis examining the internal consistency of the scales will be represented. Moreover, the main output of the factor analysis will be mentioned. Subsequently, the data gathered by the 4 different multiple regression analysis will be discussed. Finally, the hypotheses validation will show which of the hypotheses formulated in the second chapter are supported and which are rejected.

4.1 Descriptive Statistics

In total, 150 respondents completed correctly the questionnaire, distributed through Facebook and Linkedln. The inevitable choice, for economic and time-limit reasons, to provide the survey on social networks entailed a limited variety and involvement of the sample, which is supposed to have effects on the results. The questionnaire was available for five days and was opened by 199 people. However, only 150 respondents completed entirely the questionnaire and, therefore, can be used for the analysis.

The socio-demographics characteristics of the 150 respondents are provided in table 4.1.

Table 4.1 Descriptive statistics sample

The sample consists of 109 males (72,6%) and 41 females (27,3%). The majority of the respondents is lower than 30 years old (74,6%) and is endowed with a bachelor (40%) or master degree (36,6%). Furthermore, the mean and the standard deviation of the scores on the

N %

Gender Male

Female 109 41 72.7% 27.3%

Age Lower than 30 112 74.6%

30-50 23 15.4%

Over 50 15 10.0%

Level of Education Lower than high school 3 2%

High school 32 21.3%

Bachelor degree 60 40.0%

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various constructs of the questionnaire are calculated, taking in consideration the constructs derived from the previous researches. The results can be seen in table 4.2.

Table 4.2 Variables’ Scale, Mean and Standard deviation

Looking from another perspective, thus calculating the percentuages of the

respondents who score above or below the average in the various contructs, the subsequent insights about adopter and perceived innovation characteristics can be derived:

• 73.3% of the respondents have a sufficient prior knowledge of the product. • 76.7% of the sample is supposed to be innovative.

• 92% of the group considers 3D printers having a relative advantage compared to previous product.

• 76.7% of the respondents believe that 3D printers are compatible with them. • 54,7% of the sample thinks that 3D printers are complex to use.

• 86% of the respondents would prefer to try the product for a limited period.

• 34% of the group considers the benefits of 3D printing easy to observe and communicate. • 49,3% of the respondents has positive intentions to adopt a 3D printer in the future.

Finally, Table 4.3 shows the distribution of adoption intentions depending on the score given by the 150 respondents. The mode results to be 6 (Neutral), which is the answer

Factor Scale Mean Standard

deviation Relative advantage 1-7 5.14 1.17 Compatibility 1-7 4.10 1.01 Complexity Trialability Observability Innovativeness 1-7 1-7 1-7 1-7 3.86 5.37 3.24 4.40 1.01 1.05 1.42 1.28

Prior product knowledge 1-7 4.35 1.49

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chosen by 26 respondents, followed by 7 (Slightly yes) and 9 (Probably yes), chosen by 21 respondents.

Table 4.3 Distribution Adoption Intentions

4.2 Reliability Analysis

Given the fact that all the items used in this research are based on antecedent study and previously validated, it is assumed that the scales measure the examined variables reliably. However, the reliability analysis has to be performed, in order to check the internal consistency of the scales. Finally, a factor analysis will be performed to examine how the factors solution would change and to double check the reliability of the scales.

4.2.1 Cronbach’s Alpha

According to Malhotra (2009), internal consistency reliability can be used to examine the validity of a construct, formed by the sum of different items. The essential result of this analysis is called Cronbach’s Alpha, which varies from 0 to 1 and has to be more than 0,6 to indicate a satisfactory internal consistency reliability (Malhotra, 2009). Subsequently, the Cronbach’s Alpha if item deleted will be checked, which shows how the coefficient alpha would change deleting one variable of that scale. Hence, the Cronbach’s Alpha for all the

0 5 10 15 20 25 30 1 2 3 4 5 6 7 8 9 10 11 N u m b er o f r esp on d en ts

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scale of our research with more than 1 item was executed, thus for all the variables except for the dependent, 3D printer adoption intentions.

Table 4.4 Cronbach’s Alpha and improvements

As can be seen in Fig.1, almost all the alpha scores are above 0,6, except for complexity that produces a score of 0,435. This result can be motivated by the use of a reverse item in this construct, which could have confused the respondents. Therefore, it is decided to delete the reverse item and evaluate complexity with the only one item remained, “3D printer is difficult to use”, as theorized in previous researches (e.g. Moore and Benbasat 1991). Furthermore, looking at Cronbach’s Alpha if item deleted, it was possible to see that the reliability of “Prior product knowledge” and “Compatibility” scales could be improved deleting their third item. Indeed, the Cronbach’s Alpha of “Prior product knowledge” would increase from 0.75 to 0.81, while for “Compatibility” the Cronbach’s Alpha would grow from 0.63 to 0.68. Consequently, item 3 for those constructs will not be considered. Finally, all the items have internal consistency and are able to represent the specific variables.

4.2.2 Factor Analysis

According to previous researches (e.g. Verhoef and Langerak 2001), a factor analysis is performed to double check the reliability of the scales. Although every construct is based on previous literature, it is important to see if also in this research the correlations are maintained. The deleted items through the reliability analysis are not considered in the analysis, as the questions used to measure the dependent variable and the control variables. Indeed, the inclusion of these items could bring to confused results and a larger amount of factors. Therefore, 15 items are included in the “dimension reduction” and a Varimax rotation method is applied to the analysis, in order to minimize the number of variables with high loadings on a factor (Malhotra, 2009).

Variable Cronbach’s Alpha Improvement

Relative advantage 0.733 Delete item 3

Compatibility 0.639 Complexity Trialability Observability Innovativeness 0.435 0.733 0.600 0.756

Delete item 2 (reverse item)

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Firstly, the “Kaiser-Meyer-Olkin measure of sampling adequacy” equals to 0,748 showing that factor analysis should yield reliable and distinct factors. In addition, the Bartlett’s test of sphericity (Sig. = 0,000) revealed that the factor analysis is appropriate for these data. Analyzing the results and especially looking at the scree plot, a 6 factor solution is chosen. Looking at the loading of the rotated component matrix, it can be seen a strong similarity with the previous division made according to the literature. Indeed, almost every item is split in the same way. However, having one less factor, the items, that were previously supposed to measure the two variables “relative advantage” and “compatibility”, belong now to only one factor.

Finally, according to Cronbach’s Alpha and Factor analysis, the reliability of each scale was proved, thus all the items have internal consistency and are able to represent the specific variables.

4.3 Multiple Regression Analysis

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Prior Product Knowledge 0.063 n.s. 0.063 n.s. P.P.Knowledge-Relative advantage 0.060 n.s. 0.058 n.s. P.P.Knowledge-Compatibility 0.008 n.s. -0.004 n.s. P.P.Knowledge- Complexity 0.118 * 0.108 n.s. Innovativeness 0.047 n.s. 0.052 n.s. Innovativeness– Relative advantage 0.099 n.s. 0.091 n.s. Innovativeness-Compatibility -0.053 n.s. -0.048 n.s. Innovativeness-Complexity -0.059 n.s. -0.039 n.s. Gender -0.015 n.s. 0.052 n.s. Age 0.123 * 0.113 n.s. Level of education 0.058 n.s. 0.047 n.s. R2 0.450 0,474 0.463 0,484 R2 Adjusted 0.431 0,424 0.433 0,422 F (Sig) 23.597(0,000) 9.445(0,000) 15.196(0,000) 7.813(0,000) n.s.: not significant, *: p < .10, **: p < .05, ***: p < .01

Note: Dependent variable is 3D printer adoption intention. Table 4.5 Regression results for all models

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the 5 product characteristics, theorised by Rogers, had a significant effect on 3D printer adoption intention. Indeed, “relative advantage” had a p-value < 0.01 and a standardised beta coefficient of 0.439 and “compatibility” was also significant at p<0.01 with a positive effect of 0.249. Moreover, “trialability” had a significant p-value lower than 0.05, however the effect was found to be not positive as expected, as demonstrated by the standardized beta equal to -0.146. This strange result could be explained by the fact that people who would need to try the product are not convinced about it, thus the adoption intention are lower. Finally, it cannot be found a significant effect for “complexity” and “observability”, which show a p-value higher than 0.1.

The second model contained, in addition to the first, the two moderators, prior product knowledge and innovativeness, and the interactions between them and the first 3 indipendent variables: relative advantage, compatibility and complexity. Also this model was overall significant as the p=0.000 and the F=9.445 demonstrated, and the R squared increased to 0.474. Again a significant effect of relative advantage, compatibility and trialability was shown. Moreover, it was found a significant interaction effect between complexity and prior product knowledge, as hypothezed in the second chapter. All other results were not significant. Finally, the highest VIF was equal to 3.464, thus there was not multicollinearity.

The third model included all variables as the first but in addition contained the 3 control variables: age, gender and level of education. The model was significant and the R squared amount to 0.463. Beyond the “usual” significant effects of relative advantage, compatibility and trialability, it was also observed a significant effect of age with a p-value <0.1. The effects of the other 2 control variables were not significant. Again, multicollinearity was not observed, having all the VIF lower than 1.954.

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4.4 Hypotheses Validation

According to the analyses performed, the results of the hypothesis generated in chapter 2 are provided, as shown in the table 4.5 presented below.

Hypothesis Resultlt

H1: There is a positive relationship between relative advantage and

adoption’s intention of 3D printers.

Supported

H2: There is a positive relationship between compatibility and

Supported

H3: There is a negative relationship between complexity and

Not supported

H4: There is a positive relationship between trialability and

Partially supported

H5: There is a positive relationship between observability and

Not supported

H6a: Prior Product Knowledge has a positive effect on the importance

of “relative advantage” on customers’ intention to adopt 3D printer.

Supported

H6b: Prior Product Knowledge has a negative effect on the

importance of “ compatibility” on intention to adopt 3D printer.

Not supported

H6c: Prior Product Knowledge has a negative effect on the

importance of “trialability” on intention to adopt 3D printer.

Supported

H7a: Innovativeness has a positive effect on the importance of

“relative advantage” on customers’ intention to adopt 3D printer.

Not supported

H7b: Innovativeness has a negative effect on the importance of

“compatibility” on customers’ intention to adopt 3D printer.

Not supported

H7c: Innovativeness has a positive effect on the importance of

“complexity” on customers’ intention to adopt 3D printer.

Not supported

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5 CONCLUSION

The aim of this study was to identify which factors influence 3D printers’ adoption intentions, analyzing the perception of the main attributes of 3D printing and testing the moderation effect of two consumer characteristics, prior product knowledge and innovativeness. In this chapter, the discussion of the results of the previous chapter will be addressed. First, the answers to the research questions formulated in the first chapter will be provided. Second, the managerial implication derived from the results will be presented. Finally, the limitations of the study and the suggestions for further research will be discussed.

5.1 Answers To The Research Questions

Based on the results of the analyses it is now possible to answer the main research question.

• What are the main drivers of 3D printers’ adoption intention?

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significant in the most of the studies on adoption intention.

Furthermore, this study wanted to investigate the moderating effect of innovativess and prior product knowledge on the relationship between innovation characteristics and adoption intentions. The results show that in most cases these two customer characteristics are not supposed to have moderating effects. However, when not taking into account the control variables (Model 2), prior product knowledge results to have a significant positive moderation effect on the impact of complexity on adoption intentions. Finally, customer socio-demographics characteristics as age, gender and level of education, considered as control variables, have not a significant effect on adoption intentions, looking at the overall model. This is in line with the results provided by the meta-analysis of Arts, Frambach and Bijmolt (2011).

5.2 Managerial Implications

The results of this study, discussed above, have several implications for marketing managers of 3D printers’ companies. First of all, it has to be mentioned that adoption intentions can be poor predictor of the actual behaviour of the potential customers after market launch, especially when referring to really new products, as 3D printers. However, several valuable insights can be derived from our research.

Firstly, being relative advantage the most influencing attribute on adoption intentions, managers should emphatize the benefits that owning a 3D printer brings to the customers and communicate it in relation with the specific needs and characteristics of the potential adopters. Secondly, the significant positive effect of compatibility means that people who feel 3D printing compatible to their lifestyles and values are more prone to buy the product. Therefore, marketeers should make clear which are the segments compatible with 3D printing and direct the majority of the marketing campaigns to these groups of customers. On the other hand, the peculiar negative effect of trialability requires several lines of reasoning. As mentioned above, this should mean that people who need to try the product have lower intentions to buy it. Consequently, marketeers should be aware that giving the product on a trial basis is not supposed to increase adoption intentions, therefore they should spend those resources on other marketing strategies.

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knowledge of the product, increasing its visibility. Instead, innovativeness of the customer should not have effect on the perception of the product attributes. Finally, the outcomes of these analyses imply that socio-demographic customer characteristics are not strong predictor of adoption intentions, therefore should not be used to identify target of potential adopters.

5.3 Limitations And Suggestions

Several limitations to this study will be presented in this part, in order to give suggestions for further research.

First, the limited resources and time did not allow to provide an accurate sample. Indeed, the survey was distributed only on social networks, which could itself have an effect on the results. Consequently, the model should be tested through different channels and through different techniques, e.g. interviews, experts’ surveys.

Second, the sample does not represent the standard population. The majority of the sample is young, male and endowed with a degree. Therefore, a more representative sample of the standard population should be a solid enhancement to this research.

Third, the majority of the respondents are Italian or Dutch, countries where 3D printers were not easily available and distributed. Consequently, the model was based on intentions, which could highly differ from actual behavior. Therefore, the suggestion is to test the model in different countries, where the knowledge and adoption is already high, thus the actual behaviour can be examined.

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Appendices

APPENDIX A

Survey Questionnaire Question 1

I know what is a 3D printer

Strongly disagree 1 2 3 4 5 6 7 Strongly agree

I understand the various features of a 3D printer

I already came in contact with 3D printing

Question 2

I like to buy new and different things

I am usually among the first to try new products

I like to go window shopping and find out about the latest products

Strongly disagree 1 2 3 4 5 6 7 Strongly agree

Question 3

3D printer would provide me with benefits compared to previous products

The concept of 3D printing is innovative

3D printer would be useful for me

Question 4

3D printer would suit my person well

3D printer requires few adaptations in my personal life

3D printer yields little problems for me

Strongly disagree 1 2 3 4 5 6 7 Strongly agree

Question 5

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Strongly disagree 1 2 3 4 5 6 7 Strongly agree Learning to operate the 3D printer would be easy for me

Question 6

Before deciding on whether or not to adopt the 3D printer, I would need to properly try it out Strongly disagree 1 2 3 4 5 6 7 Strongly agree

I would be permitted to use the 3D printer on a trial basis long enough to see what it can do

Question 7

I have had a lot of opportunity to see the 3D printer being used

Owning a 3D printer will make me more considered among my neighbours, group of friends and/or my community

Question 8

Please indicate on the response scale from 0 to 10 to which extent you intent to buy a 3D printer in the future

Absolutely not 1 2 3 4 5 6 7 8 9 10 11 Absolutely yes

Question 9

What is your gender? ▪ Male

▪ Female

Question 10

What is your age?

•

Lower than 30

•

30-50

•

Over 50

Question 11

What is your level of education? • Less than high school

• High-school • Bachelor degree

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APPENDIX B

Output factor analysis: loadings

Items Loading Items Loading

3D printing: Analyzing the main drivers of its adoption and diffusion

3D printing:

Analyzing the main drivers of its

adoption and diffusion

by

Ivan Mauri

3-D printing:

Analyzing the main drivers of its adoption and diffusion

TABLE OF CONTENTS

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