Smart Products
Smart products: Consumer evaluations of a new product class / Serge Alexander Rijsdijk.
Proefschrift Technische Universiteit Delft. – Met lit. opg. Met samenvatting in het Nederlands
ISBN 90-8559-165-1
Trefw.: smart products, product smartness, intelligent products, product intelligence
Printed by Optima Grafische Communicatie, Rotterdam, the Netherlands Cover design by N.Y. Vink
Layout by PicaBij, Den Haag Copyright © 2006 by S.A. Rijsdijk
Smart Products
Consumer evaluations of a new product class
Proefschrift
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus prof. dr. ir. J.T. Fokkema, voorzitter van het College voor Promoties,
in het openbaar te verdedigen op dinsdag 30 mei 2006 om 15.00 uur door Serge Alexander RIJSDIJK
Dit proefschrift is goedgekeurd door de promotoren: Prof. dr. E.J. Hultink
Prof. dr. W.M. Oppedijk van Veen Samenstelling promotiecommissie: Rector Magnificus, voorzitter
Prof. dr. E.J. Hultink, Technische Universiteit Delft, promotor
Prof. dr. W.M. Oppedijk van Veen, Technische Universiteit Delft, promotor Prof. dr. ir. G. van Bruggen, Erasmus Universiteit Rotterdam
Table of contents
Acknowledgments
7
1 Introduction
11
1.1 The unique capabilities of smart products 12
1.2 The opportunities of smart products 16
1.3 Barriers to smart product adoption 18
1.4 Aim of the dissertation 20
1.5 Relevance of the dissertation 21
1.6 Overview of the book 23
2 Theoretical background and conceptual framework
25
2.1 Smart products: a new classification 25
2.2 Previous research on smart products 28
2.3 Conceptual framework 31
2.4 Conclusions and overview of the book 46
3 Defining and measuring product smartness
49
3.1 Defining the construct 50
3.2 Generating items 59
3.3 Item evaluation 60
3.4 Reliability and validity assessment 62
4 Consumer evaluations of product autonomy
77
4.1 Conceptual framework 78
4.2 Method 81
4.3 Results 83
4.4 Discussion and conclusions 88
5 Consumer evaluations of products with
multiple smartness dimensions
93
5.1 Conceptual framework 94
5.2 Method 96
5.3 Results 99
5.4 Discussion and conclusions 122
6 The effect of consumer characteristics
on smart product evaluations
127
6.1 Conceptual framework 127
6.2 Method 129
6.3 Results 134
6.4 Discussion and conclusions 146
7 Discussion and implications
149
7.1 Summary of key findings 150
7.2 Limitations 156
7.3 Theoretical implications 158
7.4 Suggestions for future research 162
7.5 Practical implications 165
Summary
171
Samenvatting
177
References
183
Appendices
197
Appendix for Chapter 3 199
Appendix for Chapter 4 219
Appendix for Chapter 5 225
Appendix for Chapter 6 251
Acknowledgments
There are several people that I would like to thank because they contributed to the realization of this book either directly or indirectly. First, I would like to thank my supervisors Erik Jan Hultink and Walle Oppedijk van Veen. Erik Jan, thanks for clearing my way to my defense and beyond. You taught me how to conduct research and many more things one needs to know as an academic. You did so in an inspiring way and I enjoyed it a lot. Walle, each question you asked me appeared to be a lesson. I am proud to be one of your Ph.D. students and want to thank you very much for your indispensable input.
I would also like to thank several other people that are affiliated with the department of Product Innovation Management that contributed to my research. Jan Schoormans, I am grateful for your input into my research project at several crucial moments. Dirk Snelders, thank you for fostering me for a while and for all the diverging talks we had. My goal-directedness needed it. Henk Arisz, thank you for answering my basic questions on statistics and the computations that you did. I would also like to thank Agnes Tan, Sandra Snoek, and Karin Langelaan for their practical support that often remains unnoticed but that can never be missed. Jetske Bouma and Bas van de Werk, your study efforts have been crucial for my thesis. Outside Delft, I would like to thank Adamantios Diamantopoulos for cooperating on the publication of Chapter 3. It was a valuable experience.
sharing an office with you for almost four years. Your enthusiasm is contagious. Niels, thank you for designing the invitation and the cover of this book.
I would also like to thank the people from the NOBO course for the great times we had in Rotterdam and Groningen and at the reunions and promotion parties that followed. I hope that there are many more to come.
I would also like to thank my colleagues from the Institute of Psychology. It is great to be part of such a young and dynamic department. I am very thankful that I got the opportunity to both develop my teaching skills and to finish my dissertation as a member of your group. Susan, thank you for being a great office mate and for frequently helping me see things more clearly.
Almost last but not least, I want to thank Bernd, Vicki, Jan Jirka, Ingrid, Marko, Saskia, Peter, Caroline, Philip and all other friends. Bernd, thanks for being my paranimf and for learning me to think critically at the time. Jan Jirka, thank you for being a great brother and for being my paranimf. Thank you also for teaching me how to read before I was supposed to. Marko, it’s you that initially made me consider writing a dissertation. Thanks for the hint, it was a good one. Peter, I enjoyed our trip to Malaysia. You always have something new or original to say, which makes you good company. Philip, your swimming achievements taught me a lot about perseverance. I suggest we keep on making dives together. Ma, pa, Bea, thanks for your continuing support. I could not have made it without you. And finally, sweet Lenny, thank you for your love, friendship and for believing in me. Yu yu! You’re next.
11
1
Introduction
In the last two decades there has been a trend towards equipping consumer products with information and communication technology (ICT) in the form of microchips, software, sensors, and other advanced electronics. An impressive and well-known example of such a product is the Sony AIBO ERS-7. The AIBO (which stands for Artificial Intelligent roBOt) is a robotic dog that walks around, lies down and plays with a ball. It recognizes up to 50 spoken words. It responds to its name and to multiple commands like ‘don’t do that’ or ‘find the ball.’ The dog can do all this and more, because it is equipped with chips, sensors and software. Due to this application of ICT the AIBO is able to collect, process and produce information and seems to be able to think. The AIBO can therefore be described as a smart product.
low, the Trilobite automatically returns to the charging station and, if necessary, resumes cleaning once they are fully charged.
Although smart industrial products (e.g., assembly robots, autopilots, and missiles) already exist for a longer period of time, the phenomenon of smart consumer durables is relatively new. The present research project investigates this new category of products and how consumers perceive the special capabilities of these products. Smart products1, for instance,
can operate autonomously (e.g., the Electrolux Trilobite), respond to their environment (e.g., the Sony AIBO), cooperate with other products (e.g., PDA’s), are multifunctional (e.g., mobile phones) or understand and produce speech (e.g., car navigation systems). First, we will provide an overview of this group of new capabilities in section 1.1 and refer to it as product smartness. We will also develop a measure for the construct of product smartness in Chapter 3 of this dissertation. Second, we will investigate how product smartness may be perceived in terms of innovation attributes (relative advantage, compatibility, observability, complexity, and perceived risk) from the diffusion literature (Bauer, 1960; Rogers, 1995). Although smart products may provide new opportunities for industry (see section 1.2), there may also be problems attached to their adoption (see section 1.3). Consumers may perceive these products as complex and risky and they may not see or appreciate their functionality and benefits. Therefore, the current research project empirically investigates consumer perceptions of smart products and aims to find out to what extent consumers observe advantages and disadvantages in these products. Also, we will investigate how consumer characteristics influence these consumer perceptions. We will elaborate upon the aim of the dissertation in section 1.4 and show how this research project may be relevant for practitioners and academics in section 1.5. Section 1.6 concludes this chapter and provides an overview of the remainder of the book.
1.1 The unique capabilities of smart products
Smart products are products that contain ICT in the form of microchips, software, sensors, and other advanced electronics. As a result of this ICT, smart products show seven specific capabilities that we will refer to as product smartness. These 1 For readability reasons, we will refer to smart consumer durables as ‘smart products’ in the
Introduction 13 seven capabilities are ability to cooperate, adaptability, autonomy, humanlike
interaction, multifunctionality, personality, and reactivity. We will refer to these capabilities as dimensions because they are capabilities that smart products show to a lesser or higher degree.
Chapter 3 will further describe how the conceptualization of product smartness was constructed and how we measure product smartness. However, in order to timely clarify what we mean by product smartness, we will describe the seven dimensions shortly below in alphabetical order. We will illustrate each dimension with examples of existing products.
1.1.1 Ability to cooperate
The first dimension of product smartness is the ability to cooperate with other devices to achieve a common goal. According to Nicoll (1999), the age of discrete products may be ending. Instead, products are becoming more and more like modules with in-built assumptions of their relationships with both users and other products and systems. An increasing number of products are thus able to communicate not only with their users, but also among themselves (Nicoll, 1999). For example, desktop computers cooperate with other products; they can be attached to scanners, printers, musical instruments, video cameras and so on. Other examples of products that can cooperate are mobile phones and PDA’s. The user of a PDA can write emails on the device and send them to the receiver via a mobile phone.
1.1.2 Adaptability
which may result in better performance. One example of a physical product that is adaptable is the Chronotherm IV thermostat developed by Honeywell. From the moment of installation, the Chronotherm collects data on the time it takes to raise the temperature in a room. While doing this, the device also takes into account the outdoor temperature. When the user instructs the thermostat to reach a certain room temperature at a certain time, the device will do so on the basis of the data it previously collected.
1.1.3 Autonomy
The third dimension of autonomy refers to the extent to which a product is able to operate in an independent and goal-directed way without interference of the user. An example of an autonomous product is the Automower of the Swedish firm Electrolux. This lawnmower is placed in the garden after which it moves through the garden and cuts the grass all by itself. By setting the limits of the garden with a metal wire the owner ensures that the lawnmower will remain within the limits of the garden. Another example is the Trilobite vacuum cleaner developed by Electrolux, which also shows a high degree of autonomy.
1.1.4 Humanlike interaction
Introduction 1 1.1. Multifunctionality
The fifth dimension, multifunctionality, refers to the phenomenon that a single product fulfills multiple functions (Poole and Simon, 1997). The application of information technology in physical products enables a larger set of attributes to be designed into one product (Dhebar, 1996). Modern mobile phones, for example, can also be used to play games or send photos and text messages. Another example of a multifunctional product is the personal computer. Poole and Simon (1997) illustrate how the present-day networked personal computer is a convergence of an answering machine, telephone, telex, fax machine, modem, arcade game, and television set. Similarly, PDA’s provide the user with multiple functions such as a calendar, email, games and a calculator.
1.1. Personality
The sixth dimension, personality, refers to a smart product’s ability to show the properties of a credible character. This dimension was also distilled from the literature on software agents. Bradshaw (1997) discusses the property of an agent to have a ‘believable personality and emotional state’. Providing an agent with a personality is supposedly beneficial for the user’s comprehension of the agent. For example, the paperclip- or Einstein assistants in Microsoft Office suggest that ‘someone’ assists the users. For physical products, the property of personality mainly refers to the way in which users interact with the product. Typical examples of products with a personality are the Furby and Sony’s AIBO. These toys express emotions and show certain emotional states.
1.1. Reactivity
environment are merely direct responses. In contrast to adaptable products, they have no internal models of their environment and are not able to learn and adapt the nature of their reactions over time.
1.1. Overall product smartness
The overall smartness of a product is formed by the sum of the degrees to which the product matches the different dimensions. A product does not need to show characteristics that match all of the smartness dimensions in order to be smart. The Hydraprotect hairdryer, for example, is only reactive. Also, several PDA’s are both multifunctional and able to cooperate with other products, but they do not match any other smartness dimensions. In this dissertation, the overall smartness of a product is determined by the extent to which a product matches one or more of these dimensions. As such, all smart products possess capabilities that match these dimensions to a certain extent and provide several opportunities for industry. 1.2 The opportunities of smart products
Making products smarter offers several opportunities for companies that develop consumer durables. First of all, companies may gain on advantage over competing products. A smarter product may perform its central function better than a non-smart competing product or it may contain non-smart features that make the product superior in terms of the benefits that it offers. An example of a smart version of a long existing product category is the Siemens WIQ 1430 washing machine. Due to the use of ICT, the WIQ 1430 is able to do several things that conventional washing machines are not able to do. The machine weighs the laundry in its drum and advises the user about the amount of detergent to use. Also, the machine has a certain extent of autonomy in that it detects how dirty the drained water is. Depending on the amount of dirt, the machine decides to re-use this water to wash the laundry or not. As such, the smartness of the washing machine results in the reduction of water and detergent use. This is environmentally friendly and also cost reducing for the owner of the washing machine.
Introduction 1 not successful in the marketplace, it caused the emergence of a new category of
products. Nowadays, a whole range of different PDA’s from different leading brands such as Palm, Compaq, Casio, and Sony populate the marketplace.
Third, smart products can serve as show- pieces for the company. Because of their technological sophistication, smart products often contain cutting edge technology. As such, smart products can provide the company with the image of being technologically advanced. Honda, for example, is developing the Humanoid Robot ASIMO (see world.honda.com). ASIMO is the result of a project that was started in 1986 with the idea to develop a new form of mobility and to create a new kind of robot that can function in society walking on two legs. Honda currently uses the most advanced version of the ASIMO, the P3 (see picture on the right), for publicity purposes by letting it travel around the world and meet prominent people such as European politicians and Olympic medalists. With its advanced robot, Honda further strengthens its image as a technological leader.
Smart products are also expected to be important in the future. Several leading institutes and companies have set up specialized laboratories to conduct research on the integration of ICT into new consumer products and people’s living environment. For example, the Massachusetts Institute of Technology (MIT) has set up House_n, a home of the future consortium (see architecture.mit.edu). This consortium has the ultimate goal to develop a house that adapts to people’s needs and reacts to environmental influences. The “n” represents adjectives such as “next generation” and “neural”. The researchers envision a house with an electronic “nervous system” that learns the habits of those who live in it and assists in their living patterns. In business, Microsoft and Philips have set up comparable projects. Microsoft set up the EasyLiving project that aims to develop architecture and technologies for intelligent environments. Philips founded the “Homelab” and envisions a future of Ambient Intelligence in which they bring advanced intelligent technologies into people’s homes. Another initiative that aims to communicate the application of smart technologies to the public is Living Tomorrow. At two locations in Europe (Amsterdam and Brussels) companies are provided with the opportunity to demonstrate their cutting edge technologies in a setting that represents the home and office of the future.
research project: Smart information technologies will form a key element in both today’s and tomorrow’s new products. Those companies that take the lead in this area may benefit by being able to create superior products and gain advantage over their competitors. However, several barriers may have to be overcome before consumers will adopt these products.
1.3 Barriers to smart product adoption
Smart products distinguish themselves from non-smart products by the fact that their functionality is largely based on the use of ICT. Besides the advantages that were described above, the use of ICT also has some disadvantages. Smart products may, for example, contain hidden functionality. Second, smart products may be perceived as complex. Third, the unique capabilities of smart products (product smartness), may encounter resistance from consumers in the form of perceived risk. We will further elaborate on these disadvantages of smart products below and will subsequently explain why the current research project will concentrate on how product smartness influences consumer perceptions.
1.3.1 Hidden functionality
Due to the fact that the functionality of smart products is largely based on microelectronics and software, the relation between a product’s form and how the product can be used is less obvious than in most traditional products. In some smart products this relation is even absent. Consumers may therefore have difficulty understanding a smart product’s functionality and how it should be operated, because product form often fulfills an important role in the communication of such information to the consumer (Veryzer, 1995). Product form provides cues about product attributes and helps consumers to understand and categorize a product (Bloch, 1995). As a result, a product that effectively communicates its function and way of operation can facilitate successful interaction between the consumer and the product and positively influence consumers’ preferences and choices (Veryzer, 1995).
Introduction 19 AIBO is able to understand. Product form cannot be sufficiently used to clarify
which 50 commands and questions the Sony AIBO understands. Another example of a product that suffers from the problem of hidden functionality is the PDA. A challenge in marketing PDA’s is to help consumers recognize and appreciate their functionality, particularly those functions that are not apparent from the product’s surface attributes (Roehm and Sternthal, 2001). In conclusion, consumers may have difficulty observing the functionality and benefits of smart products. This disadvantage may hamper the adoption of smart products.
1.3.2 Complexity
Previous research showed that product complexity negatively influences the rate of innovation adoption (Rogers, 1995). This may also be the case for smart products. Norman (1998) recognized that “as technology has advanced, we have understood less and less about the inner workings of the systems under our control” and illustrates the problem by comparing a pair of scissors to a digital watch (Norman, 1998, p.12-13):
and memory chips do their work invisibly and silently (Den Buurman, 1997). Several examples illustrate the complexity of smart products. For example, only a minority of the owners of DVD-recorders can program these devices for delayed recording. Also, users experience major problems when they have to reset channels on their TV sets and many functions permanently remain untouched (Den Buurman, 1997). Some users do not know that certain functions exist. In other cases, consumers give up on using certain functions because their operation is too difficult to learn and use (Han, Yun, Kwahk, and Hong, 2001). Such complexity problems can be barriers for the adoption of smart products.
1.3.3 Perceived Risk
A third barrier to the adoption of smart products may be the risk that consumers perceive in smart products. Although the capabilities of smart products may result in the benefits that were described in section 1.2, they also have their downsides. Consumers may, for example, have a lack of confidence in the product. The idea of an autonomous vacuum cleaner may at first seem attractive. However, consumers first need to have sufficient confidence in the vacuum cleaner before they have it clean the floors of their house. If the vacuum cleaner is not sufficiently able to avoid collisions with other objects it might cause damage. Also, consumers may feel that they loose control when smart products start to take decisions for them. A washing machine that determines itself how much detergent should be used and at what temperature the laundry should be washed may be perceived as incapable to properly conduct its task, even when the washing machine makes objectively better decisions. As such, the sometimes far reaching functionality of smart products may be a reason for consumers not to adopt members from this class of products.
1.4 Aim of the dissertation
It is the aim of this dissertation to obtain more insight into how consumers perceive smart products and to find out how the opportunities and barriers that are described above may play a role in smart product adoption. This dissertation intends to do so in three steps. First, the dissertation provides a conceptualization of the capabilities of smart products: product smartness. To ensure a timely understanding of the construct, we already described it in section 1.1. In Chapter 3, we will show how we developed its current conceptualization and an instrument to measure it.
Introduction 21 relative advantage, compatibility, observability, complexity, and perceived risk.
Relative advantage, compatibility, and observability are known to have a positive impact on adoption. Complexity and perceived risk negatively influence adoption (Rogers, 1995). Holak (1988) showed that the innovation attributes are good predictors of product adoption and can be useful tools in early product screening. As such, we will use these innovation attributes to gain more insight into the possible adoption of smart products.
Third, the dissertation will investigate consumer characteristics that may moderate the relationship between product smartness and consumer perceptions. By relating consumer perceptions of product smartness to consumer characteristics, the dissertation aims to generate knowledge on what distinguishes consumers that appreciate smart products from those who don’t.
1. Relevance of the dissertation
Overall, the results of this research project will provide knowledge on the advantages and disadvantages of product smartness in the eyes of consumers. Also, the project relates consumer characteristics to these consumer perceptions. Below we will discuss how this knowledge can be relevant for both practitioners and academics that are involved in the development, marketing or research of smart products. 1..1 Practical relevance
For professionals working in new product development (NPD) and marketing, this dissertation may be relevant in three ways. First of all, the conceptualization of product smartness supplies practitioners with a mental framework for thinking about and working with smart products. The conceptualization of product smartness may, for example, play a role in new product ideation. Previous research has shown that analogical thinking results in more original product ideas (Dahl and Moreau, 2002). Analogical thinking implies that existing, familiar information from one domain is transferred to the other. As such, innovation team members may use the conceptualization of product smartness by applying it to their own products and use it for ideas on how their products can be improved.
can explicitly pay attention to the risk that consumers perceive. One could ask specific questions concerning perceived risk in order to identify the causes of these perceptions and how they may be decreased or even removed. This may subsequently be done by, for example, equipping the product with indicators that provide the user with information on what the product is doing at a certain moment.
Third, practitioners developing and marketing smart products may benefit from this dissertation by gaining in-depth knowledge on consumers that are more likely to adopt smart products. Consumers may differ in the extent to which they appreciate smart products. A common explanation for differences in the likelihood for a person to appreciate new products is consumer innovativeness. Rogers (1995) defines innovativeness in terms of the degree to which an individual is relatively earlier in adopting innovations than other members of a social system. However, Dickerson and Gentry (1983) refined Rogers’ idea about innovativeness. They found that the adopters of home computers were mainly highly educated, middle-aged, and with a relatively high income. Also, home computer adopters were described as analytical and introvert. Dickerson and Gentry (1983) suggested that whether a person adopts an innovation or not partly depends on the characteristics of the innovation. The nature of an early adopter of a home computer may generally be different from an early adopter of a mobile phone. Taking into account the hidden functionality, complexity and perceived risk that are involved with smart products consumers with certain characteristics may be more likely to adopt smart products than other consumers. Further research into this issue is important for segmentation and target group determination purposes. As a result of such research, smart products may be marketed in a more effective way.
1..2 Academic relevance
This dissertation aims to provide academics with a framework for conducting research on smart products. As previously stated, the academic literature on smart products is limited and no conceptualization has yet been provided of the characteristics of smart products. The conceptualization of product smartness in this dissertation attempts to fill this gap thereby enabling a more systematic and structured framework for dealing with the topic. By developing a measurement scale for the construct of product smartness, the dissertation also enables empirical quantitative research into the phenomenon.
Introduction 23 Tornatzky and Klein, 1982; Waarts, Van Everdingen, and Van Hillegersberg, 2002).
Also, investigations have been performed considering the adoption and diffusion of specific product types, such as consumer durables (e.g., Bass, 1969; Foster, Golder, and Tellis, 2004; Ganesh, Kumar, and Subramaniam, 1997; Heeler and Hustad, 1980; Talukdar, Sudhir, and Ainslie, 2002; Van den Bulte and Stremersch, 2004) or high-tech products (e.g., Chryssochoidis and Wong, 1998; Easingwood and Lunn, 1992; Golder and Tellis, 2004; Norton and Bass, 1987; Raman and Chatterjee, 1995). However, no research has been conducted on the adoption or diffusion of smart products. In addition, the dissertation contributes to the literature by investigating how consumer characteristics may play a role in smart product adoption.
1. Overview of the book
Chapter 2 shows how the current research project is related to and contributes to the existing literature. The chapter shows that smart products are a subset of high-tech products and are sometimes continuous and at other times discontinuous innovations. Next, the chapter provides a literature review on previous smart product research and shows that only limited attention has been paid to the conceptualization of product smartness and to the adoption of smart products. Subsequently, the chapter provides a conceptual framework that relates product smartness to the literature on innovation adoption and diffusion. This framework also underlines the special case of the adoption of smart products. As such, the conceptual framework contains the building blocks that form the basis for this research project.
Chapter 3 focuses on the definition and operationalization of the construct of product smartness and its seven dimensions: ability to cooperate, adaptability, autonomy, humanlike interaction, multifunctionality, personality, and reactivity. The chapter also describes a scale development procedure that contains multiple empirical studies. The main and final empirical study of the scale development procedure resulted in a reliable and validated measure for the multidimensional product smartness construct.
in products with low autonomy.
Chapter 5 goes beyond the smartness dimension of autonomy and describes a study that extends the investigation into the adoption of smart products by relating an additional four smartness dimensions (ability to cooperate, adaptability, multifunctionality, and reactivity) to five innovation attributes: relative advantage, compatibility, observability, complexity, and perceived risk. In addition, the study investigates whether smartness dimensions can compensate for the disadvantages of other dimensions by taking interactions across different dimensions into account. The chapter, for example, shows that there may be a limit to how smart a new product should be. Compared to products with a medium level of multifunctionality and ability to cooperate, consumers perceive products with the highest levels of these dimensions as equally advantageous but less compatible with their current way of thinking and living. Overall, the chapter shows that the five product smartness dimensions influence the innovation attributes in different ways. Also, the chapter indicates that there are differences across product categories in how the smartness dimensions affect the innovation attributes. None of the product smartness dimensions was found to compensate for possible negative effects of another.
2
2
Theoretical background
and conceptual
framework
This chapter discusses the central concepts that constitute this dissertation and how they are related to the existing literature. Section 2.1 explains how the category of smart products relates to the classification of low versus high-tech products, and how it relates to the distinction between continuous versus discontinuous innovations. Next, section 2.2 discusses the literature on smart products and shows that previous research mainly focused on how smartness can be applied to design smart environments and interfaces. Next, section 2.3 will relate product smartness to the diffusion literature by developing a conceptual framework on the adoption of smart products. The section also develops hypotheses on how consumer characteristics may play a role in the smart product adoption process. The conceptual framework will be tested in the empirical chapters that follow. 2.1 Smart products: a new classification
to the smart/non-smart product distinction. As such, this section will clarify what the three classifications have in common and how they differ. We argue that smart products are a subset of high-tech products and that they can be either continuous or discontinuous.
Within marketing, classifying products into low-tech and high-tech products mainly flows from the necessity to emphasize the unique case of high-tech products. High-tech businesses must meet three criteria: (1) The business should require a strong scientific/technological basis, (2) new technology can rapidly make existing technology obsolete, and (3) as new technologies come on stream, their applications create or revolutionize markets and demand. Typical high-tech industries are the electronics and computer industries or the chemical and pharmaceutical industries. The products they produce strongly lean on advanced technology and developing new products in these industries demands a great amount of resources (Shanklin and Ryans Jr., 1984).
A common classification of new products discriminates between continuous and discontinuous innovations. Veryzer (1998) stated that product innovation may be described as lying along dimensions that reflect changes in product benefits, technological capabilities, and consumption or usage patterns. Product benefits refer to the capabilities of a product in terms of the needs that it satisfies. Technological capability refers to the extent to which a product concerns the expansion of technological capabilities beyond existing boundaries. Changes in consumption and usage patterns refer to the degree in which the thinking and behavior of consumers needs to be altered when they use the product. Continuous innovations are limited in the extent to which they are new along these three dimensions. A discontinuous innovation can be perceived as new or discontinuous along one or more of these dimensions.
Theoretical background and conceptual framework 2 Continuous Low -tech Hi gh -tech Discontinuous
-Smart Products
Figure 2.1 Smart products on the low-high tech and continuous-discontinous
dimensions
2.2 Previous research on smart products
So far, several studies on smart products have been published. Most of these studies take a predominantly technical viewpoint and cluster around two main themes, smart environments and smart product interfaces, which we will discuss in section 2.2.1 and 2.2.2, respectively. Next, section 2.2.3 will show that the interest for smart products in the scientific marketing and NPD literature so far has been limited. As such, we conclude that the current research project forms a valuable contribution to both the literature on smart products and to the marketing and NPD literatures.
2.2.1 Smart environments
A central idea within the area of smart products is that of smart environments in which we will be surrounded by computers. This phenomenon is referred to as ‘ubiquitous computing’. The idea of ubiquitous computing was developed by Mark Weiser (1991), a researcher at the Xerox Palo Alto Research Center (PARC), and concerns the idea of integrating computers invisibly into the world around us. Computers will vanish into the background and become indistinguishable from everyday life. Weiser draws the parallel with electric motors. Around 1900, a typical factory contained a single engine that drove dozens of different machines. Nowadays, cheap and small efficient electric motors give each tool its own source of force like in cars. Electronic engines, for example, wipe the windshield, lock and unlock the doors, and adjust the mirrors. Weiser expects the same thing to happen to the computer and imagines hundreds of computers per room because each object will have a computer in it. Computers will come out of their iron boxes and merge with the physical world. The idea of ubiquitous computing has had a strong influence on researchers working in other institutes and companies that use comparable concepts such as ‘sentient computing’ (Olivetti/AT&T), ‘pervasive computing’ (MIT/IBM), ‘aware computing’ (Georgia Tech), or Microsoft’s ‘Intelligent Environments’ (Eggen, 2005).
Theoretical background and conceptual framework 29 networking systems that require connecting different rooms and floors as found in a
real-home environment (Aarts, 2002). Research projects that have been conducted are, for example, the Home Radio (Eggen, Rozendaal, and Schimmel, 2003) and Photo Browser (van den Hoven and Eggen, 2003). The Home Radio project aims to address the need expressed by families to stay in touch with their home when they move beyond the boundaries of the physical house. Family members can tune in to Home Radio, from anywhere, to see, hear, and interact with home events, activities and information (Eggen, Rozendaal, and Schimmel, 2003). The Photo Browser is a recollection-supporting device. When brought into a smart room, the Photo Browser is able to recognize the presence of people and objects in the form of souvenirs. On the basis of that information the Photo Browser is able to present sets of photos that are related to the observed people and the souvenirs (van den Hoven and Eggen, 2003).
Vastenburg, Keyson, and De Ridder (2004) conducted a study with a more general question. In an exploratory study, they investigated how people experience interruptions by messages provided by their smart environment. This environment took into account whether people could be interrupted and the perceived urgency of the message. Their results showed that people’s appreciation of the interruptions depended more strongly on message urgency than on the extent to which people could be interrupted. These insights show the importance of properly designed interfaces in smart environments. This brings us to the second important theme in smart product research: smart interfaces.
2.2.2 Smart product interfaces
Several studies concerning smart products (Bauer and Mead, 1995; Bonner, 1996; Feldman, 1995; Freudenthal and Mook, 2003; Han, Yun, Kwahk, and Hong, 2001) observe a lack of usability of these products and emphasize the importance of the development of new ideas on interface design. One way of improving product interfaces is by using smartness. Three kinds of smart interfaces that researchers currently work on can be distinguished: personalized interfaces, tangible interfaces and multimodal interfaces.
EPG provided. A study by Waern (2004) also indicated that users themselves can improve automatically constructed user profiles.
Tangible user interfaces (TUI’s) couple digital information with the physical world (Ishii and Ullmer, 1997). Tangible interfaces allow users to operate products through physical manipulation of the products themselves or other objects. The ID StudioLab at the Faculty of Industrial Design Engineering of the Delft University of Technology, for example, developed several product concepts that use TUI’s (Keyson, Bruns Alonso, Rozendaal, and Ross, 2005). For example, users of a conceptual digital music player in the form of a cube can control the device by shaking and turning it. Another example is the Carrousel that is a system to control the atmosphere in a room. The Carrousel allows people to ‘sculpt’ atmospheres by manipulating, for example, the form or movement of the Carrousel (Keyson, Bruns Alonso, Rozendaal, and Ross, 2005). As such, the control of products becomes more physical and more compatible with the way that people are used to manipulate the physical world around them.
Another way of dealing with the complexity of products is equipping them with multimodal interfaces. Multimodal systems represent a new direction for interface design that is based on new input and output technologies that are currently becoming available (Oviatt, 1999). Multimodal interfaces enable users to control products using different input modes. Possible input modes are speech, pen, touch, hand gestures, eye gaze, and head and body movements. Also, the output of these interfaces can be multimodal. The interaction between users and multimodal systems is likely to be more natural (Eggen, 2005). For example, some PDA’s can be controlled in a multimodal way. Their users can operate them using buttons, but also by using a pen or by speech.
2.2.3 Smart products and the literature on marketing and NPD
The literature on marketing and NPD only pays limited attention to smart products. At the moment of writing, one conceptual article on smart products by Dhebar (1996) can be found. Dhebar discusses a number of implications of increasing product smartness for both users and producers. The increasing smartness of products provides the user with improved information about and control over performance, greater automation, and enhanced features, functions, and capabilities. Simultaneously, the producers of smart products can design a set of attributes into a product that is much larger. Also, certain attributes are more easily designed or changed over time at a significantly lower cost compared to non-smart products (Dhebar, 1996). Besides this conceptual article, however, no empirical studies on smart products have been published in the marketing and NPD literatures.
Theoretical background and conceptual framework 31 changing, however. In an editorial by the editor of the Journal of the Academy of
Marketing Science, ‘intelligent products’ were put on the top of a list of 15 themes that are expected to be important in the next 30 years (Zinkhan, 2003). As such, the potential relevance and future importance of marketing research into smart products has been recognized.
2.2.4 Conclusions
As the current section shows, a significant amount of research is being conducted on smart product related topics. The main themes that can be discerned within this literature are smart environments and smart interfaces. However, few publications exist on the marketing and NPD of smart products. As such, the current research project can contribute in two ways. First, this research contributes to the literature on smart products, which has mainly taken a technical approach and has paid little attention to the adoption of smart products. Second, the current research project contributes to the marketing and NPD literatures that so far have paid limited attention to the topic of smart products. In the following section we will build a conceptual framework in which product smartness and elements from the adoption literature will be interconnected.
2.3 Conceptual framework
The current section describes the conceptual framework that underlies the present research project. The conceptual framework consists of hypothesized relationships between product smartness and innovation adoption through the innovation attributes. Furthermore, the framework contains moderating effects of consumer characteristics on the relationship between the product smartness dimensions and the innovation attributes. These moderating effects imply that consumers may differ in how they perceive product smartness in terms of the innovation attributes depending on certain individual differences. Figure 2.2 shows the broad outline of this conceptual framework2.
Figure 2.2 shows that adoption is explained by the innovation attributes. The innovation attributes are explained by product smartness and by the interaction of product smartness with the consumer characteristics. The framework can be specified in the following equation:
A = a + b IA , where (1)
IA = c + d1 PS + d2 PS x CC (2)
In equation (1), A represents adoption and IA represents the innovation attributes. In equation (2) PS and CC represent product smartness and consumer characteristics respectively. The main contribution of this research project is formed by the investigation of equation (2) that represents the influence of product smartness on the innovation attributes and how product smartness interacts with the consumer characteristics in its impact on these innovation attributes. The remainder of section 2.3 discusses each path of the conceptual framework in more detail. However, we will first provide a discussion of the main concepts from the literature on the diffusion of innovations.
Product smartness: •Autonomy •Adaptability •Reactivity •Multifunctionality •Ability to cooperate •Humanlike interaction •Personality Innovation attributes: •Relative advantage •Compatibility •Observability •Complexity •Perceived risk Consumer characteristics: •Product specific variables •Personality traits •Demographic variables Adoption: •Overall appreciation H1-5 H6-13
Figure 2.2 The conceptual framework
2.3.1 The diffusion of innovations
The literature on the diffusion of innovations can be considered as a major research stream. Diffusion received attention in a large number of studies in many different disciplines, such as anthropology, sociology, psychology, geography, and marketing.
Theoretical background and conceptual framework 33 process (also called innovation-decision process) and refers to the process through
which an adoption unit becomes familiar with an innovation up to the moment at which the unit makes an adoption decision. The period from when a unit becomes familiar with an innovation until the unit adopts or rejects the innovation is called the adoption period. This is where the focus of the current project lies. The present research project investigates how product smartness may influence adoption. However, we will get to this in section 2.3.3 and will first discuss the diffusion process to which the adoption period is related.
2.3.2 The diffusion process
Diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system. Four main elements can be discriminated within the diffusion process: the innovation itself, communication channels, time, and the social system. Rogers (1995) describes these elements as follows:
The innovation is an idea, practice, or object (such as a smart product) that is perceived as new by an individual or another unit of adoption. It does not matter whether an idea is objectively new in the sense of the amount of time that passed since it came into life. The extent to which the innovation is perceived as new determines how an individual deals with it.
Communication channels are the means by which messages get from one adoption unit to another. These channels can be mass media channels, but also interpersonal channels. Mass media channels involve media such as radio, television, newspapers and the Internet. Interpersonal channels involve direct exchange between two or more individuals and are more effective in persuading an individual to accept a new idea. In the case of the present research project this would concern a consumer that could be persuaded to adopt a smart product. The third element of time plays a role within diffusion in three ways. First, time is relevant in the process by which an individual passes from first knowledge of an innovation through adoption or rejection. Second, time is used to express whether a unit of adoption is relatively early or late in adopting an innovation compared to other members of the social system. Third, time plays a role in measuring the rate of adoption of an innovation. The rate of adoption is measured as the number of members of the system that adopt the innovation in a given time period.
deal with the diffusion of innovations. Bass (1969) delivered a major contribution to the diffusion literature. He constructed the predictive Bass model that can be used to forecast the number of adoptions of a new product that will occur in future time periods. These forecasts are either made on the basis of early sales of a new product and sometimes on the basis of judgments of managers that use the diffusion history of comparable products (Mahajan, Muller, and Bass, 1990). Besides its predictive ability, the contribution of the Bass model lies in expressing the diffusion process in the form of a mathematical equation. This mathematical equation contains three parameters: (1) the innovation coefficient (also called the coefficient of mass media influence), (2) the imitation coefficient (also called the coefficient of interpersonal influence), and (3) an index of market potential. Bass (1969) assumes that innovators within a social system are influenced by mass media and that imitators are influenced by word-of-mouth. Thus, in the Bass model the innovation coefficient estimates the rate at which innovators adopt a product. The imitation coefficient estimates the rate at which imitators enter the market. A meta-analysis of diffusion studies (Sultan, Farley, and Lehmann, 1990) showed that the average innovation coefficient was .03 and the average imitation coefficient was .38. These results suggest that diffusion is a social process that takes place through interpersonal communication and to a lesser extent by innate innovativeness of consumers.
A large number of studies followed up the study by Bass (1969). These studies tested the Bass model for various types of products such as consumer durables (Ganesh, Kumar, and Subramaniam, 1997; Heeler and Hustad, 1980), industrial products (Ganesh and Kumar, 1996; Mahajan and Peterson, 1979) and services (DeKimpe, Parker, and Sarvary, 1998). Other studies added variables to the Bass model such as price (Bass, 1990; Jain and Rao, 1990) and advertising (Horsky and Simon, 1983; Simon and Sebastian, 1987). An in-depth review of this line of literature, however, is beyond the scope of this dissertation that focuses on the adoption process. Interested readers are referred to Mahajan, Muller and Bass (1990), Sultan et al. (1990) and Mahajan, Muller and Wind (2000) who provide useful overviews on the diffusion literature in marketing. We can state, however, that no studies exist that investigated the diffusion of smart products.
2.3.3 The adoption process
The second process within the diffusion of innovations literature takes place at the level of the adoption unit. This process describes how a single decision-making unit goes from receiving first knowledge of an innovation to confirmation or disconfirmation of the adoption decision.
Theoretical background and conceptual framework 3 step, the decision-making unit gets to know and understand the innovation.
Persuasion refers to the moment at which a unit forms an attitude (favorable or unfavorable) toward the innovation. The decision step refers to the time at which a unit participates in activities that ultimately lead to the decision to adopt or reject the innovation. Implementation occurs when a unit starts using an innovation. In the confirmation step, the adoption unit looks for information that either confirms or disconfirms a previously taken adoption decision. In the case of disconfirmation, the unit may reverse the previous decision.
As was noted above, time is relevant in the diffusion process in that each decision unit needs a certain amount of time to go from the knowledge of an innovation through adoption or rejection. Rogers describes this period as the adoption period. One way to increase the rate of adoption is to decrease the length of the adoption period. Two main factors influence the length of this period: innovation attributes and adopter characteristics. For example, innovations that are relatively simple in nature will be adopted more quickly. Innovations that are perceived as being more likely to malfunction will be adopted less fast. Also, some adopters are very open to new ideas and have no trouble dealing with the uncertainty that follows from this newness. Such adopters are more likely to have a shorter adoption period than adopters who do have trouble dealing with uncertainty. Within the current research project we will focus on how these two factors play a role for smart consumer durables and thereby influence the adoption period for these products. The next section will build the conceptual framework in which these factors are related to product smartness.
2.3.4 Product smartness and the innovation attributes
In the current section we will develop hypotheses about the relationships between product smartness and the innovation attributes from the diffusion literature. As we described above, the innovation attributes are important determinants of the adoption period. Holak (1988) found that these innovation attributes are good predictors for eventual product adoption. In order to develop further insight into the adoption of smart products and how its rate may be increased or decreased, we will relate product smartness to these innovation attributes.
trialability of a product. We will further discuss the five innovation attributes below. Subsequently, we will develop hypotheses on how product smartness influences each innovation attribute.
2.3.4.1 Relative advantage
Relative advantage is one of the five innovation characteristics influencing the rate of adoption (Rogers, 1995). Relative advantage is defined as the degree to which an innovation is perceived as superior to the idea it supersedes. An innovation can be superior in terms of utility, social prestige (see e.g., Hirschman and Holbrook, 1982), convenience, aesthetics (see e.g., Hekkert, Snelders, and van Wieringen, 2003) or other benefits. The nature of the innovation determines which kind of relative advantage is important to adopters. As with all innovation attributes, relative advantage should not be seen as an objective attribute. Only when adopters perceive it, relative advantage will increase the rate of adoption of an innovation. Several studies in marketing and NPD showed that relative advantage positively influences the rate of adoption (Holak, 1988; Ostlund, 1974; Plouffe, Vandenbosch, and Hulland, 2001; Tornatzky and Klein, 1982). In an early study, Ostlund (1974) investigated the extent to which perceived innovation attributes and personality characteristics predict innovativeness. The study showed that with the other innovation attributes, relative advantage was a good predictor of innovative behavior. A meta-study by Tornatzky and Klein (1982) investigated the average relative importance of the innovation attributes that were measured in multiple studies. The results showed that the effects of the innovation attributes of relative advantage, complexity, compatibility, observability and trialability all were in the expected direction. However, together with the innovation attributes complexity and compatibility, only relative advantage had a consistent significant impact on innovation adoption.
Theoretical background and conceptual framework 3 relative advantage. With respect to the dimension of autonomy, we expect that
higher levels of autonomy increase the levels of advantage that consumers perceive. This expectation is based on Baber (1996) who described that higher levels of autonomy deliver savings in time and effort. We also expect that products that are able to learn will be perceived as more advantageous. TV’s could, for example, gain a higher relative advantage by being able to provide a viewer with personal recommendations. Such recommendations could be based on information about which type of viewer uses the TV (Hara, Tomomune, and Shigemori, 2004) or on the basis of personal profiles (Murasaki, 2001). Comparably, products with a higher reactivity are likely to be perceived as offering more advantage. For example, a door that opens when someone approaches it has the advantage over other non-reactive doors in that people do not have to use muscle force.
Products that are able to cooperate with a larger number of products also deliver more relative advantage. Previous research (see e.g., Katz and Shapiro, 1985) showed that for network products, the utility of a network product strongly depends on the number of other users that are in the same network. For a telephone, for example, the utility that a consumer derives from purchasing the product depends on the number of other households or businesses that are in the same telephone network. Analogous to that, we expect that higher levels of ability to cooperate are also associated with a larger utility because they enable the product to cooperate with a larger number of products. For example, a PDA that is able to communicate with both mobile telephones and personal computers has a higher relative advantage than a PDA that can only communicate with a mobile phone. As a result, the former mobile phone can offer more advantages.
Products that communicate in a more humanlike way and that show more personality characteristics are also expected to offer more advantage. In a study on the comparison of different interfaces, Burgoon et al. (2000) showed that respondents rated the anthropomorphic interface that incorporated animated characters and speech synthesis as the most useful. Also, respondents experienced products that interact in a more humanlike as more convenient. In addition, Chan and Khalid (2003) showed that, compared to the use of a normal ATM, operating an ATM using voice control was evaluated as more fun. As such, we hypothesize: H1: Product smartness increases relative advantage.
2.3.4.2 Compatibility
adoption (Holak, 1988; Ostlund, 1974; Plouffe, Vandenbosch, and Hulland, 2001; Tornatzky and Klein, 1982).
We expect that smarter products will generally be perceived as more compatible. First of all, products with higher levels of autonomy are likely to be perceived as more compatible. Baber (1996) described how highly autonomous products may achieve a level of symbiosis in which there is a perfect match between the actions of the product’s owner and what the product does. At this level of symbiosis the presence of certain products may even become unnoticed. For example, a vacuum cleaner at this level of symbiosis would start its work when there is nobody in the house and stop its work when someone comes in.
Also, products that are able to learn will be perceived as more compatible. In fact, it is the basic idea behind the construction of, for example, user profiles to have a product better match this user’s needs. Also, as a product becomes better able to learn, for example, the user profile becomes more accurate (Waern, 2004) and will therefore be considered as more compatible.
Also, more reactive products will be considered as more compatible in that they respond to their users. For example, the reactive Hydraprotect hairdryer that was previously described, reacts to the humidity of the hair by lowering the temperature of the air. Similarly, properly functioning reactive toilets flush when needed, doors open when someone approaches, and lights switch on when a person comes into the room. As such, we expect that products with higher levels of reactivity will be perceived as more compatible.
Furthermore, the more a product is able to cooperate with other products the more it can be embedded within a network of other products that a consumer already owns. Again, the PDA that is able to cooperate with both a mobile telephone and a personal computer is more likely to be perceived as more compatible than a PDA that can only communicate with a mobile phone.
Finally, we expect that products that are able to communicate in a humanlike manner and that show personality characteristics are more compatible with their users. Such products allow a form of interaction that more closely resembles interaction between humans. In their study on the operation of an ATM, Chan and Khalid (2003) found that consumers evaluate the use of speech as more natural. Such findings can be explained by the fact that users can apply their knowledge about human conversation to their interaction with the product (Cassell and Thorisson, 1999). This leads us to hypothesize:
H2: Product smartness increases compatibility. 2.3.4.3 Observability
Theoretical background and conceptual framework 39 are easily observed, because the innovations are, for example, frequently used in
public (e.g., mobile phones). The results of other innovations may be less visible to others, because they are mainly used indoors (e.g., vacuum cleaners). Observability positively affects the rate of adoption. Although the impact of observation was not as strong as for other innovation attributes, other marketing studies have indicated the relevance of observability (Holak, 1988; Ostlund, 1974; Tornatzky and Klein, 1982).
Our hypothesis with respect to the impact of product smartness on observability is based on the observation that many smart products contain hidden functionality (see also section 1.3.1). A large extent of functionality is a result of their ICT components in the form of, for example, software. Rogers wrote that products with an important software element usually have a slower rate of adoption. In smart products, the relation between product form and how it can be used is less obvious than in non-smart products. For example, a PDA can contain functionality such as a diary, calculator, and address book. However, this functionality is not communicated by the product’s form. As a result, consumers may have difficulty in observing a product’s functionality and its operation procedure (Veryzer, 1995). We therefore expect that initially:
H3: Product smartness decreases observability. 2.3.4.4 Complexity
Complexity is a fourth innovation characteristic introduced by Rogers (1995). The complexity of an innovation refers to the degree to which an innovation is perceived as relatively difficult to understand and use. Rogers (1995) states that the complexity of an innovation, as perceived by members of a social system, is negatively related to its rate of adoption. Studies by, for example, Ostlund (1974), Tornatzky and Klein (1982) and Holak (1988) confirm this.
We expect that this complexity will play a role when consumers start using a product and also when they have used the product over a longer period of time. Several studies showed that, in first instance, users of smart products perceive them as complex. Chan and Khalid (2003) found that users did not only experience operating the voice controlled ATM as more fun and natural, but also as difficult to learn. Also, Sproull et al. (1996) found that the users of an interface that showed personality in the form of a human face were less confident than users operating a text interface. In addition to that, Alpert et al. (2003) found that users of a user-adaptive interface had difficulty to understand how it worked.
stated “as technology has advanced, we have understood less and less about the inner workings of the systems under our control.” A pair of scissors is easy to use because all operating parts are visible and the implications are clear. The holes in the scissors have a size so that only fingers will fit and the number of possible actions with the scissors is limited (Norman, 1998). For intelligent products this is not the case. Intelligent products can be considered as some of today’s most technologically advanced products and many consumers remain having difficulty understanding and using these products (Bauer and Mead, 1995). This is partly due to the fact that users do not receive feedback in the form of movements or noise when using these products. Processors and memory chips do their work invisibly and silently (Den Buurman, 1997). Concluding, we hypothesize:
H4: Product smartness increases complexity. 2.3.4. Perceived risk
Perceived risk as a construct was introduced by Bauer (1960) and later developed by Roselius (1971) and Jacoby and Kaplan (1972) to a multidimensional concept consisting of six components: performance risk, financial risk, social risk, physical risk, psychological risk, and the risk of time loss. The most important dimension of perceived risk is performance risk and is associated with inadequate and/or unsatisfactory performance of the product (Jacoby and Kaplan, 1972). The rate of adoption of an innovation is negatively affected by the risk that adopters perceive. We expect product smartness to increase the performance risk that people perceive. First of all, it is known that technologically sophisticated products lead consumers to perceive risk (Folkes, 1988). Because smart products are more technologically sophisticated they will be perceived as more risky. In addition, smart products frequently perform tasks that were previously performed by their users. It is likely that consumers will not trust these tasks to the product, because they expect that it will fail. The tasks of smart products are also frequently broader. A product will therefore be perceived as more likely to fail. It is known that a larger chance of failure increases the risk perceived (Mitchell and Greatorex, 1993). Also, Morel (2000) found that consumers doubt the quality of hybrid multifunctional products. These findings lead us to hypothesize:
H5: Product smartness increases perceived risk. 2.3. Product smartness and adopter characteristics
Theoretical background and conceptual framework 41 types. The most innovative type of adopters are the innovators, who constitute 2.5%
of the population and who are the first to decide whether they adopt a new product. Next, the group of the early adopters forms 13.5% of the population, followed by the early majority (34%) and late majority (34%). The adopters that are last to adopt a new product or do not adopt it at all are called laggards and constitute 16% of the adopter population.
Several types of adopter characteristics such as demographic variables and personality traits determine whether people are early or late adopters of innovations (Rogers, 1995). As with other innovations, we expect that adopter characteristics may play a role in the adoption of smart products. The current investigation will take into account adopter characteristics that can be divided into three groups: product specific variables, personality traits, and demographic variables. We will discuss previous research involving these variables and subsequently motivate how we expect these variables to play a role in the adoption of smart products. 2.3..1 Product specific variables
Besides the personality traits and demographic variables, this study will also take into account several variables that are directly related to the specific product categories that are investigated. The first of these variables is product involvement. The second variable refers to the frequency with which the respondents use products. We will discuss both variables below in further detail.
Product involvement
Product involvement refers to the extent to which a product category is personally relevant to a consumer (Ratchford, 1987). Previous research showed that product involvement is related to consumer decision-making. High involvement consumers have been found to be more susceptible to the quality of arguments while low involvement consumers are more influenced by the endorser of product information (Petty, Cacioppo, and Schumann, 1983). Also, high involvement consumers are more interested in information pertaining to the product, evaluate more competing alternatives, and have a more finely tuned judgment of different brands (Zaichkowsky, 1985). No previous research has related product involvement to innovation adoption. However, on the basis of the current knowledge on the relevant construct, we expect that involvement may influence the formation of smart product perceptions. We therefore hypothesize:
Use frequency
The second product specific variable concerns the frequency with which consumers currently use a product. These variables can be considered as indicators of the extent to which consumers need a product and, with that, the extent to which they are familiar with and knowledgeable about the product. Previous research showed that product expertise might also affect consumer evaluations. As with higher involvement consumers, people with much expertise appear to supply more articulated evaluations with higher internal and temporal consistency than people with lower product expertise (de Bont and Schoormans, 1995). As such, we expect that people with higher use frequency have a higher product expertise and knowledge. We therefore expect that use frequency may also play a role in the formation of consumers’ perceptions of product smartness. We hypothesize: H7: Use frequency moderates the relationship between product smartness and
the perceived innovation attributes. 2.3..2 Personality traits
Previous research showed that several personality traits play a role in the adoption of new products. We expect that these personality traits will also play a role in the adoption of smart products. These traits are desire for control, novelty seeking, and self-efficacy. For each of these traits we will discuss how they were related to new product adoption in previous research and why we think these variables may be relevant for smart product adoption.
Desire for control
Desire for control refers to the extent to which people want to exert personal control over their lives (Ashford and Black, 1996; Burger, 1990; Burger and Cooper, 1979; Trimpop, Kerr, and Kirkcaldy, 1997). People identified as having a high desire for control are highly motivated to control the events in their lives. They wish to make their own decisions, take on leadership roles in group-settings and react strongly when their perception of personal control is threatened. People having a low desire for control are less interested in exercising control over events and are more willing to allow others to make decisions and take on responsibility for group tasks (Burger, 1990; Burger and Cooper, 1979).
