THE INFLUENCE OF EVIDENCE TYPES IN
ADVERTISEMENTS ON THE EXPECTED
PERFORMANCE, EXPECTED EFFORT AND
SOCIAL INFLUENCE OF SMART THERMOSTATS
Master Thesis -‐ Business Administration – Digital Business
Floortje Plokker 11393319
Supervisor: Nick van der Meulen
Date: 18-08-2017
Acknowledgements
The successful completion of my research has been established with the support from multiple valuable resources. The multiple moments of feedback and direct communication with my supervisor Nick van der Meulen have proven to be of great value for the end product. Besides, the thoughts we have shared have given me understanding of how quantitative research should be conducted and what features of a conceptual model are important to take into account.
Furthermore, due to the internship in Dublin that was done in the meantime, I experienced that writing a thesis can be a lengthy process even if you have written two or more before. Therefore, I would also like to express my gratitude to my close friends and family who have supported me throughout this period and the company of my internship that provided me some moments during the week where I could work on my thesis.
Statement of originality
This document is written by Student Floortje Anne Plokker, who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
This research has aimed to determine the effect of the use of evidence types in advertisements on three dimensions of the Universal Theory of Acceptance and Use of Technologies (UTAUT). The context for this study was the smart thermostat, an updated version of the traditional thermostat that makes use of Internet of Things (IoT), wireless sensors and remote control. Four types of evidence have been studied (statistical, expert, anecdotal and causal) in relation to three UTAUT dimensions (performance expectancy, effort expectancy and social influence). Previous studies only discovered the effect of the use of evidence types on the intention to use, but never included any other dimensions of the UTAUT model. Furthermore, the technological context of the smart thermostat is also a new element. Through the use of a survey, data was collected and analyzed with a multiple linear regression and ANOVA test. Besides, four moderating variables were tested (age, gender, experience with smart household technologies and educational level). Outcomes of the analysis indicated that in general it did not matter what type of evidence was used, since there was no profound difference in effects on the UTAUT dimensions. The only conclusion that could be drawn was hat statistical evidence appeared to have a significant effect on the effort expectancy, indicating that the use of numbers and facts within the advertisements strengthens the consumers believe that the device is easy to use and that it is easy to become skillful at using it. The relationship between expert evidence and the experience with smart household technologies was moderated by age, whereas the relationship between expert evidence and the performance expectancy was moderated by the experience with smart household technologies.
List of Abbreviations
AE Anecdotal Evidence
CE Causal Evidence
EE Effort Expectancy
EX Expert Evidence
IoT Internet of Things KMO Kaiser-Meyer-Olkin PAF Principal Axis Factoring PE Performance Expectancy RFID Radio-frequency Identification SE Statistical Evidence
SI Social Influence
List of Tables and Figures
Table 1. Different evidence types p. 15
Table 2. Examples of marketing message in survey advertisements p. 31
Table 3. Descriptive Statistics p. 34
Table 4. Demographics p. 35
Table 5. Constructs and their corresponding items p. 37
Table 6. Description of variables p. 38
Table 7. Cronbach’s Alpha p. 39
Table 8. Summary of PE regression p. 41
Table 9. Summary of one-way ANOVA for PE p. 42
Table 10. Summary of EX regression p. 43
Table 11. Summary of one-way ANOVA for EX p. 44
Table 12. Summary of SI regression p. 45
Table 13. Summary of one-way ANOVA for SI p. 46
Table 14. Moderation effect of age on PE p. 47
Table 15. Moderation effect of age on anecdotal evidence and EX p. 48
Table 16. Moderation effect of age on expert evidence and EX p. 49
Table 17. Moderation effect of experience on PE p. 50
Table 18. Moderation effect of experience on AE and EX p. 51
Table 19. Moderation effect of gender on PE p. 53
Table 20. Moderation effect of gender on SI p. 54
Table 21. Moderation effect of educational level on AE and EX p. 55
Table 22. Moderation effect of educational level on SI p. 56
Table 23. Summary of results p. 57
Figure 1. Research design p. 12
Figure 2. Graphical overview of the UTAUT model p. 18
Figure 3. Conceptual model p. 30
Figure 4. Hypothesis 4a p. 46 Figure 5. Hypothesis 4b p. 48 Figure 6. Hypothesis 5a p. 49 Figure 7. Hypothesis 5b p. 51 Figure 8. Hypothesis 6a p. 52 Figure 9. Hypothesis 6b p. 54 Figure 10. Hypothesis 7a p. 55 Figure 11. Hypothesis 7b p. 56
Table of Content
ACKNOWLEDGEMENTS 2
STATEMENT OF ORIGINALITY 3
ABSTRACT 4
LIST OF ABBREVIATIONS 5
LIST OF TABLES AND FIGURES 6
TABLE OF CONTENT 7
1. INTRODUCTION 9
2. RESEARCH STRATEGY 12
2.1 MATERIALS 12
3. LITERATURE REVIEW 13
3.1 SMART HOMES CONTEXT 13
3.2 EVIDENCE TYPES 14
3.3 UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY 17
3.3.1 PERFORMANCE EXPECTANCY 19
3.3.2 EFFORT EXPECTANCY 22
3.3.3 SOCIAL INFLUENCE 23
3.3.4 BEHAVIORAL USE 26
3.3.5 MODERATING VARIABLES 26
4. DATA AND METHOD 31
4.1 DESIGN 31 4.2 DATA COLLECTION 32 4.3 DESCRIPTIVE STATISTICS 33 4.4 MEASURES 35 4.4.1 VARIABLES 35 4.4.2 FACTOR ANALYSIS 37 4.4.3 RELIABILITY 38 4.4.4 PRE-TEST 38 5. RESULTS 39 5.1 MAIN ANALYSIS 39 5.1.1 PERFORMANCE EXPECTANCY 39 5.1.2 EFFORT EXPECTANCY 41 5.1.3 SOCIAL INFLUENCE 43 5.2 MODERATING VARIABLES 45 5.2.1 AGE 45 5.2.2 EXPERIENCE 48 5.2.3 GENDER 51 5.2.4 EDUCATIONAL LEVEL 54 5.3 SUMMARY RESULTS 56 6. DISCUSSION 57 6.1MAIN ANALYSIS 57
6.2MODERATION 59
6.3THEORETICAL AND PRACTICAL IMPLICATIONS 60
6.4LIMITATIONS OF THE RESEARCH 61
7. CONCLUSION 63
REFERENCES 64
APPENDIX A – EXAMPLE OF SURVEY ADVERTISEMENT 69
APPENDIX B – CORRELATIONS 70
1. Introduction
Ever since the Internet has been established, the number of new applications and possibilities is rising every day (Iansiti & MacGormack, 1996). It once started with the ‘Internet of Computers’ using services like the World Wide Web. This was followed by the ‘Internet of People’ with concepts such as Web 2.0, where content is not only consumed but also created by people (Coetzee & Eksteen, 2011). One of the more recent discoveries is the so-called phenomenon ‘Internet of Things’ (IoT). IoT refers to the possibilities to connect or link multiple devices and applications. Xia et al. (2012) describe IoT as “the networked interconnection of everyday objects, which are often equipped with ubiquitous intelligence”. A combination between physical and digital elements is used to create value (Wortmann & Flüchter, 2015). The technology derives from early work on networked radio-frequency identification (RFID) (Wortmann & Flüchter, 2015).
In 2014, it was estimated that there were around 16 billion devices in use that incorporated IoT, and that this number will increase to 50 billion by 2020 (Clark 2014; Middleton et al. 2013). IoT can be applied to homes, hospitals and even cities. A clear distinction was made by Texas Instruments (2014) who distinguished the following categories where IoT plays a role (or could play a role in the future): connected home technology (thermostats, lighting and energy monitoring), wearables (fitness trackers and “smart” watches), medical/wellness devices (bathroom scales and blood pressure monitors), connected cars (dashboard controls via smartphone apps, engine diagnostics etc.), urban systems (air quality sensors, city rental bikes, parking meters/sensors).
The focus of this research will be on the implementation of IoT in the private home environment. The use of IoT that connects different devices to the internet within a private home turn it into a so-called ‘smart home’ (Portet et al. 2013). IoT in smart homes could reduce the consumption of resources such as electricity and gas and improves human satisfaction through economic and social benefits (Miorandi et al. 2012). This research focuses on a smart thermostat that uses sensors to actively monitor and optimize consumption. The replacement of the traditional thermostat allows for more accurate real-time monitoring and optimizes power flows (Komninos, 2014). The ability for consumers to generate and manage their power utility more efficiently results in time-efficiencies and cost-savings (McDaniel & McLaughlin, 2009).
Despite the hype of smart home devices such as the smart thermostat, the consumer adoption rates are low. According to Hoffman & Novak (2016), these slow rates can be attributed to three main problems: lack of awareness, consumer concerns (price, security, privacy) and an unclear value proposition. The third problem is probably most impactful, since marketers struggle to communicate the right value proposition to consumers (Hoffman & Novak, 2016). In order to get the value proposition right, it is important to determine how the marketing message should be framed. The message that is communicated to consumers usually contains a certain type of evidence. Examples of evidence types are anecdotal, statistical, causal and expert evidence (Hornikx, 2005). The type of evidence communicated in the marketing message could result in different decisions to either adopt or refuse the use of these new technologies. This is also referred to as the level of technology acceptance (Gaul & Ziefle, 2009). There are several examples of studies who have researched the influence of evidence types that are used in marketing communications for different types of contexts (Hoeken, 2001; Hornikx 2005; Rieke & Sillars 1984; Hoeken & Hustinx, 2009; Slusher & Anderson, 1996). Recently however, the topic did not receive any scientific attention. Besides, the smart thermostat introduces a new technology that uses data collection methods that changes the context from previous studies. The newness of the technology leads to a lack of knowledge upfront, as well as the requirement for certain technological expertise. Furthermore, due to the technological and interconnected nature of the smart thermostat, privacy and security issues are at stake. The introduction of IoT has created a lot of new focus points such as privacy and security concerns related to the use of data (Depuru et al. 2011; Gubbi et al. 2013). This new context opens up new ways to look at the evidence types that are used in advertising statements.
Moreover, the evidence types have thus far only been researched in relation to their persuasive effectiveness and not in relation to the intention to use the technology (Hornikx, 2005). It has, in example been found that statistical evidence, which uses numbers and statistical facts to persuade consumers, is more informative than other types of evidence (Cox & Cox, 2001). It seems relevant to explore how these evidence types interact with the intention to use of more specific technological products. Therefore, the following research question was created: “What is the influence of evidence types in advertising statements on the consumers’ expectation of performance, effort and social influence of the smart thermostat?”
If knowledge can be derived on how evidence types influence the intention to use new technological products, the evidence types might also have an effect on the using intention of
other types of products. This opens up way for more extensive research on the influence of evidence types. Additionally, a more practical goal of this study is to enhance the effectiveness of the advertisements that are used for the smart thermostat. Hoffman & Novak (2016) have stressed the importance of communicating the value proposition. By analyzing the effect of the use of different evidence types, the marketing message could be optimized. This will increase the adoption rate and sales numbers of such devices.
2. Research Strategy
First, a literature study is conducted in which the smart household devices are studied, as well as existing literature on smart thermostats. Concurrently, the different evidence types are reviewed and discussed based on previous researches. Various hypotheses are presented. In a second step, the survey is conducted in which the evidence types are tested for the dimensions of the UTAUT model by Venkatesh et al. (2003). Thirdly, the results are analyzed and discussed and a conclusion is provided. A visual overview of the different steps is provided in figure 1 below.
Figure 1. Research Design
2.1 Materials
Several materials and data sources will be used throughout the research. For the literature study, online databases such as Google Scholar and the library of the University of Amsterdam are used. Literature and journals on the drawbacks, challenges, motivations and benefits of smart thermostats leading to usage intentions are studied. In order to find relevant literature, key words such as ‘Internet of Things, ‘smart homes’, ‘smart thermostat ‘purchase intention, ‘intention to transact, ‘evidence types’, ‘advertising statements’ and ‘marketing messages’ are used. Finally, various potential consumers are questioned through an online survey.
Literature
Study
•Smart homes / smart thermostat •Evidence types
•UTAUT model
Survey
•Different evidence types
•Influence on UTAUT dimensions •Moderated by age, gender, education and
experience
Analysis
•Variation between evidence types •Discussion/Concusion3. Literature Review
This section describes the current literature on IoT and smart thermostats in general. Furthermore, the evidence types that are derived from various marketing studies are mentioned. Previous studies that have researched differences between the type of evidence are examined, as well as studies that have focused on the intention to use specific products. Since the smart thermostats are only recently introduced, scientific literature on the topic is lacking. Therefore, additional articles from magazines and conferences are also included to derive new insights and theories.
3.1 Smart Homes Context
The technology within smart homes is fairly new and its applications are in the beginning marketing and selling stages (Komninos et al., 2014). The smart thermostat is a perfect example of a recently introduced device and its features differ radically from the traditional thermostat. It connects via a wireless network to an operational center using two-way communications (Farhangi, 2010). This is made possible by the introduction of a smart electricity grid that converges information technology with communication technology using power system engineering. In order to understand the context in which these new devices operate, it is important to understand what makes this context different from the traditional situation.
Smart thermostats include a smart meter, communication infrastructure (bidirectional communication with the smart grid) and control devices (Depuru et al. 2011). They measure electricity consumption from the grid to ensure reliable bills (Depuru et al. 2011). The basic idea of the smart thermostat is to control heating, ventilation and cooling with an automatic schedule that has a set temperature when the occupants are active and one when the occupants are away or asleep (Lu et al. 2010). This is made possible by the use of many sensors that are installed throughout the home. Furthermore, the device is able to combine historical occupancy patterns to decide whether to preheat or heat after the occupants get home (Lu et al. 2010). This way, the system saves additional energy and reduces the monthly energy costs. There is a possibility to control the device remotely using a mobile phone app, which increases the amount of self-control by the consumer (Darby, 2010). For the suppliers, the smart meters enable dynamic pricing, meaning the price varies based on time, use, supply and demand (King & Jessen, 2014).
The collection of data and the two-way communication systems via wireless networks could also have its drawbacks, especially concerning the privacy of the consumers. These privacy concerns mainly relate to the collection of data performed by various devices (Weinberg et al. 2015). Consumers have several requirements that need to be fulfilled in terms of safety and security. Examples of these requirements are: data confidentiality, access control and the enforcement of privacy and security policies (Sicari et al., 2015). The number of sensors has increased, which also means the amount of data that is stored and shared with the operational energy center has increased. This data is collected by the smart thermostats that people have placed within their home. Smart meters are now near real-time communication devices (interacting with the smart grid) and thus have a vast amount of data on individual energy use (King & Jessen, 2014). The more the IoT-related systems capture data, the more it will feel as if the life of the consumer is controlled by a person, algorithm or corporation (Weinberg et al. 2015). The safety requirements of the consumer put pressure on manufacturers and policy makers. Governance in IoT is vital and policy makers play a crucial role in the successful adaptation and market penetration of these devices (Coetzee & Eksteen, 2011). However, the requirements are hard to define and fulfill due to the increasing number of interconnected devices. Moreover, the study of Sicari et al. (2015) proved that traditional countermeasures serving to protect cybercrime and unauthorized access cannot fully apply to IoT due to their limited computing power. However, without this data, IoT does not exist and cannot perform accordingly and pursue its goals of energy and cost reductions. Therefore, certain ways should be found to tackle these concerns. Besides the role of policy makers, the type of advertisement and the message it contains could help overcome these privacy concerns in order to guarantee successful adoption.
3.2 Evidence Types
To ensure the smart thermostats are adopted and purchased by consumers, it is important that the product is advertised in the right manner. Advertisements often make use of various types of evidence in order to persuade consumers of the usefulness, to create a positive attitude towards the product and concurrently to increase the behavioral use intention (Hoeken & Hustinx, 2009). Research on evidence types in relation to persuasiveness has been introduced by Cathcart (1955). He proposed that debaters, in order to be more persuasive, should use evidence for their claims. Evidence can be described as a form of proof for a claim that is used
to strengthen the probability of these claims (Hornikx, 2005). The different evidence types have been described by Rieke & Sillars (1984) and a short description is given in table 1 below.
Type of Evidence Description
Anecdotal Use of case stories, examples or illustrations to strengthen argument quality.
Statistical Numerical compacting of a series of instances.
Expert Citing experts to enhance the credibility of the advertising statement.
Causal Explanation of the occurrence of an effect.
Table 1. Different evidence types (Rieke & Sillars, 1984)
Later on, the use of evidence types in scientific studies and its effects on persuasiveness has been reviewed by Hornikx (2005). He found that many studies appeared to be inconsistent and differed from each other concerning the operationalization of the evidence types. The acceptance of the evidence claim, often measured as the dependent variable, is also measured differently by various scholars. The various methods measured an effect on attitude towards the product or buying behavior (Hornikx, 2005; Haans et al. 2013; Hoeken & Hustinx, 2009; Slater & Rouner, 1996). However, the behavioral use was never included as a dependent variable. Even more confusing is the fact that all studies came to different conclusions concerning the persuasive effectiveness of evidence types. The dependent variable, often measured as the acceptance of a certain claim, showed different levels for different studies.
Moreover, none of these studies have focused on specific types of products. Haans et al. (2013) have tried to discover the influence of the four different evidence types on search engine advertisement. They have measured the influence that the different types could have on the click-through-rate and the conversion rate. The conversions that have been measured are comparable to behavioral use, since it implies an online purchase has been made. However, the research did not take into account product-specific characteristics but took a broad perspective on several retailers. Besides, a conversion referred to an actual purchase whereas the intention to use refers to a certain intentional behavior that might establish a purchase in the future. The concept of using evidence types is mostly linked to persuasive effectiveness, which seeks to
explain whether the proposed behavior is better than the alternative (Hoeken & Hustinx, 2009). In the case of the smart thermostat, the example would be that the proposed behavior is to possess a smart thermostat, whereas the alternative is not possessing a smart thermostat. It could be that effective persuasion and a positive perception of the proposed behavior could in turn lead to a higher intention to use.
As mentioned in the introduction, marketing messages for new technologies are expected to be perceived differently by consumers (Thompson, 1997). Many influential concepts play a role, for instance the extent to which people are willing to adopt a new technology (Legris et al. 2003). This technology acceptance level differs from person to person. Furthermore, the level of technology acceptance has thus far been included in some studies but none of them specifically related to the smart thermostat. Slater & Rouner (1996) have found that statistical evidence and its persuasive advantage depends on the extent to which the evidence relates to the personal values of the consumer. Anecdotal evidence worked the other way around and seemed to be more effective when incongruent with the values of the consumer. If the desirability of a consequence is not very obvious, evidence is needed to convince people of the outcome. This could be much better realized with anecdotal evidence than statistical evidence (Hoeken & Hustinx, 2009). This is due to the fact that anecdotal evidence might provide a clearer picture on the desirability of the new situation. It could be that this is needed for a product like the smart grid, for which the desirability is not directly evident. Another comparison between these evidence types was conducted by Allen & Preiss (1997), who described ‘anecdotal’ evidence as ‘narrative’ evidence. They have compared narrative to statistical evidence in messages and found that the latter is more persuasive. Therefore, it could be that generating a conclusion on which evidence type prevails is dependent on the context and product at stake. It makes sense from a scientific perspective to discover how behavioral use is influenced by these evidence types. Additionally, since the smart thermostat is a product that people might not be aware of or knowledgeable about yet, it seems interesting for marketers to discover which type of evidence the consumer prefers. Cox & Cox (2001) indicated that statistical evidence tends to be more informative than other types of evidence. It could be that the lack of information and the fact that this product could create social and economic benefits creates the need for more statistical evidence.
3.3 Unified Theory of Acceptance and Use of Technology
The evidence types in this research are used to determine the consumer’s intention to use smart thermostat. Venkatesh et al. (2012) have used different technology acceptance models to create the so-called unified theory of acceptance and use of technology (UTAUT). UTAUT includes four core variables (performance expectancy, effort expectancy, social influence and facilitating conditions) and four moderating variables (age, gender, experience and voluntariness of use). The model identifies these drivers as the constructs that have an influence on the adoption of information systems (Venkatesh et al. 2003). This adoption is indicated as the usage intention and behavioral use. The model has primarily been used for information systems and included as an independent variable in multiple studies (Gupta et al., 2008; AlAwadhi & Morris, 2008; Yu, 2012; Zhou & Wang, 2010). Even though the model has several limitations, its usefulness has been widely acknowledged and they have made use of a large real world data set. The UTAUT model has been tested and applied to several technologies, but never for the different evidence types or in the context of the smart thermostat. Its comprehensiveness, validity and reliability encouraged the use of the model for this study. It is amended and adjusted to the context of the smart thermostat. The UTAUT model dimensions have often been used as the independent variables that predict the intention to use and behavioral use. This study extends the model by hypothesizing that the dimensions are influenced by evidence types that are used in advertising. The second part of the model (the impact of performance expectancy, effort expectancy and social influence on the behavioral intention) is taken as a fact and not disclosed in the study. This research differentiates itself from other studies due to the fact that it proposes multiple new constructs to influence the dimensions of the UTAUT model and in that way includes a marketing element. A graphic overview of the model is presented in figure 2 below. For simplicity and relevance reasons the facilitating conditions and its influence on the use behavior are not included in this study.
Figure 2. Graphical overview of the UTAUT model from Venkatesh et al. (2003)
Previous studies, as well as information on the influence of various evidence types, contribute to some predictions related to their impact on the intention to use the technology. Thus far, the studies that included comparisons between evidence types came up with mixed results. Out of the four different types, statistical and anecdotal evidence have received the utmost of attention from scientific scholars (Hornikx, 2005).
The UTAUT model explores the relationship between the four dimensions and the behavioral use (Venkatesh et al. 2003). The model expects an influence of the dimensions on the intention to use the technology. Hausman & Siekpe (2009) came to the same conclusion and also concluded that i.e. the perceived usefulness had a positive impact on the purchase intention. Here, the perceived usefulness is similar to the performance expectancy of the UTAUT model (Venkatesh et al. 2012). Following this theory, users will experience an improved feeling of performance when they use the technology. People might find the ability to manage the energy within their house remotely and individually attractive. Besides, they are now able to lower their energy costs, making it a viable long-term investment. Gao & Bai (2014) accordingly stressed that service convenience increases the satisfaction level of consumers and affects their intentions. When facilitating the consumer’s daily life, it seems likely that the adoption rates and buying intentions increase.
3.3.1 Performance Expectancy
Performance expectancy (PE) is defined as “the degree to which using a technology will provide benefits to consumers in performing certain activities” (Venkatesh et al. 2012, p.159). Not many of the concepts that might play a role in the relationship between the evidence types and the purchase intention of the smart thermostat have been touched upon by scientific scholars. However, some of the concepts related to IoT have been studied extensively in other domains or categories of IoT. One of these concepts that has been mentioned in relation to the performance expectancy of new technologies is the usefulness of the technology. It is defined as the degree to which someone believes that a technology would enhance his or her performance, and has been part of the technology acceptance model (TAM) (Davis, 1989). It is also adopted by Venkatesh et al. (2003) as one of the main items relating to the performance expectancy. To a certain extent, technological expertise is required from consumers when they decide to purchase a smart thermostat. The smart thermostats differ radically from traditional thermostats and other traditional household devices since they are connected via sensors and networks. The evidence types could have an influence on the perceived usefulness.
Aldrich (2003) conducted a study on the obstacles that consumers encounter when taking up technologies that are used in smart homes. These include:
- Dependence on old housing stock
- Lack of common protocol between other devices - High initial investment from the consumer
- Little usability evaluation by suppliers (little attention paid to usability) - “Technology push” by suppliers
Especially the latter two could determine which evidence types leads to a higher performance expectancy and adoption rates. Anecdotal evidence might highlight the usability. Since the smart thermostat is a rather new technology people might also be unaware of its possibilities and might be unable to operate the device. Hoffman & Novak (2016) therefore stressed the importance of having people experience the opportunities of a device. The new device should eventually be embedded in existing institutionalized practices, like the regulation of temperature within a house and from the house (Munir & Philips, 2005).
Anecdotal evidence is expected to have an effect on the perceived usefulness, and thereby on the construct performance expectancy. Anecdotal evidence makes use of storylines and
narratives to create an impact on the consumer. This can for example be done by telling the story of another consumer who already makes use of the new technology. If the consumer sees that the device is of use to other people, they will start considering a purchase. Storylines are able to create a strong case, since it stimulates people to relate (Kim et al. 2016). Consumers engage with the brand or product and a sense of involvement is created that refers to the interest of the consumer in a product or brand category (Kim et al. 2016). Storylines, or narratives, are used to convey meanings and intangible benefits associated with the consumption (Mattila, 2000). Therefore it is expected that this type of evidence influence the performance expectancy.
H1a: The use of anecdotal evidence in the marketing message of the smart thermostat will have a positive significant effect on the extent to which consumers perceive that the technology will provide benefits in performing certain activities
Another concept that is related to the performance expectancy and has repeatedly been studied, is the role of trust. However, opposed to privacy and security, its precise meaning differs. Therefore, the main problem with the concept ‘trust’ is that it cannot easily be measured by evaluation methodologies (Sicari et al. 2015). Gao & Bai (2014) researched the consumer acceptance and willingness to use make use of products using IoT and wireless two-way communication devices. With the use of a technology acceptance model (TAM) they have tested three technology factors (perceived usefulness, perceived use and trust), one social factor (social influence) and two individual user characteristics (perceived enjoyment and perceived behavioral control). The main finding of their study was that trust did not play a role in predicting the intention to use the technology. This indicates that people are willing to use and purchase technologies with IoT such as the smart thermostat and should not be convinced of its trustworthiness. Evidence types that focus on the creation of trust, such as anecdotal and causal evidence, might therefore have less evident influence than statistical and expert evidence (Hoeken & Hustinx, 2009). Besides the possible disadvantages, using IoT in devices such as the smart thermostat could also lead to several benefits. Weinberg et al. (2015) indicated that IoT increases access and control of Internet-connected devices, allowing for a more customized experience. Additionally, it leads to many efficiencies such as energy reduction and increased individual control over costs and usage. Consumers will need to consider and evaluate the trade-offs between the possible challenges and benefits. The use of evidence types within the advertising statements could have an effect on the way in which the technology is expected to perform.
For causal evidence the possession of a smart thermostat is contrasted against the possession of a traditional thermostat. In order to persuade them of the usefulness, the possibilities and opportunities are clearly highlighted. In order to do so, the causal evidence that is used in the message favors the smart thermostat over the traditional thermostat. Not using the smart thermostat will create unfavorable consequences for the consumer. Causal evidence is regarded as a strong quality argument (Slusher & Anderson, 1996). The consumer is confronted with many arguments in favor of using the smart thermostat and its favorable consequences. For this reason, it seems likely that causal evidence will in turn also have a positive influence on the perceived usefulness, and therefore on the performance expectancy. Additionally, next to seeing the positive side of consumption (as with anecdotal evidence), causal evidence also includes the downside of not using the device. It seems logical that this will positively influence the expectation that the device is useful.
H1b: The use of causal evidence in the marketing message of a smart thermostat will have a positive significant effect on the extent to which consumers perceive that the technology will provide benefits in performing certain activities
Hoeken & Hustinx (2009) argued that for some purposes the desirability of a result is more easily assessable than the probability of a result. They explicitly mentioned the example of a new heating system that might lead to cost reductions. The probability and certainty of the cost reduction is harder to assess than the desirability and therefore probability claims are used more often to strengthen their persuasion power. The probability that the positive effect of a smart thermostat occurs can be derived through expert and statistical evidence. Reinard (1988) supports this view by mentioning that in Western cultures, statistics are seen as indicators of objectivity. Since the smart thermostat establishes cost reductions and individual as well as societal benefits, the use of numerical information will likely positively influence the intention to purchase. Statistical evidence might reduce the feeling that the technology is pushed by suppliers, which was mentioned by Aldrich (2003) as a negative consumer perception. Besides, source credibility is higher for statistical and expert evidence (Haans et al. 2013). The participants could be perceptive to heuristics such as the source credibility. Therefore, it is also expected that these types of evidence have a positive impact on the performance expectancy. Reinard (1988) additionally found that well-referencing increases the persuasive effect of a message.
H1c: The use of expert evidence in the marketing message of a smart thermostat will have a positive significant effect on the extent to which consumers perceive that the technology will provide benefits in performing certain activities
H1d: The use of statistical evidence in the marketing message of a smart thermostat will have a positive significant effect on the extent to which consumers perceive that the technology will provide benefits in performing certain activities
3.3.2 Effort Expectancy
Effort expectancy (EE) is defined as “the degree of ease associated with consumers’ use of technology” (Venkatesh et al. 2012, p.159). It is strongly related to constructs such as the ease of use and complexity of the product. Previous studies suggest that the higher the complexity of a technology, the lower the adoption rate of consumers (Venkatesh & Brown, 2001). Effort expectancies will often be formed after experience (Venkatesh & Davis, 1996). Pre-usage or experience with similar technologies will therefore have a moderating effect. In the context of the smart thermostat, EE refers to the extent to which the consumer believes he or she can use the meter effortlessly. This will result in a higher degree of consumer intention to use the smart thermostat.
Anecdotal evidence makes uses of a storyline that aims to engage and persuade consumers based on identification with the story and the extent to which they can relate (Hoeken, 2001). Especially for inexperienced consumers (people without knowledge about the smart thermostat or similar technologies), the use of this type of evidence will have a positive impact on the effort expectancy. A storyline will bring people at ease and reduce feelings of possible complexity (Kim et al. 2016). Even though the study of Kim et al. (2016) has looked into the effects on luxury products, it is expected that similar effects will apply to the smart thermostat. If they see that others are able to operate the device, they will have more confidence in their own usage capabilities. Furthermore, expectations on the ease of use are closely linked to the extent to which consumers have self-confidence in their usage capabilities. Since the storyline of anecdotal evidence is able to bring people at ease, this level of self-confidence is likely to increase. The other types of evidence do not (or to a lower extent) make use of storylines and narratives and focus less on the real use of the product. Especially statistical and expert evidence make more use of facts about the product features, whereas causal evidence focuses more on
the benefits of the new technology over the traditional technology. Therefore, they are not expected to have an effect on the effort expectancy.
H2: The use of anecdotal evidence in the marketing message of the smart thermostat will have a positive significant effect on the extent to which consumers perceive the use of the technology as easy
3.3.3 Social Influence
Social influence (SI) is “the extent to which consumers perceive that important others (e.g., family and friends) believe they should use a particular technology” (Venkatesh et al. 2012, p.159). The social influence includes more subjective norms and determines to what degree social factors play a role in the decision to adopt the technology. Since the smart thermostat is a fairly new technology on the market that not many people might possess as of yet, they could be perceptive to the influence of close family and friends and other subjective influences.
After being exposed to the judgments of others, consumers will evaluate the judgement and create their own opinion or adopt the opinion of others as their own (Wood, 2000). The attitude towards the product can be changed by stories or experiences that are collected through opinions of social others. The social influence construct will be dependent on several concepts that play a role. One of the most vivid examples of an extensively researched concept on this matter, is privacy. Privacy and security requirements play a fundamental role in the level of trust that people will have in new technologies. Moreover, AlAbdulkarim et al. (2012) found that data security plays a vital role in the decision to adopt. Vividly visualizing the potential and use of the smart thermostat could enhance the level of trust people have in the technology and their data being shared among others. This will likely diminish their privacy concerns. When they have a clear picture of the data security and are persuaded of the safety matters that are included, they are more confident that the technology is safe and interesting for them to use. The more people, that are seen as important others from the perspective of the consumer, adopt the smart thermostat and decide that these concerns can be neglected, the more it will influence the decision of the consumer to adopt. The importance of privacy and the concerns that come along might create a need for a type of evidence that is able to diminish these concerns. A lack of trust in the privacy matters will likely result in a less favorable product attitude.
Moreover, the subjective norm is also influenced by external sources such as mass media reports, expert opinions and other non-personal information (Bhattacherjee, 2000). External sources will have its effect on the population in general. This in turn will have an effect on the opinion of important others and transfer these opinions to the consumer. Whenever positive news is brought to the attention of consumers related to the smart thermostat, the social influence will be higher. Currently, consumers are struggling to find the value in replacing their traditional thermostat for a more expensive smart thermostat. According to the customer, these new technologies do not offer many benefits besides the novelty element (Hoffman & Novak, 2016). Furthermore, they are likely to encompass technological difficulties (Hoffman & Novak, 2016). Hoffman & Novak (2016) additionally stressed that current marketing practices need to focus on the emergence of an experience. It is also the experience that might establish a positive attitude towards the product. Furthermore, it is about the interactions and not the components that the device contains. This is also where the element of privacy concerns comes in. People are interested in what the technology can do for them and not how it is built (Walker & Johnson, 2006). If the device is able to share their personal data, or is receptive of security risks the consumer might develop a less favorable attitude towards the project. Based on these findings, one could argue that the focus on experience rather than cases could best be established by the use of anecdotal evidence. Anecdotal evidence can give consumers a vivid visualization of what the technology could do. If the privacy concerns are brought to a lower level, this will in turn increase the favorable product attitude and the extent to which people will share positive opinions about the product with important others. Anecdotal evidence inhibits more vividness and is easier to imagine than statistical evidence (Hoeken, 2001). The consumers interpret brand or product stories and transform them into a memorable experience (Kim et al. 2016). The receiver (or consumer) enters a world that is evoked by a story. Moreover, a narrative format is easier to understand since it is more similar to life experience (Mattila, 2000). The storyline often uses a personal touch and makes use of subjective persuasion techniques (Hoeken, 2001). This in turn will give consumers the feeling that the device is used and understood by others and is seen as a positive contribution to their lives. Based on these findings, it is expected that important others will influence the consumer perception that they should use the smart thermostat when anecdotal evidence is used.
H3a: The use of anecdotal evidence in the marketing message of a smart thermostat will have a positive significant effect on the extent to which consumers perceive that important others believe they should use that smart thermostat
For causal evidence the availability of explanations increases, making people more willing to accept the claim. They are presented with the positive effects of adoption and the negative consequences when neglecting to adopt. This will enable the consumers create to build a model of how an effect (the adoption) occurs in their personal environment (Hoeken, 2001). Even though the study of Hoeken (2001) studied the acceptance of a certain claim (related to a life threatening disease such as aids) and not the intention to use a technological device, it is still expected that using causal evidence has a positive effect on the reduction of privacy concerns. Narrative presentations are easily processed and recalled, providing a sense of recognition and plausibility (Slater & Rouner, 1996). Moreover, by contrasting the desired effect of purchasing a smart thermostat against not possessing a smart thermostat increases the desirability of the product and will lower the possible concerns that consumers had upfront. Their attitude will positively change, especially when hearing that more people use the technology and do not fear the misuse of their data. The smart thermostat has multiple benefits that are not only limited to costs, but also incur environmental and efficiency related advantages. Through a multitude of factors, they can be persuaded that the smart thermostat should be favored over the traditional thermostat. Therefore, the following hypothesis was created:
H3b: The use of causal evidence in the marketing message of a smart thermostat will have a positive significant effect on the extent to which consumers perceive that important others believe they should use that smart thermostat
The influence of external sources has proven to have a significant impact on the subjective norm of the social influence (Bhattacherjee, 2000). Especially the topic of sustainability and environmental concerns have received a lot of attention in the past years. Besides the cost-reductions, more societal benefits can also be derived by using the smart thermostat (Hoffman & Novak, 2016). Therefore, it is expected that if an expert provides information on the benefits of the device, this will have a significant impact on the social influence. The social influence construct focuses on people in the direct surrounding of the consumer that could exert an influence on his opinions and decisions. Especially when the expert can be seen as a trustworthy source who might have an independent perspective, consumers could be persuaded by these individuals (Marr & Prendergast, 1993).
H3c: The use of expert evidence in the marketing message of a smart thermostat will have a positive significant effect on the extent to which consumers perceive that important others believe they should use that smart thermostat
The statistical evidence is not expected to have an influence on the extent to which consumers perceive that important others believe they should use the smart thermostat. Since this evidence is mostly numerical and based on facts and objective expressions, it will most likely not affect the subjective perception of the consumers.
3.3.4 Behavioral Use
One of the dependent variables in the model of Venkatesh et al. (2003) is the intention to use the technology. The intention to use describes a change in behavior of consumers on whether they are willing to use a certain product. It is defined as the subjective probability that an individual will use the product or purchase the product from the online seller or offline store in the future (Chiu et al. 2012). Other concepts that refer to a similar outcome are: willingness to use and behavioral use. Many scholars have used these constructs in academic studies, however none of them in relation to IoT devices. The behavioral use might differ for the type of product that is at stake (Haans et al., 2013). Scholars have made a distinction between search, experience and credence products (Darby & Kami, 1973). The evaluation of search products can be done by externally provided information, whereas experience products also require a try-out, and finally credence products are hard to evaluate even with information and try-outs. These categories could be linked to the smart thermostat, because this study focuses on a search method. This is the case, since it is only based on externally provided information through the advertisements. Additionally, it has been proven that the consumers who show higher levels of behavioral use are more likely to actually buy the product in the future (Berkman & Gilson, 1978). The relationship between the three dimensions (performance expectancy, effort expectancy, social influence) and behavioral use is taken as a fact and was not studied further.
3.3.5 Moderating Variables
The moderating variables that have been used by Venkatesh et al. (2003) include age, gender, experience and voluntariness of use. Age could have a possible influence on the variation in the intention to use the technology using different evidence types. This influence of age in relation
to technologies has been studied by many scholars. A certain age usually relates to a certain competency in using new technologies and being familiar with data sharing devices (Gaul & Ziefle, 2009). Since different age numbers might have different reasons to adopt or purchase a certain product, it could be that they are persuaded in different levels by the various types of evidence. This is also due to the fact that age might determine the level to which they are familiar with the use of technologies (Gaul & Ziefle, 2009). Furthermore, Morris & Venkatesh (2000) indicated that older people often encounter difficulties when absorbing new material or demonstrating competence at new tasks, it is therefore that age is also expected to have a moderating effect on the effort expectancy. Besides, they often face anxiety towards the new technology since they are less familiar with other types of technology. The moderating effect of age on the relationship between the evidence types and social influence is not hypothesized to be present. Social influence often comes from important others around you and age will not determine how perceptive you are for this type of influence. It is only the type of social influence and especially the source that will create differences in social influence levels, and not the age of the consumer.
H4a: The relationship between the four evidence types and performance expectancy will be moderated by the age of the consumer
H4b: The relationship between anecdotal evidence and effort expectancy will be moderated by the age of the consumer
Another moderating variable is the experience with smart household technologies. It could be that people who currently possess a smart household device or people who are more familiar with these new technologies tend to have higher purchase intention rates.
Furthermore, people who are more familiar with technological products might already be more knowledgeable about the features of the smart thermostat. Consumers that are more experienced with the use of technological devices could be influenced in different ways by evidence types than people who are less experienced. Since they are more able to make comprehensive use of new technologies, they might see the perceived usefulness of a new technology more easily than non-experienced consumers. Current knowledge or possession of other smart household technologies is expected to have a positive impact on the perceived usefulness. Since these people have encountered interactions with these types of products in the past they are more knowledgeable of the features and how it will improve their lives. If the device is completely
new to people they might have a certain level of suspicion before they are able to see how its use can have a positive impact (Hsiao, 2003).
Many scholars have also argued the impact of learning from past experiences (Bentler and Speckart, 1979). Additionally, Conner & Armitage (1998) argued that considerations of past behaviors have a significant impact on consumer’s intentions to use a certain product. By experience, the consumer has already been able to try-out the product and to overcome certain difficulties they have encountered when using the technology. Therefore, they might be less influenced by the evidence types when it comes to the effort expectancy. Their expectancy on the ease of use is already higher than consumers who do not have the experience. Concerning the social influence, it is not expected that the relationship with the evidence types is moderated by experience. Important others have an influence by focusing on subjective norms. Having experience with a certain technology will not influence the extent to which they are perceptive to the meaning of others. Important others around the consumer are still able to influence the opinion of the consumer even though he has much or none experience with the technology.
H5a: The relationship between the four evidence types and the performance expectancy will be moderated by experience, meaning the higher the experience level the weaker the relationship between the evidence types and the performance expectancy
H5b: The relationship between anecdotal evidence and effort expectancy will be moderated by experience, meaning the higher the experience level the weaker the relationship between anecdotal evidence and the effort expectancy
Venkatesh (2003) additionally used gender as a moderating variable in his study. His study indicated that for males, the performance expectancy is more important, and for females the effort expectancy will play a bigger role. Especially in the context of the smart thermostat, which includes an element of newness, it is expected that females have less confidence in their abilities to use such a product (Gefen & Straub, 1997). Males might have more self-confidence in the using capabilities but are skeptical towards the use of the product and how it will improve from their current situation. Morris et al. (2005) found on this matter that men place greater emphasis on the attitude towards use, while in contrast women are more influenced by the subjective norm. Also, the social influence seemed to play a bigger role for women, since they are more receptive of social pressure (Venkatesh et al., 2012). Besides, women discourse tends to be more tentative and socially oriented, whereas men are more categorically oriented
(Preisler, 1987). No studies have found that gender had an effect on the effort expectancy and ease of use. The smart thermostat is a new technology and men and women will be equally unaware of how easy to use the technology will be. Therefore, it is not expected that gender has a moderating effect on this construct.
H6a: The relationship between the four evidence types and performance expectancy will be moderated by gender, since males are expected to weaken the relationship between the evidence types and the performance expectancy
H6b: The relationship between causal, expert and anecdotal influence and social influence will be moderated by gender, since males are expected to weaken the relationship between these evidence types and the social influence
Furthermore, the final moderating variable -voluntariness- was considered by Venkatesh et al. (2003). However, the use of smart thermostats by consumers is by definition voluntary and will therefore be omitted (Hövels, 2010). This variable will be replaced by a moderating variable for the educational level. Wu, Tao & Yang (2007) determined that this moderating variable is relevant to add when it concerns technologies, since consumers with higher educational levels are better able to assess the effort expectancy upfront. Since they are highly educated, they are more confident in their skills and capabilities compared to other lower educated individuals, and might therefore expect that they are more able to learn how to make use of the technology. Besides, it appears that more education also means more interaction with technology, leading to lower levels of technology anxiety, which could also influence their perception on the ease of use (Czaja et al., 2006). Furthermore, the educational level could also play a role in the social influence. This is indicated in a non-scientific study conducted by Universal Mc Cann (2008), which emphasized that higher educated people have more trust in reviews of strangers. Furthermore, higher levels of education often come with a highly educated surrounding of friends and family (Clasen & Brown, 1985). Here, keeping up with trends to maintain your status and standing out by means of new technologies and appliances are important factors. Lu & Yu-Jen Su (2009) found that especially early adopters are likely to be well educated and have a higher socioeconomic status. They are influenced by their peers and feel pressured to live up expectations. When hearing from others that they have decided to adopt the smart thermostat technology, they might be more likely to copy their behavior in order to belong and keep up with the standards of their surroundings. Chao & Schor (1988) argued on this matter that status consumption was positively correlated with income and education. Since the educational level
often refers to a certain learning capability or social network and is not related to expectations of performance or the personal perception of how useful a product is, it is not expected that the educational level has a moderating effect on the relationship between the evidence types and the performance expectancy. Perceived usefulness and performance expectancy is more related to the individual context of the consumer and how often he has encountered or used new technologies within her life.
H7a: The relationship between anecdotal evidence and effort expectancy will be moderated by educational level, meaning the higher the educational level the lower the relationship between anecdotal evidence and the effort expectancy
H7b: The relationship between causal, expert and anecdotal evidence and social influence will be moderated by educational level, meaning the higher the educational level the lower the relationship between those evidence types and the social influence
Figure 3. Conceptual Model
Anecdotal Causal Expert Statistical Social Influence Age Effort Expectancy H3a Experience Educational Level H2 Performance Expectancy H1a H1b Behavioral Use H1d H3b H1c H3c Gender
4. Data and Method
4.1 DesignThe research is a correlational study since it explores the relationship among multiple variables. The study is cross-sectional since it measures the conditions at a single point in time. This type of research has been chosen due to the limited amount of time and the fact that it involves a thesis for a University program that might not need a follow-up. Furthermore, the use of a survey could provide easy access to participants by spreading it out through online channels. Again, the limitations of time and also money made it less convenient to opt for another type of study, such as an experimental design. Different types of consumers (in terms of age, experience, educational level and gender) have filled out the survey. The influence of the performance expectancy, effort expectancy and social influence are studied for different types of evidence. These different evidence types with different purposes are used in order to highlight the differences and variation between them. These are shown in the form of advertisements with a marketing message. The evidence types are used in advertising statements that could potentially be found in offline and online display marketing. It is not necessarily the medium or method through which the consumer is reached, but rather the message or statement that producers wish to carry out. A factorial design with four groups will be used since every participant only reads one type of advertising statement. First, an introductory text is shown to every participant with a small background and explanation of the smart thermostat. Next, the advertisement with a different type of evidence will be shown. The formation of these texts will be explained in the following sections. To conclude, demographic information is collected such as age, gender, educational level and expertise. The experience is derived through questions on their experience with smart household devices. These questions refer to the moderating variables. The messages that are included in the survey advertisements are indicated in table 2 below. Participants are questioned on whether they perceive the smart thermostat as useful, easy to use and whether they perceive important others of having an influence on their decision to adopt.
Type of Evidence
Textual Advertisements
Statistical The Environmental Protection Agency has estimated that homeowners can save up to 15-25% on their heating and cooling, resulting in a solid €160 per year in energy costs. Some even claim that savings of 37% can be reached, depending on your current usage behavior. In Europe and North America, the number of homes with a smart thermostat are rapidly increasing. Berg Insight forecasts that the numbers will increase with a solid 54.5% growth in the next upcoming five years. The installed base in Europe is expected to reach 18.9 million homes by 2020.
Anecdotal Jane (45) has always been keen on reaching an optimal level of efficiency while pursuing her busy career. She has been looking for ways to reduce her energy costs and control the temperature and humidity remotely while she is at work or doing the groceries. Now she finally found the solution; the smart thermostat. This device allows her to automatically and remotely control her energy usage. The device will know when she is at home or away and adjusts the temperature accordingly. An installed app on her telephone provides her an updated overview of her current costs and usage and she is able to manually change the settings via her Phone. This gives her an optimal level of comfort and efficiency.
Causal The smart thermostat introduces a whole new approach to energy
management within your house. The thermostat allows for more comfort and lower energy usage than the traditional thermostat. Furthermore, it is able to reduce your energy costs significantly. You do not have to control the thermostat manually (along with a high chance of forgetting/neglecting to lower the temperature while you are out), but this will all be done
automatically. Not using the smart thermostat will also mean you are paying on average 15% more than people who already make use of the smart
thermostat. Missing out now also means that in the future you will not be able to connect this device to other smart household appliances.
Expert According to many experts, the smart thermostat provides several benefits. Tom Scarangello (General Manager of Scaran Heating and Air Conditioning) states that "it is well worth the costs and a - set it and forget it - experience". The Environmental Protection Agency claims it can save up to hundreds of euros per year on your energy bill. The smart thermostat was even included on the list from the British Newspaper ‘The Independent’ as one of the best smart home technologies. The use of smart thermostats has also been discovered by multiple companies such as Exelon who are trying to reduce their costs as well as environmental impact.
Table 2. Marketing messages in the survey advertisements
4.2 Data collection
The research was conducted by the use of an online survey, distributed with Qualtrics research software. The questionnaire is conducted between 9 July – 20 July. The statistical analyses are performed with SPSS (Statistical software Package for Social Sciences). Data is collected from different people of different ages. The survey is mainly sent through digital media and social platforms. Anonymous links have been posted on social media such as Facebook, LinkedIn and
Twitter that were linked to the online survey. Informed consent is obtained by informing the participants on the background of the researcher, the study, and their right to not take part. Their participation is not rewarded by any means. When clicking on the link and opening the survey, the participants were informed about the study subject. However, no references have been made to evidence types and the influence on the three UTAUT dimensions to avoid response bias.
Upfront, the respondents were thanked for their participation, informed about the subject of the study, and given the opportunity to ask questions or leave comments. First, every participant was shown the same short introduction and description to smart thermostats and smart household technologies. Right after, they were randomly assigned to one of the four evidence type advertisements: (a) statistical evidence, (b), anecdotal evidence, (c), causal evidence and (d) expert evidence. Subsequently, the participants responded to the dependent measures: the performance expectancy, effort expectancy and social influence. Finally, the participants answered questions about basic demographics namely age, gender, educational level and experience with smart technological devices. By enabling the Force Response option in Qualtrics, the participants had to answer each question before they could move on to the next in order to avoid missing data. The design of the advertisements was professionally edited and presented in the way that consumers could encounter them on websites, in magazines or newspapers. An example of one of the advertisements can be found in Appendix A.
4.3 Descriptive Statistics
A non-probability sampling technique is used where all potential respondents have an unknown chance to be selected to participate in the study since it is conducted on voluntary basis. For the sample a 0.95 confidence interval (Z-score 1.96) was chosen with a 0.5 standard deviation. The size of a statistical study is important to achieve the goals of the study. This study makes use of multiple linear regression analysis and is followed by a one-way ANOVA. The online and freely available tool called “G*power”1 is used to compute the necessary sample size relating to the power of the results. Here, it was computed that a minimum number of 15 respondents per factor is needed (since the study uses four evidence types, or factors, this would mean that a total of 60 respondents for every one of the three dimensions is needed).
1 http://www.gpower.hhu.de/en.html
In total a number of 322 respondents filled out the survey. After deleting the responses that contained missing values or durations below a minute, the total number of respondents equaled 252. To ensure similar sized groups for the analysis of the evidence types, 240 of the responses were used for the analysis. This created a total of 80 respondents per dimension, with each 20 respondents per evidence types.
Variable Mean Std. Deviation N
Performance Expectancy 62.867 9.60555 80
Effort Expectancy 86.5333 13.96867 80
Social Influence 46.4000 10.90740 80
Table 3. Descriptive statistics
De distribution between males and females is almost even. Regarding the education, it can be concluded that most people had a bachelor or master’s degree. Age is for simplicity divided into four groups to show that most of the respondents were between 16 and 25 years old. The youngest respondent was 16 years old and the oldest respondent was 88 years old.
Demographics N=240
Gender Frequency Percentage
Male 115 48% Female 125 52% Age 16-25 109 44% 26-50 97 40% 51-75 28 14% 76-88 6 2% Educational Level None 2 1%
High School (Middelbaar Onderwijs MAVO/HAVO/VWO) 6 2%
Intermediate Vocational Education Degree (MBO) 6 2%
Bachelor Degree (University of Applied Sciences / HBO) 46 20%
Bachelor Degree (University / WO) 70 30%
Master’s Degree (WO) 113 45%
Experience smart household technologies
No experience 38 17%
Very Inexperienced 25 12%
Moderately inexperienced 21 11%
Slightly Inexperienced 14 7%
Neither inexperienced or experienced 19 8%
Slightly experienced 49 17%
Moderately experienced 52 18%
Very experienced 17 6%
Highly experienced 9 3%
