Smart meter data unlocking energy saving & load shifting potential

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1 Smart meter data unlocking energy saving & load shifting potential Changing the energy consumption behavior of consumers through smart meter data Master Thesis by Anneke Geerts Student ID: Final version University coach: Marcel van Oosterhout Co-reader: Ting Li

2 Graduation committee University coach Dr. Marcel van Oosterhout Academic researcher/ project manager Department of Decision and Information Sciences Rotterdam School of Management (RSM) Co-reader Dr. Ting Li Assistant professor Department of Decision and Information Sciences Rotterdam School of Management (RSM) External coach Bas Ranzijn RE Manager Business Intelligence KPMG Advisory N.V. IT Advisory Business Intelligence The work in this thesis was supported by KPMG Advisory N.V. Their cooperation is gratefully acknowledged. 2

3 Executive summary The domain this thesis focuses on is the Dutch energy market. This market is moving towards a decentralized smart grid were smart energy meters will be in place. This research takes a specific look at smart energy meter and the way these meters influence the energy consumption behavior of consumers. We argue that through the consumption data that gets available through these meters, market parties can influence the energy consumption behavior of consumers. The goal of this thesis is to contribute to the design of energy programs. The literature distinguishes two types of energy programs: energy efficiency programs and demand response programs. Demand response programs focus on load shaping and reducing demand during peak times. Energy efficiencies programs tend to focus on energy reduction all the time. Energy programs can change the behavior of consumers. In behavioral research the behavioral intention of consumers is often measured with the application of the theory of planned behavior. The theory of planned behavior measures the attitude of a consumer, the perceived control and subjective norms regarding a certain behavior. With these three variables the theory measures the behavioral intention of a certain behavior. In this research we argue that through the introduction of smart meters other elements also influence the behavioral intention. To validate this statement we link the theory of planned behavior to the theory of informedness. We argue that with the introduction of smart meters the informedness of consumers and energy suppliers rises and influences the behavioral intention. Furthermore, we take gamification into account in this research. Gamification can be related to behavioral change and we therefore argue that this element can influence the behavioral intention of consumers. The elements that will be considered in this research are the elements of the theory of planned behavior, feedback on energy consumption, normative comparison, gamification, and lastly price differentiation. Our conceptual model proposes that the elements of the theory of planned behavior, namely attitude, perceived control and subjective norms have a positive effect on the behavioral intention to save energy or shift load. Furthermore, in the literature there is a distinction made between direct and indirect feedback. Indirect feedback is provided after consumption occurs and direct feedback is real-time. We propose that the type of feedback on energy consumption influences the behavioral intention. It is argued that direct energy feedback will result in a higher intention to save energy and a higher intention to shift load than indirect feedback. The second variable we researched is normative comparison. Numerous studies suggest that the effects of normative social influence have powerful effects on individual behavior. According to Arahamse et al (2005), comparison between households provides a feeling of both competition and social pressure. Our research proposes that normative comparison positively influences the behavioral intention. Gamification refers to the trend were recently digital designers have begun to integrate game elements and mechanics into nongame applications, systems and services, to better engage end users. Goal setting and competition are the two game elements applied in this research. We argue that gamification elements have a positive effect on the behavioral intentions. In addition, the model proposes that there is a moderating variables in place. Variable pricing will moderate the effects of feedback, comparison and gamification on the behavioral intention to save energy and shift load. To research these impacts both an 2x2x2 factorial experimental design and interviews were conducted. To test the hypothesizes a scenario-based survey was conducted. To get more 3

4 background on the constructs and their relationships interviews were held. The interviews were also used to validate the conceptual model. In the survey research, the independent variables feedback, comparison, gamification and variable pricing were altered as stimuli over the eight different The variables of the theory of planned behavior were measured consistently in each scenario using adapted scales. The dependent variables for this research were the intention to shift load and the intention to save energy. In total 236 people filled in the questionnaire correctly and four experts participated in the interviews. Based on the input we gained during the interviews we validated our conceptual model. Based on our survey results we created three models using the Partial Least Square and Bootstrapping method. One model were no price differentiation was in place, one with block prices in place and one with hourly energy prices in place. If we look at the theory of planned behavior we can conclude that the hypothesizes for the attitude towards energy saving and energy shifting were confirmed. The hypothesizes in which the subjective norms on energy saving and load shifting were tested, a also confirmed. Most of the hypothesizes with the stimuli feedback, comparison and gamification have been rejected. Nevertheless, comparison positively effects the behavioral intention to save energy when variable prices are in place. Our research suggest that the attitude towards a behavior has to largest effects on the behavioral intention. During the development of energy efficiency programs this has to be kept in mind. We argue that through the development of energy programs and energy efficiency applications energy suppliers and grid operators can influence the behavior of consumers. Our research suggests that market players can change the behavior of consumers by focusing on changing the attitude of consumers. Therefore, the main conclusion of this thesis, in regard to the design of energy programs, is that these programs should have a wider focus then just the development of an energy efficiency application. First, the focus of these programs should lie on an attitude change. Based on our finding we conclude that attitude shows the largest effect on the intention to save energy and the intention to shift load. Based on our findings we argue that a change in the attitude toward energy conservation and energy shifting can lead to a change in the intention to perform the behavior. Secondly, in our interviews it came forward that energy programs should be designed so they can take consumers with them through the energy saving process step by step. When designing an energy program it is important to take this into account. Looking at the learning styles of Kolb (1974), the four learning styles have to be taken into account when designing an energy program. In relation to the energy market the style concrete experiencing could be put to practice by using a prepaid billing solution. Secondly, looking at reactive observation, frequent visual feedback on energy consumption is important in the next phase of the energy program. We suggest energy consumption should be made visual in relation to the day and hour it was used. When using the abstract conceptualization learning style, comparison to personal historic consumption or normative comparison is important. We argue that once consumers are familiar with their own consumption the next step would be to compare this consumption with others. The final learning style and last step in the program is experimentation. This is a style where learning results from actions initiated by the consumer. Lastly, for the energy program to be well rounded we argue a change in attitude has to be made. Based on our findings we conclude that during the whole program actions aimed at a change in the attitude towards energy saving and load shifting has to run sideways to the program. 4

5 Table of contents Graduation committee... 2 Executive summary... 3 Table of contents Introduction What is going on in the energy market? Changing consumer behavior through smart meters Business relevance: the design of a combined energy program Scope: elements of energy programs researched in this thesis Scientific relevance Research goal and research questions Theoretical framework Domain Smart meters Smart meters and big data Theory of planned behavior Background theory of planned behavior The theory of planned behavior in environmental research Informedness Information as competitive advantage Firm informedness Customer informedness Revenue management and price differentiation Revenue management as a tool for demand side management Price differentiation Customer profitability gradient WTP and pricing strategies Hourly energy prices Block pricing Conclusion price differentiation Feedback on energy consumption

6 6.1. Energy saving through feedback Feedback characteristics Direct and indirect feedback Actions arising from feedback programs Feedback and learning Comparison Normative comparison Gamification What is gamification? Gamification loop Gamification and energy behavior Goal setting in energy literature Competition in energy literature Methodology Conceptual model Hypothesizes Research design Survey design Stimuli Feedback on energy consumption Normative comparison Gamification Price differentiation Measurement and operationalization Dependent variables Independent variables Data collection Interviews Coding strategy Analysis of the interviews Social business case Energy saving Energy saving steps Insight per device

7 10.5. Comparison Gamification Development energy efficient application Pilots Commitment energy saving Load shifting & revenue management Other uses smart meter data Screenshots Conclusions with regard to our conceptual model Data analysis of the survey Descriptive statics: dataset and demographics Valued importance of independent variables Independent and dependent variables Variance of the different stimuli Relations individual variables on intention to save/shift Covariates and correlations on demographics Linear regression on independent variables Partial least square analyses Why we used PLS? Outer model (measurement model) Inner model Price differentiation Dependent variables when price differentiation is in place Variance of the different stimuli Relations individual variables on dependent variables Linear regression of variables theory of planned behavior Partial least Square analyses when price differentiation is in place Direct effect when block prices are in place Direct effects when hourly energy prices are in place Conclusions Theory of planned behavior Stimuli Research questions Discussion

8 13.1. Managerial implications Theoretical implications Limitation and further research Literature Appendixes

9 1. Introduction 1.1. What is going on in the energy market? It is assumed that, in the near future, electricity will have a larger share of the total energy consumption. This is partly due to the increasing electrification of consumer goods and the adoption of plug-in hybrids and electric vehicles (Lampropoulos, Vanalme et al. 2010). When argued above is true this will lead to an increase in the demand for electricity. To be able to cope with this trend the energy market is working towards a major transition and moving towards a decentralized market. In the traditional setting energy is mostly generated centrally by major power plants. The European Union wants to promote disparate generation of energy by renewable sources. Additionally, the energy infrastructure has to move towards a smart grid, which implies moving towards a digitally enabled electrical grid. In this future market it is complicated to predict the supply and demand of energy, because renewable source are less predictable. In order to balance supply and demand, better insight in accurate and detailed consumption of energy is important. Furthermore, the European union want to promote energy saving. In 2008 the Dutch government agreed with the proposal for a new market model for small scale energy users (Dutch Parliament 2008). The parliament decided to implement a Dutch Advantaged Metering Infrastructure, which is a network that can intelligently integrate the behavior and actions of all users connected to it in order to efficiently deliver sustainable, economic, and secure electricity supply. With this infrastructure in place electricity grids can better accommodate and balance the transmission load in order to fulfill the transaction needs. The Dutch Ministry of Economic Affairs has decided that in principal every small scale user in the Netherlands will receive a smart meter (Dutch Parliament 2008). Smart meters are electrical meters that identify the consumption of energy in a more detailed fashion then conventional energy meters. Because these meters are connected to a network, grid operators can read these meters from a distance. They enable bi-directional communication between the meters and the systems of other players. Additionally, these meters give consumers insight in their energy consumption patterns, which helps them to actively reduce their energy consumption. During 2009 and 2014 large smart metering deployment projects will be set up, and around % of the households must have a smart meter (Energy Research Centre for the Netherlands 2007). When working towards a decentralized energy market smart meters are important, because load balancing in such a market is only possible with the help of these meters. The ultimate goal of grid operators and the Dutch government is to lower the peaks in the demand of energy and to save energy. This will help them to better balance supply and demand and will contribute to the goal of the European Union to reduce energy Changing consumer behavior through smart meters According to NetbeheerNederland (2012), smart grids and smart meters have different advantages for the different players in the energy market (NetbeheerNederland 2012). For consumers, it is expected that they will become more involved in their energy consumption and can more easily contribute to energy savings (NetbeheerNederland 2012). With the help of smart meters consumers 9

10 can now contribute to sustaining energy supply while maintaining comfort. In theory smart meters can measure the consumption of electricity every 10 seconds. This would give consumers the possibility to see their near real-time consumption. Market players can built services and applications that can help consumers to get insight in their consumption behavior. This is called feedback on energy consumption. It is argued that feedback on energy consumption can change the energy consumption behavior of consumers (Abrahamse, Steg et al. 2007, Bonino, Corno et al. 2012a, Ehrhardt-Martinez, Donnelly et al. 2010, Faruqui, Sergici et al. 2010, Fischer 2008, Houwelingen, Raaij 1989, Karjalainen 2011, McCalley, Midden 2002, Sexton, Johnson et al. 1987). Installing the actual smart meter, without giving consumers insight in their consumption behavior or motivating them to save energy, will most likely not result in large changes in the behavior of consumers. Other market parties, such as the business community, have to make it convenient for consumers to save energy. Indirectly these market players influence the amount of energy consumed in the market. For instance, the type of feedback on energy consumption affects the amount of energy saved. (Ehrhardt-Martinez, Donnelly et al. 2010, Karjalainen 2011, McCalley, Midden 2002, Wood, Newborough 2003). Market players who would supply consumers with these add-on application therefore indirectly affect the amount of energy saved. In the Dutch energy market this market player could be an energy supplier, grid operator or an external party in the business community Business relevance: the design of a combined energy program The business relevance of this thesis is that this research will contribute to the design of these energy programs. The literature distinguishes two types of energy programs by which market players can indirectly affect the energy consumption behavior of consumers. Energy efficiency programs and demand response programs (Ehrhardt-Martinez, Donnelly et al. 2010). In basic, the behavioral change market players want to achieve is a reduction of the use of energy (energy efficiency) or a change in the time periods energy is used in order to shape the load curve of energy (energy demand). Energy reduction is driven by the increase use of electricity. It is expected that the grid will not be able to cope with this in the future. Additionally, environmental concerns drive the thought that energy use has to be reduced. Energy load shaping is driven by the fact that energy supply become more dependent on unreliable supply quantities (sustainable sources). Furthermore, during peak times the high pressure on the grid significantly leads to more outages compared to off peak times (Allcott 2011a). The ultimate objectives of energy efficiency and energy demand programs differ. Demand response programs focus on load shaping and reducing demand during peak times when reliability may be threatened. When flexible prices are in place, flatter peaks in power demand can be realized, by supplying instead of consuming energy at peak moments or by rescheduling electricity usage (Gottwalt, Ketter et al. 2011). Energy efficiencies programs tend to focus on energy reduction all the time. In general, an energy efficiency program is likely to reduce demand in addition to saving energy, but a demand response program may not necessarily save energy (Goldman, Reid and Levy 2009). Energy reduction can be achieved by reducing the waste of energy or forging some type of energy amenity. For instance by replacing light bulbs with LED bulbs or turning off the airconditioning. Energy load shifting occurs when running a washing machine at night when night tariffs are in place. Load shifting generally yields no overall energy savings. 10

11 In 2008, only 43 out of the 1707 energy programs in Canada and the USA focused on both energy efficiency and demand response (Goldman, Reid and Levy 2009). Nevertheless, because the differences between these two programs are unclear, packaging the two types of programs together seems logical. (Pratt et al. 2010). Martinez (2010) argues that advanced metering initiatives, provide the theoretical opportunity to increase both energy savings (through conservation and efficiency measures) and peak load savings (through conservation and load shifting activities). A multidimensional behavioral approach to program design including demand response and energy efficiency would increase the potential for consumer savings and offer other significant societal benefits. (Ehrhardt-Martinez, Donnelly et al. 2010). Lastly, this research can help application suppliers to select elements they should include in their smart meter application Scope: elements of energy programs researched in this thesis Energy programs can change the behavior of consumers. In behavioral research the behavioral intention of consumers is often measured with the application of the theory of planned behavior (Ajzen, 1985). The theory of planned behavior measures the attitude of a consumer, the perceived control and subjective norms regarding a certain behavior. With these three variables the theory measures the behavioral intention of a certain behavior. An overview of the model is given in figure 3. In this research we argue that through the introduction of smart meters other elements (in addition to attitude, perceived control and subjective norm,) influence the behavioral intention. To validate this statement we link the theory of planned behavior to the theory of informedness (Li, 2009). We argue that with the introduction of smart meters the informedness of consumers and energy suppliers rises and influences the behavioral intention. Furthermore we will take gamification into account in this research. Gamification can be related to behavioral change and we therefore argue that this element can influence the behavioral intention of consumers. This thesis will specifically focus on elements that get available through smart meters and will relate these elements with the theory of informedness. The elements that will be considered in this research are feedback on energy consumption, normative comparison, gamification elements, and lastly price differentiation. The elements related to customer informedness are the elements feedback on energy consumption and normative comparison. Price differentiation is an element that can be related to firm informedness. Feedback on energy consumption is directly related to smart meters. Large scale feedback on energy consumption was not possible before the introduction of smart energy meters. In recent years feedback on energy consumption became a prominent subject in the energy literature. A major push in research around this subject took place after the EU set the goal to replace 80% of all meters with smart meters by Furthermore, in the last few years, normative comparison has been researched widely. Many research around this subject has been done since the subject feedback on energy consumption became popular. Obviously, before anyone will compare the consumption behavior of other households with that of their own, households first have to get insight in their own behavior. Another reason this element is included to the scope of this thesis is because in the Netherlands several energy supplier included this element in their applications, for instance Nuon (Nuon, 2013). 11

12 The Nuon E-Manager allows customers to compare their energy usage with that of similar households. To remain closely involved with the market we choose to include this element. Additionally, another element added to the scope of this research is price differentiation. Price differentiation was not possible before the introduction of smart meters, because conventional meter do not support the measurement of energy consumption over different time frames. We argue that through the introduction of smart meters energy companies will get access to the consumption data of their clients and can better tailor their revenue management programs. They now know what type of consumer reacts to the program in a certain way. In theory these organization can, with the help of smart meters, offer their customers personalized contracts. Through revenue management and price differentiation energy companies can indirectly influence the behavior of their customers. With the use of smart meter data energy supplier can adjust their revenue management strategies more accurate. Lastly, the element gamification was included in this research. Gamification is a relatively new subject within the energy literature. Not much research has been done around the subject. Gamification can be related to behavioral change and is therefore added to the scope of this thesis thesis. A second reason this element was included is because during all interviews conducted for this research the respondents state that their company is looking into adding game elements into their energy efficiency or energy demand programs Scientific relevance The scientific relevance of this thesis lies in the contribution to the energy research field. We will research if the theory of planned behavior can be applied in this research field. Specifically the contribution of the theory of planned behavior in relation to energy consumption behavior. The theory of planned behavior has been used widely in behavioral science, only has not much been applied in energy and environmental research. Additionally, we will link smart energy meters to the theory of informedness and subsequently we will link the theory of informedness with the theory of planned behavior. Lastly, the element gamification has not much been research in the energy literature. Nevertheless, gamification has been research in relation to behavioral change. Adding this subject to this thesis will give the energy saving literature a new angle Research goal and research questions On a high level the goal of this research is to research if the theory of informedness and gamification can contribute to a behavioral change in energy consumption. We argue that smart meter data will higher the informedness of consumers and firms. More specific we will research if the elements feedback on consumption, normative comparison, gamification and price differentiation effect the energy consumption behavior of consumers. Looking at the managerial implications of this research our goals is to research what would be the best way to design an energy program with regard to energy load shifting and energy conservation now smart energy meter become available. 12

13 The main question of this thesis is: How can smart meter data change the energy consumption behavior of consumers, and how can these insights help to optimize energy programs? The research questions relevant for the thesis are: 1. How does the energy market look like? 2. How can energy consumption data help market players change the energy consumption behavior of consumers? 3. How can energy consumption data help consumers change their energy consumption behavior? 4. How can game aspects be integrated into the energy saving discussion? 5. What combination of feedback, revenue management, insight in consumption and gamification elements will lead to the highest intention to save energy and what to the highest intention to shift load? 6. Based on the outcomes, what is the best way to design an energy program? 13

14 Theoretical framework 2. Domain As outlined in the introduction the domain this thesis will focus on is the Dutch energy market. In 2006 the European Parliament published a directive on energy end-use efficiency, demand management and the promotion of renewable energy (European Parliament Council 2006). In this directive it is described that, in order to meet the Kyoto commitments, the emission of CO 2 and other greenhouse gas has to go down. EU countries have to aim to achieve an overall national indicative energy savings target of 9% in 9 years. Additionally, the goal was set to enable the transmission and distribution of up to 35% of electricity from dispersed and concentrated renewable sources by 2020 and to make electricity production completely decarbonizes by 2050 (Commission of the European Communities 2007). In order to achieve these goals energy has to be saved and a larger percentage of renewable energy has to be consumed. With current initiatives to stimulate consumers to invest in renewable energy initiatives, such as solar panels on houses, this could be achieved. Making consumers prosumers, the market is moving towards a decentralized market. Prosumers are consumers who do not only consume energy but also produce energy through solar panels or other technologies which mostly make use of renewable resources (Lampropoulos, Vanalme et al. 2010). Additionally, caused in part by the increasing electrification of energy consumption, greater transmission needs have to go through the infrastructure. This would not be possible without major network upgrades. Smart grids could partly solve this problem by using the existing infrastructure more intelligently. Furthermore, in the traditional network single points of failure exist, because energy is moving hierarchical from the power plant to the consumer. In a smart grid the total network is less vulnerable and more reliable, as it is organized more like a mash typology network. Figure 1 Traditional grid versus Smart grid 14

15 A goal set by the European Union says that in % of all households must have a smart meter. The Dutch Ministry of Economic Affairs has decided that in principal every small scale user in the Netherlands will receive a smart meter (Dutch Parliament 2008). Additionally, the Dutch Ministry of Economic Affairs initiated the drafting of the NTA 8130 document by the Netherlands Standardization Institute, which was finalized in April 2007 (KEMA Consulting 2008). This documents sets the basic functionalities for connecting the consumer to the energy distribution infrastructure. The functionalities are important due to the interoperability of the total system in the European market, in which also other countries are involved. The various grid operators will be responsible for the physical rollout of the smart meters. The meters will become part of the physical infrastructure of the grid operators. In the Netherlands there are approximately seven million household and small business users that fall under this role-out (KEMA Consulting 2008). Because of these changes the dynamics in the energy market will change. Already in basics the electricity market is a complex market, due to the characteristics of the end product, which cannot be stored, meaning supply and demand have to be tailored. The market is supported by systems and devices and a change in the system will have huge implications on the operations. Additionally, there are many actors in this market, which leads to high complexity. Due to the new regulations and initiatives the energy market has become an innovating and interesting market to research Smart meters The network created by connecting all smart meters to the smart grid is called the Dutch Advanced Metering Infrastructure (AMI). A minimum set of basic functions for the meter is set in the meter definition NTA 8031 and the corresponding document Dutch Smart Meter Requirements (DSMR). These documents are generated by The Netherlands Standardization Institute assigned to them by the Dutch Ministry of Economic affairs (KEMA Consulting 2008). The emphasis of these documents lies in the description of the basic functionalities for smart gas and electricity meters. For instance agreements on communication protocols; where it is stated that the meter should remotely read the energy consumption and that both actual as interval value have to be metered. Additionally, it should be possible to connect external service devices to the meter via the P1 port. These devices could be applications that give a detailed overview of energy consumption in order to stimulate energy saving. Figure 2 Smart meter design 15

16 In basic a smart meter entails two functions: (1) measuring, a smart meter measure the energy consumption every 15 minutes and (2) communicating in a network to transfer metering data. For communication purposes a smart meter has four ports (KEMA Consulting 2008). The first port P1 can be used by the consumer to access the meter. Some meters that are available in the market already measure 10 second interval data that can be accessed through the P1 Port. A Home Area Network can be formed through this port by connecting different types of devices to the smart meters. This could be devices that monitor and control the energy consumption. Through this port you cannot send data to the meters, only read the data from the meter. The P2 Port can be used for communication between the smart meter and other metering instruments, for instance the gas meter or grid operators equipment. The third port, Port P3 can be used for communication between the smart meter and the Central Access Server (CAS). In March 2007 this CAS database was set up. Through this server, authorized parties can access the metering data. This process can both be triggered on the request of an actual meter read by a market participant or done automatically. CAS is managed by Energy Data Service Netherland (EDSN), who is responsible for the data exchange between the actors in the electricity market. When an energy suppliers want to access data about usage from consumers this request had to go through EDSN. Therefore, EDSN is an important player when you look at the use of energy consumption data in the energy market. They will make sure privacy rules are not broken. Lastly, port P4 is a port on the CAS server, run by EDSN. Through this port market parties like energy supplier or independent service suppliers can access CAS. If an energy supplier wants to gain access to the energy consumption data of its customers it has to go through EDSN and send them a report with the information they need from which consumers. EDSN will check if the energy supplier is allowed to read this data. If so, EDSN will give the supplier access to the database, so they can read the data. Due to Dutch law, an energy supplier currently only able to access this data 6 times a year. Energy suppliers can get around these regulations by providing an application to their customers which consumers can connect to the P1 port. If a customer chooses this, and chooses to opt-in, energy suppliers would be able to collect 10 second consumption data via an internet connection. Already several Dutch energy suppliers have such applications in place. For instance the Nuon E-manager, of Toon from Eneco (Nuon, Eneco ). A smart meter can generate the following data. This data is divided in personal identifiable information and technical information to point out the level of privacy concerns of each data item. Tabel 1 Smart meter data Personally identifiable information - Personal info (name, sex, age) - Connection information (address) - Consumption information (measurement data). Total consumption data has to be stored 40 days, 15 minute interval data has to be stored 10 days In consumption data, both energy usage data as energy production data is included. (KEMA Consulting 2008). - Monitoring information Technical information - Connect and disconnect commands - Software - Keys and passwords - Device settings - Other information at application level 16

17 2.2. Smart meters and big data As outlined smart meters will measure energy consumption every 15 minutes. The meter stores this data and when a consumers chooses to opt-in several market players can access the data. When a player would like to use this data for analysis they would have to store this data in order to draw conclusions from the data. The Netherlands consist out of approximately 7.4 million household and small business users who would require a smart meter (NetbeheerNederland 2012). If every meter would generate 15 minute interval data this would lead to enormous amounts of data, namely: 7.4 million households / (1/4 hours x 60 minutes x 60 seconds) = 8222 messages per second A market player would have to store 8222 messages each second. At package level this would lead to the following: a meter generates consumption and production data and the assumption is that a single meter produces packages with 6-diget numbers. A 6-diget number can be stored in 12 bytes resulting in 8222 messages x 12 bytes = bytes per second. Per second a market player would have to store bytes. Big data is a term that is used for prodigious amounts of fast-moving data that typically cannot be handled by existing data tools. Big data has high volumes, velocity and variety. (Manyika, Chui et al. 2011). The term big data is applicable for data that is generated by smart meters. According to McKinsey Global Institute (Manyika, Chui et al. 2011) the amount of big data in the world has been expanding rapidly and will continue to grow exponentially for the foreseeable future. Data generated by smart meters will contribute to the growing amount of data, because these meters produce high volumes data. When the full potential of the smart grid is in place, smart meters will enable device to device communication. For instance, in the case of smart neighborhoods where meters communicate with each other about trading energy at the neighborhood level. This device to device communication is part of the Internet of Things research wave. This terms refers to objects used in daily life that have a virtual connection with each other and can therefore communicate and become intelligent. Data generated from the Internet of Things will grow exponentially as the number of connected nodes and objects increases (Manyika, Chui et al. 2011). According to McKinsey s Big Data report companies that can extra benefit the big data trends are companies that sit in the middle of large information flows where data about products and services, buyers and supplier, and customer preferences intent can be captured and analyzed (Manyika, Chui et al. 2011). Within the future smart grid environment energy supplier could fit this role, because their role will have a big focus on balancing supply and demand of energy, while still remaining in close contact with their customers. Energy suppliers will be the players in the future energy market who will have access to the energy consumption data in order to schedule and balance energy. On the other hand they will have information about customers, their preferences and customer data. Therefore, energy supplier will have access to information about consumer consumption behavior. This indicates that energy suppliers will be the party most likely to benefit from big data initiatives. Analyzing their data in order to find opportunities to leverage the data will be most relevant for these organizations. 17

18 3. Theory of planned behavior As outlined in the introduction the goal of the EU and the Dutch government is to stimulate energy saving and to shape the load curve. With the help of smart meters they want to change the behavior of consumers. To measure the energy consumption behavioral intentions we will use the theory of planned behavior. We will look into what element with change the behavioral intentions of consumers. The behavioral intention to save energy and the behavioral intention to shift load will be the dependent variables used in our research. We will use the theory of planned behavior as the structural bone of our conceptual model. In this research we argue that besides behavioral attitude, subjective norms and perceived behavioral control, other elements influence the behavioral intention of consumers. Specifically, we will look into elements that get available with the introduction of smart meters. We therefore combine the theory of planned behavior with the theory of informedness and gamification Background theory of planned behavior The theory of planned behavior is designed by Ajzen in 1985 to help explain people s behavioral choices in specific contexts. We use the model to get a full view on the behavior our respondents are likely to perform in real life. Figure 3 The theory of planned behavior, Ajzen (1985) An overview of the theory is given in figure 3. The theory of planned behavior is an extension of the theory of reasoned action and is about the link between beliefs and behavior (Ajzen 1991). The theory of reasoned actions is originated from various theories of attitude such as learning theories. The theory assumes that attitude towards a behavior, subjective norms, and perceived behavioral control together influence and shape the behavioral intentional and the actual behavior of a person. According to this theory people s behavior is influenced by the behavioral attitude a person has towards the behavior. This perspective behavioral attitude means that if a person evaluates a suggested behavior as positive they will have a higher intention to perform the behavior and they will be more likely to do so. Furthermore the subjective norms are an individual s perception about a 18

19 behavior. This perception is influenced by the opinion of significant others. This can be parents, a spouse, friends, coworkers and other important others in a person s life. Some individuals find the opinion of others more important than others and will therefore more easily change their own perception about a certain behavior if others think differently. Lastly, perceived behavioral control, is influenced by how easy a person can perform the behavior or thinks he or she can perform the behavior. An individuals perceived ease of preforming the behavior is measured with this variable. In the model behavior intention means an indication of an individual s readiness to perform a given behavior. It is based on attitude towards behavior, subjective norms, and perceived behavioral control, with each predictor weighted in relation to the behavior and population of interest (Ajzen 1991). The actual response in a given situation is the behavior a person preforms. The more favorable the attitude towards a behavior and subjective norm, and the greater the perceived behavioral control, the stronger the intention to perform the behavior will be and the higher the change the behavior will be performed. To check the variance of the theory of planned behavior on the behavioral intentions to save energy or shift load we use the following hypothesizes: H1: The more positive the attitude towards energy saving the higher the intention to save energy. H2: The more favorable the subjective norm on energy saving the higher the intention to save energy. H3: The greater the perceived behavioral control the stronger the intention to save energy. H4: The more positive the attitude towards energy shifting the higher the intention to shift energy. H5: The more favorable the subjective norm on energy shifting the higher the intention to shift energy. H6: The greater the perceived behavioral control the stronger the intention to shift energy The theory of planned behavior in environmental research Not much research in energy conservation literature has been done with the use of the theory of planned behavior (Laudenslager, Holt et al. 2004, Laudenslager, Holt et al. 2004). Nevertheless, there has been some research done that supports the application of the theory to understand consumer behavior in the field of environmental protective research. Because environmental research can be related to CO 2 reduction we find it interesting to take a further look at these papers in regard to our research question. In environmental literature it has been concluded that behavioral intentions are strongly related to the actual behaviors (Laudenslager, Holt et al. 2004). This indicates that when we use the theory of planned behavior in our research this will not negatively affect the reliability of our research. Furthermore, it has been concluded that variance in environmental behaviors cannot always be related with an environmental attitude (Scott 1994). Research findings have suggested that the other concepts that are part of the theory of planned behavior are important when trying to understand people's behavioral intentions regarding environmental programs. This indicated that 19

20 the independent variables perceived control and subjective norms are important looking at environmental research. Research on recycling behavior is in line with this and found that consumers find the opinion of their peers important when making recycling decisions(bratt 1999). In other words consumers beliefs regarding their peers attitude towards behavior predicted their own attitude towards recycling. Peers could be spouses, children, neighbors, etc. This effect on recycling decisions is directly related to the behavioral intention, which is related to the actual behavior that will be performed in real-life. Bratt s study also suggested the relevance of the perceived behavioral control component of the theory of planned behavior in environmental research. He argues that there is a relationship between personal control, conservation behavior and recycling behavior. In most of the literature found around energy conservation, energy feedback and price differentiation real-life pilots were conducted. This results in a higher construct validity, because the realism of context is clear and people are within their normal life routines. Due to the scope and goal of this research we choose not to conduct a pilot but use the theory of planned behavior instead. 20

21 4. Informedness In this chapter the terms firm informedness and customer informedness will be introduced and outlined in regard to our research question Information as competitive advantage Rather than IT, information has been argued to be the source for competitive advantage for firms (Li 2009). IT has become a strategic necessity, as it has been commoditized (Carr 2003). An organizations information capability is an organizations ability to capture the complete customer behavior information (Li 2009). IT can help to higher an organizations information capability by collecting and processing available information about customer preferences. In this way IT initiatives focusing on areas that are in line with a firm specific skills, like for instance customer orientation, could lead to a competitive advantage. In achieving high levels of customer orientation, organizations have found IT to be an indispensable factor. A key capability for superior customer orientation is the ability to track and predict changing customer preferences, especially in volatile markets. IT enables firms to track shifts in customer choices much more rapidly (Anandhi S Bharadwaj 2000). In light of the domain of this research, this information could be energy consumption information that becomes available with the use of smart meters. As a result, smart meters can higher the information capability of an energy supplier, when data of these meters is used in their business processes and decision making. Organizations with a high information capability will have better firm informedness. Firm informedness has been related with overall better firm performance. (Li 2009). Informedness is the level at which organizations and consumers are able to make informed decisions, based on complete, reliable and timely information. More specific, at what level does a firm know its customers and is the organization able to learn their preferences and decision impacts. On the other side, at what level do consumers know what organizations are offering them (Li 2009) Firm informedness Firm informedness refers to what a firm knows about its customers and the capability of learning what the customer wants in order to satisfy customers and impact their willingness-to-pay (WTP) (Li 2009). As outlined in the previous paragraph firm informedness is related to IT and information management. Looking at the energy market, IT can help to capture and store customer data (purchase, demographic, preference data) and transform this data into information through data analysis. Smart meter applications can capture consumer energy consumption data through the P1 port and the appropriate analysis could make this data useful information for players in the energy market. Willingness to pay (WTP) is related to the price a consumer is willing to pay for a certain good. Under what circumstances is a consumer willing to pay the premium price and under what circumstances is a consumer looking for the lowest price? Consumers trading-down and trading-out behavior is related to a consumers WTP (Clemons, Gu et al. 2002). Trading down means that the consumer wants to buy something because it is inexpensive, even if it does not offer the highest satisfaction, but substitutes are more expensive. Trading out, on the other hand, means that a consumer is looking for the product or service that offers the best fit, without taking price into consideration. In light of the energy market and the scheduling of energy usage, trading down would mean that a 21

22 consumer is willing to schedule its energy usage to an inexpensive time interval, even if this would not be the most convenient time interval. For instance, when a consumer is willing to let its washing machine run at night, because with day/night tariff energy is less expensive at night. Trading out would, for instance, occurs when a consumer is watching television during an expensive time interval. He is not willing to change this behavior, but willing to pay the premium price for energy to be able to watch television at this time. Looking at the energy market, trading out and trading down behavior can be related to revenue management and demand side management energy programs. In chapter 5 we will look into revenue management and price differentiation in the energy market. Trading out and trading down can be related to behavioral change and will change the behavioral intention of a consumer. In this research we will measure the behavioral intention and can therefore link firm informedness to the theory of planned behavior Customer informedness Looking into the WTP for energy, energy consumption data is argued to help shape the behavior of consumers (KEMA Nederland B.V. 2010). Currently, consumers do not have full information about their own energy usage and the prices of energy. This means that they cannot make a thoughtful decisions about their energy consumption. Smart meters in combination with extra applications can overcome this, by giving consumers perfect insight in their consumption and the prices they pay for energy. In figure 4 an example of such an energy management application is given. In this application a consumer sees an overview of its total consumption per day. Figure 4 Example of an energy management application Customer informedness refers to the degree to which consumers know what product or service is available, with precisely which attribute at precisely what price (Li 2009). Smart meters will positively affect the customer informedness of consumers, as these meters give consumers insight in their energy usage. Consumers are expected to change their behavior directly through increasing their informedness. This behavioral change can be related to the behavioral intention to save energy of shift load. Therefore in this research we link customer informedness with the theory of planned behavior, were behavioral intention is measured. 22

23 Using the consumption information that gets available through smart meters, consumers get a full overview of their energy consumption. The customer informedness of the consumers increases through this data. With the help of applications, such as Plugwise (Plugwise 2012), consumers can get real-time insight in their usages per device. They now have a good overview of the total amount of money spend on energy for each appliance at what time during the day. Customer informedness plays a critical role in determining a customers WTP. In the presence of increased information, some customers will exhibit stronger preferences for choosing the cheapest product (evidence of trading down behavior), whereas other consumers will exhibit stronger preferences for choosing products that best fit their needs (evidence for trading out behavior). Chapter 6 and 7 of this literature review are related to customer informedness. In these paragraphs feedback on energy consumption and normative comparison of energy consumption will be discussed. 23

24 5. Revenue management and price differentiation Consumption data that gets available through smart meters gives the market many new opportunities related to energy saving and load shifting. Taking into account the perspective of energy suppliers, this paragraph will look into how smart meter data can contribute to a behavioral change that leads to energy saving or load shifting. When looking at the consumer perspective an important note is that consumers only have direct access to their own consumption data. Energy supplier, on the other hand, are in theory possible to access consumption data of all their customers. This results in energy supplier having different opportunities that can lead to energy saving or load shifting compared to consumers. In this paragraph it is researched how energy supplier can indirectly influence energy saving and load shifting. As outlined, in theory energy supplier will have access to interval consumption data of all their clients. Currently the Dutch law restricts grid operators to read the consumption data more than six times a year. Only, if you look at the directive of the European union it is stated that one of the goals is that energy can be traded within communities, in which prosumers also participate. It is argued, in order to make this possible, energy prices will have to move towards more flexible structures (Commission of the European Communities 2007). In case of flexible energy pricing; billing and managing accounts can only be possible when interval consumption data of consumer is available to market parties. Therefore in this research we assume that in the future energy market energy supplier will have access to this data. Energy supplier can also implement their own solution to go around the regulations. This solution could be an application directly on the P1 port of the meter, which pushes the consumption data to the energy supplier. Several Dutch energy supplier already have implemented such an application (Nuon, Eneco ). As outlined earlier, insight in consumption data will increases the firm informedness of an energy supplier. A question that has to be answered to get value out of this increased informedness is what an energy supplier can do with this data related to energy saving? In this chapter we will look into revenue management and how this is linked in research to energy saving and load shifting. Related to this, we will look into research around variable pricing structures Revenue management as a tool for demand side management Revenue management is short-term demand management to promote flexible real-time capacity allocation, customer segmentation, and pricing optimization (Li 2009). Revenue management is based on price elasticity and focuses on selling the right amount of products, to the right customers at the right time. As outlined in the definition, revenue management can have an impact on flexible real-time capacity allocation. In other words: how do you stimulate demand to use fixed capacity? Because energy cannot be stored easily, because the network can only handle up to a certain pressure and because the supply of energy is increasingly dependent on unreliable supply quantities (sustainable sources), fixed capacity is applicable to this market. Additionally, an energy supplier, but also grid operators, are looking into ways to lower the peak of energy consumption and trying to shift demand of energy to off peak hours. Stimulating demand to use fixed capacity and discouraging demand at peak times is part of demand side management. 24

25 Price differentiation Price differentiation is an important revenue management technique (Tobias Von Martens, Andreas Hilbert 2011). Price differentiation is a pricing strategy in which an organization sets different prices for the same product or service. Price differentiation commonly focuses on specific segments, but it is also common that different prices are set for the same service based on time of use. Consumers would be able to self-select themselves to a time interval. By allowing consumers to self-select, an organization is able to be more precise in their segmentation strategy and therefore drive higher revenues (Irene C L Ng 2006). In revenue management, price differentiation is commonly applied to influence demand, skim consumer surplus or generate additional demand from a customer segment with a higher WTP. This could be achieved by focusing on, for instance, temporal, regional, costumer, quantity or service related characteristics (Tobias Von Martens, Andreas Hilbert 2011). Real-time customer consumption data is a very effective way to capture customer behavior characteristics. An organization who is able to capture this type of data and use it for managerial and business practice has a higher firm informedness then an organization who is not able to do this (Li 2009). The introduction of smart metering would create opportunities for rating and pricing variations for energy (Jagstaidt, Kossahl et al. 2011). Higher prices will be set for peak-times and lower for off-peak times. The average distribution of energy usage in the Dutch market is shown in figure 5. In this figure peak versus off-peak times have been separated. An organization who does not have this data, does not have full insight in the demand of different customer groups and cannot fully leverage their revenue management strategy. It is argued that by using real-time customer data in the differentiation technique, operations and revenues can be improved (Li 2009). Figure 5 Distribution of energy in the Dutch energy market. Adapted from KEMA Consulting b.v. In previous research conducted in the public transportation industry price differentiation, customercentric revenue management and informedness have been research (Li 2009). Fixed capacity with daily peak hours is applicable to this industry and equal to the energy sector. Influencing customer behavior to shape the capacity allocation, in order to lower peak moments in the Dutch public 25

26 transportation infrastructure, was one of the goals of this research. Based on travel data gathered by a stated choice experiment and a computational simulation Li proposed self-selecting contracts, based on price differentiation, that can help to level out the travel peaks in the Dutch transportation industry (Li 2009). Currently, the energy sector has not yet fully taken advantage of price differentiation. Fixed energy prices are popular contract forms in the Netherlands. Currently, only day and night tariff contract exist in the consumer market. This is because the conventional meter does not support the comprehensive billing process that flows from variable prices. Additionally, detailed consumption behavior is not available widely. With smart meters installed this could be overcome. In the transportations industry, with the availability of smart cards and mobile devices, service providers are informed of consumers individual demand preferences and, thus, can better leverage the effective use of price differentiation (Li 2009). Looking at the energy industry, smart meters give insight in consumers individual demand preferences, as these meters can give insight in interval energy consumption data and therefore can give a better and more detailed insight in the demand of energy. Using this real-time data will positively impact the firm informedness of energy suppliers. This enables these organizations to develop and implement informed revenue management programs Customer profitability gradient The customer profitability gradient refers to the presence of extreme differences in profitability between the best and the worst customers in a market. This is most frequently due to uniform pricing in the presence of great differences in customers cost to serve, although other forms of simplistic pricing can produce similar effects (Clemons, Gu et al. 2002). Often, providing services during peak-hours, compared to off-peak hours, is very expensive. In the transportation industry, it is argued that it is profitable to focus on servicing the most profitable customers during peak hours and offload the unprofitable ones to the off-peak (Li 2009). Looking into the energy market this could translate into the search how much a consumer is willing to pay for energy at peak hours, before trading down and shifting towards a less expensive time interval. It is researched that low income households have a lower WTP for energy (Bonino, Corno et al. 2012a, Allcott 2011a). These households are more likely to reschedule energy consumption instead of buying more energy efficient appliances. High income houses, on the other hand, are overall more likely to investment in new devices (Bonino, Corno et al. 2012a, Allcott 2011a). Low income houses are more willing to change their behavior. The customer profitability gradient is strongly related to WTP and demand management. Based on the customer profitability gradient it is the task of a service provider to segment the market and to design service offerings were discount for time intervals are in place. The customer profitability gradient can help service providers to change customer behavior and/or to higher profit by focusing on the most profitable customers. Organizations with an information based strategy can better profit from the customer profitability gradient. On the other hand, firms who do exploit the heterogeneity of their consumers to persuade customer to shift might also tailor service offerings to satisfy individual demand (Clemons, Gao 2008)(Li 2009). Customer who have a lower WTP and who are willing to use less energy at peak time by trading down, will end up paying less for their energy. This can lead to a higher customer satisfaction. On the other side, if a service providers does choose to 26

27 higher prices for customers who are consuming during peak-time, higher revenue can be generated short term. Nevertheless, It is argued that it is better to persuade customers with a discount for offpeak hours then to overcharge for peak hour travelers (Li 2009). Looking at revenue management and the profitability gradient in relation to the energy market the literature suggest that it is wise to serve consumers who are willing to pay full price at peak hours and consumers with a lower WTP on off peak hours. This could lead to load shifting (Li 2009). The next paragraph will take a closer look at different pricing strategies WTP and pricing strategies The willingness to pay (WTP) for energy is expected to differ for different customer groups. As outlined partly due to income differences, but most likely also other characteristics will underlie this difference. To take a good look at the WTP for energy, reactions of consumers on price differentiations can be helpful. In the last few years several pilots in which price differentiation was in place, were conducted. Different reactions in energy consumption behavior occurred Hourly energy prices In 2003 a pilot program was conducted in Chicago called Energy-Smart Pricing Plan (Allcott 2011a). In this pilot households could opt in into a large experiment with hourly energy prices. Before four p.m. the day before hourly energy prices were announced, in which the price of energy was based on a daily forecast of the next day. During expected peak hours energy was more expensive than during off-peak. The goal of this pilot was to research how price differentiation based on time of use, could affect load shifting. Knowing the precise energy prices for the next day increased the customer informedness of the households that participated in the experiment, because they have full information about what product is available; at what time for what price. The households selected into the experiment showed significant price elasticity. In this particular pilot program households used less energy during peak hours, were energy prices tend to be higher, and more energy during off-peak hours. For instance, hourly energy prices lead to using the washing machine during low price hours instead of during the afternoon. Did not lead to energy saving, but substitution of electricity consumption from one hour to another. Allcot (2011) argues that: intra-day substitution elasticities may be increased by energy management devices that automatically allocate activities such as clothes washing to low price hours (Allcott 2011a). Based on these findings it can be argued that revenue management can lead to load curve shaping. During this experiment households got an overview of the next day hourly energy prices, only there were no smart meters in place were current energy price could be read. Nevertheless, the pilot provided a small group of households with Pricelights. This are glowing orbs that change color corresponding with the electricity price. The group using Pricelights shift significantly more energy. The research found that because the variation of hourly price is small, reducing the cost of observing and responding to energy prices can substantially affect households behavior (Allcott 2011a). Smart meters can reduce the cost of observation and responding to energy prices, because these meters give near real-time insight (10 seconds). A logical observation in light if this research thesis would be that smart meters could decrease the cost of observation which could increase the 27

28 price elasticity of energy. This relationship has been discussed in the paper, where it was concluded that energy management and information technology can significantly increase households price elasticity (Allcott 2011a). It is argued that new technologies in consumer energy management can increase households price elasticity and increase to willingness to buy an advanced energy smart meter to register energy interval consumption (Allcott 2011a). Insight in consumption data and energy feedback can be related to customer informedness. Additionally, the design of an effective pricing strategy can be done more effectively when a firm is more informed of the customer preferences Block pricing Reiss and White (2005) examined increasing block pricing, where the marginal price increases by the total quantity consumed (Reiss, White 2005). In a real-life pilot a multi-part tariff structure for residential electric services was introduced. The price per kwh depended on the quantity used per month. The more energy used the higher the price. For the first x kwh all consumers pay a certain price. Consumer who use more than x energy per month pay a higher price for the energy consumer above the x point. This goes up to five consumption levels. On average energy consumption decreased with 10% under this new five-tier tariff system. In this pilot revenue management is directly linked to energy saving. In this pilot considerable heterogeneity in the price elasticity of households was found. Households price elasticity vary across households not just by energy consumption level, but also related to the appliance a households belongs and uses. Households with electric space heating or air conditioning exhibit a much higher electricity price elasticity than households without such systems (Reiss, White 2005). These households saved a larger percentage of their total consumption. It was argued that electric appliances who use of lot of electricity, such as air conditioning, will contribute to the price elasticity of a household Conclusion price differentiation While feedback programs focusing on load shaping and rescheduling of energy usage are often successful in rescheduling the consumption of energy from peak to off-peak periods, they are less successful in generating overall energy savings, compared to feedback programs focusing purely on energy saving. It is shown that studies focusing only on energy reduction at a certain point of time safe less energy than programs who focus on energy saving at all times (Ehrhardt-Martinez, Donnelly et al. 2010)(). According to Martinez (2010) feedback studies focusing on rescheduling of energy to off-peak hours saved on average 3% energy. Programs focusing on overall energy efficiency have an average saving of 10%. Only, it is suggested to conduct more research about programs who combine both goals. In this thesis both goals are central, as we look at both energy saving from the consumer perspective (who want to save energy), as from the firm perspective (who want to shape their load). 28

29 6. Feedback on energy consumption If we want to research how smart meter data can contribute to a behavioral change in energy consumption we will secondly look at this question from the consumer perspective. Firms are expected to influence energy consumption behavior through revenue management. Only, consumers are expected to change their behavior directly through increasing their informedness. Using the consumption information that gets available through smart meters, consumers get a full overview of their energy consumption. The customer informedness of the consumers increases through this data. With the help of applications, such as Plugwise (Plugwise 2012), consumers can get real-time insight in their usages per device. These developments give new innovative angles to the energy saving discussion. In this paragraph it will be described how the current literature relates energy saving and load shifting to feedback on energy consumption Energy saving through feedback Energy saving can be achieved giving consumers insight in their energy consumption (Bonino, Corno et al. 2012a, KEMA Nederland B.V. 2010). This way smart meter data gives rise to changing consumption behavior, different behavioral intentions, energy conservation and more efficient use of the energy infrastructure (Bonino, Corno et al. 2012a). Whether consumers will accept the smart meter infrastructure and how consumers will use this infrastructure are important questions that can influence the final end goal: energy saving and CO 2 reduction. The basic principle behind the European directive is that the smart meter infrastructure gives rise to more and improved feedback on energy consumption and results in behavior that will lead to energy saving (Dutch Parliament 2008). Feedback programs give consumers a detailed overview of their energy consumption. These programs are useful tools that make modern energy resources, such as electricity, more visible to consumers and makes it possible for consumers to actively manage their energy consumption. These feedback programs can be designed so they can higher the customer informedness. In energy saving literature it is argued that feedback on energy consumption through smart meter data can give rise to improved energy consumption behavior in regard to load shifting and energy saving (Bonino, Corno et al. 2012a, KEMA Nederland B.V. 2010). According to Abrahamse et al (2007), giving consumers feedback on their energy savings may further boost them to reduce more energy, because they would feel that their self-sufficiency has increased (Abrahamse, Steg et al. 2007). Informing, engaging, empowering and motivating consumers in combination with useful technology is important when designing energy saving programs. Achieving maximum feedback-related savings requires a two-sided approach. Not only giving insight into the data that is generated by smart meters can be used; actively giving feedback on a detailed level is argued to save even more energy (Karen Ehrhardt-Martinez, Kat A. Donnelly, John A. Laitner 2010) Feedback characteristics In 2010 a comparison paper was published in which a systematic assessment of information gathered from 57 primary studies around energy savings and energy feedback was outlined (Karen 29

30 Ehrhardt-Martinez, Kat A. Donnelly, John A. Laitner 2010). In these studies different approaches on energy usage feedback are researched. In the paper it was concluded that feedback is important when empowering consumer to manage their energy consumption behavior. Additionally, it was concluded that potential energy savings on household level could range from 4 to 12%, depending on the type of feedback given. Of critical importance is the way the feedback is provided and whether people understand the information, believe that they are capable of making a difference, and are motivated of action (Karen Ehrhardt-Martinez, Kat A. Donnelly, John A. Laitner 2010). Again, in the paper is referred to the two-sided approach important to change energy consumption behavior. Nevertheless, there is still disagreement about the most effective program. This disagreement is an important issue this thesis will contribute to. In the comparison study it is argued that some feedback strategies tend to give more energy savings than others. As outlined earlier, potential energy savings on household level could range from 4 to 12%, depending on the type of feedback given. Daily/weekly feedback is argued by Martinez (2010) to be less effective than real-time feedback and feedback on device level. This is shown in figure 6. Figure 6 Type of feedback and energy savings. Adapted from Martinez (2010) Enhanced billing means that information about your energy consumption is provided on a monthly, quarterly or yearly bill. This could for instance be an overview of the total consumption per day for the last month. In this case a consumers can see what days in the month the consumption of energy was above average. Enhanced billing is argued to lead to 3.8% energy savings. Estimated feedback means that a consumers provides information about a households energy usage online. During these audits questions like how often do you use your washing machine and on what temperatures are answered. Feedback and energy saving tips are given afterwards. On average energy efficiency programs focusing on estimated feedback result in energy savings of 6.8%. Daily/weekly feedback would occur when a consumers gets feedback on the amount of energy they use on a daily or weekly basis. Daily/weekly feedback let on average to 8.4% energy saving. All off these three feedback mechanisms are indirect ways of giving feedback, meaning that feedback is given after the consumption occurs. It is argued that direct feedback leads to more energy savings (Ehrhardt- Martinez, Donnelly et al. 2010). Real time feedback is argued to save 9.2%. Real time feedback means 30

31 that a consumers is able to get insight in consumption real-time at the moment the actual consumption is occurring. Through an in-home screen, such as Toon from Eneco (Eneco ), consumers get insight in the amount of energy currently used in their house. Additionally, they would be able to see the amount of energy used in the last day, week of month. Martinez (2010) also distinguished real-times feedback down to the appliance level. In her comparison study she concluded that this type of feedback can lead to 12% energy savings. In this case consumers would be able to see their current consumption per device they have in their household. They would be able to see how much energy each device is consuming in real-time. Feedback on appliance level is argued to save more energy than total energy used per day (Bonino, Corno et al. 2012a, Ehrhardt-Martinez, Donnelly et al. 2010, Abrahamse, Steg 2009). Partly because users see how much each device uses and which devices are energy inefficient. In the Netherlands environmental efficiency labels are given to appliances ranging from A (best) to F (least energy efficient) (MilieuCentraal ). It is assumed that that through this increase of informedness, these households will more likely buy energy efficient appliances and therefore save more energy (Fischer 2008, Steg 2008) Direct and indirect feedback In the literature there is a distinction made between direct and indirect feedback (Bonino, Corno et al. 2012a, Karen Ehrhardt-Martinez, Kat A. Donnelly, John A. Laitner 2010). Indirect feedback is provided after consumption occurs. This data gets processed and a consumer can for instance find this data on the website of their energy supplier or on their monthly bill. This could be total consumption per day or month. The key characteristic is that this data is processed and accumulated before presented to the consumer. The main difference with direct feedback is that direct feedback is real-time. This type of feedback is often displayed using an in-home energy display. Direct feedback can be about current energy use which can be appliance specific for a more detailed insight. A major advantage of direct feedback is that a consumer can directly see the changes he makes in his energy usage. This leads to a better involvement in energy saving (Bonino, Corno et al. 2012a). The type of feedback given can be related to the level of customer informedness. Direct feedback gives consumers more insight in their own exact energy usage and can be directly relate to devices. Therefore direct feedback gives consumers a higher customer informedness compared to indirect feedback. Direct feedback provides a wide range of contextual knowledge to consumers and enables consumers to learn by doing. Overall, direct feedback is considered in the literature, to be more effective than indirect feedback. According to KEMA (2010) (KEMA Nederland B.V. 2010) practical experiments demonstrated that feedback does lead to energy conservation. And well, 0-10% for indirect feedback an 5-15% for direct feedback. Furthermore, consumers do not want to go to an effort themselves to receive energy saving information. According to KEMA (2010) this is crucial to the energy conversation. In-home displays often provide direct feedback and can be situated in the living room, making them easy to approach. Based on the literature we propose the following hypothesizes: 31

32 H7: Direct insight into consumption data will generate a higher intention to save energy as opposed indirect insight into consumption data. H8: Direct insight into consumption data will generate a higher intention to shift load as opposed indirect insight into consumption data Actions arising from feedback programs Actions performed by consumers arising from feedback programs are generally related to consumer behaviors, technology choices or purchasing decisions and involve the adoption of more energyefficient products and appliances. Laitner et al. (2009) groups energy savings as a result of three categories of actions: 1. Simple changes in routines and habits 2. Infrequent and low-cost energy stocktaking behaviors (replacing light bulb, weather stripping) 3. Consumer investments in new energy-efficient appliances, devices. An interesting notion is that energy feedback programs achieve most energy savings through changes in behavior, not investment in new appliances. Only, consumers who put effort and money into the investment in new appliances tend to save more energy (John A. Laitner, Chris Knight, Vanessa McKinney, and Karen Ehrhardt-Martinez 2009). Overall, more energy savings can be achieved by energy efficient appliances, compared to changing our energy consumption behavior. Households that are more likely to invest in new energy efficient appliance are often high income households, resulting in high income houses saving more energy. Low income households are more likely to engage in energy rescheduling behavior and are more willing to change their habits and routines (Karen Ehrhardt-Martinez, Kat A. Donnelly, John A. Laitner 2010). This indicates that households with a lower income have a lower willingness to pay for energy. Nevertheless, only focusing on designing an energy efficiency programs aimed at the installation of new devices alone will result in only a small percentage of the potential energy savings, because people save energy out of two basic principles. 1. Self-interest (bill saving) and 2. Civic concerns and more altruistic motives (Karen Ehrhardt-Martinez, Kat A. Donnelly, John A. Laitner 2010). Only focusing on self-interest will not lead to full energy saving potential Feedback and learning Feedback on energy consumption can be related to learning theory (BRONNEN. Already in early research around feedback and learning some researchers began to emphasize that feedback is part of a learning process, in which people are information processors who actively make sense of the world around them (Ellis and Gaskell 1978). With energy consumption feedback available through smart meters some researchers took a specific look at learning and energy feedback {{81 Grønhøj,Alice A. 2011; 81 Grønhøj,Alice A. 2011; 82 Darby, Sarah 2006}}. According to Darby (2006) direct feedback can be related to learning by looking or paying. Furthermore, indirect feedback can be related to learning by reading and reflecting. The goal of feedback on energy consumption and learning is that feedback contributes to building tacit knowledge {{81 Grønhøj,Alice A. 2011}}. Tacit knowledge is knowledge that is difficult to transfer from one person to another. The literature argues that feedback on energy consumption makes it possible for consumers to take in information about their energy use and their acting. By doing so consumers gain understanding of what has happened 32

33 by interpreting the feedback. It is argued that when consumer go to this process repeatedly, a consumer will learn about its energy use as it becomes tacit knowledge. In figure 7 a model for the development of tacit knowledge through feedback is shown. Figure 7 Model about the development of tacit knowledge. Adapted from Grønhø (2011) It is argued that consumers go through learning phases when saving energy through feedback. Van Houwelingen and van Raaij (1989) argue that energy feedback has three functions in regard to learning. First, feedback has a learning function. Through feedback consumers learn what the connection is between their energy behavior and the amount of energy they use. Secondly, consumers have to habit this information. Consumers put the information they have learnt into practice and may develop a change in their behavior. Lastly, when consumers develop new habits, they will change their attitude to suit this new behavior. This is called the internalization of behavior and can be related to tacit knowledge. In line with this are the learning styles of Kolb (1974). Consumers who save energy by feedback on their energy consumption have to go through a learning process. Only, because every person learns differently feedback must be provided in a way that is consistent with the learning style a specific consumers feels comfortable with (KEMA Nederland B.V. 2010). In the learning cycle shown in figure 8 four different learning styles are shown (Kolb, Fry 1974). Consumer normally go through all of these styles when learning. The styles each transfer logically into another. Figure 8 Learning styles of Kolb (1974) 33

34 7. Comparison Comparison to personal historic consumption or bench marking (comparison to similar groups of consumers) is a subject researched in relation to energy savings. Additionally, the application of social norms in the energy saving discussion have been researched. Fischer (2006) argues that in basic there are two types of basic comparison. Namely, historic comparison and normative comparison. With historic comparison a consumer would compare its own consumption with that of previous periods. Historic comparison relates actual to prior consumption. Normative comparison compares consumption to that of other households. This could be comparison with an regional average, other households in the neighborhood, households of relatives or households that are in some way similar (type of house, size). It is argued that normative comparison may stimulate specific motives for energy conservation, for example, competition and ambition. When a consumer is able to compare its own consumption with that of others, their informed rises as they are better informed about their own position in the market. Therefore we argue that customer informedness can be related to normative comparison. In this chapter we will take a specific look at normative comparison. Looking more in detail to normative comparison, Ayres (2009) distinguishes three different types of comparison strategies that can be applied. (Ian Ayres, Sophie Raseman et al. 2009): 1. Comparison with standard consumption ( type of building or number of residents) 2. Comparison with a comparable group of consumers (young families, student dorms) 3. Peer comparison (family and friends) In the Netherlands several utilities included normative comparison to their energy efficiency program. For instance Nuon who make it possible for their customers to compare their consumption with that of other consumer groups (Nuon ) Normative comparison Numerous studies suggest that the effects of normative social influence have powerful effects on individual behavior. Approximately 25% of the feedback studies reviewed in the study of Martinez (2010) attempts to use social norms to help to change the energy consumption behavior of households. Using social norms in an energy efficiency program could for instance be achieved by adding close friends and family in the online environment. These would then compare their achievements with those of family and friends. According to Arahamse et al (2005), comparison between households provides a feeling of both competition and social pressure. However, results from actual studies indicate mixed results for programs that compare households with other households and suggest that specific elements of program design are likely to play an important role in energy savings. Of critical importance is the way in which the comparison group is determined and whether or not households believe the comparison to be appropriate. A study by Egan (1999) indicates that households do not necessarily save energy when compared to other households, particularly if people question the validity of the groups to which they are assigned. Therefore it is important to give reasoning why a household is 34

35 compared to a specific group and what the characteristics of this group are. Additionally, comparison with a peer group could also result in undesirable behavior because of another important reason: if the consumption would be lower than the peer group. This could de-motivate energy saving (Ian Ayres, Sophie Raseman et al. 2009). On the other hand Fischer (2006) argues that comparison can point out if a households energy consumption is out of norm and can thereby alert consumers, which can lead to a better consumers attention in energy conservation. Recent reviews of the enhanced billing interventions performed by OPOWER reveal that innovative combination of monthly feedback and normative data can achieve low-cost energy savings of 1.2% to 2.5% ((Ehrhardt-Martinez, Donnelly et al. 2010, Allcott 2011a, Ian Ayres, Sophie Raseman et al. 2009)). OPOWER s approach provides households with monthly Home Energy Reports that include both targeted and contextualizes information, including 1) household level data on current and comparative historical energy consumption, 2) semi-tailored energy saving tips, and 3) information concerning the energy consumption patterns of other households. This third component provides households with a social or normative context in which to compare and assess their own energy use patterns. By understanding the normative context, households can evaluate whether their consumption is abnormally high or low and adjust their energy behaviors as necessary. In order to reduce the probability that low-level electricity consumers will increase their consumption, the reports of OPOWER use injunctive norms and include energy use comparisons with energy-efficient neighbors (Allcott 2011b). Cialdini, Kallgren, and Reno (1991) argue that combining injunctive norms (norms that express social values rather than actual behavior) with descriptive norms (norms that express the behavior in a specific situation) and normative comparison can benefit energy saving. As outlined, households who use less energy than the average household can be de-motivated through comparison. It is argued that injunctive norms and descriptive norms can overcome this problem. Nevertheless, results from several other studies indicate mixed results for programs that compare households with other households. OPOWER shows us a positive relation between peer comparison and energy savings. Fishers (2008) noted that of the twelve studies she reviewed in her study around energy comparison, not one shows an effect (Fischer 2008). For the construct comparison we propose hypothesizes in line with the finding of the research of OPOWER, which is often cited. We propose the following hypothesizes: H9: Normative comparison will generate a higher intention to save energy as opposed to no comparison. H10: Normative comparison will generate a higher intention to shift load as opposed to no comparison. In relation to revenue management and price differentiation the following hypothesizes have been proposed for the construct comparison and its effect on the dependent variables: H11: The introduction of variable pricing strengthens the relationship between normative comparison and the intention to save energy. H12: The introduction of variable pricing strengthens the relationship between normative comparison and the intention to shift load. 35

36 8. Gamification In energy research it is argued that pilots that last longer overall safe less energy (Karen Ehrhardt- Martinez, Kat A. Donnelly, John A. Laitner 2010). It is concluded that respondents are more involved in the energy saving conversation in the beginning stage of the pilot as when the pilot evolves. Therefore we argue it is very important to motivate consumers to stay involved with energy saving. In recent literature about smart meters and energy saving it is outlined that adding game elements to an energy program could overcome this problem (Reeves, Cummings et al. 2012). Here, gamification is linked with behavioral change. A second reason gamification was added to the scope of this research is because gamification was pointed out during all four interviews conducted for this thesis. The respondents outlined that their companies were looking into adding game elements into their energy efficiency or energy demand response programs. In this chapter we will outline energy saving and peak shaping in regard to gamification What is gamification? Gamification refers to the trend were recently digital designers have begun to integrate game elements and mechanics into non-game applications, systems and services, to better engage end users (Liu, Alexandrova et al. 2011)). This is related to web 2.0 technologies, which makes it possible for consumers to interact with the system and become a content creator. This shift towards customer engagement led to a research wave around the question how a consumer develops a relationship with a certain platform. It is argued that consumers have to be incentivized in order to let them change their behavior. The ultimate goal of gamification is to incentivize a non-game system user to have so-called game-like behavior (Liu, Alexandrova et al. 2011). Both social psychology motivations as economic incentives are important to engage consumers (Reeves, Read 2009). This would indicate that both price differentiation as more socially focused approaches would benefit a behavioral change of energy consumption. Social facilitation is an approach supported within the gamification literature (Liu, Alexandrova et al. 2011). Social facilitation refers to the inclination that people perform better while someone else is watching. On the other hand, when they are working alone people tend to perform less. In order to support social facilitation within an application the effort of someone should be easily traceable for others, and these individuals should know that others can trace their work. Additionally, the unique work and contribution of each individual that contributed to the end goal should be remarked. Social psychological incentives, like comparison to historical data or ranking of contributions compared to other participants can significantly increase the amount of times a user contributes to a platform (Cheshire, Antin 2008). Economic incentive are real money or other commodities that trigger users to contribute to a platform. This is a relatively straight forward incentive and related to the energy sector this could mean lower energy prices or refunds when using energy at off-peak times. Game elements have been around for years. For instance frequent flyer programs at airline companies. Often these programs offer elite status for a certain amount of miles flown. 36

37 8.2. Gamification loop The idea behind gamification is to create a gamification loop within an non-game system environment (Reeves, Read 2009). The gamification loop is based on non-monetary incentives, but can be better related to social psychological incentives. Win condition Challenge Rewards Social network & Status Leader board Badges Figure 9 Gamification loop The user of the system is put in front of a clear goal or challenge and when he or she achieves this goal a reward is given. This could be in the form of an economic or social incentive. Based on historical achievements a leaderboard or badges can be provided for motivating competitiveness. Lastly, and most important, the players virtual status in their social network or in the network of the system should be updated to stimulate the best behavior. It is also common that levels are designed in the platform. If a consumers reaches a certain goal he is upgraded to a higher level Gamification and energy behavior Some research has been done linking gamification and energy consumption behavior. In this paragraph we will make a summary of the research conducted around this subject. Even though not much research has used the term gamification, there is much research in the energy field around goal setting and competition. This are elements also used in the gamification literature. We will first look into research that focuses specifically on gamification and energy behavior. Secondly we will look into energy research that has a focus on goal setting and competition. Gamification research seeks to examine how the engagement mechanisms common in popular games may be leveraged to promote desired real-world energy behaviors among players (Reeves, Cummings et al. 2012). It is researched that 22% of the energy consumption of households could be eliminated if consumers are involved with their energy behavior. Reeves (2011) argues that so far millions have been spend on the transformation to the smart grid, based on the idea that people will use less energy and make better consumption decisions if they are more informed about their own energy usage. He argues that the process by which consumers interact with the current designed smart meter system is not engaging. According to Reeves (2011) presenting energy consumption information in a game like environment is a solution to this problem. A game like environment could present consumers elements like self-representation, feedback, virtual connections, goal setting, 37

38 levels and rewards (Reeves, Cummings et al. 2012, Liu, Alexandrova et al. 2011, Reeves, Read 2009, Palmer. D, Lunceford S, Patton. A.J. 2012). A good example of gamification elements in the energy market is the customer engagement platform of OPOWER. This platform is developed to help deliver energy efficiency programs to utilities. Energy consumption is normally not something people usually talk about, but on this app people are talking a lot. Users are leaving tips, provide support, share successes and have fun as they challenge each other to reduce their energy usage (Palmer. D, Lunceford S, Patton. A.J. 2012). In the platform of OPOWER game elements are added to the energy feedback features. In the platform real-world smart meter data is combined with the ability to socialize, collect points, and receive rewards for challenges. The goal of the platform is to change the energy behavior of customers in real-life. In the platform of OPOWER this is realized by connecting the platform to the real-life smart meter a household uses. Another real-life example of an application that uses game elements to attempt to improve energy efficiency is Simple Energy. In this application social elements and data analytics of the data from smart meters is combined. Furthermore, the incentive to win real-world prizes is used to motive consumers. Pilot research has been conducted with this application in San Diego (Palmer. D, Lunceford S, Patton. A.J. 2012) households participated in a three month pilot and on average energy savings doubled compared to only feedback on consumption Goal setting in energy literature Even though the term gamification has not yet been used widely in energy literature, some research has been done around goal setting and competition in relation to energy saving. During the comparison study of Martinez (2010), in which she compared 57 energy feedback studies, 18 mention the use of gamification-like elements (Ehrhardt-Martinez, Donnelly et al. 2010). Of which, four include the use of goal setting and two include the use of competitions. In this paragraph we will give an overview of the most cited studies. In the energy literature it has been argued that goal setting can have a positive effect on the amount of energy saved in energy efficiency programs (McCalley, Midden 2002). Research concluded that giving households a conservation goal resulted in more energy savings compared to giving households only feedback on energy consumption. In this particular case a goal was set to save 10% gas (Houwelingen, Raaij 1989). The group in the experiment for whom a goal was set saved 12,3 % more gas than the group who only received feedback on their consumption. In this case a clear distinction is made between goal setting and only receiving feedback. Therefore in the thesis we argue that gamification elements, such as goal setting, positively affect the consumption behavior of households. Also the level of difficulty of goal setting has been researched. Seligman et al (1978) researched if feedback would lead to more energy saving if individuals were asked to adopt a difficult conservation goal rather than an easy one. Of the 100 households involved in the study, 40 households were given a difficult conservation goal (20% energy savings) while 40 were given an easy conservation goal (2% energy savings) and 20 households served in the control group. The group with the 20% goal saved 13 % less energy than the control group. Between the group who had the goal to safe 2% energy and the control group was no significant difference. 38

39 Competition in energy literature Two studies have been found were the competition element was researched. In both studies a competition was set up under students. In one of the studies 20 Dutch student households were studied, in the other 18 student dorms of the Oberlin University. In the dormitory energy competition aggregated, real-time feedback has been used (Petersen 2007). An automated data monitoring system was developed that provided dormitory residents with real-time web-based feedback on energy. For two weeks long dormitories competed to reduce their resource use. Conservation incentives were given to students to reduce their energy usage. The competition resulted in average electricity savings of 32%. After the experiment was conducted a survey was conducted under the student. At the end of the competition the winning dorm would be given an ice cream party for the whole dorm. Only the attendance of this ice cream party was very low. The researchers concluded that factors other than the incentive of a reward were responsible for the changes in behavior. The researchers argued that the challenge itself and the social interaction involved in meeting the challenge were more important for motivation than the reward offered. The experiment conducted in the student households in the Netherlands generated energy savings up to 45% for the most efficient household. The average savings were 25% electricity (Geelen, Keyson et al. 2012). After the experiment was finished the consumption of energy increased but remained below the level before the experiment was conducted. This experiment was based on the game: the energy battle. In this experiment the households were given direct feedback on energy usage, ranking of the competing teams, tips and access to the energy battle game. The researchers suggest that is it beneficial to explore whether the gamification of energy efficiency can be used in real-world settings to reduce energy consumption. This can be done by applying the rules of the energy battle to other target groups such as families with children. For the construct gamification the following hypothesizes can be made based on the literature: H13: Using game elements will generate a higher intention to save energy as opposed to not adding game elements. H14: Using game elements will generate a higher intention to shift load as opposed to not adding game elements. In relation to revenue management and price differentiation the following hypothesizes have been proposed for the construct gamification and its effect on the dependent variables: H15: The introduction of variable pricing strengthens the relationship between gamification and the intention to save energy. H16: The introduction of variable pricing strengthens the relationship between gamification and the intention to shift load. 39

40 9. Methodology This chapter will focus on the methodology of this thesis. First the conceptual model and hypothesis will be outlined followed by the research design, measurements and operationalization. For this thesis both interviews and a scenario-based survey were conducted. This chapter will first focus on the survey and secondly on the interviews Conceptual model Figure 11 shows the conceptual model of the proposed research based on the theoretical framework. The model proposes that the elements of the theory of planned behavior, namely attitude, perceived control and subjective norms have an effect on the behavioral intentions. It is argued that the more positive the attitude towards energy saving and load shifting, the more favorable the subjective norm and the greater the perceived behavioral control the stronger the intention to perform the behavior. Secondly, the model proposes that the type of feedback on energy consumption influence the behavioral intention. It is argued that direct energy feedback will result in a higher intention to save energy and a higher intention to shift load than indirect feedback. Furthermore, the model proposes that normative comparison influences the behavioral intention. In case normative comparison is in place there will be a higher intention to save energy or to shift load (compared to no comparison and only feedback on consumption). In addition, the model proposes that gamification elements will have an effect on the behavioral intentions. We argue that when gamification elements are in place the intention to safe energy and shift load will be higher. Lastly, the model proposes that there is a moderating variables in place. Variable pricing will moderate the effects of feedback, comparison and gamification on the behavioral intentions. It is argued that when variable pricing is introduced the relationship of feedback, normative comparison and gamification on the behavioral intentions will be stronger. 40

41 Theory of Planned Behavior Customer informedness Attitude towards behavior Perceived behavioral control Subjective norms H1, H2 + H3, H4 + + H5, H6 Behavorial intention Intention to save energy Intention to shift load + H7 H8 H9, H H11, H12 H15 H16 H13, H14 Feedback on consumption Comparison Gamification Price differentiation Firm informedness Figure 10 Conceptual model 9.2. Hypothesizes An overview of all the hypothesizes is given below. H1: The more positive the attitude towards energy saving the higher the intention to save energy. H2: The more favorable the subjective norm on energy saving the higher the intention to save energy. H3: The greater the perceived behavioral control the stronger the intention to save energy. H4: The more positive the attitude towards energy shifting the higher the intention to shift energy. H5: The more favorable the subjective norm on energy shifting the higher the intention to shift energy. H6: The greater the perceived behavioral control the stronger the intention to shift energy. H7: Direct insight into consumption data will generate a higher intention to save energy as opposed indirect insight into consumption data. H8: Direct insight into consumption data will generate a higher intention to shift load as opposed indirect insight into consumption data. 41

42 H9: Normative comparison will generate a higher intention to save energy as opposed to no comparison. H10: Normative comparison will generate a higher intention to shift load as opposed to no comparison. H11: The introduction of variable pricing strengthens the relationship between normative comparison and the intention to save energy. H12: The introduction of variable pricing strengthens the relationship between normative comparison and the intention to shift load. H13: Using game elements will generate a higher intention to save energy as opposed to not adding game elements. H14: Using game elements will generate a higher intention to shift load as opposed to not adding game elements. H15: The introduction of variable pricing strengthens the relationship between gamification and the intention to save energy. H16: The introduction of variable pricing strengthens the relationship between gamification and the intention to shift load Research design This study looks into the impact of the elements of the theory of planned behavior, feedback, normative comparison, gamification and price differentiation on energy consumption intentional behavior. To research these impacts both an 2x2x2 factorial experimental design and interviews were conducted. To test the hypothesizes a scenario-based survey was conducted. To get more background on the constructs and their relationships interviews were held. The interviews were also used to validate the conceptual model. In paragraph 9.8 more information is provided about the setup of these interviews. In this paragraph will be described how the 2x2x2 factorial experimental design was conducted. The three independent variables feedback, comparison and gamification were altered as stimuli over the eight different scenarios of the 2x2x2 factorial experimental design. The variables of the theory of planned behavior were measured in the same way in each scenario and not altered. For the variables feedback, normative comparison and gamification we measured the between variance of the respondent. This between-subjects distribution helped to avoid any carryover effects, common for many within-subjects designs (Field, Hole 2003). Carryover effects could cause altered behavior based on the acquired knowledge from a previous scenario (Field, Hole 2003). To measure the effect of the independent variable price differentiation we measured the within variance of a respondent. First we measured how a respondents reacts when no price differentiations is in place. Secondly, we measured the independent variables feedback, comparison and gamification again when block or hourly prices were in place. 42

43 This led to the following eight scenario s: Tabel 2 Eight scenarios of the 2x2x2 factorial design Scenarios Indirect insight into consumption (monthly) Direct insight into consumption (real-time) Comparison to average household 1 x 2 x x 3 x x 4 x x x 5 x 6 x x 7 x x 8 x x x Game elements (goal setting & point collection) As outlined, the independent variables of the theory of planned behavior, namely attitude towards the behavior, perceived behavioral control and subjective norms were measured in each of the eight different scenario s the same way and not altered. The independent variables feedback, comparison and gamification were tested on the dependent variables intention to save energy and intention to shift load. The independent variable price differentiation took the form of hourly energy prices or block prices. A scenario with hourly energy prices was shown to respondents in the scenarios in which direct feedback was in place. Block prices were shown in the scenarios were indirect feedback was in place. We argue that hourly energy prices cannot be related to indirect feedback, because indirect feedback gives a summary of the consumption in a certain period that has been passed. In these scenarios hourly energy prices would lead to confusion, therefore we chose block prices for scenario s were indirect feedback was in place. The design consisted out of eight different scenarios which all represented a different screenshot of a smart meter application. Screenshots with hypothetical but close to real-life circumstances has also been used in earlier survey research around energy feedback conducted by Bonino (Bonino, Corno et al. 2012a). In their scenario based survey Bonino et al divided their respondents in groups and showed each group different visuals. The researchers made sure the different stimuli differed notably per scenario. This research also used screenshots of a hypothetical but realistic in-home device as their visuals. This was corresponding with our visuals Survey design Because our research consists out of eight scenario s, eight different surveys were set out. In each version a different screenshot of a smart meter application was shown to the respondents. As shown in the table each scenario represented different combinations of stimuli. To keep the respondents focused on the manipulated factors any distractions were deleted. The stimuli visualized in the screenshots were feedback, comparison and gamification. The stimuli price differentiation was only visualized in the second half of the survey after first a scenario without price differentiation was completed. After the first half of the survey respondents were pointed out that in the next scenario price differentiation would be in place. The respondent were randomly assigned to one of the eight 43

44 designs and were asked to complete a questionnaire regarding the dependent variables intention to shift load and intention to save energy. Additionally, question regarding the theory of planned behavior were shown. The independent variables related to the theory of planned behavior, namely attitude, perceived control and subjective norms, were be measured in all the eight different scenario s consistently at the end of the survey. During the 2x2x2 factorial survey we visualized the stimuli as close to real-life as possible in order to measure truthful behavioral intentions. In the screenshots we tuned the usage data to values (in kwh) close to real-life settings. We included appliances that are often used in European households, to make it as authentic for the respondent as possible. The values of usage in kwh we used were adapted to a household with two children and two adults. During the survey respondents saw an introduction text. Depending on the stimuli shown in the screenshot afterwards, different texts were prepared. Each independent variable was described in words and depending in the variables present in the screenshot different pieces of text were added. For the independent variable comparison this was the following text: The energy company classified your energy consumption in the category: young families. This are families with two adults and two children who live in an one family house. Figures that are significantly different from your own consumption are automatically shown in a red frame. For the independent variable gamification the following text was used to described the variable to the respondents: Your energy company gave your family the target not to exceed 800 kwh per month in exchange for 75 points. Your smart energy meter is connected to an online environment where households collect and accumulate points. So, when you use less than 800 kwh in January you earn 75 points. On the right side of the screen you can see that so far you earned 55 points. The de Smits family, who is the family of your sister, earned 155 points so far. De Vries is the family of a good friend who have so far 107 points. Your neighbor is also present in you online environment and so far has gathered 75 points. Appendix 3 shows the visuals of the eight different screenshots that followed these introduction texts. First, each respondents was asked to answer question regarding their energy consumption behavior while keeping the screenshot in mind. Secondly, each respondent was communicated that in the next scenario price differentiation would be in place. A visual representation of the price structure was shown and described. Afterwards, for the second time the screenshot with stimuli was shown. This means that for effect of price differentiation on the behavioral intention within variance is measured. Again the respondents were asked to answer questions regarding their energy consumption keeping the screenshot and price differentiation in mind. Next, the scales to measure the variables of the theory of planned behavior were shown. These question were measured in all the eight different scenario s consistently. Lastly, questions about how important the respondent finds the different stimuli, were asked. For each scenario only the variables present in the scenario were included in this question. This question was asked to get some additional information about the opinion of the respondent. The survey ended with demographic questions around age, gender, income and education. 44

45 9.5. Stimuli The stimuli in the survey were the independent variables feedback, comparison, gamification and price differentiation. These stimuli were shown to the respondents using different visuals. For each stimuli is described where it derives from and how it was used in the scenarios Feedback on energy consumption There are many different ways feedback on energy consumption can be presented to consumers. As outlined in the literature review feedback down to the appliance level is argued to save the most energy (Bonino, Corno et al. 2012a, Faruqui, Sergici et al. 2010, Fischer 2008, Karen Ehrhardt- Martinez, Kat A. Donnelly, John A. Laitner 2010). Furthermore energy feedback can be divided in direct and indirect feedback. Direct feedback is argued to safe more energy (Karen Ehrhardt- Martinez, Kat A. Donnelly, John A. Laitner 2010), only indirect feedback down to the appliance level has not been research much. Therefore in this research we will distinguish direct from indirect feedback and in both cases we will display the feedback down to the appliance level. In line with the literature we argue that direct feedback will result in a higher intention to save energy, only we do keep in mind that indirect feedback down to the appliance level has not been researched extensive. Therefore, during the analysis, we will take a specific look on what is most favorable in this setting. Direct and indirect feedback will be the basis of each of the eight screenshots. Comparison and gamification cannot be visualized without basic insight into consumption data. To visualize feedback we adopted the screenshots used by Karjalainen (2011) in her research around customer preferences for energy feedback (Karjalainen 2011). We slightly adopted the screenshots to fit our setting. For instance, the period was changed to December 2012 to make it more like a real-life setting for respondents. The two screenshots are shown in figure 12 and figure 13. In figure 12 direct feedback is visualized. The current consumption is shown and respondents are asked to answer the Figure 11: Direct feedback, adapted from Karjalainen (2011) question with this situation in mind. In this setting it is January 4 th 2013 in the late afternoon. In total 1150 Watt is used. It is explained to the respondents that this means that when the consumption stays on this level for an hour 1,15 kwh is used. Lightning, television and the washing machine are currently the most energy consuming. 45

46 In figure 13 indirect feedback is visualized. In this screenshot the energy consumption of the previous month is shown, which was December 2012 when the survey was online. Because the screenshots gives an overview of energy consumption that already occurred you can talk about indirect consumption. The total consumption for December was 958 kwh, which costs 115. Indoor lightning is the highest energy consumer in this setting. The value of kwh for each device was adopted from Karjalainen (2011), to keep the screenshots as reliable as possible. Figure 12: indirect Indirect Feedback, feedback, adapted from Karjalainen (2011) Normative comparison The second stimuli we visualized is normative comparison. Comparing consumption against that of other households is argued to give consumers a good overview where their consumption differs from normal. This gives them an overview of the devices they might be able to save energy on (Ian Ayres, Sophie Raseman et al. 2009). According to Abrahamse et al (2005) the comparison between households provides a feeling of both competition and social pressure. However, results from actual studies indicate mixed results for programs that compare households with other households and suggest that specific elements of program design are likely to play an important role in energy savings. Of critical importance is the way in which the comparison group is determined and whether or not households believe the comparison to be appropriate. To overcome this problem we described that the group whom a respondents was compared to was a similar households. Obviously, not all of the respondents can relate the values of kwh in the screenshot to their real-life situation, which could lead to bias. To overcome this we asked them what they would do if they saw such a screen on their in home device. Values that differ a lot from the value of a households own consumption are put in a red box. Besides the actual comparison in kwh per device, total consumption is compared on a scale. In figure 14 comparison is visualized for indirect feedback. As you can see the Figure 13 Stimuli normative comparison indirect feedback 46

47 comparison element is added on the basic feedback screenshot. Fridge/refrigerator and indoor lightning is put in a red box because they significantly differ from the average households. In the bottom of the screenshot you can see the scale on which the total consumption is compared. In figure 15 comparison is visualized for direct feedback. Again, the stimuli is added to the basic screenshot. Figure 14 Stimuli normative comparison direct feedback Gamification The third stimuli that was visualized in the screenshots it gamification. In the literature review is outlined that not much research has been done around gamification in the energy literature. Nevertheless, goal setting and competition have been researched. Both elements are argued to benefit energy saving. Especially the competition element is argued to lead to big energy savings. In one pilot an average energy saving of 32% was realized (Petersen 2007). We argue that adding gamification elements to an energy efficiency program can positively affect the amount of energy saved. Gamification elements can take many forms, only because we want to be able to compare our finding to that of other research we choose only to include the elements goals setting and competition. The element goal setting was visualized by showing the respondent a target with a maximum amount of kwh. The competition element was put in practice by the ability to collect points. A respondent could earn 75 points if they reached the target. To put a meaning to the amount of points and to further explore the competition element a point breakdown was shown. In this breakdown the total amount of points of other families was given. This was the family of a friend, a relative, a neighbor and an average. In figure 16 this stimuli is shown. Figure 15 Stimuli gamification 47

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