Keywords

1 Introduction

The energy revolution and smart grids (SG) are two of the most important energy topics today. Recently, the demand for energy is growing while traditional resources of energy supply (coal, natural gas, and oil) will not meet the increasing energy demand any longer [1]. In order to face ecological challenges a flexible power grid enabling the integration of renewables - temporal dynamic energy sources - is required [2]. As a result, distributed energy resources (DERs) have become increasingly important because of their advantages for the grid. DERs enable demand response, grid stabilization and reduce the distribution of transmission costs. Consequently, integration of DERs is more energy-efficient [9] and saves money for the customers.

However, the increase of DER integration probably results in a reduction of energy sales of the distribution system operators (DSOs). In consequence, the DSOs are predicted to increase their grid tariffs in order to counterbalance their sales loss, which in turn might attract more consumers to become prosumers, who probably increase self-consumption and invest in local storage technologies or even disconnect from the grid - called the spiral of death [3]. Consequently, in order to maintain their market role, DSOs have either to adjust their business models or create new business models that integrate DERs into the SG. There are some studies [4, 5], which examine financing topics of renewable infrastructure barriers for future business models. Renewable Energy Cooperatives were found to facilitate the market uptake of renewable energies by applying community-based marketing initiatives [6], but business models of DER integration have not been in the focus of research yet.

New energy business models will involve new market players and give the opportunity of active con- and prosumer involvement in terms of optimal production and consumption of energy. Their acceptance is a main contributor to the adoption [7] of these models. Hence, integration of user preferences and barriers is of fundamental importance in the design process, but research on acceptance of DERs and business models is scarce until now [7, 8]. To enlarge the body of user research, this study focuses on the empirical assessment of underlying variables for acceptance.

2 Related Work

2.1 Acceptance

There is sparsely empirical research on technology acceptance of DER business model so far due to the novelty of the topic. Recently, Von Wirth, Gislason and Seidl [10] investigated drivers and barriers for the social acceptance of DERs. Results of the literature research and semi-structured interviews with representatives of pilot regions indicate that the awareness of local advantages could be a decisive argument promoting these systems. Furthermore, the authors conclude that the ownership of infrastructure fosters the acceptance of such systems. There is a plethora of research focusing on the concept of acceptance of new technologies. As DER systems, combining renewable energy generation, energy conversion, and energy storage on different local scales [10] technology acceptance is one crucial construct when it comes to user adoption.

Technology Acceptance Model

A well-known theoretical framework to describe technology acceptance is the Technology Acceptance Model (TAM) proposed by Davis [11]. According to the author, acceptance of new technologies depends on the perceived usefulness and the perceived ease of use of the technology. The first is defined as “[…] the degree to which a person believes that using a particular system would enhance his or her performance […]”, whereas the latter refers to “[…] the degree to which a person believes that using a particular system would be free of effort.” [11]. The combination of these concepts determines a person’s attitude towards the usage of a technology, which influences the intention to use and finally the actual use of a technology. With respect to SGs, beside all the merits of DERs in a SG, the integration of multiple features in a business model might hamper the ease of use even though usefulness ratings are high and lead to reduced acceptance consequently.

Norm Activation Model

Another main motivator for the acceptance of DERs in general might be their positive outcomes. Beside monetary savings, DERs allow an environmentally friendly energy production and grid stabilization. Therefore, mere technology acceptance falls too short describing user adoption. According to Schwartz [12, 13] the behavior of using technologies that benefit others or the environment is motivated by personal norms or self-expectations: “Personal norms focus exclusively on the evaluation of acts in terms of their moral worth to the self.” [14, p. 245]. Therefore, personal norms might contribute to the accepance of future energy business models with a higher amount of DERs.

Responsible Technology Acceptance Model

The Responsible Technology Acceptance Model (RTAM), [15] combines aspects of the TAM [11] with the Norm Activation Model (NAM) [12, 13]. Here acceptance of new technologies depends on the rational cost-benefit assumptions and personal moral deliberations. A survey with 950 subjects from Denmark, Switzerland, and Norway showed that the RTAM successfully predicted the acceptance of the SG application [15]. Rational assessments as well as personal norms triggered by “[…] feeling of moral obligation or responsibility towards the environment and a positive contribution to the society […]” [15, p. 398] are essential for a positive evaluation. Individual benefits like monetary saving or incentives are not the only motivator for usage [16]. The authors discuss, that societal as well as environmental benefits should be stressed when promoting new technologies, which is in line with research by Bolderdijk et al. [17], who found, that pointing out societal and environmental benefits in the communication induces more positive feelings than solely mentioning private ones.

2.2 Hypotheses

As present research [10] showed, SG actors who own parts of the energy infrastructure - such as prosumers - should have a higher acceptance of future energy business models than consumers. Furthermore, the acceptance should be higher the more local advantages an energy model provides. Accordingly, it can be derived from the NAM [12, 13], that the acceptance of business models, that integrate personal norms like pro-environmental and pro-social behavior, is higher. This is confirmed by the research of Toft et al. [15], and Bolderdijk et al. [17], who integrated environmental and societal benefits. The Peer-to-Peer (P2P) model described above represents the most complicated model in terms of (technical infra-) structure, but also the most locally anchored one in terms of social involvement and energy infrastructure. Therefore, the following hypotheses are derived from the literature for underlying varibles of acceptance:

H1.1::

The current business model (BaU) is perceived more easy to use than any future business models (DSO, Volt, P2P).

H1.2::

With increasing complexity, due to more infrastructure and interaction with the user needed, ratings on perceived ease of use diminish. Hence lowest ratings are expected with P2P, followed by Volt and DSO.

H2::

Perceived usefulness is rated higher for future energy business models (DSO, Volt, P2P) than the current model (BaU).

H3.1::

The influence of personal norms is highest for future business models (DSO, Volt, P2P) compared to the currently used business model (BaU).

H3.2::

Ratings on personal norms will be higher for models with a high level of DERs (P2P, Volt) compared to the other presented energy business models (DSO, BaU).

H4.1::

The attitude towards future energy business models (DSO, Volt, P2P) is more positive than towards the current model (BaU).

H4.2::

Compared to consumers, prosumers` attitude rating is higher with models with a high level of DERs (P2P, Volt).

2.3 Method

Participants

Overall, N = 21 persons had been interviewed (9 prosumer, 18 male). The sample consisted of three Swedes and 18 Germans. On average they were M = 43 years old (SD = 13.75; min = 21; max = 71). The most interviewees (n = 18) indicate holding a university degree or higher. The average household size was 3.6 residents. The income was indicated by 20 interviewees with a most frequently (n = 7) income category of “3000-4500€”, followed by “more than 6000€” (n = 6).

Material

Three future business models were investigated: 1.) DSO controlled (DSO); 2.) Voltage-Tariff (Volt) and 3.) Peer-2-Peer (P2P). The business models incorporated different degrees of DER ranging from large to small-scale distributed energy generation and were developed within the research project “NEMoGrid - New Energy Business Models in the Distribution Grid”. Business as Usual (BaU) represents a baseline measurement. In order to facilitate comparability, the descriptions of the models comprised following characteristics: (1) Energy source (e.g., PV plant or DSO), (2) Basic tariff structure (e.g., static depending on the consumption), (3) Installed infrastructure (soft-/hardware; e.g., algorithms and storage), (4) Possible effect for daily life (e.g., shifting of energy consuming activities), (5) Composition of the energy bill (e.g., quota of network service usage, time-specific energy costs), and (6) Financial benefits. Detailed business model descriptions can be found in the project deliverable “D2.3” [19].

Interviews with Swedish participants were conducted in English and with English materials. For German interviewees material and interview questions were translated.

Procedure

Demographic variables like user type (pro-/consumer) were assessed in a pre-questionnaire, which was used to preselect subjects for the interviews. Subjects were provided with the energy business models descriptions before the interview. These descriptions varied depending on the pro-/consumer classification. The interviews were conducted via Skype or phone call. After the introduction on the project subject and interview procedure, consent was obtained. Subsequently interviewees were asked to evaluate each of the four energy business models, which were presented in randomized order, by answering closed- and open-ended questions. The questions on the variables presented in this paper (1) the perceived ease of use, (2) the perceived usefulness, (3) personal norms, and (4) the attitude, remained the same for each model. Interviewees were asked to give their evaluation on a scale ranging from 1 to 6 for the respective items of each variable and were asked to explain their ratings subsequently. The interview lasts for about one hour and the interviewees received a remuneration of 40€ for their participation.

Data Analysis

Quantitative data was analyzed descriptively. After verifying distribution of normality parametric inferential statistics were applied. Since the research question was to identify differences between the user group and/or the business models, a repeated measurement analysis of variance (RMANOVA) was calculated with model as within-subject factor and user type as a between subject factor.

The open-ended questions of the interview have been transcribed using the software easytranscript (Version 2.50.7) [20]. Answers of each interviewee were split up into single statements and categorized into a bottom-up built category system (example statements for the reported categories are listed in Annex A). The category system for the variables (except personal norms) distinguished between positive and negative statements on an overall level. On a sub-level, the categories contain detailed information. A second coder was included to ensure reliability of codings. Intercoder-reliability (unweighted Kappa) on the sub-level varied between κ = .72 (ease of use) and κ = .86 (personal norms). According to literature this is a “substantial”/”almost perfect agreement” [18]. Discrepancies of codings were identified and eliminated. The frequencies of the sub-level categories of this consensus solution are reported relatively to the overall amount of either positive or negative statements.

2.4 Results

Perceived Ease of Use

Interviewees rated BaU as the most easy to use, followed by DSO (Table 1). Perceived ease of use was rated the lowest with the P2P. RMANOVA showed, that models significantly differed from each other (F(3, 57) = 29.24, p < .001, η2 = .60). Post-hoc pairwise comparisons became significant (p < .001) for all except DSO and the Volt.

Table 1. Mean evaluation of perceived ease of use (N = 21).
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In total 162 single statements explaining the ease of use ratings had been categorized. With exception of BaU - evaluated mainly positively (n = 40; 85%), the future energy business models received more critique than positive statements (P2P: 37; 82%; DSO: 30; 65%; Volt: 24; 61% negative statements).

Positive Statements. BaU was appreciated for the “Low Cognitive Effort” (50%), the “Predictability of Costs” (23%) and its “Accessibility” (20%). DSO and Volt were appreciated for the “Low Cognitive Effort” (DSO: 56%; Volt: 33%) and the “Accessibility” (DSO: 25%; Volt: 13%) as well. For P2P various other aspects were pointed out positively, e.g., “Financial Incentives” (38%).

Negative Statements. BaU received critique for the “Initial Installation Effort” (57%). In particular, prosumer criticized the initial bureaucratic effort for an energy plant installation. The future models especially P2P (54%) and Volt (46%) were criticized for the “Increased Cognitive Effort” the interviewees assumed. The “Uncertainty of Costs” (27%) was mentioned negatively during the P2P evaluation. Furthermore, the interviewees named that DSO will probably lead to a “Discrepancy with their own Habits” (27%) and criticized the “External Control” applied within this model (23%).

Perceived Usefulness

Usefulness was rated the highest for the Volt, followed by the P2P and the DSO model (Table 2). BaU was rated the least useful. Future models were all perceived significantly more useful than BaU (F(3, 57) = 31.75, p < .001, η2 = .62, pairwise comparisons: p < .001). However, there were no significant differences between future models. RMANOVA revealed a significant interaction (F(3, 57) = 5.82, p = .002, η2 = .23) for user type and model. Whereas prosumers rated BaU more useful than consumers, the DSO was rated better by consumers.

Table 2. Mean results for perceived usefulness (N = 21).
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The open-ended explanations resulted in 155 single statements. Overall, all future business models were evaluated positively (positive statements: DSO: 21; 60%; Volt: 33; 75%; P2P: 32; 73%). Underlining the quantitative ratings, the majority of statements (32; 76%) about the BaU model was negative.

Positive Statements. BaU was especially valued because of its “Grid Stabilization and the Secure Supply” (40%). This advantage was also mentioned for DSO (43%) and Volt (36%). Furthermore, diverse other categories were mentioned during BaU evaluation: “Increased Level of Freedom on Influence and Consumption Costs” (20%), “Predictability of Costs” (10%), and “Integration of Renewables” (10%). In general, participants prefer the “Increased Level of Freedom” with future models, especially on consumption costs within the Volt (33%) and the P2P (31%). The interviewees liked the “Effective Usage of Energy” with DSO (24%) and P2P (25%).

Negative Statements. Considering BaU the interviewees mainly fear the “Destabilization of the Grid” (38%) and complain about the “Few Incentives to Adjust Behavior” (25%). DSO was criticized even more frequently for “Few Incentives to Adjust Behavior” (43%) compared to BaU. Again, the “External Control” was evalauted disadvantageously (36%). Regarding P2P interviewees worried about “Destabilization of the Grid” (50%) and again the “Increased Cognitive Effort” (17%). Least was also the main critique on the Volt (27%).

Personal Norms

Interviewees’ personal norms towards P2P were the highest, followed by the Volt and DSO model (Table 3). There was a significant effect of the model (F(3, 57) = 3.96, p = .012, η2 = .17). However, pairwise comparisons only became significant for BaU and P2P (p = .045). Again, prosumers and consumers differed in their rating between models (F(3, 57) = 4.06, p = .011, η2 = .17; see Fig. 1). Whereas Volt and P2P result in similar ratings, consumers see DSO more positive than BaU, whereas prosumers are in favor for BaU (Fig. 1).

Table 3. Descriptive results on personal norms (N = 21).
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Fig. 1.
figure 1

Personal norms (1 = strongly disagree to 6 = strongly agree) by user type with 95% confidence interval (N = 21).

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Overall, 150 open-ended question statements had been analyzed. Especially with BaU, the influence of personal norms seems low (30; 71% rejection). Contrary to that, this is the case for the majority of DSO (22; 63%) and Volt statements (24; 63%, P2P: 18; 51%).

Social Norms Present. For BaU the interviewees most frequently named “Ecological reasons” (42%). This agreement was caused by prosumers, e.g. by stating that the integration of renewables is feasible already. For DSO (27%) and Volt “Grid Stabilization and Secure Supply” (21%) was mentioned. “Financial Incentives” (Volt: 25%; P2P: 22%) and “Ecological Reasons” (P2P: 22%; DSO: 18%; Volt: 17%) were pointed out for the future models. “Innovativeness” was particularly pointed out with P2P (22%) and Volt (17%).

Social Norms Not Present. In contrast, some participants denied the influence of personal norms. For BaU “Non-Ecological Production” of energy was mentioned (23%) as a reason. Furthermore, there was criticism to the extent, that there is “Room for Improvement” (20%) and the model leads to a “Destabilization of the grid” (17%). Especially for the DSO evaluation, interviewees criticize the “Dependence on Others” (46%). In general the interviewees see “Room for Improvement” (36%) and explicitly mention that “Norms are not Applicable in this Context” (29%) for Volt. Least was also stated with regard to P2P (29%). Again, and strictly speaking for P2P the interviewees fear the “Increased Cognitive Effort” (24%).

Attitude

Overall, the attitude towards the business models is rather neutral to slight positive. BaU is rated the safest and P2P the least safely. However, a vice versa pattern was found for the evaluation on the scale from very bad to very good, where P2P and Volt received the highest ratings. Feelings towards P2P were the most positive (Table 4).

Table 4. Descriptive results on attitude (N = 21).
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RMANOVA of overall score revealed no significant differences between models or user group. However, an interaction between both factors occurred (F(3, 57) = 5.65, p = .002, η2 = .22). Whereas prosumers were in favor for BaU and P2P, consumers prefered the DSO and the Volt model.

The open-ended question, gathering reasons for the attitude ratings, lead to 168 single statements. Overall, the evaluation of BaU indicated a positive attitude (37; 71%). There were less positive statements for DSO (25; 58%), Volt (24; 51%) and P2P (18; 46%).

Positive Statements. “Low Cognitive Effort” (27%) and the “Effectiveness and Reliable Functionality” (27%) were appreciated most frequently for BaU. Interestingly, the “Effectiveness and Reliable Functionality” were mentioned also for all future models (DSO: 28%; P2P: 22%; Volt: 21%). Additionally, future models (esp. Volt: 46%), were appreciated because of their “Increased Level of Freedom”. Statements belonging to this category refer mainly to an increased influence on the consumption costs. Furthermore, the interviewees appreciated the “Innovativeness”, especially for P2P (28%). Additionally, P2P was valued because of its potential of “Ecological Integration of Renewables” (28%).

Negative Statements. BaU received critique because the interviewees broadly see 1) “Room for Improvement” (67%) and think that; 2) energy is produced “Non-Ecological” (20%). A negative attitude towards the future models was explained by the “External Control and Dependence” on other actors. This was the case for DSO (61%) and Volt (30%). In contrast, P2P was criticized for “Increased Cognitive Effort” (43%). Furthermore, the interviewees complained about the “Uncertainty of Costs” with P2P (33%).

2.5 Discussion and Conclusion

Our interview study aimed on the identification of factors fostering or preventing the acceptance of future energy business models. In general, the quantitative ratings of the interviewees showed that acceptance of future models is relatively high.

In accordance with the hypotheses (H1.1, H1.2) we found that perceived ease of use diminished with increasing complexity of the business model. Not surprisingly, it was rated the best for the current business model, possibly because user already interact with it. However, perceived usefulness was rated significantly higher for future business models, even though characterized by higher complexity, which is in line with our hypothesis (H2). Toft et al. [17] stated personal norms should be taken into consideration for the evaluation of smart grids. Assumptions on the influence of personal norms can only be confirmed partly. The only statistically significant difference from business as usual is to be found in comparison to P2P. Therefore, H3.1 must be rejected. However, prosumers and consumers do rate some models differently. Wherereas, the Voltage-Tariff and the P2P model result in similar ratings, consumers see the DSO controlled model more positive than BaU, whereas prosumers are in favor for BaU. Therefore, H3.2 can only be confirmed partly. Acceptance in terms of a positive, good and safe attitude towards the model is higher for all future models but not significantly higher. Hence H4.1 must be rejected. However, taking into account the different user groups we see that prosumers were in favor for the current business model and the P2P model, whereas consumers seemed to prefer the DSO and the Volt model, which speaks for a partial confirmation of H4.2. Therefore, we can confirm and extend the results of Von Wirth, Gislason and Seidl [10], who underlined the importance of infrastructure ownership for the acceptance of distributed energy systems. Qualitative data helps to better understand these results.

The analysis of qualitative data showed that future energy business models received critique due to their specific characteristics, such as the increased external control within the DSO controlled model. Probably this affected specifically prosumer ratings, as they value independence of energy supply and therefore acceptance is diminished for this model. In contrast, consumer acceptance is remarkably higher for this model. Probably they are more concerned about the destabilization of the grid, as they are more depending on a secure supply. Least could be a reason for the different perspectives on the DSO controlled model. In turn, the appreciated “Increased Level of Freedom” (resp. influence on consumption costs) especially for the Voltage-Tariff is probably more decisive for consumers than for prosumers, as they do not have any possibilities to influence their costs at the moment. The P2P model was criticized for its complexity and the uncertainty of costs, which could be more discouraging for consumers than prosumers, who are used to a certain degree of complexity, high investments for energy generation or the – also financial – security of self-consumption.

It can be concluded that as energy business models acceptance differ with regard to user group, they should be adapted to the specific pro- and consumer requirements. Generally speaking our interviewees valued the innovativeness of future energy business models, especially the P2P model. We can conclude that they see need for change as they criticized the current model for its backwardness.

2.6 Limitations and Future Work

As DER systems imply the installation of technical infrastructure the theoretical foundation of this study built up on research of technology acceptance. Even though the RTAM model [15] focuses on the technology acceptance of smart grid infrastructure and incorporates personal norms, aspects of social acceptance are neglected. Moreover, some interviewees denied influence of personal norms in this study. Interviewees mentioned economical thinking mostly drives acceptance. Therefore, future work should enlarge the scope of variables including acceptance on a social and financial level.

The present findings should be considered with care because sample size is relatively small and self-selection bias can lead to increased acceptance and interest in the topic. Due to regional differences in energy market, national differences should be considered more intense. Findings in other countries might differ from the ones generated here.

Further, in order to identify tendency more clearly, we used 6-point scales instead of 7-point scales suggested by Toft et al. [15]. Hence, comparisons with other technologies ratings are not possible with the present results.

Regarding the qualitative data, the categories identified for each variable were very similar. Positive and negative category statements frequently represent opposite opinions. Future work could investigate main motives and barriers in a quantitative manner, avoiding contrary queries of motives and barriers.

For our interview study different business models descriptions and user group have been prepared. The descriptions were phrased in colloquial language to ensure comprehension of the models, but could be improved with regard to implementation aspects to improve con- and prosumer ideas of the future energy business models.