Eugênio José

Silva Bitti
email: ebitti@usp.br

Cintya Lanchimba
email: cintya.lanchimba@epn.edu.ec

Muriel Fadairo
email: muriel.fadairo@univ-st-etienne.fr

Franchisors' choice between royalties and fixed fees evidence from Brazil

Keywords: contract design, brand strategy, agency theory, distance C12, L14, M21 JEL C12, L14, M21) contract design, brand strategy, agency theory, distance

In franchise contracts, the royalty rate and the fixed entrance fee are the main monetary clauses defining the payment scheme between the franchisor and the franchisee. In the traditional agency view, the presence of distant outlets leads the franchisor to choose a payment mechanism designed to provide incentives to the franchisee; that is, a low royalty rate associated with a high fixed fee. Based on a unique panel dataset, we provide evidence that, in the Brazilian context, spatial dispersion has the opposite impact, with interesting practical and research implications.

I. INTRODUCTION

The importance of contact theory was recently emphasized by the choice of the central bank of Sweden to award the 2016 prize in economic sciences in memory of Alfred Nobel to Oliver Hart and Bengt Holmström. Contract design raises issues of incentives, trust, and monitoring, with associated questions of remuneration and compensation payments. While this research field concerns many domains of economics, business-format franchising presents itself as a relevant area for applied studies on contract design, thanks to the availability of data. Franchising networks contractually bind an upstream party, the franchisor, to a network of retailers using its brand name and business format, the franchisees. This organizational form is used internationally in all retail and services sectors, and stands out as the dominant model of trade in this early 21 st century.

Since the 80s, franchising has been the focus of a wide empirical literature in economics, entrepreneurship, marketing and strategy.1 However, as underlined by [START_REF] Fadairo | Franchising in Latin America[END_REF], relatively few works for an international audience deal with data from emerging economies. Yet in recent years statistics have highlighted the remarkable dynamism of franchising in Brazil, Mexico, China, Turkey, and in several African countries. This trend continues, despite the multidimensional crises impacting the above-mentioned countries, and is attracting a growing scientific interest.

According to the Brazilian Franchising Association [START_REF] Abf | Números do setor_Desempenho do Franchising[END_REF], regardless of the current economic and political crisis in Brazil, notably the low economic performance and modest results of the Brazilian retail sector since 2014, franchising continues to grow in this country.

The ABF estimates that the total income of Brazilian franchising grew 7.7% in 2014 / 2013, and 11.2% during the first half of 2015 compared with the same period in 2014. In addition, franchise networks are present in diverse regions of this huge country, including the most remote and least developed. Although 53.2% of the networks were located in São Paulo in 2016, significant increases in franchising were recorded in the other Brazilian states [START_REF] Abf | Números do setor_Desempenho do Franchising[END_REF]. In this context, spatial dynamics and related issues are critical.

At the same time, despite some exceptions (e.g. Mathewson and Winter, 1994), most discussions in the empirical literature on the locational aspects of franchising seem shortsighted, addressing the geographic dimension only in so far as it is a means to evaluate monitoring costs between franchisors and the franchised outlets (e.g. [START_REF] Norton | Franchising, Brand Name Capital and the Entrepreneurial Capacity Problem[END_REF][START_REF] Combs | Why Do Firms Use Franchising as an Entrepreneurial Strategy?: A Meta-Analysis[END_REF][START_REF] Perryman | Who Should Own It? An Agency-Based Explanation for Multi-Outlet Ownership and Co-Location in Plural Form Franchising[END_REF]). Yet franchising represents a rich framework within which to study the impact of spatial organizational choices, while, in the literature on retailing, location has long been clearly considered a major determinant of firm performance (e.g. [START_REF] Jones | Location, Location: Analysing the retail environment[END_REF]. This is a context of economic analysis where "space is back",2 with the renewal driven by [START_REF] Krugman | Increasing Returns and Economic Geography[END_REF] and the "New Economic Geography", as well as recent works at the microeconomic level on spatial competition, or on the economic impact of distance [START_REF] Alderighi | Localized Competition, Heterogeneous Firms and Vertical Relations[END_REF][START_REF] Kalnins | Too far away? The effect of distance to headquarters on business establishment performance[END_REF].

Aiming at addressing these gaps in the literature on franchise data, our paper deals with the impact of network spatial organization on contractual choices. We focus on the payment terms linking the franchisor and the franchisees, in an agency-theoretic framework. The relevance of agency theory in franchise contracting is widely accepted, starting from the contributions of [START_REF] Rubin | The Theory of the Firm and the Structure of the Franchise Contract[END_REF], [START_REF] Mathewson | The Economics of Franchise Contracts[END_REF], and [START_REF] Rey | The Logic of Vertical Restraints[END_REF]. In addition, the empirical literature on franchise data emphasizes the role of agency-theoretic arguments in explaining the organizational choices in franchise networks (e.g., [START_REF] Blair | The Economics of Franchising[END_REF][START_REF] Lafontaine | Incentive and strategic contracting: implications for the franchise decision[END_REF]. In this framework, two main monetary provisions, considered as substitutable, define the payment terms in franchise contracts: the fixed fee, also called the franchise fee, which is an upfront fixed payment given by the franchisee when entering the network; and the royalty rate, an ongoing payment usually expressed as a percentage of the franchisee's sales.

Our goal in this paper is to address the following questions: in continent-sized Brazil, does the spatial organization of franchising networks influence the franchisor's choice regarding the payment devices? Is there any influence from the specific context -an emerging country undergoing rapid evolution in the development of franchising -on this monetary choice? Is there any influence from the support devices characterizing the relationship between the franchisor and the franchisee? Finally, what are the performance outcomes, at the network level, of that monetary choice?

The rest of the article is organized as follows. Section II presents the related literature and develops the testable predictions, based, first, on the traditional agency argument regarding monetary provisions. In addition, we discuss an alternative view specific to the context of franchising in an emerging country. Additional hypotheses are developed to study the influence of the franchise relationship on the payment terms, and the performance outcomes.

Section III describes the data and the study variables. We use a unique and recent panel dataset, customized for the purpose of the investigation in this paper. The estimation strategy and the results are presented in Sections IV and V. Section IV provides evidence concerning the empirical determinants of the monetary choice (royalties versus fixed fees) in Brazilian franchising. Section V highlights the performance outcomes of the network in terms of market share. To control for endogeneity bias, we perform the estimations using the two-step methodology developed by Murphy and Topel. Section VI concludes with final remarks on the implications for franchising and academics that go beyond the Brazilian case.

II. RELATED LITERATURE, STYLIZED FACTS, AND HYPOTHESES

A. Traditional dilemma between incentives versus monitoring and the impact of geography

Business-format franchising is characterized by the following vertical relationship: the franchisor develops a brand and a business concept, which is then operated by independent outlets (the franchisees). Specifically focusing on the delegation of tasks -here the use of the franchisor's brand name and business concept -an agency-theoretic context, with the franchisor being the principal and the franchisee the agent, constitutes a relevant framework for the study of franchise contracting. Indeed, as emphasized by [START_REF] Wimmer | Moral Hazard, Asset Specificity, Implicit Bonding, and Compensation: The Case of Franchising[END_REF], many of the elements of moral hazard models emerge in franchising. Thus, the literature highlights moral hazard on the franchisee's side regarding the franchisor's brand reputation; the common brand indeed being affected by free-riding behavior in the franchised network.

Despite the above generic definition and features, a great variety of franchise contracts can be distinguished, both in business relationships and analytically. In particular, in the moral hazard context, the franchisor faces the choice between contracts oriented towards the monitoring of the independent outlets, with the associated costs related to a more integrated vertical relationship, versus contracts targeting the provision of incentives to the franchisees, which involves a greater level of delegation (e.g. [START_REF] Lafontaine | Incentive Contracting and the Franchise Decision[END_REF]Lal 1990;[START_REF] Michael | Do Franchised Chains Advertise Enough?[END_REF][START_REF] Fadairo | Franchising in Latin America[END_REF].

Figure 1 illustrates the trade-off between monitoring and incentives faced by the franchisor in franchise contracting. In the agency-theoretical framework, the royalty rate is considered as the main monetary provision, and as the "share parameter" determining the partition of residual claimancy rights between the counterparties [START_REF] Lafontaine | Incentive Contracting and the Franchise Decision[END_REF]. For this reason, contracts with a low royalty rate play an incentive role, motivating the effort of the franchisee (selling effort, effort in the promotion of the common brand). Then, the franchisor mainly pays itself with other monetary compensations like the upfront fee. This argument was developed for the first time by [START_REF] Rubin | The Theory of the Firm and the Structure of the Franchise Contract[END_REF] in his early analytical work regarding the determinants of the payment design in franchise contracts. On the other hand, the franchisor may choose a payment mechanism characterizing a more integrated vertical relationship, based on an ongoing payment term (royalties) associated with a low fixed fee.

FIGURE 1 Payment terms and franchisor's dilemma between monitoring and incentives

Monitoring

High royalty rate Low upstream fixed fee Low royalty rate High upstream fixed fee

Incentives Alternative payment terms with an increasing delegation level

In the franchise literature, as previously mentioned, location has often been taken into account as a proxy for the monitoring cost. Indeed, network expansion and spatial dispersion increase the agency-monitoring problem. In other words, with the geographical dispersion of the network, the cost of monitoring the outlets increases. This argument finds empirical support in the econometric work on franchise data. As emphasized by [START_REF] Perryman | Who Should Own It? An Agency-Based Explanation for Multi-Outlet Ownership and Co-Location in Plural Form Franchising[END_REF], the most common test of the monitoring cost prediction concerns the relationship between the geographic dispersion of a network and its ownership composition, that is the proportion of franchised outlets or, conversely, the proportion of company-owned outlets (i.e.

fully integrated retail outlets). The argument is as follows: monitoring the outlets in geographic markets far from headquarters is more costly, due to the number of monitoring personnel required and the related travel expenses [START_REF] Carney | Vertical Integration in Franchise Systems: Agency Theory and Resource Explanations[END_REF]; because they are independent firms, franchisees are more likely to make significant efforts, thus requiring less monitoring. This situation would impact the ownership structure of retailing networks, strategically mixing company-owned units and franchised units as part of their location choices. Several studies (e.g. [START_REF] Norton | Franchising, Brand Name Capital and the Entrepreneurial Capacity Problem[END_REF][START_REF] Lafontaine | Agency Theory and Franchising; Some Empirical Results[END_REF][START_REF] Combs | Why Do Firms Use Franchising as an Entrepreneurial Strategy?: A Meta-Analysis[END_REF] provide evidence that outlets located close to headquarters are more likely to be company-owned, while retail units in more distant locations are more likely to be franchised.

Moreover, monitoring costs arising from the geographical dispersion of the network would be sufficient to explain the franchise option. More generally, at the network level, the higher the geographical dispersion, and thus the related monitoring cost, the lower the proportion of company-owned outlets. Therefore, a clear link is established between spatial choices and ownership structure of franchising networks. Based on this background literature and previous evidence, we push the argument a bit further, considering the different options for payment mechanisms in franchise contracts, as presented in Figure 1. The related predictions are as follows:

H1: The royalty rate decreases with the geographical dispersion of the network

H2: The fixed fee increases with the geographical dispersion of the network

The reasoning being that the geographical dispersion of the network increases the monitoring cost. Then the franchisor chooses payment terms oriented towards incentives, i.e.

a low royalty rate. In this case, the franchisor's payment is mainly based on the fixed fee.

B. Alternative view of the monetary choices within the spatial development of an emerging franchise system

The specific context of franchising in Brazil may justify an alternative view on the impact of spatial development on monetary choices. Three main stylized facts characterize the Brazilian franchise sector. First, as highlighted by Figure 2, Brazilian franchising is dominated by domestic brands (average of 91% over the period 2007-2016), in common with the developed countries and contrary to the position in the small economies of the zone like Ecuador, Guatemala, and Uruguay.

FIGURE 2

Domestic brands in Latin American franchising Source: Authors generated from the Ibero-American Federation of Franchising (2017)

However, compared to the developed countries, the dynamism of the Brazilian franchise sector is remarkable, as highlighted by the evolutions pointed out in Table 1. As previously mentioned, business-format franchising is based on having a common brand name and concept within the network, developed by the franchisor. As intangible assets, the brands and the related business concepts do not all have the same profitability. This idea is captured by the synonymous notions of "brand-name capital" or "brand-name value" related to the reputation of the network.

A. Data collection

Our first source is the ABF's Official Franchise Guide. This annual publication provides valuable information on the Brazilian franchise sector. More precisely, the guide contains data concerning network characteristics, notably the franchise fee, total number of outlets, required investment level of the franchisees, number of employees, and so on. We obtained data for the period 2011-2016. In order to achieve a better understanding of how the ABF collected the information used to construct our dataset, we performed interviews with executives from this organization: specifically, the President of the ABF, his executive director, and his marketing analyst, who is responsible for both collecting and pooling information provided by the franchised networks. The ABF has an internal information system accessible to associated franchised networks, which are able to update information regarding their network in real time. The networks have an interest in keeping information up to date, since the Guide is an important communication channel with potential franchisees. Divergent or even fake information could damage the reputation of the network, or make the search for potential franchisees more difficult. Therefore, there is a strong incentive for franchising networks to offer good quality information. In addition, the ABF controls for the consistency of the data, systematically analyzing the annual variations before publishing official information. Any abnormal variation is checked and corrected. However, the ABF does not have a scientific advisory council: for this reason, the ABF marketing department performs both the monitoring and the validation of provided data. If necessary, specialized consultants are hired for more specific studies.

The websites of the sample franchise networks comprise our second main source of information. The manual capturing of website data was used to perform control checks, and to complete the missing values from the ABF guides. In addition, we used this data source to collect spatial information regarding the Zip codes of cities where the franchised networks are established, in addition to the proportion of network outlets operated in shopping malls.

Where the network websites were incomplete, we used the PEGN publication, which is a SME-specialized Brazilian media.

As our analysis requires localized data, we use a third source of information, the Brazilian Institute of Geography and Statistics (IBGE). We collected information about geo-referencing (longitude and latitude) of the municipalities where the sample networks are established.

We pooled the different sources of information and performed crosschecks to match the data. Finally, we obtained a unique panel data sample (6 years) of 2010 franchised networks in Brazil, containing localized variables.

B. Dependent variables

Three dependent variables are distinguished: the two main monetary provisions, and the performance indicator.

The Royalty Rate is defined as the percentage of the downstream sales accruing to the franchisor.

The Fixed Fee is defined as the upfront fixed amount paid by the franchisee when entering the network.

As Network Performance variable, we use the market share, measured as the franchisor's turnover divided by the sector turnover.

C. Independent variables

The Network Geographic Dispersion is our first variable of interest. To construct this variable, we first surveyed the addresses of all the sample network outlets. We thus created a dataset containing information regarding the Brazilian municipalities where the franchising networks are established. Then we geo-referenced the cities where the stores are located, obtaining their latitude and longitude. We used the software R, specifically the GEOCODE function of the package GGMAP. These artifacts are available at the Comprehensive R Archive Network (CRAN). GEOCODE uses Google Maps to geo-reference the addresses entered in the dataset.

On this basis, we constructed geographic clusters for each network, grouping together stores located in the same area. Each cluster is thus an area of agglomeration of network outlets. We defined the center for each cluster so that the cumulative distance between the stores and the cluster center is the smallest possible. The distance criterion is the Euclidean distance based on the latitude and the longitude of the cities where each store is located. For example, assuming that a store A is located at the point (xA, yA) and a store B is located at the point (xB, yB), the Euclidean distance between them is defined as follows: 𝐷(𝐴, 𝐵) =

((𝑥 ! -𝑥 ! ) ! + (𝑦 ! -𝑦 ! ) ! ).
The Euclidean formula only applies to metric geographical coordinate systems. Thus it underestimates the true distance that would be travelled by a road connecting the two points.

This bias is greater at points distant from each other. However, alternative measures of distance were not available: the Euclidian distance was the best and most intuitive proxy available. Our variable, the Network Geographic Dispersion, is measured as the distance between the stores in the clusters, to which we add the distance between the centers of the network clusters.

The data related to city of occupation by the networks of the sample were available for the For every support, the information is available as a dummy variable. Summing these categorical variables for each network, we created the Support Devices variable as an ordered multinomial variable.

D. Control variables

To deal with the monetary contractual choice (level of the royalty rate; level of the fixed fee), we consider the following structure variables related to the franchise contract general feature (contract length), or to the characteristics of the network (age, proportion of franchised units) as control variables.

Contract length: this variable allows us to control for the influence of the duration of the contract in the determination of the monetary clauses. It is indeed relevant to assume that the contract length may impact the royalty rate and/or the upfront fixed payment given by the franchisee when entering the network.

Age of the network: this variable refers to the difference between the year of creation of the first franchised unit, and the present year in the panel data. We assume that the network experience may influence the contractual design.

Proportion of franchised units: this variable is measured as the number of franchised units in the network divided by the total number of outlets in the network. In addition to the proportion of franchised units, this variable can also be studied as a proxy for the reputation of the brand name, which is related to our second set of hypotheses and to the specific context of an emerging franchise system (H3-H4).

Regarding the performance outcome of the monetary contractual choice, we add the following control variables.

Economic sector: this variable allows us to control for the influence of operating in a specific sector. We made the choice to follow the ABF taxonomy, distinguishing 11 economic sectors3 to construct a set of sector dummies.

Localized HDI: we use this multidimensional indicator to control for the performance impact of the socioeconomic development level of the network location. The IBGE provides information about Brazilian HDI (Human Development Index) scores at the municipality levels. To construct our localized HDI variable, for each network we measured the average HDI of the cities where the network operates. Information from IBGE refers to the year 2010.

However, for each network, the localized HDI changes every year with its geographical expansion.

Population: Brazilian government conducts the population census approximately every 10 years, with the last three censuses conducted in 1991, 2000 and 2010. Outside this period, IBGE estimates the population by municipality. Using the same approach as for the localized HDI, for each network we measured the average population of the cities across which the network operates.

As for the Network Geographic Dispersion, we filled the missing values of the localized HDI and population variables with linear coefficients, through the linearization of the trends indicated by the censuses.

E. Summary statistics

Summary statistics are provided by Table 2, and highlight the heterogeneity of the sample networks regarding the variable Network Geographic Dispersion. This is also the case with the performance variable. The other variables are quite homogenous in the sample, as shown by the means, which are higher than the standard deviations. Lastly, the correlation matrix shows that it is necessary to check for multi-collinearity relating to the following variables: population and dispersion, age, and percentage of franchised units.

IV. ESTIMATION STRATEGIES

A. Step one: determinants of the monetary choice

Methodology -To test H1-H5, we estimate the following linear model:

𝑀𝐶 !" = 𝜇 + 𝜋 ! 𝐷 !" + 𝜋 ! 𝑆 !" + 𝜋 ! 𝜌𝑋 !" + 𝑢 ! + 𝜖 !" (1) i = 1, …, 2010 t = 2011, …, 2016
where 𝑀𝐶 is the monetary clause, 𝜇 the constant term, 𝐷 the network geographic dispersion, 𝑆 the support devices, 𝑋 the matrix of control variables, 𝑢 ! the random disturbance that characterizes the ith observation (constant over time) and 𝜖 !" the error term.

Three regressors raise a potential problem of endogeneity because they are managerial variables deriving from the franchisor's choices. These are the network geographic dispersion, the support devices, and the contract length. We perform exogeneity checks using the Hausman test, which allows us to compare model ( 1) with an instrumental model in two stages including the lagged variable as an instrument in random-effects and fixed-effects specification. The results presented in Table 3 confirm that there is no problem of endogeneity. We complete the specification tests with a likelihood ratio test regarding heteroskedasticity at the panel level for both monetary clauses. The results (𝜒 !"#$%&# ! =2.5e+08 *** , 𝜒 !"" ! = 2.06 *** ) show that the data in the sample do not have a common disturbance variance, thus providing evidence for heteroskedasticity across the panel. In addition, the Wooldridge test for autocorrelation highlights a problem of autocorrelation affecting the royalty rate variable, which is not the case with the fixed fee (𝐹 !"#$%&# = 119.947 *** , 𝐹 !"" =2.157).

To handle these problems we use the generalized least squares method (FGLS). The FGLS estimator is asymptotically normal, even under weak conditions -that is, even if the errors do not follow a normal distribution. Two stages are required. First, a consistence covariance matrix is built with the residuals of an OLS or another estimation. In the second stage, the FGLS is calculated, and replaces the value of the unknown covariance matrix for its estimation.

Results -The estimation results are presented in Table 4. A first comment concerns the good global significance of the econometric models, highlighted by the Wald Chi 2 tests. Furthermore, the results are robust, since they are qualitatively similar in all the models, whether or not including time dummies or control variables.

The influence of network geographic dispersion is clearly significant in all the models. This variable positively impacts the royalty rate, and negatively impacts the fixed fee, which is consistent with the alternative view of the monetary choices, thus providing evidence for hypotheses H3 and H4, while H1 and H2 are rejected. The significant and negative influence 0.000

We deal with this endogeneity problem by using the two-step methodology developed by [START_REF] Murphy | Estimation and Inference in Two-Step Econometric Models[END_REF]. This methodology allows the replacement of an unobservable variable in the equation of interest by the predicted values from another model. Similar methodologies using seemingly unrelated regressions are not adequate here because of the features of the panel, where N (the number of franchised networks) is big whereas T (the number of years) is rather small. In addition, again to avoid any endogeneity bias in the estimations, we include the royalty rate and fixed-fee variables separately in the econometric models.

Regarding heteroskedasticity, we perform a likelihood ratio test at the panel level for both monetary terms. The results (𝜒 !"#$%&# !" !"#$% ! = 1.7e+08 *** , 𝜒 !"" !" !"#$% ! = 1.5e+08 *** ) confirm that the data in the sample do not have a common disturbance variance, thus highlighting a problem of heteroskedasticity. Concerning autocorrelation, we use the Wooldridge test. The results do not provide evidence of a problem of autocorrelation (𝐹 !"#$%&# !" !"#$% = 2.312, 𝐹 !"" !" !"#$% =1.972) in the sample.

Based on the results of these specification tests, we choose to use the feasible generalized least squares method (FGLS).

Results -Our second set of results, regarding the influence of the monetary clauses on the network performance, are presented in Table 6. All the estimations have a good global significance (Wald chi2 results), and the results are robust to the inclusion of control variables or sector dummies.

The main result from this stage of estimation is the clear, robust, and significant influence of the monetary clauses on network performance. Consistent with the assumption of substitutability between the two provisions, the royalty rate and the fixed fee have an opposite effect, with a negative impact of the royalty rate, and a positive one of the fixed fee.

V. CONCLUSION Dealing with the impact of spatial organizational choices of franchising networks in Brazil, this article develops an analytical framework based on the agency explanation of monetary contractual clauses. In franchise contracts, these are the royalty rate and the fixed entrance fee.

In the agency-theoretic context, both devices are considered as substitutes. Within the traditional agency view, the presence of distant outlets, related to higher monitoring costs for the franchisor, results in a vertical relationship (franchisor/franchisee) based on the delegation of tasks and a payment mechanism designed to provide incentives to the franchisees, that is a low royalty rate associated with a high fixed fee.

Providing stylized facts, we argue that in the specific Brazilian case, the opposite impact of spatial dispersion may occur. Indeed, a payment mechanism based on monitoring (versus incentives), with a low fixed fee and high ongoing payments (royalties) is a way for the franchisors to keep control over the exploitation of their brand names by the franchisees, in a context of network inception and expansion.

We use a unique and recent panel dataset to provide evidence for this alternative view: in Brazilian franchising, the network geographic dispersion leads the franchisors to choose a payment contract based on a high level of royalties and a low fixed fee. However, studying the performance outcome of these contractual choices with a two-step Murphy and Topel econometric model, we show that the traditional orientation for delegation and incentives would produce higher performances.

These results have important practical implications for organizational and contractual choices in franchising, suggesting that the payment mechanism (payment terms linking the franchisor and the franchisees) should be focused on the provision of incentives for the franchisees operating the common brand name, while alternative tools, like the "support devices" also studied in our paper, are more appropriate for brand-name management.

  Distinguishing emerging versus mature franchise sectors, we assume that in the first case, a significant part of the local franchisors have not yet established their reputation, because they are at the first stages of their network development. The geographical expansion of a franchise network in a continent-sized market is then associated with the need to set efficient enforcement of quality standards for the brand and business concept throughout the network. In this context, franchisors might choose a payment mechanism allowing more control of the franchisees, i.e. a lower level of delegation (cf. Figure1). For this reason, we formulate the following counter-propositions expressing an alternative view of the monetary choices in the specific context of an emerging franchise sector, on a wide market:H3: The royalty rate increases with the geographical dispersion of the network H4: The fixed fee decreases with the geographical dispersion of the network C. Support devices as an alternative to monetary provisions in maintaining the common brand reputationConsistent with the preceding reasoning, we here develop the idea that support devices can be an alternative to monetary provisions in maintaining a common brand reputation. As previously mentioned, franchising covers a variety of organizational forms and vertical integration levels. Still in the context of an emerging franchise sector, with network reputations yet to be settled, the franchisor can use non-monetary contractual terms to maintain a close relationship with the franchisees, and then to keep control over the operating of the brand. These support devices may act as substitutes for the monetary clauses (royalty rate, fixed fee), which leads to the following prediction:H5: The higher the support devices in the franchise relationship, the lower the monetary clausesD. Performance outcomeWhile the literature on franchise data often addresses the performance outcome of organizational choices (e.g[START_REF] Madanoglu | Franchising and Firm Financial Performance Among U.S. Restaurants[END_REF][START_REF] Fadairo | Organizational Choices and Performance in Distribution Systems[END_REF], few empirical studies deal with the impact of contractual terms, and more precisely of the royalty rate, on network performance.[START_REF] Shane | The Effects of New Franchisor Partnering Strategies on Franchise System Size[END_REF] study the impact of strategic decisions on the network performance and their evolution over time, measured as increase in the network size. The royalty rate is taken into account as one of the pricing policy decisions. The authors provide evidence that the size of a franchise system is negatively related to its royalty rate, and that, as the system ages, the relationship between the network size and the royalty rate becomes more negative. In the study ofKosová and Lafontaine (2010), the royalty rate is a control variable in econometric models for the growth and survival of franchised networks, with the age and size of the network being the core explanatory variables. Based on US panel data, they do not find evidence for any influence of the royalty rate on network performance, which is measured in terms of franchising growth and exit from franchising. Recently, based on US retail and service network data, El Akremi et al. (2015) find that upfront fees, level of internationalization, network age, training, and experience before franchising have a positive impact on performance of franchise network. However, the influence of the royalty rate on sales (as a performance measure) is negative. More recently, combining French franchise and financial panel data, Lanchimba et al. (2017) provide empirical support for the hypothesis of a negative relationship between risk and the royalty rate, which contradicts the prediction of the standard agency theory. They highlight the positive significant influence of an adjusted royalty rate on network performance. Based on these contrasting previous empirical results, we formulate the following prediction: H6: The payment mechanism affects the network performance III. DATA AND MEASUREMENT We use recent panel data for the period 2011-2016, regarding 335 franchise networks implanted in the Brazilian market. The sample franchise networks occupy 1,397 Brazilian municipalities, and are present in the 26 Brazilian states. Our unique dataset compiles information from three main sources: 1) ABF's Official Franchise Guides, 2) websites of franchise networks, and 3) Brazilian Institute of Geography and Statistics (IBGE). In what follows, we present the data collection process and the study variables derived from the analytical framework. Finally we provide summary statistics.

  years 2011, 2014 and 2016. To avoid using an unbalanced panel, we have chosen to fill the missing values related to the other years, assuming linearity in two sub-periods: 2010-2014 and 2014-2016. Then we filled the dispersion missing values with linear coefficients. Our second variable of interest is the Support Devices provided by the franchisor to the franchisees, representing the level of control versus delegation in the vertical relationship regarding the exploitation of the franchisor's brand name. We constructed this variable based on the ABF indications. Thirteen types of support are indicated in the guide:

TABLE 1

 1 Dynamism of franchising in BrazilInternational comparisons for the evolutions of brands per capita

		Growth of the number of	Growth of the number of outlets	Growth of employment in the
		brands per capita (%)	per capita (%)	franchise sector per capita (%)
	Countries	2010-2011	2011-2012	2010-2011	2011-2012	2010-2011	2011-2012
	Brazil	8.45%	18.33%	6.66%	11.36%	5.99%	11.24%
	France	5.72%	5.16%	-5.80%	3.76%	5.81%	4.36%
	Germany	2.91%	-0.19%	9.13%	-0.19%	4.05%	-2.93%
	Spain	1.03%	-1.44%	3.56%	-3.85%	2.88%	-3.21%
	United Kingdom	2.42%		13.12%	-7.55%	3.08%
	United States	-0.74%	-0.74%	-12.01%	21.68%	-1.25%	2.09%

Source: Authors generated from European Franchise

Federation (2013), World Bank (2014), and PricewaterhouseCoopers (2013)

Finally, still compared to the developed countries, Figure

3

regarding the evolutions of brands per capita suggests a significant development potential.

Source: Authors, generated from the Ibero-American Federation of Franchising, the European Franchise Federation, and the World Bank (2017)

Based on these three stylized facts, it is relevant to assume that the franchise sector in Brazil is still emerging. This argument is consistent with the institutional features. Indeed, Brazil is one of the few Latin American countries to have a specific law about franchising. Yet this law is recent, having been established in 1994, whereas in Europe the Treaty and Commission Regulation was introduced in 1967, and in the United States the rules of the Federal Trade Commission for franchising date back to 1978.

TABLE 4

 4 Results for the monetary clauses

		(1)	(2)	(3)	(4)	(5)	(6)
		Royalty rate	Royalty rate	Royalty rate	Fixed fee	Fixed fee	Fixed fee
	Net. Geo. dispersion	1.31e-05 ***	1.29e-05 ***	1.11e-05 ***	-6.49e-03 ***	-5.48e-03 ***	-5.25e-03 ***
		(1.20e-06)	(1.45e-06)	(1.87e-06)	(8.9e-04)	(7.64e-04)	(7.22e-04)
	Support devices	-2.31e-04 ***	-3.16e-04 ***	-1.67e-04 ***	-0.150 *	-0.0786 *	-0.262 ***
		(2.72e-05)	(2.74e-05)	(0.3e-04)	(0.0626)	(0.00750)	(0.0770)
	P. of franchised units		-2.88e-05	-2.97e-04		-14.72 ***	-15.27 ***
			(8.04e-05)	(3.32e-04)		(0.538)	(0.685)
	Age of the network		-3.08e-04 ***	-2.79e-04 ***		0.0461 ***	0.0241 **
			(2.86e-06)	(9.77e-06)		(0.0109)	(0.00778)
	Contract length		5.08e-06	2.99e-06		0.170 ***	0.166 ***
			(3.6e-06)	(3.95e-06)		(0.00999)	(0.0113)
	Year dummies	no	no	yes	no	yes	yes
	_cons	0.0576 ***	0.0657 ***	0.0629 ***	39.64 ***	39.25 ***	33.11 ***
		(0.000365)	(0.000346)	(0.000544)	(0.662)	(0.729)	(1.143)
	N	2010	2010	2010	2010	2010	2010
	Wald Chi2	190.18	11805.90	903.42	59.15	1244.69	825.94
	Standard errors are in brackets
	* p < 0.1, ** p < 0.05, *** p < 0.01

TABLE 6

 6 Performance outcome of the monetary choice

		(7)	(8)	(9)	(10)	(11)	(12)
		Performance	Performance	Performance	Performance	Performance	Performance
		(Market Share)	(Market Share)	(Market Share)	(Market Share)	(Market Share)	(Market Share)
	Royalty rate	-0.349 ***		-0.539 ***		-0.437 ***
		(0.0479)		(0.0547)		(0.0269)
	Fixed fee		8.00 e-4 ***		1.08 e-4 ***		7.00 e-5 ***
			(0.0000434)		(0.0000624)		(0.0000280)
	Population			2.59e-10 ***	2.59e-10 ***	8.65e-11 ***	1.14e-10 ***
				(1.46e-11)	(1.36e-11)	(5.19e-12)	(5.09e-12)
	Localized			0.126 ***	0.109 ***	0.132 ***	0.127 ***
	HDI
				(0.0124)	(0.0123)	(0.00607)	(0.00594)
	Sector	no	no	no	no	yes	yes
	dummies
	_cons	0.0460 ***	-0.00238	-0.0506 ***	-0.107 ***	0.0378 ***	-0.00929
		(0.00274)	(0.00158)	(0.00991)	(0.00943)	(0.00578)	(0.00595)
	N	2010	2010	2010	2010	2010	2010
	Wald chi2	53.11***	339.34***	193.82***	443.10***	6024.62***	6063.84***
	Standard errors are in brackets

* p < 0.1, ** p < 0.05, *** p < 0.01

  This article has interesting implications for future research. By reference to performance outcomes, we here provide clear and robust evidence that spatial organizational choices impact contractual design. Yet the limitations of this study open new research opportunities. It would be interesting to address the same issue in different empirical contexts, i.e. other continent-sized emerging countries like China; or with a longer panel (we dealt here with a six-year panel dataset, and perhaps a longer perspective would provide complementary results); or with types of vertical contracts other than the franchise contract, such as patent license contracts.

See Combs et al. (2011), Dant et al. (2011) for a survey of the literature on franchise data.

The initial work dates back to A.[START_REF] Marshall | Principles of Economics[END_REF].

Advertising, Informatics & Electronics; Business, Services & Other Retails; Cars; Cleaning; Clothes; Education; Food; Hoteling & Tourism; House & Building; Shoes & Accessories; Sports, Health, Beauty & Leisure.

* The authors are grateful to Prof. Vivian-Lara Silva for her insight and support, and to the Brazilian Franchising Association (ABF) for valuable help regarding the construction of our dataset. This research has received funding from the University of São Paulo/Comité Français d'Evaluation de la Coopération Universitaire et Scientifique avec le Brésil Programme (USP-COFECUB 2017-2020) under grant agreement no. Uc Sh 165-17. The usual caveats apply.

of support devices on both monetary clauses suggests that these are substitutes, thus providing evidence for hypothesis 5.

Regarding the control variables, we find a significant positive effect for the age variable on the fixed fee, and a negative one on the royalty rate. Those results provide evidence that the network experience impacts the contract design, and that more experienced networks, with a well-established reputation, delegate more; which is also consistent with the alternative view of the monetary choices. The proportion of franchised units has no significant influence on the royalty rate, but impacts the fixed fee significantly and negatively. Finally, the contract length has a significant positive effect on the fixed fee, and no significant influence on the royalty rate.

B. Step two: performance outcome

Methodology -To test H6, we estimate the following linear model:

(2)

where 𝑃, the performance variable, is the market share, 𝜑 is a constant term, MC is the monetary clause (royalty rate versus fixed fee), Z is a matrix of control variables, 𝑣 ! is a random disturbance that characterizes the ith observation (constant over time), and 𝜓 !" is the error term.

Here again, a problem of endogeneity may be suspected, regarding two regressors, the royalty rate and the fixed fee. Following the same methodology as previously (4.1.1.), we confirm the endogeneity bias. The results of the Hausman tests are presented in Table 5.


J Ér Émy Wambecke
email: jeremy.wambecke@univ-grenoble-alpes.fr

Georges-Pierre Bonneau
email: georges-pierre.bonneau@inria.fr

Renaud Blanch
email: renaud.blanch@univ-grenoble-alpes.fr

Romain Vergne
email: romain.vergne@inria.fr

Activelec: an Interaction-Based Visualization System to Analyze Household Electricity Consumption

Everyone can now record and explore the evolution over time of his/her personal household electricity consumption. However understanding what links this data to our behavior remains a challenge. In this paper, we present a visualization tool based on the direct manipulation, by the users, of their behavior. Users can select and modify their actions over time, evaluating the results on the data with the visualization. We also conduct a user study, showing that our method allows users to understand the links between actions and data, and to use this knowledge in order to test and evaluate changes in their behavior.

INTRODUCTION

With the development of personal informatics systems [START_REF] Li | A stage-based model of personal informatics systems[END_REF], we can now easily access temporal personal data, for example about home energy consumption [START_REF] Costanza | Understanding domestic energy consumption through interactive visualisation: A field study[END_REF] or physical activities [START_REF] Li | Using context to reveal factors that affect physical activity[END_REF]. However, the links between one's behavior and the resulting data are complex, and their understanding is a challenge for a layperson. We express the behavior of users in terms of actions, which stands for real actions of users in their environment, for example actuating electrical appliances. The use of visualization is justified by the fact that only users know the context in which their actions are taken, and thus can link this information with the resulting data during the visualization process [START_REF] Huang | Personal visualization and personal visual analytics[END_REF]. The main challenge is to allow interaction with the data, which has been proven to be crucial for non-expert users [START_REF] Fischer | Feedback on household electricity consumption: a tool for saving energy?[END_REF]. Our contribution is a system that allows users to analyze and manipulate their electricity consumption data. Our focus is on the interaction with a representation of the activities rather than directly with the collected data. We express the activities of users in terms of actions on appliances over time, which are linked to the temporal data. By using this representation of the activities, we provide to users the ability to test What if scenarios, by selecting and virtually modifying their actions in the visualization. The power of information visualization to analyze and understand temporal data does not have to be proven. However, we now little about how it can be used by non-expert users on their own data. From a researchers perspective, our work allows to reflect on this question, as well as to investigate the efficiency of the What if approach for users to analyze their data. Several previous works focus on visualizing personal data, but provide only limited interactions restricted to exploration or annotation. Costanza et al. [START_REF] Costanza | Understanding domestic energy consumption through interactive visualisation: A field study[END_REF] presented Figure Energy, a system based on the visualization of household electricity consumption. They provide to the users a way to interact with their consumption by annotating their activities and removing appliances usage, but selections and modifications do not include temporal aspects as in our system.

Li et al. [START_REF] Li | Using context to reveal factors that affect physical activity[END_REF] designed IMPACT, a prototype for the visualization of physical activity data. The activities done during the days are displayed on the curve of the user's steps to provide context, making the visualization close to ours. But unlike our system, no selection of activities over time nor modifications are provided to the user. Badam et al. [START_REF] Badam | Timefork: Interactive prediction of time series[END_REF] presented TimeFork, an interactive prediction technique for time series. Like our method, TimeFork allows what if experiments to be conducted. However, it is designed for direct manipulation of raw data and the target users are domain experts. Section 2 presents the design rationale of the tool, Section 3 the system design. To assess the usability of our tool, we conduct a user study described in Section 4. The results show that our tool enables users to find relevant actions for saving energy consumption, while being compatible with an effort they are willing to make. Finally we conclude and give possible future works in Section 5.

DESIGN RATIONALE

Interaction

Our tool is based on a computer software using information visualization approaches. The design of interaction is at the heart of our contribution, especially allowing the users to interact with their actions on electrical appliances rather than the recorded data. We argue that this way of interaction, by contrast with interacting with raw data, leads to more knowledge about how to adapt the behavior in a given situation to change the data, for example to save energy. Our system allows users to select their actions via temporal criteria to visualize their impact on the data. Then they can conduct What if experiments about their behavior by virtually modifying it. It is hence useful for users who want to fully understand their data, as well as for users who just want to know what are the actions to be changed to reach a given objective. Moreover, it is usable by users that do not have knowledge about the domain (e.g. electricity consumption) as well as experienced users. To support our method, the system is based on a hierarchical model of the data, which is composed of raw data and user's actions data. Raw data describes the evolution of the instantaneous electric power of the appliances, and is measured directly on the appliances or simulated when a what if scenario is applied. Note that our input is disaggregated energy data, i.e. each appliance is associated to a temporal dataset. To express the relationship between raw data and the behavior of the users, we model the appliances as having states over time. At a given point in time, the appliance is in a given state, which depends on the action of the users. We define a set of states for each appliance, e.g. for a TV: Off, Standby and Usage. Along with its raw data, an entity is associated with the evolution of its state over time. Thus, this evolution is represented by time intervals corresponding to the state in which the entity is at a given time. We call such an interval an instance of this state. The evolution of raw data during an instance is linked to the state of this instance. More precisely, we assume the average of the values during an instance to be similar between the instances of the same state. Figure 1 shows an example of the evolution of the state of a TV over time, where the state is represented with colors.

Data Model

SYSTEM DESIGN

User Interface

Our system is built on two modes: appliances mode to explore and compare the consumption of all appliances, and instances mode to focus on an appliance and interact with its data. Appliances mode is the first one that appears when launching the application (Figure 2). It allows to explore and compare the consumption of all appliances of the house, by visualizing them on the same plot. When clicking on the edit button of an appliance (in the list to the left), the application switches to instances mode. Instances mode allows users to focus on a given appliance, and to interact directly with their data (Figure 3). They can obtain information about the usage of the appliance via exploration and selection, and test several changes of actions over time via modifications. Hence, in this step, users acquire knowledge about the consumption data and the links with their actions. The instances and states of the appliance are displayed to users only in instances mode. Since the units of energy, such as kWh, are often misunderstood by users, we need to convert them into meaningful information [START_REF] Strengers | Designing eco-feedback systems for everyday life[END_REF]. Therefore we chose to always express the amounts of energy in terms of cost. It could as well be expressed in terms of emitted CO2 if the user's focus is on environmental concern rather than cost saving.

Visualization

To display the evolution of the consumption over time, we chose to use curves, since it is a well known metaphor for temporal data. Other less standard visualization such as Braided Graphs [START_REF] Javed | Graphical perception of multiple time series[END_REF] would lead to a steeper learning curve and could be misunderstood by people with low visualization literacy [START_REF] Boy | A principled way of assessing visualization literacy[END_REF]. Standard interaction tools to explore the data, such as zooming and panning, are provided. To better understand the links between the behavior and the consumption of an appliance, in Instances mode we color the curve according to the instances of the appliance. More precisely, we fill the part below the curve with the color of its current state. The evolution of the color along the curve represents the evolution of the state of the appliance, and the curve itself represents the instantaneous electric power. We use a multi-hue color scheme from ColorBrewer [START_REF] Brewer | Colorbrewer[END_REF]. Since the state of the appliance is directly linked to the actions of users, they can visualize the consumption along with their actions.

Selection

In instances mode, users can create selections of instances of the appliance by two means: by clicking directly on the curve, or by defining criteria. Selections have three main purposes. Firstly, selecting a set of instances allows one to obtain information on them. For example, users may want to check if the durations of the selected instances are similar. Secondly, they can also see which parts of the curve are associated to the selected instances, enabling to link the selection with the consumption. Finally, selecting instances allows to apply a modification on them, as explained in the next section. The first way to create a selection is to do it directly from the visualization by clicking on the instances. The second way to select instances is through the definition of criteria on their properties. For example, users can select all the periods of Standby of a computer whose length is greater than one hour, during the weekend. Adding criteria is made via widgets displayed to the right of the interface. Once a criteria is modified, the set of selected instances is updated in real time, and these instances are highlighted on the curve.

The benefits of selecting the actions via the definition of criteria are twofold. First, it extends classical exploration, since users can explore the space of criteria by using widgets. This method allows for fast selections and feedback, with progressive refinements of the set of selected items [START_REF] Ahlberg | Visual information seeking: Tight coupling of dynamic query filters with starfield displays[END_REF]. The second motivation for selecting actions from criteria is that it allows users to generalize a pattern found during the direct exploration step. Hence, defining criteria allows habits to be easily visualized and modified.

Modification

Once users have selected several instances of the appliance, it is possible to modify them. For now, we provide two types of modifications. The first one is changing the state of the instances, for example switching the states of the instances to Off to remove the corresponding usages of the appliance. This modification is related to a change in the real usage of the appliance. The second one is limiting the time of the instances to a given duration, for example limiting all instances of Standby of a computer to 2 hours. The extra part, i.e. after the limit, is removed and replaced by Off. As a real action, it is equivalent to switch off the computer when it is in Standby for 2 hours. When a modification is applied, the raw consumption data is updated accordingly, by using a model to compute the affected part. Then, the visualization is updated for users to visually evaluate the impact of the modification. A summary of the modification is displayed, containing a feedback about the price earned or lost with the modification, and the gain / lost percentage relative to the total consumption of the appliance. Thus, users can use the visualization, the numeric value or both to evaluate the modification. Items containing the summaries of the modifications are displayed in a list to the right of the interface. Users can do as many modifications as they want, which are added to the list to be compared. Not only users can apply modifications, but they can manipulate them: along with the summary, the modification item contains interaction widgets. Users can decide to apply or not the modification, to modify it (i.e. modify the selection and the type of modification), or to delete it. To allow the manipulation of the modifications, we applied the concept of reification, as stated by Mackay and Lafon [START_REF] Mackay | Reification, Polymorphism and Reuse: Three Principles for Designing Visual Interfaces[END_REF]. It means that the concept of modification of a set of instances is represented by an object, made of a selection of instances and a type of modification.

EVALUATION

We conducted a user study to evaluate the usability of our tool and especially to assess if users are able to apply selections and modifications to the data. To do so, we designed a controlled experiment, where participants were asked to use the tool to save energy while minimizing their required effort to do so. 13 users (11 males, 2 females) participated in the study (none of the authors). Among them, 2 users were visualization specialists and 7 users could define electrical power and energy. 8 participants were motivated in saving energy and 4 wanted to save energy without too much effort. One participant did not follow the given instructions, thus we did not consider his results in the analysis of the study. Before the experiment, users got used to the tool with the help of the authors. We ensured that they understood the visualization of the curves and the interaction processes, i.e. selections and modifications. The learning phase took approximatively 25 minutes in total, after which participants told us that they understood the system.

Task : Free Usage

Method. The task was designed to study the usability of the method when freely used. The consumption data used in the study comes from the Household Electricity Survey [START_REF] Energy | Household electricity survey[END_REF], from which we selected six month data from ten common appliances. Participants were asked to use the tool as if they were at home with their own data, and had to find relevant actions to save energy. They were asked to apply modifications that they could apply in reality, and had to rate the effort of each modification (between 1 and 5). They did not have any saving target, and had 10 minutes to apply modifications.

Results. The results of the task are summarized in Figure 4. On average, participants saved 36.73% (σ = 12.53) of the total energy consumption and modified 6.75 appliances out of 10 (σ = 1.06) with an effort of 1.48 (σ = 0.39). Participants usually put higher effort on the most consuming appliances such as the heater, the computer or the TV. On the opposite, the light and toaster consume less energy and were only slightly modified. We can note that users with energy knowledge saved 37.67% (σ = 13.13) of the energy and modified 6.71 appliances (σ = 1.25), while novice people saved 29.53% (σ = 18.50) of the energy and modified 6.17 appliances (σ = 1.72). Users from both groups managed to successfully use the system. This task also allowed to identify common patterns between users to find and modify relevant behavior changes. They usually select one of the most consuming appliances using the visualization and consumption indicators provided in the appliances mode. Users next switch to the instances mode and analyze the usage of this appliance over time to define criteria that correspond to a change in behavior. The instantaneous feedback of the selection process allows to adjust the criteria until the ratio gain/effort is acceptable. They then apply a modification and evaluates it via the visualization and the gain value. With low savings, users tend to refine their selection criteria with more effort, or apply a second modification. If the saving is high, they tend to refine criteria to relieve their effort. When a suitable modification has been made, users go back to the appliances mode to select another appliance, whose ratio consumption/effort to put seems acceptable.

Interview

At the end of the study, participants were asked to fill a questionnaire to evaluate the usability of the method, based on a Likert scale from 1 (I totally disagree) to 5 (I totally agree). Appendix A shows the resulting averaged rates and standard deviations for each of the questions. From the rates, we can deduce that participants found the tool easy to use and felt confident in using it, and no misunderstanding appears. We also asked to participants which functionalities they had appreciated/disliked the most. All participants gave 2 to 4 items to explain what they preferred in the tool. Among them, 7 participants mentioned the interaction, 6 mentioned the usefulness of a cost feedback, and 5 mentioned the design of the visualization, including the choice of the colors. Concerning the negative points, 4 participants mentioned potential improvements for the user interface, such as displaying more information about the states of the appliances.

Discussion

Our main objective was to evaluate whether our method and its usage are understood by the participants. Although participants did not use their own data during the study, they could interpret the usage of the appliances with visualization and exploration. Moreover, creating selections, especially from criteria, helped the participants to understand patterns of use as well as the consumption of the appliances. Results show that they could easily test behavior changes by applying modifications on the appliances. Depending on their personal interests, the participants used the visualization and/or the aggregated saving expressed in money to evaluate the modifications. This shows that the method could be used by people that prefer to use visualization as feedback, as well as by people that rely on a value feedback. Moreover, participants managed to find relevant actions to save energy regardless of their knowledge about energy, validating our design that targets both experimented users and novices. The results show that the participants could evaluate on which appliances efforts should be put to save energy. Indeed, finding the relevant appliances to save energy on is one of the problems that users face when using eco-feedback [START_REF] Strengers | Designing eco-feedback systems for everyday life[END_REF]. Participants were also able to find actions easy to apply in reality, for instance by switching off appliances when not needed.Thus, our method allows to evaluate the compromise between energy saving and comfort.

CONCLUSION AND FUTURE WORK

In this paper we introduce a system that allows to analyze and especially interact with electricity consumption. By focusing on the actions of users rather than directly on the raw collected data, our goal is to improve the understanding of the links between the behavior of users and their data. Users can select actions based on criteria, virtually modify these selected actions, and visualize the corresponding modified data. We evaluate our system in a user study were users are asked to freely use the system to save energy. As future work, we plan to include additional features which where suggested by the users during the study, such as more criteria for selections, other types of modifications and a real time preview of the modifications before they are applied. Using the same color palette for both appliances mode and instances mode did not seem to be disturbing during the study, however we could consider the use of different palettes to avoid overloading colors. We also envison to provide suggestions to the users for common and easy changes to apply, for example switching the appliances from Standby to Off. However, the strength of the system is to let users freely find the modifications that fit them, which cannot be achieved by an automatic system, since only users have knowledge about how and why they use their appliances. For example, turning Off a TV when it is not watched can only be decided by users. To extract the instances from raw data we use a simple threshold algorithm, which gives acceptable results. To apply a what if scenario, for example to change the state of an instance, our system needs to simulate new raw data, which is currently done with a simple copy from existing data. In future works it would be interesting to incorporate more elaborated algorithms for these two processes. In the longer term, we could envison to incorporate our tool in a home automation system such as OpenHab [1]. In such a system, our tool could take the values of sensors as input, and export the chosen modifications of the users on the appliances as rules. For example, if the user chooses to switch off an appliance at 10 P.M during week days, the corresponding rule that automatically switches off the appliance could be generated.

A RESULTS FROM FINAL INTERVIEW

Figure 1 :

 1 Figure 1: Data related to a TV.

Figure 2 :

 2 Figure2: Interface of the appliances mode. Four appliances are displayed: a heater (blue), a computer (orange), a dishwasher (green) and a toaster (red). The heater has been highlighted by the user, and the item of the dishwasher has been extended to display its consumption values.

Figure 3 :

 3 Figure 3: Interface of the instances mode. A computer has been chosen by the user, whose states are Usage (orange) and Standby (blue). At the right, we can see that the user has applied two modifications, the first one to remove instances of Standby after 9 P.M, and the second one to limit the instances of On to 4 hours during the weekend. When the user is selecting instances, this panel displays information about the selection.

Figure 4 :

 4 Figure 4: Results of Task 2. Consumptions gains are normalized relative to the highest consumption, efforts are normalized on a scale from 0 to 5 and numbers of modifications are normalized relative to the highest value (15).

Figure 5 :

 5 Figure 5: Rates from final interview. "→ understanding" means helps in understanding consumption and behavior and "→ actions" means helps to find relevant actions to save energy

ACKNOWLEDGMENTS

This work has been partially supported by the LabEx PERSYVAL-Lab (ANR-11-LABX-0025-01) funded by the French program Investissement d'avenir.