Lucas Mello Schnorr

Arnaud Legrand

Visualizing More Performance Data Than What Fits on Your Screen

Keywords: Data aggregation, trace visualization, alternative visualization techniques Agrégation de données, visualisation de traces, autres techniques de visualisation

High performance applications are composed of many processes that are executed in large-scale systems with possibly millions of computing units. A possible way to conduct a performance analysis of such applications is to register in trace files the behavior of all processes belonging to the same application. The large number of processes and the very detailed behavior that we can record about them lead to a trace size explosion both in space and time dimensions. The performance visualization of such data is very challenging because of the quantities involved and the limited screen space available to draw them all. If the amount of data is not properly treated for visualization, the analysis may give the wrong idea about the behavior registered in the traces. This paper is twofold: first, it details data aggregation techniques that are fully configurable by the user to control the level of details in both space and time dimensions; second, it presents two visualization techniques that take advantage of the aggregated data to scale. These features are part of the VIVA open-source tool and framework, which is also briefly described in this paper.

Introduction

High performance computing systems today are composed of several thousands and sometimes millions of cores. The fastest parallel machine as defined by the Top500 [START_REF] Dongarra | Top500 supercomputer sites[END_REF] in June 2012 has more than 1.5 million cores. Parallel machines with billions of cores are expected in the way towards exascale computing. Parallel applications running on these large-scale platforms are therefore composed of many processes and threads that together explore the extreme concurrency available to solve problems in a variety of domains.

A possible way to conduct a performance analysis of such applications is by recording in trace files the behavior of all processes of the same application. The behavior of each process can be defined by a succession of timestamped events that register the important parts of the application, as defined by the analyst. With low-intrusion tracing techniques, the events can be as frequent as one event per nanosecond. Because of the quantity of processes and the amount of details we can collect about each of them, traces become very large, at least in the space and time dimensions.

Despite the technical challenge of managing possibly millions of very large traces that are scattered in the platform, another aspect is how to extract useful information from them. A possibility is to use performance visualization techniques to visually represent the behavior described in the traces. Several techniques exist, but the most prominent is the traditional space/time view -also known as timeline view or Ganttlike chart, shared by many trace visualization tools [START_REF] Brunst | Comprehensive performance tracking with vampir 7[END_REF][START_REF] Coulomb | Visual trace explorer (vite)[END_REF][START_REF] Kalé | Scaling applications to massively parallel machines using projections performance analysis tool[END_REF][START_REF] De Kergommeaux | Pajé, an interactive visualization tool for tuning multi-threaded parallel applications[END_REF][START_REF] Pillet | Paraver: A tool to visualise and analyze parallel code[END_REF]. Other techniques such as classical histograms, communication matrices, and kiviat diagrams also appear in the literature [START_REF] Brunst | Comprehensive performance tracking with vampir 7[END_REF][START_REF] Heath | Visualizing the performance of parallel programs[END_REF]. Independently of which visualization technique is chosen to represent traces, the task is commonly very challenging and complex because of the quantities involved -number of processes, amount of details -and the limited screen space available to represent all the information.

Most of performance visualization tools today have implicit assumptions about how traces should be represented. Let us consider a very detailed trace of an application that executes for several days. When visually representing an overview of that trace on the screen, a pixel might represent many hours of execution. Some tools [START_REF] Brunst | Comprehensive performance tracking with vampir 7[END_REF] take the more common application state on that time interval and choose its color to draw the pixel. Although this works well to scale the visualization, it might not work for all scenarios if, for example, the state chosen to draw the pixel is not the one that is the focus of the analysis. This also has the potential disadvantage of dropping important information when two different states have similar presence in the time interval of a given pixel. Some other tools [START_REF] Coulomb | Visual trace explorer (vite)[END_REF] simply rely on graphical rendering -thereby ignoring completely the problem. This second case might lead to an extreme negative scenario where the understanding of the trace depends on the graphics card and library used to draw its representation. Generally, these implicit assumptions mixed with large-scale traces may give the wrong idea about the behavior registered in the traces. We argue that all assumptions done when visualizing trace data should be taken by the analyst, and not built-in in trace visualization tools.

This paper addresses the issue of visualizing more performance data than what could fit on the available screen space. Instead of directly drawing the trace events with a visualization technique, our approach transforms raw traces into aggregated tracesaccording to analyst needs -and then visually represent the transformed traces. Therefore, this paper is twofold: first, it details data aggregation techniques that transforms the raw traces and are fully configurable by the user to control the level of details in both space and time dimensions; second, it presents two visualization techniques that take advantage of the aggregated data to scale.

The paper is organized as follows. Section 2 presents an extended discussion about the problem of scalable performance visualization and a classification of tools considering implicit or explicit trace aggregation. Section 3 describes the multi-scale aggregation techniques applied to transform trace data before visualization. Section 4 presents two interactive visualization techniques designed to explore aggregated traces: the squarified treemap and the hierarchical graph views. Section 5 presents the VIVA visualization tool, which implements the data aggregation algorithms and the visualization techniques. Finally, Section 6 draws a conclusion and future work.

Motivation and Discussion

The performance analysis of parallel applications must deal with the problem of trace size. This increase in size is present in different forms. The more common situations are the following: spatial size increase, when the application is large with many processes; temporal, when many details for each process must be interpreted -even if there is only a few processes to be analyzed; or both. The use of visualization to analyze large-scale traces is especially influenced by this increase in size, since traditional visualization have scalability problems.

We take as example the Sweep3D benchmark [START_REF] Lubeck | Implementation and performance modeling of deterministic particle transport (sweep3d) on the ibm cell/b.e[END_REF] to illustrate the scalability issue in trace visualization. The experiment is configured as follows. The MPI application is executed on 16 nodes of the Griffon cluster, part of the Grid'5000 [START_REF] Bolze | Grid'5000: a large scale and highly reconfigurable experimental grid testbed[END_REF] platform. The TAU library [START_REF] Shende | The tau parallel performance system[END_REF] traces all the MPI operations, resulting in traces that are merged into a single file. This single file is converted to the Paje File Format [START_REF] Schnorr | Pajé trace file format[END_REF] using the tau2paje1 tool and finally exported to CSV (comma-separated values) using the pj_dump2 tool. A combination of R [START_REF] Ihaka | R: A language for data analysis and graphics[END_REF] and the ggplot2 package [START_REF] Wickham | ggplot2: elegant graphics for data analysis[END_REF] is used to plot the resulting CSV file to a graphical representation based on the space/time view into a vector file.

Figure 1 shows two visualization of this vector file: one as seen by the Gnome Evince (left); the other by Acroread (right). As traditional space/time views, processes are listed in the vertical axis while the horizontal axis depicts the behavior of each processes along time. The different colors (or gray scales) represent different MPI operations. We can see that while both tools visualize the same file, the visualization is completely different depending on the viewer chosen, misguiding the analysis. If only Evince is used, the analysts might conclude that there are very few states denoted by the red color; if Acroread is used, it is hard to take the exact same conclusion, since we can see that red states are quite present for all processes.

The extreme negative scenario shown in Figure 1 clearly illustrates the problem when visualizing too much data in the same screen without care. The trace obtained from Sweep3D, even for this small run of 2 seconds, has many events in time -more events than the horizontal resolution is capable to contain. Since the vector file contains all events -most of them in the microseconds scale -many states have to be drawn in the same screen pixel. The color choice for a given pixel is taken by the renderer and the anti-aliasing algorithm, which is different depending on the visualization tool and does not make any sense for such Gantt-charts. That is why we get different views depending on the tool. This issue about implicit data aggregation also appears on performance visualization tools which are specific to trace analysis. Considering space/time views, the scalability problem can appear in both dimensions: in the horizontal dimension when there is too much detail about each process (as in Figure 1), in the vertical dimension when the application is composed by many thousands processes. Many data reduction techniques exists in several forms [START_REF] Nickolayev | Real-time statistical clustering for event trace reduction[END_REF][START_REF] Aguilera | A systematic multi-step methodology for performance analysis of communication traces of distributed applications based on hierarchical clustering[END_REF][START_REF] Joshi | Measuring benchmark similarity using inherent program characteristics[END_REF][START_REF] Knupfer | Construction and compression of complete call graphs for post-mortem program trace analysis[END_REF][START_REF] Lee | Towards scalable performance analysis and visualization through data reduction[END_REF][START_REF] Mohror | Evaluating similarity-based trace reduction techniques for scalable performance analysis[END_REF][START_REF] Schnorr | A hierarchical aggregation model to achieve visualization scalability in the analysis of parallel applications[END_REF] to try to reduce the amount of data that is going to be visualized. They might be broadly classified in two groups: selection and aggregation. The first group -based on selection -encompasses all solutions that select a subset of the data according to some criteria, which on its turn can be either fully based on a direct choice made by the analyst or not. Clustering algorithms considering behavior summaries as similarity pattern are an example of automatic selection. The second group -based on aggregation -acts upon the data, transforming into another kind of data whose intent is to represent the aggregated entities.

There is always some kind of aggregation considering performance visualization of large traces, although it is often implicit and uncontrolled. We explain now the three possibilities that appear on performance visualization tools regarding trace aggregation:

Explicit Data Aggregation. The analyst keeps control of the aggregation operators and the data neighborhood that is going to be aggregated. Moreover, the visualization tool gives some feedback to let the analyst know that a data aggregation takes place and is being used in the visualization.

Implicit Data Aggregation. There is no way to distinguish in the visualization something that has been aggregated from raw traces. The Figure 1 is an example of implicit data aggregation where the renderer takes the decision and the analyst is unaware about what is being visualized, if it is aggregated or not.

Forbidden Data Aggregation. The performance visualization tools forbids data aggregation at some level, commonly to avoid an implicit data aggregation that could mislead the analysis. The analyst is aware of that since the tool blocks, for example, an interactive operation, such as zoom out to get an overview.

Performance visualizations tools are sometimes present in more than one of these data aggregation categories. Paje's space/time view [START_REF] De Kergommeaux | Pajé, an interactive visualization tool for tuning multi-threaded parallel applications[END_REF], for example, has explicit data aggregation in the temporal axis -the user realizes that an aggregation took place since aggregated states are slashed in the visualization, while forbidding data aggregation on the spatial axis -minimum size for each resource is one pixel. Another tool that falls in more than one category depending on the represented data is Vampir's master timeline [START_REF] Brunst | Comprehensive performance tracking with vampir 7[END_REF]. For the temporal axis, it has implicit and explicit data aggregation. Implicit aggregation happens for function representations (colored horizontal bars): the tool choose the color of each screen pixel according to a histogram and selecting the more frequent function on that interval of time [START_REF] Gmbh | Vampir 7 User Manual[END_REF]. The user has no visual feedback if an aggregation takes place or not. Explicit aggregation appears in Vampir for the communication representation (arrows): a special message burst symbol [START_REF] Gmbh | Vampir 7 User Manual[END_REF] is used to tell the user that a zoom is necessary to get further details on the messages. Vite's timeline [START_REF] Coulomb | Visual trace explorer (vite)[END_REF] has implicit data aggregation on spatial and temporal dimensions, since it draws everything no matter the size of the screen space dedicated to the visualization. Triva [START_REF] Schnorr | Detection and analysis of resource usage anomalies in large distributed systems through multi-scale visualization[END_REF] has explicit data aggregation for spatial and temporal dimensions, but it uses different visualization techniques, such as the aggregated treemap, to visualize performance data.

Thus, the best scenario for a performance visualization tool is to provide pure explicit data aggregation. This choice enables users to realize what is happening with eventual trace transformations through a visual feedback in the visualization technique. This is commonly present on traditional statistical plots, where the amount of data is reduced with statistical mechanisms which are fully controlled by the analyst. Therefore, the goal on performance visualization is to propose explicit data aggregation which enables visualizations that are richer than classical statistical plots and, at the same time, safer than traditional trace visualization. Next section details our approach to explicit data aggregation for visualization.

Multi-Scale Trace Aggregation for Visualization

We briefly detail how data aggregation is formally defined in our approach. Let us denote by R the set of observed entities -which could be the set of threads, processes, machines, processors, or cores -and by T the observation period. Assume we have measured a given quantity ρ -which is a tracing metric -on each resource:

ρ : R × T → R (r, t) → ρ(r, t)
In our context, ρ(r, t) could for example represent the CPU availability of resource r at time t. It could also represent the execution of a function by a thread r at time t.

In most performance analysis situations, we have to depict several of such functions at once to investigate their correlation.

As we have have illustrated in Figure 1, ρ is generally complex and difficult to represent. Studying it through multiple evaluations of ρ(r, t) for many values of r and t is very tedious and one often miss important features of ρ doing so. This is also one of the reasons explaining the previous visualization artifact seen in the previous section.

Assume we have a way to define a neighborhood N Γ,∆ (r, t) of (r, t), where Γ represents the size of the spatial neighborhood and ∆ represents the size of the temporal neighborhood. In practice, we could for example choose N Γ,∆ (r, t) = [r -Γ/2, r + Γ/2] × [t -∆/2, t + ∆/2], assuming our resources have been ordered. Then, we can Inria define an approximation F Γ,∆ of ρ at the scale Γ and ∆ as:

F Γ,∆ :    R × T → R (r, t) → NΓ,∆(r,t) ρ(r , t ).dr .dt (1)
Intuitively, this function averages the behavior of ρ over a given neighborhood of size Γ and ∆. For example a crude view of the system is given by considering the whole system as the spatial neighborhood and the whole timeline as the temporal neighborhood. Since ∆ can be continuously adjusted, we can temporally zoom in and consider the behavior of the system at any time scale. Once this new time scale has been decided, we can observe the whole timeline by shifting time and considering different time intervals.

The analyst have to be careful about the conclusions that are taken during an analysis based on aggregated data. The nature of the data aggregation technique as presented here leads to the attenuation of behaviors registered in scales smaller than the one used to aggregate the data. For example, if a temporal aggregation is configured to integrate data using a two seconds interval, all the details smaller than two seconds are attenuated by the integration.

At the same time, the analyst needs to be aware that, although some information is lost, such aggregation generally lead to better visualization which can allow for the detection of anomalies that could pass undetected without data aggregation. Our approach deals with such questions by letting the analyst choose freely which scale is used to aggregate trace data. That's why this approach can be considered a pure explicit data aggregation method, where traces are transformed before being visualized. Next section details two visualization techniques for data that is aggregated following this method.

Visualization Techniques

Previous sections have shown the importance of explicit data aggregation algorithms for performance analysis through visualization. We present now two visualization techniques that have interactive mechanisms to deal with different aggregation levels, at the same time giving the user a feedback when the information in traces is aggregated: the squarified treemap and the hierarchical graph view. Their common characteristic is the lack of a timeline, as the one used on Gantt-charts, because the trace information is temporally aggregated. This lack of timeline also enables the use of both screen dimensions to draw observed entities and thus display more information.

Squarified Treemap View

The treemap technique [START_REF] Johnson | Tree-maps: a space-filling approach to the visualization of hierarchical information structures[END_REF] represents an annotated hierarchical structure on the screen using a space-filling approach. The recursive technique starts on the root of the tree, dividing the screen space among its children depending on their values. The screen surface each node occupies is proportional to its value. This mechanism allows an easy comparison of the characteristic of the different nodes of the structure, even in large-scale scenarios.

The squarified treemap view [START_REF] Schnorr | A hierarchical aggregation model to achieve visualization scalability in the analysis of parallel applications[END_REF], in our approach, is capable to represent performance data that is temporally and spatially aggregated: one screenshot represents therefore the application behavior in a time slice and spatial cut. These levels of detail are configurable by the user and can be changed interactively during the analysis. However, spatial aggregation is only calculated when the available performance data is hierarchically organized (threads grouped by process, process by machine, and so on). The hierarchy is therefore used as spatial neighborhood criteria (as presented in Section 3). Whenever the time slice or the spatial cut is changed, a new squarified treemap is calculated and drawn. Three interactive transitions of this kind are depicted in Figure 2.

Figure 2 shows four treemaps of the same trace at a given time interval configured by the analyst. The top-right treemap of the Figure shows, for instance, the Executing and Blocked state for the six clusters of this synthetic example (as indicated by the rounded dashed rectangles). We can see the three clusters per site and the two sites. The values for the states for a cluster are calculated by the aggregation algorithm considering the Blocked and Executing states for the machines belonging to that cluster. The main advantage of the original treemap representation over traditional space/time is its visual scalability. The technique is, as any other visualization technique, also limited by the screen size. The treemap A of Figure 3 shows the representation of synthetic trace with 100 thousands processors. We can see that with that large number of processors, the visualization suffers from implicit data aggregation during graphical rendering -there is a lack of pixels to draw all processors in a way that we could extract useful information from them. With the aggregated treemap view, the scalability limits of the representation are pushed forward since it is no longer necessary to view the behavior of every single monitored entity in the analysis. Treemaps B -E of Figure 3 depicts aggregated behavior of groups of processes. The scalability limit lies on how deep the trace hierarchy is for a given trace. If there is not enough levels on this hierarchy, they can be created by grouping nodes according to some analysis criteria.

The aggregated treemap view, as presented here, is a complementary technique in performance visualization. Although it offers a very scalable way to represent aggregated trace data, it has some disadvantages. It lacks, for example, support for a causal order analysis, which is obvious when using space/time views. By using treemaps as a complementary view, it is possible to get the best of both techniques. Another problem of treemaps (and space/time views) is that it lacks topological information, which might be crucial when analyzing performance behavior considering the network bandwidth limitations.

Next section details the second visualization technique based on hierarchical graphs to offer a topological analysis of parallel applications behavior.

Hierarchical Graph View

The traditional timeline view is expected by most of users of high performance computing, as can be observed by the number of visualization tools that implement it. Although useful to show the causal order in program behavior, timeline views lack topological information. Contrasting behavior with topological data is sometimes crucial for the comprehension of application behavior, especially because it allows to find the origin of contentions and better adapt the application to these constraints. The hierarchical graph view, presented in this section, enables an analysis that correlates all program behavior with a graph. The hierarchical aspect is used to tackle the scalability issues of graphs. We detail here two basic aspects of the hierarchical graph view: how temporal-aggregated metrics are mapped to the graph; and how the spatial-aggregated data is used to achieve visualization scalability.

The mapping from the trace to the graph works as follows. All monitored entities are mapped to nodes, while edges indicate a connection between two monitored entities. Monitored entities can be physical components of the system (machines, network links) but also the applications components (threads, processes). They are differentiated in the representation through different geometric shapes, while their attributes (size, color, filling) are mapped from the trace metrics associated to each monitored entity. Generally, all geometric shapes and properties can be somehow configured depending on trace metrics. Figure 4 shows an example of six variables (two per resource) showing resource availability (normal line) and consumption (dotted line) and three graph mappings depending on the selected time intervals (A, B and C). Hosts are mapped to squares, while links are mapped to diamonds. The size of the geometric forms are equivalent to the time-integrated resource availability, while the filling comes from resource consumption. The spatial aggregation algorithm presented in Section 3 also influences how the graph representation in our approach. Figure 5 shows an example that illustrates how the spatial aggregation affects the representation. As before, hosts are represented by squares filled by their utilization, links by diamonds, also filled according to their utilization. We consider for this example that the time-slice is already fixed. In the left of the figure, GroupA indicates the first neighborhood taken into account during the first spatial aggregation. All data within this group is space aggregated following the Equation 1. The resulting representation is depicted in the center of the figure, surrounded by the dashed gray line: it combines a square, representing all hosts, and a diamond, representing all links (in this case there is only one). The properties of these two geometric shapes are calculated according to the space-aggregated values of the traces, considering all the entities within the group used to do the aggregation. The example ends with a second spatial aggregation, considering the whole GroupB, with all monitored entities. As of result, in the right of the figure, there are only one square and one diamond representing all the hosts and all the links of the initial representation.

Spatial aggregation as defined in Section 3 plays a major role in the scalability of the hierarchical graph view. Graphs are by nature non-scalable, as we increase the number of nodes, the harder it gets to analyze and understand patterns. The possibility to interactively aggregate a portion of the graph, while keeping its general behavior through the use of aggregated values, enables an analysis of large-scale scenarios. Figure 6 shows an example of this change of spatial detail with the Grid5000 platform and its network topology. Each graph node represents a machine, and its size is equivalent to the computing power of the machine. The leftmost graph depicts all the 2170 Inria 1st Space Aggregation 2nd Space Aggregation GroupA GroupB computing nodes. Subsequent plots represent higher-level cuts on the hierarchy defining aggregated graphs -in the cluster and site levels. The rightmost graph represents the full aggregation considering the computing power of all hosts (on its left) and the bandwidth of all network links (on its right). 5 The VIVA Visualization Tool

The VIVA3 visualization tool implements the multi-scale aggregation algorithm (Section 3) and the two visualization techniques of the previous section. It is implemented in C++ using the Qt libraries as user interface. The tool also uses the PAJENG4 framework to deal with traces. This framework encloses all the basic building blocks such as reading trace files, simulating their behavior and offering access to trace data through the Paje protocol.

Conclusion

The performance visualization of traces might be a complex task because of the size of parallel applications and the amount of detail collected for each process. Besides dealing with the technical issues of large-scale traces, there is the problem of how to keep a given visualization technique useful, capable of detecting performance problems, on scale. We have shown that if traces are represented without care, the visualization might misguide the analysis.

This paper addresses the issue of visualizing more performance data than what could fit on the available screen space. Instead of directly drawing the trace events, our approach is to use a multi-scale data aggregation algorithm to transform raw events into aggregated traces. These transformed traces are then visualized by two visualization techniques especially tailored to handle aggregated data: the squarified treemap and the hierarchical graph view. The squarified treemap view enables the comparison of processes behavior by mapping per-process trace metrics to screen space. The hierarchical graph view enables the correlation of application behavior with the network topology by mapping trace metrics to geometrical attributes of a graph representation. Both techniques can scale since they expect spatial-aggregated data as input.

Relying on data aggregation is fundamental to scale the visualization techniques. However, it also has some disadvantages. As defined in Section 3, our multi-scale aggregation algorithm averages behavior in space and time dimensions. Depending on the situation, this average might smooth or even completely hide a certain behavior from the analysis. In addition, it is likely that space and time scales should be linked -a zoom in/out in one dimension implicates a zoom in/out in the another one -since it is meaningless to visualize the behavior of several thousands processes in a onemicrosecond time interval. Currently, we transfer to the analyst the responsibility to choose a space/time neighborhood in order to mitigate these issues.

Beyond this space/time rescaling issue, we can identify at least three other interesting research topics. The first one is to revisit space/time representations (also known as timeline views) to draw aggregated data instead of raw events, diminishing the problems detailed on Section 2 and especially in Figure 1. The second topic consists in studying new aggregation techniques and operators that take into account the uncertainty of events in the temporal dimension, in particular to deal with large-scale scenarios and in the presence of time synchronization issues. And finally, since aggregations smooth and may lead to potential loss of information, being able to quantify such loss could be used to provide feedback to the analyst. This feedback would indicate where particular attention is necessary due to aggressive aggregation.

Figure 1 :

 1 Figure 1: Performance visualization of the Sweep3D MPI application with 16 processes -the vertical axis lists the processes, while the horizontal axis depicts timeas visualized with two PDF viewers: Gnome Evince (left) and Acroread (right): depending on the viewer, the analyst takes different conclusions about the performance behavior.

Figure 2 :

 2 Figure 2: Four treemaps to show the per-level aggregation of Blocked and Executing states.

Inria

A

  Hierarchy: Site (10) -Cluster(10) -Machine (10) -Processor (100) B Hierarchy: Site (10) -Cluster(10) -Machine (10) -Processor (100) C Hierarchy: Site (10) -Cluster(10) -Machine (10) -Processor (100) D Hierarchy: Site (10) -Cluster(10) -Machine (10) -Processor (100) E Maximum Aggregation

Figure 3 :

 3 Figure 3: Normal (A) and four aggregated treemap visualizations (B -E) of two states for 100 thousand processors (based on synthetic trace).

Figure 4 :

 4 Figure 4: Mapping temporally-integrated trace metrics (left) to three graph representation (right) depending on the selected time intervals, considering that hosts are mapped to squares, links to diamonds.

Figure 5 :

 5 Figure 5: Two spatial-aggregation operations and how they affect the topology-based representation.

Figure 6 :

 6 Figure 6: Grid5000 network topology with 2170 computing nodes, depicted in four different aggregation levels of the hierarchical graph view: resource capacity is used to draw the size of geometric shapes.

RR n°8079

Part of Akypuera toolset, available at https://github.com/schnorr/akypuera

Part of PajeNG, available at https://github.com/schnorr/pajeng Inria

Available at http://github.com/schnorr/viva/

Available at http://github.com/schnorr/pajeng/ RR n°8079

Acknowledgments

This work is partially funded by the french SONGS project (ANR-11-INFRA-13) of the Agence Nationale de la Recherche (ANR). We thank Augustin Degomme for providing the sweep3D MPI traces. We also thank the organizers of the 6th International Parallel Tools Workshop for the invitation.


Rachel Bocquet

Christian Le Bas

Caroline Mothe

Nicolas Poussing
email: nicolas.poussing@ceps.lutel

Are firms with different CSR profiles equally innovative? An empirical analysis with survey data

Keywords: Corporate Social Responsibility, Innovation, Product, Process, Strategic Profiles

Are firms with different CSR

Introduction

Corporate social responsibility (CSR) has received increased attention from managers and researchers in the past decade (e.g. [START_REF] Baden | The effect of buyer pressure on suppliers in SMEs to demonstrate CSR practices: An added incentive or counter productive[END_REF][START_REF] Banerjee | Corporate social responsibility: The good, the bad and the ugly[END_REF][START_REF] Chassagnon | The law and economics of the modern firm: A new governance structure of power relationships[END_REF][START_REF] Fernando | Corporate social responsibility in the wake of the Asian tsunami: Effect of time on the genuineness of CSR initiatives[END_REF][START_REF] Harwood | On the resilience of corporate social responsibility[END_REF][START_REF] Knox | Corporate social responsibility: Moving beyond investment towards measuring outcomes[END_REF][START_REF] Muller | Global versus local CSR strategies[END_REF][START_REF] Perrini | Building a European portrait of corporate social responsibility reporting[END_REF][START_REF] Weber | The business case for corporate social responsibility: A company-level measurement approach for CSR[END_REF]. These studies focus on various dimensions (and related measurement criteria) of CSR, such as the determinants of CSR engagement, the specificities of CSR in small and medium-sized enterprises (SMEs), and the link between CSR and firm performance or value creation. Although the business benefits of CSR also have been subject to substantial research (for reviews, see [START_REF] Knox | Corporate social responsibility: Moving beyond investment towards measuring outcomes[END_REF][START_REF] Weber | The business case for corporate social responsibility: A company-level measurement approach for CSR[END_REF], a significant gap remains "between the goals of the European Commission (which saw CSR as a vital link between innovation and competitiveness on the one hand and social inclusion on the other) and, with a few exceptions, the perceptions of scientific CSR academics" (European Commission, 2011, p. 12). With this research we attempt to address one of the main weaknesses of extant European CSR research and respond to the demand for a better theoretical and empirical understanding of the relationships between CSR and innovation, especially because "Driving innovation (e.g. fuel innovation or packaging innovation)" (European Commission, 2011, p. 28) is one of the main benefits of engaging in CSR. Accordingly, our research reflects the European Commission's (2011) seventh Framework Program's agenda, which set "more ambitious goals … relating CSR directly to economic growth (and) innovation" (p. 33).

In this effort, we recognise that most CSR literature has tried to identify an effect on financial performance and produced contradictory results. As [START_REF] Mcwilliams | Corporate social responsibility: A theory of the firm perspective[END_REF] note, this lack of consensus might reflect model specification problems, such as omissions of R&D spending. [START_REF] Surroca | Corporate responsibility and financial performance: The role of intangible resources[END_REF] also demonstrate that intangible resources, including innovation, might be a missing link to explain relationships between CSR and financial performance. Despite recognition of the importance of innovation as a means to understand the impact of CSR, we remain in a "continuing state of emergence" (Lockett et al., 2006, p. 133) as far as theoretical approaches, assumptions, and methods are concerned.

We also find that previous studies focus almost exclusively on a specific aspect of CSR, namely, environmental practices. This perspective derives from the Porter hypothesis, which asserts that well-designed regulations can "trigger innovation that may partially or more than fully offset the costs of complying with them" (Porter & van der Linde, 1995, p. 98). Various studies test and confirm this predicted positive effect of environmental regulation on firms' innovation (e.g. [START_REF] Jaffe | Environmental regulation and innovation: A panel study[END_REF][START_REF] Rennings | The impact of regulation-driven environmental innovation on innovation success and firm performance[END_REF]. More recently, this notion also has extended to CSR in an effort to understand the extent to which practices that go beyond the letter of the law might drive innovation [START_REF] Mcwilliams | Corporate social responsibility: A theory of the firm perspective[END_REF]. The relationship among CSR, environmental practices, and innovation can reflect different theoretical perspectives. For example, using supply and demand theory, [START_REF] Mcwilliams | Corporate social responsibility: A theory of the firm perspective[END_REF] argue that CSR is an investment in product differentiation that sparks both process and product innovations. Applications of the resource-based view of the firm (RBV; [START_REF] Hart | A natural-resource-based view of the firm[END_REF][START_REF] Surroca | Corporate responsibility and financial performance: The role of intangible resources[END_REF] suggest environmental strategies can lead to the development of firm-specific capabilities (including innovation) that can be sources of competitive advantage. Yet [START_REF] Gallego-Alvarez | Corporate social responsibility and innovation: A resource-based theory[END_REF], analysing the bi-directional relationship between CSR and innovation, offer evidence that sustainable practices do not always lead to value creation and innovation, contrary to previous findings [START_REF] Wagner | Corporate social performance and innovation with high social benefits: a quantitative analysis[END_REF].

Still other authors (e.g. [START_REF] Carroll | Corporate social responsibility: evolution of a definitional construct[END_REF][START_REF] Hart | A natural-resource-based view of the firm[END_REF]Wartick & Cochrane, 1985;[START_REF] Wilson | What one company is doing about today's demands on business[END_REF] attempt finer characterisations of the types of CSR responses that firms adopt, situating them on a continuum between reactive and proactive end-points. [START_REF] Wilson | What one company is doing about today's demands on business[END_REF] identifies four generic firm-level approaches (reactive, defensive, accommodative, and proactive); [START_REF] Hart | A natural-resource-based view of the firm[END_REF] attempts to link them to innovation, though his analysis remains limited to the environmental elements. [START_REF] Sharma | Proactive corporate environmental strategy and the development of competitively valuable organizational capabilities[END_REF] extend his theoretical approach by reintroducing the idea of significantly different effects of proactive versus reactive strategies.

That is, proactive responsiveness to uncertainties in business and ecological issues appear associated with the emergence of unique organizational capabilities. Another characterisation, provided by [START_REF] Burke | How corporate social responsibility pays off[END_REF], explicitly links innovation to social, environmental, and economic CSR issues. Porter andKramer (2006, 2011) also argue that poor alignment between firms' CSR approaches and business strategies obscures the greatest opportunities for firms, namely, to gain a sustainable competitive advantage and create shared value that also benefits society.

Despite all these theoretical contributions, no empirical tests or cluster analyses describe the direct effect of CSR strategies on innovation; despite various attempts to differentiate strategic CSR profiles, the actual impact of these various profiles on technological innovation remains unclear. According to Wagner (2010, p. 583), "corporate sustainability and [corporate social performance]/CSR are less narrowly defined measures…. Hence, a study using one of [them] contributes by extending earlier analyses whilst building on their conceptual arguments and rigid methodology". We adopt this perspective and attempt to address the research gap pertaining to the complex relationship between innovation and CSR. Specifically we ask, Which CSR practices do firms adopt, and how to they relate to different types of technological innovation (product, process, or both)? Such relationships may be key to understanding firms' competitive position, because the impacts of product and process innovations on market shares and profit differ greatly [START_REF] Fagerberg | Innovation, a guide to the literature[END_REF]. We therefore work to delineate the links between firms' various CSR strategies (proactive or strategic, reactive or responsive) and the different types of innovation (product/process).

Using a merged data set that combines an original CSR Luxembourg firm-level survey with the fifth Community Innovation Survey, we specify CSR firms' profiles and assess their impacts on product and/or process innovations. To characterise the firms' profiles, we adopt a strategic approach to CSR that clearly acknowledges the link between CSR and innovation.

With regard to CSR practices, our cluster analysis reveals two CSR firm profiles (clusters): strategic (Cluster 1) and responsive (Cluster 2). We test whether belonging to a CSR cluster is a key determinant of a firm's (type of) innovation. By estimating a bivariate probit model, we also show that firms with strategic CSR profiles are more likely to innovate in both products and processes, whereas adopting responsive CSR practices significantly alters their innovation process, such that CSR even might create barriers to innovation.

The remainder of the paper is organized as follows: Section 2 contains our analytical framework related to CSR practices and their potential link with innovation. In Section 3, we describe the data and sample. Section 4 outlines the cluster analysis; Section 5 presents the results from the estimated bivariate probit model. In the conclusion, we offer both theoretical and managerial implications, as well as describe the main limitations of our study and some avenues for further research.

Literature review: A framework of CSR profiles and innovation

An understanding of the process by which "companies integrate social and environmental concerns to their business operations and in their interactions with stakeholders on a voluntary basis" (Commission of the European Communities, 2001, p. 6) emerges from strategic management literature, which not only identifies CSR practices but also addresses the link between CSR and innovation. As [START_REF] Lockett | Corporate social responsibility in management research: focus, nature, salience and sources of influence[END_REF] note though, CSR in strategic management literature is far from an established field; in particular, no consensus exists about the impact of CSR on firms' innovation.

In this sense, strategic management literature comprises two main streams. First, studies that link environmental practices to innovation have been extended to the broader concept of CSR. Second, literature in this field has established that types of strategic behaviour, and especially environmental strategies, lead to diverse performance effects. However, no studies sit at the intersection of these two streams, which prompts our main research interest: Are firms' CSR profiles (proactive/strategic versus reactive/responsive) linked to different levels of technological innovation performance and different types of innovation (product and/or process)?

Environmental practices, CSR, and innovation

The impact of CSR, through the adoption of environmental practices, on innovation has been extensively studied. A strategic perspective explicitly incorporates innovation and views it as an endogenous force that drives new resource combinations to sustain competitive advantages. Innovation heralds the introduction of new products and processes that, if embraced by the market, will enhance firms' performance. As firms work to recognise, manage, and reduce environmental impacts, they potentially reap competitive advantages [START_REF] Hart | A natural-resource-based view of the firm[END_REF][START_REF] Porter | Toward a new conception of the environmentcompetitiveness relationship[END_REF][START_REF] Russo | A resource-based perspective on corporate environmental performance and profitability[END_REF], captured in the form of enhanced innovation. Nidumolu et al. (2009, p. 57) even state explicitly that CSR and sustainability are "key drivers for innovation".

Using supply and demand theory as a framework, [START_REF] Mcwilliams | Corporate social responsibility: A theory of the firm perspective[END_REF] show that the adoption of environmental practices, going beyond legal requirements, may promote investments in Research and Development (R&D), which in turn can produce both process and product innovations. They also argue that there is a presumption that firms that actively support the stewardship of the environment are more reliable and offer products of higher quality.

Other frameworks, such as those based on the RBV of the firm [START_REF] Hart | A natural-resource-based view of the firm[END_REF], take into account the multi-dimensional character of CSR. For example, [START_REF] Wagner | Corporate social performance and innovation with high social benefits: a quantitative analysis[END_REF] uses panel data to test empirically the relationship between corporate social performance measured by the Kinder Lydenberg Domini Inc.1 index and innovation. That study reveals that CSR, conceptualised as a multi-dimensional appraisal of a firm's responsible performance, leads to innovation and strong social benefits. Using a two-step estimation that corrects for endogeneity, [START_REF] Surroca | Corporate responsibility and financial performance: The role of intangible resources[END_REF] demonstrate that intangible resources (including innovation, which they measure as the ratio of R&D expenses to the total number of firm employees) mediate the bi-directional relationship between CSR and firms' financial performance. This "virtuous circle" appears stronger in growth industries than in non-growth industries. Using a dynamic panel data model to control for unobservable heterogeneity, Gallejo-Alvarez et al. (2011) also analyse this bidirectional relationship but rely on the RBV and suggest a negative effect of CSR on innovation. Thus, not all CSR practices seem to create value. In particular, we predict that the type of CSR strategy-reactive or proactive-may have differentiated impacts [START_REF] Mcwilliams | Corporate social responsibility and financial performance: Correlation or misspecification?[END_REF][START_REF] Porter | Strategy and society[END_REF][START_REF] Sharma | Proactive corporate environmental strategy and the development of competitively valuable organizational capabilities[END_REF]. Moreover, many studies have used narrow measures of innovation (i.e. R&D) that cannot fully capture innovation efforts [START_REF] Mairesse | Using innovation surveys for econometric analysis[END_REF] or prevent the differentiation of various types of innovation (product/process), despite their potentially varying effects on performance [START_REF] Bocquet | Product and process innovations in subcontracting: Empirical evidence from the "Sillon Alpin[END_REF]. The use of such proxies also might help explain the contradictory results proposed by [START_REF] Wagner | Corporate social performance and innovation with high social benefits: a quantitative analysis[END_REF] versus [START_REF] Gallego-Alvarez | Corporate social responsibility and innovation: A resource-based theory[END_REF].

Strategic CSR behaviour and the missing direct link to innovation

Strategic management literature reveals that the Porter hypothesis does not always dominate firms' CSR actions in environmental or social areas. Some authors thus try to identify the type of CSR strategy that best favours innovation, mainly by distinguishing between proactive and reactive firms. However, they focus almost exclusively on environmental strategies. Building on [START_REF] Hart | A natural-resource-based view of the firm[END_REF] classification, [START_REF] Buysse | Proactive environmental strategies: A stakeholder management perspective[END_REF] assess the linkages between proactive environmental strategies and stakeholder management with an empirical application to large, polluting firms active in Belgium. Specifically, they use a cluster analysis to classify firms according to their environmental management practices and identify three dominant environmental management strategies: (1) reactive, (2) pollution prevention, and (3) environmental leadership. Many firms in their sample had shifted from a reactive to a pollution prevention strategy, but only a minority (mostly multi-national firm affiliates) had adopted an environmental leadership strategy.

One study links environmental strategic behaviours to organizational capabilities, including innovation. That is, [START_REF] Sharma | Proactive corporate environmental strategy and the development of competitively valuable organizational capabilities[END_REF] find that firms with a positive environmental strategy develop unique capabilities, such as higher order learning and continuous innovation, which they cite as outcomes of environmental responsiveness. They find that proactive firms seek opportunities and experiment at the business/environment interface. Other evidence also indicates that developing environmentally friendly processes and products requires firms to be more innovative than firms that choose not to pursue such environmental actions [START_REF] Aragón-Correa | A contingent resource-based view of proactive corporate environmental strategy[END_REF][START_REF] Dibrell | Natural environment, market orientation, and firm innovativeness: an organizational life cycle perspective[END_REF].

However, these studies limit their analyses to environmental aspects, without introducing a general CSR construct to capture effects on innovation. This limit is unsatisfactory, because it ignores a potentially important area demarcating the relationship between CSR and corporate performance [START_REF] Devinney | Is the socially responsible corporation a myth? The good, bad and ugly of corporate social responsibility[END_REF]. Therefore, we extend these studies to CSR as a whole, not just to its environmental aspects, to analyse whether firms with different CSR strategic profiles innovate differently. [START_REF] Burke | How corporate social responsibility pays off[END_REF] transpose the CSR strategic approach to assert that engaging in social, societal, and/or environmental actions provides opportunities for value creation. [START_REF] Porter | Strategy and society[END_REF] framework completes this approach by introducing a clear distinction between proactive and reactive strategies and their potential (distinct) effects on innovation. With the exception of [START_REF] Husted | Strategic corporate social responsibility and value creation among large companies[END_REF], such frameworks have led to few empirical applications; most investigations of CSR remain conceptual or rely on qualitative case studies (e.g. [START_REF] Jenkins | Small business champions for corporate social responsibility[END_REF][START_REF] Moore | Criteria for responsible business practice in SMEs: An exploratory case of U.K. fair trade organisations[END_REF][START_REF] Murillo | SMEs and CSR: An approach to CSR in their own words[END_REF][START_REF] Tencati | New tools to foster corporate socially responsible behavior[END_REF]. These methodologies certainly are well adapted to descriptions of CSR practices, but they cannot reveal the impact of the different types of CSR (proactive and reactive) on innovation. [START_REF] Husted | Strategic corporate social responsibility and value creation among large companies[END_REF] empirical study offers a partial test of Burke and Logdson's model; it cannot not demonstrate how value creation is associated with the positive alignment of all strategic dimensions that characterise proactive strategies [START_REF] Porter | Strategy and society[END_REF]. Therefore, we aim to complete the strategic CSR framework [START_REF] Carroll | Corporate social responsibility: evolution of a definitional construct[END_REF][START_REF] Mcwilliams | Corporate social responsibility and financial performance: Correlation or misspecification?[END_REF][START_REF] Porter | Strategy and society[END_REF][START_REF] Sharma | Proactive corporate environmental strategy and the development of competitively valuable organizational capabilities[END_REF][START_REF] Wilson | What one company is doing about today's demands on business[END_REF]Wartick & Cochrane, 1985), beginning with [START_REF] Burke | How corporate social responsibility pays off[END_REF] framework as a foundation, for two main reasons.

Main hypothesis: CSR strategic profiles and types of innovation

First, their framework can establish a strong link between the strategic CSR profile and innovation in terms of products and processes. It "appears to be useful in understanding value creation via CSR projects" [START_REF] Gallego-Alvarez | Corporate social responsibility and innovation: A resource-based theory[END_REF], p. 1713), and "value creation is necessarily about innovation" (Husted & Allen, 2007, p. 597). Value accrues when consumers are willing to pay more for the firm's products and services because of the social issues it defends (product innovation). Value creation also occurs when resources are combined in new ways to increase their efficiency (process innovation). Distinguishing between these two main types of technological innovation is crucial [START_REF] Rehfeld | Integrated product policy and environmental product innovations: An empirical analysis[END_REF], in that they have unique effects on market share and profits and clearly induce different levels of economic performance. With respect to their determinants, some factors are common, but others are specific to either product or process innovation [START_REF] Bocquet | Product and process innovations in subcontracting: Empirical evidence from the "Sillon Alpin[END_REF][START_REF] Cabagnols | The determinants of the type of innovative behaviour at the firm level[END_REF].

Second, we aim to distinguish the effects of two CSR profiles (strategic versus responsive) on innovation. [START_REF] Burke | How corporate social responsibility pays off[END_REF] framework explicitly introduces the strategic dimension of CSR as a means to understand the extent to which CSR leads to value creation. Consistent with strategic management literature, they argue that value creation through CSR, in addition to distinctive capabilities, requires a positive alignment of all strategic dimensions (Burke & Logsdon, 1996, p. 497), as follows:

-Centrality, or "closeness of fit to the company's mission and objectives"; -Proactivity, defined as the "degree to which the program is planned in anticipation of emerging social trends and in the absence of crisis"; -Voluntarism, which is "the scope for discretionary decision-making and the lack of externally imposed compliance requirements"; -Visibility, or "observable, recognizable credit by internal and/or external stakeholders for the company"; and -Specificity, indicating the "ability to capture private benefits by the company".

These five dimensions can serve to characterise firms according to their CSR practices, with splits based on high (strategic CSR) versus low (responsive CSR) scores. This empirical strategy is consistent with [START_REF] Porter | Strategy and society[END_REF] identification of two firm profiles that reflect whether the firm considers CSR a response or a strategic lever. This distinction thus indicates the way the firm integrates all strategic CSR dimensions into its strategic vision.

Strategic CSR requires an alignment between CSR and the firm's growth strategy, which then creates a virtuous circle that allows innovation activities to develop. [START_REF] Husted | Strategic corporate social responsibility and value creation among large companies[END_REF] omit two key variables (centrality and proactivity) that refer to strategic alignment with the core business and CSR strategy [START_REF] Porter | Strategy and society[END_REF], such that their results may underestimate the impact of CSR on value creation. Strategic CSR seemingly should turn the related expense into a measurable potential return [START_REF] Mcwilliams | Corporate social responsibility: A theory of the firm perspective[END_REF][START_REF] Wagner | Corporate social performance and innovation with high social benefits: a quantitative analysis[END_REF], as well as a unique, distinctive competitive position (e.g. "lower cost, better service"). We capture these effects through process and product innovations, from which perspective strategic CSR, based on a symbiotic relation between society and a firm's competitiveness, offers a main determinant of both product and process innovations. Sharma and Vredenburg (1998) also find that proactive companies perceive competitive benefits emerging from their environmental engagement, and those benefits include innovations in processes and products. Thus, we hypothesise:

Hypothesis 1: The engagement of a firm in strategic CSR has a positive effect on its propensity to innovate (in both products and processes).

In contrast, responsive CSR corresponds to the most basic level of CSR, that is, "acting as a good corporate citizen, attuned to the evolving social concerns of stakeholders, and mitigating existing or anticipated adverse effects from business activities" (Porter & Kramer, 2006, p. 85). The implementation of best practices, as characterises responsive CSR, may contribute to continuous improvement, but they are often disconnected from the firm's overall strategy. Although it ensures firm legitimacy, this approach may cause tensions and conflict with an internal logic of efficiency [START_REF] Fiss | The symbolic management of strategic change: Sensegiving framing and decoupling[END_REF]. Such a "decoupling" also could hinder the firm's ability to innovate in processes and/or products. In this sense, responsive practices place additional constraints on the innovation process and might slow its pace [START_REF] Castiaux | Responsabilité d'entreprise et innovation: Entre exploration et exploitation[END_REF]. They can limit the freedom and creativity needed for innovation processes, even creating a vicious circle in that respect. This consideration matches [START_REF] Miles | Organizational strategy, structure and process[END_REF] identification of a "reactor" type as a form of "strategic failure," because of the inconsistencies across its strategy, technology, structure, and process. Studying the impact of supply chain pressures on SMEs to engage in CSR, [START_REF] Baden | The effect of buyer pressure on suppliers in SMEs to demonstrate CSR practices: An added incentive or counter productive[END_REF] show that such pressures can be counter-productive in the absence of intrinsic motivations of owner/managers. Imposed practices increase bureaucracy costs and set lower standards than firms would set for themselves. Moreover, Sharma and Vredenburg (1998, p. 735) find that reactive companies "perceived environmental responsiveness as detracting from this objective" of improving the creation of value. Thus:

Hypothesis 2: The engagement of a firm in responsive CSR has a negative effect on its propensity to innovate (in both products and processes). The CSR survey was carried out in Luxembourg during the same period but included all economic sectors. Firms with at least 10 employees appeared in the survey. From this population of 3,296 companies, we built a sample of 2,511 firms that employed at least 50 people and then created stratified random sampling for companies with 10-49 employees. The data collection took place by mail, with a questionnaire in French and German (English was available on request). We received 1,144 valid questionnaires,3 most of which (51%) indicated the firms had adopted CSR for more than two years (37% for more than five years).

Methodology

To analyse simultaneously CSR and innovation, we merged the responses obtained from the two surveys. This final sample consists of 266 enterprises that responded to both surveys, which we categorised into three size classes (10-49 employees, 50-249 employees, and 250 or more employees). Considering its relatively small size, we simply break the sample into two economic sectors: industry (mining and quarrying, manufacturing, electricity, and gas and water supply) versus services (wholesale trade, transport, storage and communication, financial intermediation, computer and related activities, architectural and engineering activities, and technical testing and analysis).

To produce valid results for the target population (i.e. result representativeness), we also weighted the survey results to adjust for the merging procedure, sampling design, and unit nonresponse. A calibration, carried out with the SAS macro developed by INSEE in France, or CALMAR (Calibration on Margins), used auxiliary information: the number of firms per sector and the number of employees per sector. Our statistical analyses rely on this weighting.

Our empirical procedure included two steps. First, using the CSR survey, we conducted a non-hierarchical cluster analysis to classify the firms in relation to their CSR practices. Second, we used the sample that we obtained after merging the CIS and CSR surveys. With a bivariate probit model, we then considered the link between CSR profiles and technological innovation (product and/or process).

A Cluster analysis for CSR profiles (Step 1)

We first classified firms that had responded to the CSR survey according to their CSR practices.

Variables and measures

Our survey data describe CSR practices according to the five dimensions identified by [START_REF] Burke | How corporate social responsibility pays off[END_REF]:

-Centrality. This dimension, which [START_REF] Husted | Strategic corporate social responsibility and value creation among large companies[END_REF] excluded from their empirical model, consists of two binary variables:4 the existence of documents that describe values and motivations in terms of social issues (DOC) and whether the firm describes its CSR practices in its annual report, in a dedicated report, or on its website (SUP). As Porter andKramer (2006, 2011) show, creating value through CSR requires not just additional capabilities but also a fit among CSR values, goals and practices, and the core business strategy. Firms that formalise their CSR strategy likely integrate CSR into their corporate business, to gain reputational advantages.

-Proactivity. Also excluded from [START_REF] Husted | Strategic corporate social responsibility and value creation among large companies[END_REF] test, we measure this dimension with two binary variables: the existence of CSR action planning (PLAN) and the existence of an agenda (AGENDA). These variables capture the firm's ability to gain a first-mover advantage by anticipating changes in social issues.

-Voluntarism. This binary variable assesses whether the firm has identified stakeholders for its CSR actions (STAKE). Similar to [START_REF] Husted | Strategic corporate social responsibility and value creation among large companies[END_REF], we use this consideration to determine if voluntary actions are undertaken freely or under pressure, to fit the firm's strategy.

-Visibility. We use another binary variable for this dimension to assess whether the firm has an external or we use another internal communication plan (COM). Unlike Husted and Allen (2007) but similar to [START_REF] Buysse | Proactive environmental strategies: A stakeholder management perspective[END_REF], we focus on the means of communication (internal and external) that firms use to increase their visibility, rather than on the purpose of the communication. Thus we avoid any confusion with the "specificity" dimension and better align with [START_REF] Burke | How corporate social responsibility pays off[END_REF] definition.

-Specificity. The three variables for this dimension use four-item Likert scales, ranging from 1 ("no perceived benefits") to 4 ("strong perceived benefits"): attract new clients (CLIENT), enhance the firm's image (IMAGE), and differentiation from competition (COMP). These items reflect the assumption that each element will lead to value creation and private benefits for the firm. [START_REF] Husted | Strategic corporate social responsibility and value creation among large companies[END_REF], who call this variable "appropriability", suggest that one of best ways to extract economic benefits from CSR is product differentiation, through the development of a new product or process. With these three variables, we aim to capture various sources of private benefits, whereas [START_REF] Husted | Strategic corporate social responsibility and value creation among large companies[END_REF] used a more general measure linked to "the achievement of social objectives … necessary to reach financial objectives" (p. 608).

Recall that firms with high scores on these dimensions have strategic CSR, whereas those that score low represent the responsive CSR group.

Principal component and non-hierarchical cluster analyses

We conducted a principal component analysis (PCA)5 on the nine core variables we use to operationalise the CSR dimensions (DOC, SUP, CLIENT, IMAGE, COMP, PLAN AGENDA, STAKE, COM). The measure of sampling adequacy (MSA) confirmed that the variables were good candidates for a PCA (MSA > 0.5). In addition, the Kaiser-Meyer-Olkin (KMO) measure and Bartlett's test of sphericity met common standards (KMO = 0.55, p < 0.001). The PCA also identified two factors, in good summary of the theoretical dimensions (49.8% of total variance). Next we undertook a non-hierarchical cluster analysis, using the scores revealed by the factor analysis. To determine the final number of clusters, we used three common criteria: (1) the statistical accuracy of the classification measured by the ratio of within-cluster and between-clusters variances (Fisher's test), (2) the number of firms per cluster, and (3) the economic significance of the clusters identified. According to these criteria, the version with two clusters of CSR practices is best. 6 To interpret the two clusters, we calculated the mean of each CSR indicator in each cluster (see Table 1).

Cluster analysis results

The results may be interpreted by comparing the means in each cluster. In Cluster 1, the 114 firms are intensive CSR adopters, in terms of the number and intensity of the various types of CSR practices they undertake. Engaging in CSR is not only supported by the definition of clear economic objectives at the firm level but requires the adoption of various formalised practices. These practices reflect the firms' ability to incorporate stakeholders' objectives into their business operations. In this sense, CSR means more than the pursuit of common objectives (i.e. firms' and stakeholders' benefits); it is a process in itself that applies to all kinds of decision making, evaluations, and measures. These firms prefer visibility, and they "say what they do". In contrast, the 95 firms in Cluster 2 are poor CSR adopters: They may adopt all kinds of CSR practices, but they score systematically lower than Cluster 1 firms.

They have not attained full CSR adoption, lack an asserted economic objective, do not clearly identify their stakeholders, and have not really succeeded in formalising their CSR practices. Notes: We interpret Clusters 1 and 2 according to the variables in the cluster analysis. We computed the mean of each variable for each cluster. The mean appears in bold when it is significantly higher in the considered cluster. For example, strategic CSR profiles (Cluster 1) use significantly more documents describing values and motivations in terms of social issues (DOC) than do Cluster 2 firms.

In line with [START_REF] Burke | How corporate social responsibility pays off[END_REF] predictions, strategic CSR no longer reflects the presence of a single dimension but instead requires a positive alignment of all strategic dimensions. This result is consistent with [START_REF] Porter | Strategy and society[END_REF] distinction between firms with strategic or responsive CSR. For Cluster 1 firms, expectations of private benefits from CSR are strongly associated with "doing things together". These strategic firms seek to exploit complementarities between inside-out and outside-in linkages, because they take stakeholders explicitly into account as strategic resources. In contrast, Cluster 2 firms offer a responsive vision: They adopt goals and practices but at a low level of intensity. They never reach the "threshold" (here, the mean) that would allow them to go beyond best practices, assuming that their current level of adoption involves good practices.

The Relationship between CSR profiles and innovation (Step 2)

To test our main hypothesis, we also must determine whether there is a relationship between innovation and CSR profiles. A descriptive analysis of the proportion of firms with innovative behaviours shows that Cluster 1 firms differ from those in Cluster 2. In Cluster 1, 58% and 55% of firms innovate in products and processes, respectively; these proportions are lower in Cluster 2, especially for process innovation: 41% innovate in products and 23% in processes. We thus infer that belonging to a specific type of cluster affects the probability that a firm innovates in products and/or processes.

Dependent variables: Two types of technological innovation

The two dependent variables, product and process innovations, or several of the following processes?" (INSPSPD). Such process innovation might include significant changes in the way that goods or services are produced or provided; the measure differentiated between processes new to the business or new to the industry.

Independent variables: Types of CSR profiles

To determine the effects of each CSR profile, we introduced the two CSR profiles stemming from the previous classification procedure (CLUSTER_1 and CLUSTER_2), which constitute our main independent variables. We also added the firms' intention to adopt CSR or not, to assess whether firms planning to adopt CSR practices exhibit particular behaviours in terms of the types of innovation. We thus introduced two dummy variables, PLAN_CSR and NO_CSR, 8 to account for a larger sample.

Control variables

Finally, we included in the group of regressors the implementation of organizational innovation, to confirm that CSR practices are not simply encompassed in broader organizational practices, which would eliminate any specific effect of CSR. Organizational innovation is a determinant of technological innovation (e.g. Mothe & Nguyen, 2011). 7 In [START_REF] Burke | How corporate social responsibility pays off[END_REF] model, the dependent variable is value creation. We focus on a more precise aspect of value creation, innovation, in line with Husted and Allen's (2007, p. 597) claim that value creation always and necessarily pertains to innovation. 8 Including these variables enables us to use all available questionnaire data. These firms constitute part of the overall population that has not implemented CSR practices in the time period under observation.

Accordingly, we measured four organizational practices (OECD, 2005): (1) new business practices for organizing work or procedures; (2) new knowledge management systems; (3) new methods of workplace organization for distributing responsibilities and decision making; and

(4) new methods of organizing external relations with other firms or public institutions. These four practices were aggregated into one variable to represent the introduction of (at least) one new or significantly improved organizational practice (INORG).

Following a strategic management perspective, the probability of innovation also depends on a mix of firm-specific characteristics and sector configurations [START_REF] Cohen | Empirical studies of innovative activity[END_REF][START_REF] Teece | Reflections on "Profiting from Innovation[END_REF], which are common variables in empirical studies [START_REF] Mairesse | Using innovation surveys for econometric analysis[END_REF]. Firm capabilities are crucial to its long-term success and innovative performance [START_REF] Teece | The dynamic capabilities of firms: An introduction[END_REF]. A strong knowledge base includes R&D capacity and a well-trained workforce [START_REF] Cohen | Absorptive capacity: A new perspective of learning and innovation[END_REF]. We also included data on whether the firm undertakes internal R&D (RRDIN), in line with McWilliams andSiegel's (2000, 2001) recommendations. Because R&D expenditures cannot always capture innovation efforts, especially for small firms [START_REF] Mairesse | Using innovation surveys for econometric analysis[END_REF], we also considered the proportion of employees with a higher education degree (NEMPHI) as a proxy for the level of human capital dedicated to innovation.

Another important driver relates to technological opportunities (NPRODPER), that is, the velocity with which products and services become old-fashioned. To control for competitive intensity [START_REF] Covin | Pioneers and followers: competitive tactics, environment and firm growth[END_REF][START_REF] Ozsomer | What makes firms more innovative? A look at organizational and environmental factors[END_REF], we included a dummy variable (NMARCONC), equal to 1 when competition in the firm's market is very intense, and 0 otherwise. Appropriability conditions [START_REF] Teece | Firm organization, industrial structure, and technological innovation[END_REF] can help generate and maintain rents from leadership in technological activities. Firm size also likely matters in terms of innovation (e.g. [START_REF] Russo | A resource-based perspective on corporate environmental performance and profitability[END_REF][START_REF] Wagner | Corporate social performance and innovation with high social benefits: a quantitative analysis[END_REF], because large firms have the resources to invest in technological activities and exploit external opportunities, though small ones can also be very innovative, especially in high-technology sectors [START_REF] Cohen | Empirical studies of innovative activity[END_REF]. We accounted for these effects with a measure of the firm's size (T1 = 10-49 employees, T2 = 50-249 employees, T3

= more than 249 employees, according to European classifications). We also added two traditional controls, widely used in studies of CSR and innovation: the sector of activity (e.g. [START_REF] Gallego-Alvarez | Corporate social responsibility and innovation: A resource-based theory[END_REF][START_REF] Husted | Strategic corporate social responsibility and value creation among large companies[END_REF][START_REF] Wagner | Corporate social performance and innovation with high social benefits: a quantitative analysis[END_REF] (manufacturing/services, INDUS) and a variable indicating whether the firm belongs to a group (GROUP).

Table 2 contains the definitions of all these variables. Appendices A and B provide summary descriptive statistics for the main variables and their correlations, respectively.

Table 2. List of variables

Model and estimation

The basic model for determining whether a set of variables exerts an impact on the decision to innovate is a probit model, which is standard in empirical works that deal with firm behaviour.

We could estimate a probit model for each type of innovation (product and process), which would account for the probability that a firm implements each type of innovation. However, the decision to adopt product innovation may relate to the decision to implement process innovation, and firms define their technological strategy for all types of technological aspects.

Thus, process and product innovations may not be independent choices, which means that estimating two separate probit models leads to a loss of efficiency and possibly misleading results [START_REF] Rouvinen | Characteristics of product and process innovators: Some evidence from the Finnish innovation survey[END_REF]. For this reason, we estimated a bivariate probit model (Table 3) to assess the possible relationship between the CSR profiles and the two types of innovation. 9 The special attention we pay to the possible interaction between product and process innovations differentiates this study from most works, which focus on one or the other [START_REF] Weiss | Adoption of product and process innovations in differentiated markets: The impact of competition[END_REF].

Moreover, this distinction enhances understanding of innovation determinants [START_REF] Damanpour | An integration of research findings of effects of firm size and market competition on product and process innovations[END_REF]. 10 We use the same set of previously defined regressors. The status of belonging to a CSR cluster describes our two core variables. Table 3 provides the coefficient estimates, through marginal effects on the probability of innovating. We performed a statistical test for ρ = 0 that indicates the interdependence of the two adoption decisions. The correlation coefficient differs significantly from zero. We assume in our model that the random error terms in the two equations are correlated, so the test confirms the statistical link between the two innovation choices (product and process). 9 To investigate two related decisions by the same individual, a bivariate probit model (or bi-probit) fits the data better than two separated probit models. We test if the bi-probit model is better with widely available software (e.g. [START_REF] Baum | An introduction to modern econometrics using Stata[END_REF]. We further presume that the choice to innovate products correlates to the choice to innovate processes. Thus the covariance between the two terms of errors can differ from zero. 10 We acknowledge [START_REF] Hoetker | The use of logit and probit models in strategic management research: Critical issues[END_REF] advice about using probit models in strategic management research.

Results

The results indicate succinctly whether belonging to Cluster 1 or to Cluster 2 has an impact on the probability of implementing a specific type of innovation, after controlling for several different factors. In comparison with firms that do not adopt CSR, and as expected, firms with strategic CSR profiles (Cluster 1) are more likely to innovate in both products and processes.

Hypothesis 1 thus receives support. The results for firms belonging to Cluster 2 (responsive firms) indicate a negative effect on the probability of adopting product or process innovations, though the effect is significant only for process innovation. Hypothesis 2 is thus partially supported. Responsive CSR seems to represent an obstacle, especially to process innovation.

The positive impact of variables that account for the firm's capabilities (RRDIN and NEMPHI) is consistent with strategic management literature. That is, undertaking internal R&D and hiring many employees with higher education favours firms' product and process innovations. The variable for technological opportunities (NPRODPER) has a significant negative sign, in contrast with prior findings, which might reflect measurement issues and the difficulty of finding a good proxy for this factor, which often interacts with firm innovative performance. The proxy for incentives is significant only for the process innovation equation.

Belonging to a market in which competition is very intense enhances the probability of innovating in processes. This result is in line with an industry life-cycle approach: In later stages, when their production volumes have increased, firms try to optimise their production and devote their R&D investments to process innovation (Klepper, 1996). 11 The dummy variable for industry is significant for both models; manufacturing firms, not surprisingly, innovate more in products and processes than service firms. Firm size has no overall impact on the probability of innovating. This result is not surprising though, because small firms can benefit from additional resources, especially when they are part of a group.

Discussion, conclusion, and implications for further research

The objective of this research has been to assess whether firm CSR profiles and practices relate to technological innovation performance, in particular to product and process innovations. Firms in Cluster 1, which take a strategic orientation toward CSR, are more innovative in terms of products and processes, after we control for firm characteristics and key innovation drivers.

In line with [START_REF] Baden | The effect of buyer pressure on suppliers in SMEs to demonstrate CSR practices: An added incentive or counter productive[END_REF], we find that firms have no chance of benefiting from CSR in "the absence of intrinsic motivation to engage in CSR" (p. 432). This result offers empirical validation of [START_REF] Burke | How corporate social responsibility pays off[END_REF] theoretical predictions; it also extends an existing empirical study [START_REF] Husted | Strategic corporate social responsibility and value creation among large companies[END_REF] that used only three dimensions. Consistent with [START_REF] Miles | Organizational strategy, structure and process[END_REF] model, we confirm that organizational performance depends on the degree of consistency (fit) that managers establish across strategic, organizational, and environmental elements. All five dimensions [START_REF] Burke | How corporate social responsibility pays off[END_REF] should be aligned to create innovation and its competitive advantage. This internal fit does not imply simply the adoption of good practices, which are both insufficient and possibly poorly aligned with the firm's existing organization. In this case, they could lead to a failure of established strategic routines and declines in performance [START_REF] Porter | Contextuality within activity systems and sustainability of competitive advantage[END_REF].

A lack of alignment also may translate into barriers to innovation, which can produce a vicious circle when the firm cannot link its CSR practices with its strategy [START_REF] Castiaux | Responsabilité d'entreprise et innovation: Entre exploration et exploitation[END_REF]Galego-Alvarez et al., 2011). This lack of alignment in responsive CSR firms affects their technological innovation negatively, though the negative impact is significant only for process innovation. To explain this result, we note that the best CSR practices adopted by firms thus far have been related mainly to cost reduction objectives. Because they were not integrated into the firm's strategy and organization, they produced a countervailing effect that hindered the firm's capacity to innovate in processes.

These results offer several implications for theory and research on CSR and innovation.

In particular, our results offer empirical confirmation of previous theoretical assumptions about the link between CSR practices and innovation, though they address this link much more carefully. This finding also confirms an OECD (2009) report about the "new nature of innovation": Social concerns increasingly drive innovation. Moreover, our results suggest that a firm with strategic CSR can sustain a competitive advantage and ensure better, longer-lasting economic performance than a firm with responsive CSR. From an evolutionary perspective, their likelihood of survival is higher. The social technology supported through strategic CSR also offers more chances for growth, whereas responsive CSR firms engage in behaviours more closely related to lower economic performance, so their probability of failure in the medium term increases. Institutions put forth (responsive CSR) thus might bar innovation. To put it simply (at least as a prediction of our study), responsive CSR seems unsustainable and not viable. Strategic CSR better fits the socio-economic context and enables both the improvement of stakeholder revenues and enhanced life quality with constraints only due to stockholders' demands.

For CSR and innovation managers, we reveal that to foster product and process innovations, firms should adopt formalised CSR practices and establish procedures and tools aligned with their corporate strategy. When they have strategic CSR [START_REF] Porter | Strategy and society[END_REF], firms clearly identify their stakeholders and focus on external visibility, such that they "say what they do" in formal documents that clearly describe their CSR practices. Furthermore, managers might employ our results to guide their use of CSR practices to enhance technological innovation or their exploitation of technological innovation to introduce CSR practices. Strategic orientation is key in both areas. In turn, this finding indicates major implications for public policy support for innovation and/or CSR firm engagement-a link that requires further studies to detail the relevant complementarities [START_REF] Mohnen | Complementarities in innovation policy[END_REF]. A package of policies may be needed to help firms engage in either CSR or innovation processes.

Our study is not exempt from limitations. A primary one relates to the specific economic structure of Luxembourg, where service firms have a very important place.

Additional research should replicate this study in countries where industry and service sectors play equally important roles. Longitudinal data would be useful to assess whether strategic CSR behaviour is linked to persistent technological innovations. Further research also could take into account the likely complementarities between the different types of innovation; [START_REF] Polder | Product, process and organizational innovation: Drivers, complementarity and productivity effects[END_REF] already have shown that product and process innovations have positive effects on productivity when combined with organizational innovation. It thus seems relevant to test for complementarity through tri-variate probit models that account for the combined effects of product, process, and organizational innovations. Other types of non-technological innovations (e.g. marketing, business models) could also be considered. More generally, our analysis might be extended with additional research on the complex relationship among CSR, innovation, and value creation that examines further the differentiated impacts on types of innovation.

CLUSTER_2

Data

  To test our main hypothesis, we used data from two surveys conducted by CEPS/INSTEAD (Luxembourg): the fifth Community Innovation Survey (CIS 6) and a survey relative to CSR practices in firms. Both provide information about CSR practices and innovation performance at the firm level. The Community Innovation Survey (CIS 6) was conducted in each member state. The quality dimensions are based on the standard defined by Eurostat. We used data collected in Luxembourg, by CEPS/INSTEAD on behalf of STATEC with financial support from the European Commission (Eurostat), which includes the following economic sectors as the core target population for the CIS 6: manufacturing (NACE 2 15-37); electricity, gas, and water supply (NACE 40-41); wholesale trade (NACE 51); transport, storage, and communication (NACE 60-64); financial intermediation (NACE 65-67); computer and related activities (NACE 72); architectural and engineering activities (NACE 74.2); and technical testing and analysis (NACE 74.3). All the enterprises included in the target population have at least 10 employees. The observation period covered by the survey is 2004-2006, which entails a threeyear period from the start of 2004 to the end of 2006. The reference period for CIS 6 is 2006. The sample was drawn from the national business register provided by the National Institute of Statistics in Luxembourg (Statec) at the end of 2006. We obtained 568 responses.

  7 come from the 2006 Community Innovation Survey, which asked, "During the years 2004 to 2006, did your firm introduce a new or significantly improved product (good or service) or process for making or supplying them?" (INPDGD) and "During the years 2004 to 2006, did your firm introduce one

  and does not plan to adopt CSR INORG Organizational innovation: the firm introduces a new organizational method into its business practices (including knowledge management), workplace organization, or external relations RRDIN The firm undertakes internal R&D activity NEMPHI Percentage of employees with higher education (post-secondary college and university) NPRODPER The products and services become rapidly old-fashioned (dummy variable, yes = 1, no = 0) NMARCONC The competition in the market is very intense (dummy variable, yes = 1, no = 0) T1 The total number of employees is between 10 and 49 T2 The total number of employees is between 50 and 249 T3 The total number of employees is more than 249 INDUS Belongs to the manufacturing sector GROUP The firm is part of a group Note: Dependent variables are in bold.

Table 1 . CSR clusters

 1

	Cluster		DOC	COM	SUP	CLIENT IMAGE COMP AGENDA PLAN STAKE
	1	Mean	1.70	1.62	1.77	3.01	3.81	3.52	1.96	1.82	1.46
		N	114	114	113	107	111	110	113	110	114
	2	Mean	1.31	1.27	1.42	2.54	3.47	2.97	1.51	1.51	1.13
		N	95	95	95	92	94	94	95	91	95
	F-test (sig.)		***	***	***	***	***	***	***	***	***
	Total Mean	1.52	1.46	1.61	2.79	3.66	3.27	1.75	1.68	1.31
		N	209	209	208	199	205	204	208	201	209

Table 3 . Determinants of innovation behaviours (bivariate probit model)

 3

		Marginal Effect (standard error)
		INPDGD	INSPSPD
	CLUSTER_1	0.635531***	0.5472143***
		(0.143411)	(0.1407221)
	CLUSTER_2	-0.053847	-0.7756979***
		(0.1401149)	(0.1450609)
	PLAN_CSR	-0.2778489**	-0.1881734
		(0.1231161)	(0.1178849)
	NO_CSR	Reference category	Reference category
	INORG	0.8850447***	0.5828004***
		(0.0871822)	(0.0831286)
	RRDIN	1.339211***	0.648653***
		(0.1229137)	(0.1076452)
	NEMPHI	0.6959584***	0.8727128***
		(0.1383004)	(0.1362914)
	NPRODPER	-0.6329258***	-0.1873012**
		(0.0960763)	(0.0905903)
	NMARCONC	0.053673	0.3571119***
		(0.0880101)	(0.0824224)
	T1	0.098066	0.10163
		(0.1069987)	(0.1027805)
	T2	Reference category	Reference category
	T3	0.1193759	-0.3159759*
		(0.2053129)	(0.1838261)
	INDUS	0.268331**	0.3838273***
		(0.105868)	(0.0977941)
	GROUPE	0.4554853***	0.7604304***
		(0.0883534)	(0.0866091)
	CONST	-1.53966***	-1.78133***
		(0.1480217)	(0.1486636)
	Sample size	266
	-Log-likelihood	-1487.7105
	Rho	0.4728654 (0.0455743)
	Likelihood-ratio test of rho =	chi2(1) = 82.6787 Prob > chi2 = 0.0000
	0

This global sustainability index benchmarks the top companies in all sectors across the Asia-Pacific, Europe, and North America.

This acronym refers to the statistical classification of economic activities in the European Community.

The descriptive statistics about the population and sample by NACE code and firm size are available on request.

All dummies for the variables in this cluster analysis take values of 1 = "no" or 2 = "yes".

We do not present the PCA results here because they are only preparatory stages for our cluster analyses. They are available on request.

For all comparisons of variances, Fisher's test is significant at the 0.000 level and indicates good differentiation among between firms.

We bear in mind that existing results are sensitive to the indicator used to measure intra-industry competition. When we retain the concentration ratio, some authors might assert a stronger positive impact of concentration (i.e. less competition) on product innovations than on process innovations (see[START_REF] Cabagnols | The determinants of the type of innovative behaviour at the firm level[END_REF].