Influence of personal attributes and demographic diversity on Nzoia basin negotiation outcomes

: The Kenyan government has made signiﬁcant advances in water resources management at the local authority (county) level with little or no cooperation at the drainage basin level. Research on critical determinants of cooperation amongst transboundary water negotiation teams is limited. In this paper, we assess whether personal attribute diversity (PAD) is a stronger factor than demographic diversity (gender, age, and education play) in determining whether the negotiation team will cooperate or make unilateral actions. We use a negotiation game to study decisions taken by water policymakers. After that, we conduct a multiple discriminant analysis (MDA) to assess the inﬂuence of PAD, gender, age, and education on water negotiation outcomes. The ﬁndings indicate that PAD plays a signiﬁcant role in determining whether the group will cooperate or compete. Gender, education, and age barely inﬂuence the outcome. Only upon removal of the PAD variable do we see an increase in the discriminant power of gender and education. Age has minimal inﬂuence on the negotiation outcomes. We apply the research at a lower level of governance (Nzoia River Basin). However, results might be extrapolated to a bigger basin, like the Nile Basin, through future multiple level analysis which takes account of the complex socio-technical systems.

Introduction
Water scarcity is one of the major challenges facing the world today [1][2][3][4][5][6]. UN-Water defines water scarcity as either physical water shortage or inaccessibility caused by failed water supply systems or inadequate water infrastructures [3]. The problem with this defnition is it combines water deficit and water scarcity in one defintion, which can be misleading or inaccurate in circumstances where there is no water deficit but only water scarcity. Jaeger et al. [7] explain that water scarcity should be distinguished from water deficit. Water scarcity is normative and anthropocentric in nature because it varies based on temporal, spatial and social values (direct and indirect ) regarding multiple water uses. We adopt the Kampas and Rozakis [8] (p. 1258) definition of water scarcity as "the opportunity costs of forgone human options that result from a specific water use decision". Jaeger et al. [7] explain that water deficit is purely descriptive. Kampas and Rozakis [8] (p. 1258) define water deficit as "the case where the water is not enough for a specific biophysical process".
United Nations (UN) Food and Agriculture Organization (FAO) statistics indicate that approximately one-fifth of the global population is living in a water scarce area and one quarter suffer as a result of economic water scarcity [2]. Economic water scarcity occurs when there is sufficient water (no water deficit) but no infrastructure or strong governance system to increase access. values (including attitudes) have barely been researched. Kakabadse et al. [45] (p. 23) affirm the importance of personal attributes, specifically cognitive abilities, values, background and experiences, in influencing decisions.
Previous research has mainly focused on demographic diversity (DD), with limited studies on underlying personal attributes diversity (PAD) [46]. Jackson [46] (p.805) identifies the most studied attributes in diversity research. Sex (DD) was the highest studied, followed by age (DD), racio-ethnicity (DD), education level (DD), functional background (PAD), tenure in organisation (DD), tenure in job/team (DD), cognition/mental models (PAD), personality (PAD), education content (DD), cultural values (PAD) and finally nationality (DD). Most of the studies focused on performance outcomes with a small percentage focusing on process and affective outcomes.
Haidt [42] argues that different forms of diversity lead to different effects and a general study on diversity that does not disaggregate it into its attributes may not produce useful policy insights. Kakabadse [45] concludes that most of the available research is at the conceptual level, with very limited empirical research on the value addition of diversity. Figure 1 contains literature review on DD and PAD studies and their respective added value including the elements that contributed to value-creation [45]. Most of the studies are at the conceptual level with empirical research limited to studies on: values, cognitive, nationality and age diversity. Previous research has mainly focused on demographic diversity (DD), with limited studies on underlying personal attributes diversity (PAD) [46]. Jackson [46] (p.805) identifies the most studied attributes in diversity research. Sex (DD) was the highest studied, followed by age (DD), racioethnicity (DD), education level (DD), functional background (PAD), tenure in organisation (DD), tenure in job/team (DD), cognition/mental models (PAD), personality (PAD), education content (DD), cultural values (PAD) and finally nationality (DD). Most of the studies focused on performance outcomes with a small percentage focusing on process and affective outcomes.
Haidt [42] argues that different forms of diversity lead to different effects and a general study on diversity that does not disaggregate it into its attributes may not produce useful policy insights. Kakabadse [45] concludes that most of the available research is at the conceptual level, with very limited empirical research on the value addition of diversity. Figure 1 contains literature review on DD and PAD studies and their respective added value including the elements that contributed to value-creation [45]. Most of the studies are at the conceptual level with empirical research limited to studies on: values, cognitive, nationality and age diversity.  According to Figure 1, gender diversity is the most studied with mixed results on its added value and the contributing elements to value-creation [45,[47][48][49][50][51][52][53][54][55]. From the literature, age, was the least studied, with consistent results. Age contributed to improved decision making by balancing personal drive with caution [55,56]. Moreover, older persons were considered an asset because they possessed a broader network that was essential in the decision-making processes [56]. Ethnicity is critical when: social capital needs to be built; new life experiences or unique social connections are missing in the current network; or influential social personalities are needed [36,45,50,57,58]. Nguyen (2015) indicates that increased cognitive ability leads to improved review and probing [55].
Functional diversity led to improved decisions and services due to one major element: increased team cognitive ability [45,55,56,59,60]. The cognitive results were the most consistent. Cognitive diverse teams made better decisions due to increased team thinking ability [45,[60][61][62]. A value diverse group improves the decisions made due to: increased innovation when working in mixed teams [45], introduction to unique networks [42], increased cognitive ability [63] and the unique life experiences of each team member [64].
Demographic diversity is costly, especially in the short-term [36]. Based on Twigg and Taylor [65], diversity led to reduced levels of trust and social cohesion. The study mainly looked at demographic diversity (age, ethnicity, income level, education level, occupation, tenure and place of residence) [65], (p. 1429). De Oliveira and Nisbett [66] conclude that demographic diversity does not lead to wiser decisions. Putnam [36] (pp. 149-150,165) adds that demographic, specifically ethnic, diversity triggers anomie and social isolation. He adds, that in the short-term ethnic diversity is negatively correlated with social capital and engagement in cooperative actions. According to Putnam [36] (p. 149), increased demographic diversity has demonstrated:

1.
Declined confidence in one's ability to influence the situation and bring about change; 2.
Lesser frequency to participate in democratic processes, with increased interest and political knowledge that is essential for protests; 3.
A lower expectation of cooperation by the opposing team to resolve the collective dilemma; and 4.
Declining interest to participate in joint actions.
There is barely any research on the discriminant power of DD and PAD in a given group [45]. We conducted this research to address the identified research gap with the aim of reducing the water management costs that arise from heterogeneity, by predicting the possible future group configuration and its strongest diversity attribute. In addition, there is no study that assesses what happens when the heterogeneous groups transform into a demographic homogenous (DH) or a personal attribute homogenous (PAH) group. Do other subdued diversity attributes take prominence and change the group dynamics? The outcomes may guide water resources management experts to emphasize on the most powerful diversity attribute, so as to enhance its benefits, reduce the costs and focus less on attributes that have limited or no impact on the final outcome. Based on the research outcomes, further diversity studies in water resources management can be conducted on how to introduce and strengthen SSI using the most powerful diversity attribute, so that the heterogeneous group enjoys the benefits of diversity and homogeneity (SSI).
Therefore, we hypothesize that PAD is a stronger predictor than DD (age, education level, and gender) of water negotiation outcomes (whether a water negotiation groups will develop SSI surpassing in and out-group differences and cooperate or they will act unilaterally), and will diminish in PAH groups leading to a change in group dynamics as DD takes prominence. With the use of a negotiation game known as Nzoia WeShareIt, we examine seven teams of negotiators from four county governments (Bungoma, Trans Nzoia, Kakamega and Busia) in the western part of Kenya. By analyzing the in-game and post-game data, we assess which, if any, of these four variables, are useful in predicting whether the composition of certain negotiation teams will cooperate or act unilaterally, leading to unhealthy competition for scarce water resources.
We conducted a discriminant analysis (DA) as a grouping and predictive technique, with the pre-game and post-game questionnaire data. First, we maintained the status quo, the players negotiate within an environment where the four variables are under consideration. Afterward, we conducted a follow-up analysis where we excluded PAD, which is the variable with the highest discriminant power. We then assessed the power of the other three variables in predicting whether the negotiation groups will cooperate or unilaterally act. The two instances are compared to assess the discriminating power of PAD and DD (gender, age, and education level).
The remainder of the paper is organized as follows. First, we provide a detailed description of the Nzoia river basin case study, the water negotiation game (Nzoia WeShareIt), the procedure we employed to play the game and collect the research data and how we analyzed the data. The third section discusses the results of the in-game, and post-game questionnaire data. The final section discusses and makes concluding remarks.

Materials and Methods
In this section, we will explain the case study, the design and application of the Nzoia WeShareIt negotiation game and a description of the four-pronged data analysis procedure. In step one we selected the methodology. In step two we conducted a crosstabs procedure. Crosstabs procedure is used to create cross tables or contingency tables to disaggregate the data according to gender, age and education. These tables are useful for examining the relationship between two categorical variables. The crosstab contains the number of cases for all the probable combinations of the three variables. The third step in data analysis was assumption testing for the multiple discriminant analysis (MDA) procedure. In the third step, we conducted the MDA. We used the MDA to predict the values of a grouping variable, based on the independent variables values. After that, we conducted a follow-up MDA analysis where we excluded the variable with the strongest discriminant power.

The Nzoia WeShareIt Water Policy Game
The Nzoia WeShareIt game was inspired by the original Nzoia WeShareIt game, which had been designed for the Nile river basin where different upstream and downstream countries had to negotiate to earn 'happy faces' [16][17][18][19]21,67]. Later, we customized the game for the smaller Nzoia basin within the larger Nile river ecosystem [17,20,67]. Water policy gaming is selected as a quasi-experiment to understand the contribution of PAD and DD to water negotiation outcomes. Policy games are defined by Mayer [68] (p. 535) as: "Reality is simulated through the interaction of role players using non-formal symbols as well as formal, computerized sub-models where necessary. The technique allows a group of participants to engage in collective action in a safe environment to create and analyze the futures they want to explore. It enables the players to pre-test strategic initiatives in a realistic environment".
Water policy games have been used due to a number of benefits. A policy game may lead to the building of trust, relationships, and coalitions, through repeated constructive social interactions [69] (pp. 5-7). Meaningful social interactions [69][70][71], challenge players existing mental blocks that hinder cooperation [14]. In the course of the interactions, players clarify existing values and judgments [72] through "step-wise, round-based interactions [69] (p. 6)" that enable the players to frame and reframe issues [73]. The players can also jointly co-create knowledge [69] (p. 6), by communicating deep-seated tacit information that they would never articulate in a formal setting [69] (p. 5). A game setting can reveal what diplomacy seeks to conceal [74] thus drawing the participants closer to the core problem. Once the tacit and explicit knowledge is clarified and reframed, a shared understanding of the issues may develop.
Games have been proven to influence real world outcomes through knowledge diffusion. When players continuously communicate their shared understanding, it may lead to diffusion of knowledge [69] (p. 6). During the debriefing session, the players discuss the lessons learned and how they plan to apply them it in real life settings, leading to knowledge integration [69] (p. 7).
Knowledge integration also occurs when games simulate a real-life policy challenge and provide the players with the opportunity to test viable options before applying them to real life circumstances [75] (pp. 825-826). In a game, the risks are low because there are no real-life consequences to the decisions made. As a consequence, games provide policymakers with a safe environment to test various policy options and find the set of options that may work to their advantage [68,[75][76][77].
Conversely, the design of a policy game requires a comprehensive approach to ensure that knowledge learnt and created during a game session, is transferred into real life. To address this sustainability challenge, Medema [69] proposes that policy games provide actors with precise mechanisms that can be incorporated into ongoing processes. We designed the Nzoia WeShareIt game based on an ongoing policy reform of the overall water governance systems in Kenya [78][79][80]. The policy game would provide an opportunity for the policymakers to test prospective policies, which have already been proposed or may be proposed and identify the most viable provisions on diversity in the water sector. Some research findings recommend embedding a game in an already ongoing process whose outcomes are critical for the players, to facilitate knowledge diffusion, transfer, and integration [69][70][71]. The ongoing processes that we embedded the game in are the current legislative reviews aimed at developing new laws and regulations that are aligned to the 2016 Water Act [81]. Additionally, research findings recommend that knowledge integration [69] (p. 7) should be one of the goals of a game designed for social learning [69,82]. Therefore, we designing the game we incorporated three game mechanics for cooperation to ensure that there is knowledge diffusion. First, the players have a shared goal of jointly managing the Nzoia river basin. Second, players had their own individual goals of making their county government residents happy. Third, the game was designed to ensure that the competition between the shared goal and the individual goals is healthy and leads to the development of complementary roles and niches within the basin [16][17][18][19][20][21]41,67].
The Nzoia WeShareIt game was designed for application in the Nzoia river basin, in Western Kenya. This river basin comprises of a surface area of 12,900 km 2 and is home to approximately 3.5 million persons. The primary economic activity is agriculture (mainly rain-fed), and the basin produces 30% of Kenyan maize and sugar [23,[83][84][85] [85]. Currently, most of the residents rely on wood fuel as their primary source of energy. Also, most county governments do not have enough resources to abstract the water for food and/or energy production. However, with increased economic growth and a rapidly growing population, the competition for the shared resource is increasing [23,[83][84][85].

Overview and Design
The Nzoia WeShareIt game's essential characteristics are the counties in the Nzoia basin and it allows participants to negotiate over resources and products, thus generating an artificial water-food-energy nexus. Participants in the Nzoia WeShareIt game were 35 water-policymakers from the Nzoia river basin in Kenya, all of them dealing with water policy and water scarcity challenges in their daily work. In each game session, there were five players representing the five distinct counties in the game. The policy makers' assignment was to fulfil their constituents needs with respect to food, energy and investment in public services. By fulfilling these needs, they could earn 'happy faces', representing their happy citizens. Each player's resources (food, energy, water and income), generation capacity and required turnover to generate happy faces were different according to the characteristics assigned to their specific county.
In total there were seven game sessions. We played two game sessions in Busia, Bungoma and Trans Nzoia county governments, and one game session in Kakamega county government [86]. In total, there were 12 females and 23 males. Most of the participants had a bachelor's degree (20). The age range of most of the participants was 25 to 34 (11), 35 to 44 (7) and 45 to 55 (10) [67].
At the start of the Nzoia game, the players were given a limited volume of water with pre-determined food, energy and nature parcels based on the amount of water. Thereafter, players can sell or purchase food and hydro-electric energy for 500 euros per food or energy parcel, to address their shortages or earn money to supply a certain amount of basic needs to their citizens and thus generate 'happy faces'. However, in successive rounds, they have to negotiate the price, so scarcity may lead to higher prices; abundance to lower prices. Also, the players need to identify which county governments have a comparative advantage regarding food and/or hydro-electric power production and negotiate food and energy production quantities and prices. For instance, Busia could negotiate for a constant supply of food and energy from two different neighboring county governments based on an agreed price. Thus, the other two county governments would produce excess food and energy and Busia would ensure that they have the agreed finance to purchase the excess production. The money earned from Busia would allow the other districts to invest in public services to generate their 'happy faces'. The game mechanism guarantees that cooperation leads to better outcomes especially when scarcity of resources occurs (in a drought period); going alone in such circumstances leads to critical shortcomings and unhappy faces.

Procedure
Five participants entered into a hotel meeting room, pre-arranged with five board games and five iPads and all the supporting gadgets and information ( Figure 2). As they waited for the other participants, they first filled in an online pre-game questionnaire. On their desk there was also an information sheet on how to play the game. Each player was guided by a game facilitator, on the general tasks and the game instructions. At the conclusion of each session, the participants rated the Dyad negotiations during that particular trading round, in the form of an in-game questionnaire. We visualized the results in a leaderboard projected on the screen. In each round, Dyads or Triads were given 30 min to negotiate during the trading round. The results of the negotiations are recorded in the iPad and projected in real-time on the whiteboard screen [67]. their shortages or earn money to supply a certain amount of basic needs to their citizens and thus generate 'happy faces'. However, in successive rounds, they have to negotiate the price, so scarcity may lead to higher prices; abundance to lower prices. Also, the players need to identify which county governments have a comparative advantage regarding food and/or hydro-electric power production and negotiate food and energy production quantities and prices. For instance, Busia could negotiate for a constant supply of food and energy from two different neighboring county governments based on an agreed price. Thus, the other two county governments would produce excess food and energy and Busia would ensure that they have the agreed finance to purchase the excess production. The money earned from Busia would allow the other districts to invest in public services to generate their 'happy faces'. The game mechanism guarantees that cooperation leads to better outcomes especially when scarcity of resources occurs (in a drought period); going alone in such circumstances leads to critical shortcomings and unhappy faces.

Procedure
Five participants entered into a hotel meeting room, pre-arranged with five board games and five iPads and all the supporting gadgets and information ( Figure 2). As they waited for the other participants, they first filled in an online pre-game questionnaire. On their desk there was also an information sheet on how to play the game. Each player was guided by a game facilitator, on the general tasks and the game instructions. At the conclusion of each session, the participants rated the Dyad negotiations during that particular trading round, in the form of an in-game questionnaire. We visualized the results in a leaderboard projected on the screen. In each round, Dyads or Triads were given 30 min to negotiate during the trading round. The results of the negotiations are recorded in the iPad and projected in real-time on the whiteboard screen [67]. One key observation from this game session was the participants barely sat at their tables, they moved around to find solutions to their common challenges and did not make decisions before discussing with other members of the water negotiation team.
Following the game session, participants completed a post-game online questionnaire. They reported their learning outcomes from the negotiation on a 5-point Likert scale, with endpoints of 1 (Very Inaccurate) and 5 (Very Accurate). The learning outcomes were phrased in statements like: Figure 2. Nzoia WeShareIt game session 6 in Trans-Nzoia county government. One key observation from this game session was the participants barely sat at their tables, they moved around to find solutions to their common challenges and did not make decisions before discussing with other members of the water negotiation team.
Following the game session, participants completed a post-game online questionnaire. They reported their learning outcomes from the negotiation on a 5-point Likert scale, with endpoints of 1 (Very Inaccurate) and 5 (Very Accurate). The learning outcomes were phrased in statements like: through the game 'I became more aware of the need for joint action.' More information on the game design, application and assessment is available as supplementary information S1.

Step 1: Selection of Methodology
To conduct our analysis, we needed a statistical procedure that is both a grouping method and a predictive technique. Discriminant analysis (DA) is used to predict the values of a grouping variable (which is the dependent variable) based on the values of the independent variables. The grouping variable is categorical (usually dichotomous, but it can be also multinomial) and the independent variables should be continuous (however, ordinal variables are also used).
Amongst the grouping techniques, we had to select between cluster analysis (CA) and MDA. CA groups the population members in homogeneous classes or clusters (or groups). The researcher does not know the groups in advance; they will be created during the clustering procedure. The cluster analysis technique computes the distances between cases and then builds a similarity or proximity matrix based on those distances. The cases that are close to one another are included in the same cluster.
We already knew the groups in advance and did not need to undertake cluster analysis. We needed a grouping methodology for predictive purposes. We seek to assess whether PAD is a stronger predictor than DD (age, education level, and gender) of water negotiation outcomes (whether a water negotiation groups will develop SSI surpassing in and out-group differences and cooperate or they will act unilaterally), and will diminish in PAH groups leading to a change in group dynamics as DD takes prominence. Therefore, MDA was found to be the most relevant procedure, based on the nature of the data and the purpose of the analysis. The game data for conducting MDA is available as supplementary information S2.

Step 2: Creating the Crosstabs
We used SPSS to create crosstab contingency tables, which are useful for examining the relationship between two categorical variables. The crosstab tables are in Appendix A. We extracted the grouping variables and the three independent variables from the pre and postgame questionnaires. The grouping variables are the 7 game sessions; and (2) four independent variables, namely age range, gender (male, female), education level and the respondent ID (35 respondents). Table A1 (Appendix A) are the results from conducting a cross-tabulation of Gender * Education. The results indicate that the highest number of player's highest attained education was bachelors (8 females and 12 male). The lowest education completed was primary education, by one female. The highest education completed was a Master's degree. Only one woman and two men had completed a Master's degree. There were six male and one female whose highest level of education was a college diploma. Table A2 (Appendix A) are the results from conducting a cross-tabulation of Game * Age. The results indicate that the highest number of players were in the 25 to 34 age brackets (#11) and the lowest was the 55 to 64 age brackets (#3). Trans-Nzoia county government policymakers were mainly young persons (25 to 44), and Bungoma county government mainly comprised of elderly persons (45 to 64). Only one game session (Busia_2) had an even distribution of players in all the age sets: 1 player in each age bracket (see also Tables A3 and A4).
Table A5 (Appendix A) are the results from conducting a cross-tabulation of Game * Gender. The results indicate that three groups purely comprised of male players. The other three game sessions had an acceptable gender balance (3 × 2). One group had four females and one male. No group had no male represented. We performed a chi-square test and tabulated the results in Table A6 (see also Table A7).   Table A8 is not disaggregated by gender. All the game sessions had players with a bachelor's degree. The players with the highest education were from Trans Nzoia county government. The player with the lowest education was from Busia county government. Trans Nzoia is the upstream county government, and Busia is the downstream county government (see also Tables A9 and A10).

Step 3: Assumption Testing
Before conducting the MDA procedure, we first checked some assumptions that are required to be tested before running an MDA procedure, to ensure that the model is robust [87]. The three assumptions that were tested and met are: (1) the dependent variable is categorical, with disjoint groups; (2) the independent variables are continuous (or at least ordinal); and (3) there is no critical multicollinearity (the independent variables are not strongly correlated with each other). The detailed results of the assumptions testing are in Appendix B.
The last assumption we tested was that the data do not present significant outliers. The outliers may represent a danger for our proposed analysis because they affect the mean and standard deviation. We conducted a graphical test, using boxplot chart histograms to identify outliers ( Figures A1 and A2 in Appendix B). Based on the graphical representation of all the assessment results of the 20 dependent variables distributed according to gender ( Figure A1) and the seven game sessions ( Figure A2), we identified multiple outliers. Some outliers appear to be extreme because of their distance from the mean. We proceeded to conduct the MDA, after testing the robustness of the model to determine whether the outliers affected the model. The model was found to be robust enough not to be affected by the significant outliers, as described in Section 2.3.2 and Appendix B.

Step 4: Conducting a Multiple Discriminant Analysis (MDA) Procedure
To conduct the MDA procedure, we used the SPSS discriminant analysis function. For the MDA classification, we treated all groups as equal and used the within-groups covariance matrix. The MDA functions assigned each case to one group by computing the probability of belonging to one group based on the scores of the independent variables (IDVs). From the MDA we were able to differentiate the groups of the response variables. The MDA procedure aims to use the results to identify what differentiates various negotiators (female, male, young, elderly, and their education level). The MDA procedure creates the following discriminant function: where the dependent variable is y, the x's are the explainers and the b, s are the coefficients.
In MDA, the number of discriminant functions created by the algorithm is min (c − 1, k), where c represents the number of categories in the dependent variable, and k is the number of independent variables. Therefore, we created the discriminant functions by the following algorithm: min (7 − 1, 4).

In-Game Data Results
There were two datasets of the in-game data that we found relevant for this particular analysis. The first dataset is the changes in the resources (Food, Energy in the form of Hydroelectric Power and Money) in the seven game sessions for the five county governments. The second dataset is the grand total of smileys earned in the seven game sessions for the five county governments. We analyzed the dataset using the one vital element of cooperation, SSI [88,89]. Figure 3 visualizes the changes in the food, energy and money resources, based on the negotiations between the five policymakers in each of the seven game sessions. The in-game data is available as supplementary information S2.
better results in game 6, followed by 4 and finally 5. Busia and Bungoma also pursue SSI with the highest changes in the money resource in the game session 6, followed by game session 4 and then 5. Therefore, from the results, game session 6 negotiation outcomes lead to cooperation, evidenced by the five county governments pursuing SSI in an environment where there are goal asymmetries.    Based on the results, there was more evidence of SSI in the game sessions 4 and 6 where the critical producers of food (Trans Nzoia and Kakamega), increased their production to be able to sell the surplus to the other local authorities. In the game session 4, Trans Nzoia also increases food production, thus pursuing SSI based on its comparative advantage, but Kakamega does not increase its production in the game session 4. Uasin Gishu county government pursues SSI in 4, 5 and 6, with better results in game 6, followed by 4 and finally 5. Busia and Bungoma also pursue SSI with the highest changes in the money resource in the game session 6, followed by game session 4 and then 5. Therefore, from the results, game session 6 negotiation outcomes lead to cooperation, evidenced by the five county governments pursuing SSI in an environment where there are goal asymmetries. Figure 4 visualizes the total of smileys earned based on the negotiations between the five policymakers in each of the seven game sessions. Participants earned smileys when the county government met its residents' food, energy, and investments in public service needs. Based on the results, there is more evidence of SSI in the game sessions 4 to 6 because inequities reduce. Game session 7 has the least negative results whereas game session 2 has the highest negative results. better results in game 6, followed by 4 and finally 5. Busia and Bungoma also pursue SSI with the highest changes in the money resource in the game session 6, followed by game session 4 and then 5. Therefore, from the results, game session 6 negotiation outcomes lead to cooperation, evidenced by the five county governments pursuing SSI in an environment where there are goal asymmetries.  Figure 4 visualizes the total of smileys earned based on the negotiations between the five policymakers in each of the seven game sessions. Participants earned smileys when the county government met its residents' food, energy, and investments in public service needs. Based on the results, there is more evidence of SSI in the game sessions 4 to 6 because inequities reduce. Game session 7 has the least negative results whereas game session 2 has the highest negative results.  Based on the results in Figures 3 and 4, we grouped the seven teams as follows: 1.
Group 1: Unilateral actions (Game session 1, 2 and 3 comprising of 15 policy makers in 3 water negotiation teams).
Based on the varied outcomes (cooperation and unilateral actions), it was not clear what diversity attribute was the strongest predictor of the water negotiation outcomes. To gain more insights, we proceeded to conduct an MDA procedure, as explained in the subsequent parts of this section.

Strength of the MDA Procedure
The program computed four discriminant functions based on four independent variables, education, gender, age, and respondent ID. Every function derives a meaning depending on the variables that it is correlated. Also, with the help of the discriminant function, the program computed the probability whether k belongs to one category or another and assign each k to a category. Table A11 in Appendix C represents the Eigenvalues of each function. These values show the importance of each function. The first function has an Eigenvalue was equal to 56.94 and explains 97.1% of the total modal variance. By contrast, the second function is less critical, its Eigenvalue is only 1.38, and explains only 2.3% of the total modal variance. The third function has an Eigenvalue of 0.28 and explains 0.5% of the total modal variance. The final function has an Eigenvalue of 0.03 and does not explain any percentage of the modal variance. In the last column of Table 1 are canonical correlation coefficients. These squares show the percentage of the variation in the response variables, which is an explanation of the variation by each function. The canonical correlation associated with the first eigenvalue was equal to 0.99, followed by 0.76. The first function explains the highest variation whereas function 4 explains the lowest.  (Table A11 in Appendix C) indicates how strong is the discriminating power of the independent variables or how well the independent variables explain the dependent variables. The lower the Wilks' Lambda, the stronger the discriminating power of the independent variables. The first line represents the Wilks' Lambda for all the four functions, which was found to be statistically significant (Wilks Λ = 0.006, F (24, 148.248), p = 0.0005). Additionally, the Wilks' Lambda for function 2 through 4 was found to be statistically significant (Wilks Λ = 0.319, F (15, 32.557), p = 0.005). Since the Chi-square test p-value for functions 1 through 4 and 2 through 4 is lower than 0.005, it indicates that the MDA model is robust.
By contrast, the 3 through 4 and function 4, were not found to be statistically significant (Wilks Λ = 0.758, F (8, 7.887), p = 0.445) and (Wilks Λ = 0.972, F (3, 0.810), p = 0.847). Moreover, the p-value in the two tables indicates the distances between the group centroid are significant. Table A12 (Appendix C) contain the functions of group centroids for functions 1 through 4, evaluated at group means. For each function, we have at least seven centroids. Since the p values are lower than 5%, then the distances between the centroid are large enough to be considered significant for functions 1 through 4. Therefore, there are good chances that the discriminant functions classify the cases correctly. Table 2 is the test of equality of group means. It presents the Wilks' Lambda values for each of the independent variables. Respondent ID has the lowest Wilks' Lambda, thus the highest discriminating power, which is statistically significant. The results of the tests of equality of group means of gender and education and respondent ID are statistically significant. The results of age are not statistically significant. Based on the pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions listed in Table A13, the MDA computed the structure matrix for function 1 to 4. Based on the discriminant functions, the SPSS program computed, for each case, the probabilities of belonging to each of the seven original groups.

Test of Equality of Group Means
The table of the classification of the results (Table A14 in Appendix C), indicates that all the classification results for the groups: Busia_1, Busia_2, Kakamega and Trans Nzoia_2 were correctly classified. However, Bungoma_1, Bungoma_2, and Trans Nzoia_1 were incorrectly classified. Trans Nzoia_1 is the only group where the females largely outnumbered the males (4 females, 1 male). Bungoma_1 and Bungoma_2, comprised of purely male teams. Therefore, the incorrectly classified groups were the extremes. Overall, an outstanding percentage of 94.3% were correctly classified, proving the power of the discriminant function in our model. Table 3 on the structure matrix, describes the correlation between each discriminant function and the independent variables. The first function is positively correlated with respondent ID, education and age and negatively correlated with gender. We could assume that this function is associated with a negotiation team selected based on PAD, education, and age, with little consideration of education and no consideration of gender. In function 1, the highest (0.937) positive discriminating factor is PAD and the only negative discriminating factor is gender (−0.023). The second function is positively correlated with ID, age and gender. Function 2 is negatively correlated with education. We could assume that this function is associated with a negotiation team selected based on PAD, age, and gender with little consideration of education. In function 2, the highest (0.668) positive discriminating factor is gender, followed by age and then PAD/skills (ID). Function 2, as explained earlier, explains only 2.3% of the total modal variance. Function 3 and 4, will not be considered, because they are statistically insignificant, based on the previous analyses.

MDA Procedure 1: Four Function Structure Matrix
When gender, age and education are combined with respondent ID, the discriminating power of PAD (respondent ID) shows overwhelmingly that the effect of gender, age and education is negligible, as evident in function one results of the structure matrix ( Table 3). The first canonical discriminant function is crucial because it explains 97.1% of the variance (Table A11). Additionally, the Wilks' Lambda for a test of functions 1 through 4 is only 0.006, and its p-value is 0.0005, which confirms the immense power of the function 1 (Table A11). Function one is heavily controlled by PAD. The influencing power of education, age, and gender is negligible. Due to the high discriminating power of PAD, in the next MDA model, we removed the respondent ID from the structure matrix, in favor of looking for generic gender effects. Thus, when we left the model with only three functions, the structure matrix changed (see Table 4). Gender and education gained more prominence. The first function is positively correlated with education and negatively correlated with gender and age. Function 1 was found to be statistically significant (Wilks Λ = 0.252, F (18, 39.969), p = 0.002). Function 2 is positively correlated to all the three independent variables, which was found not to be statistically significant (Wilks Λ = 0.685, F (10, 10.967), p = 0.36). Function 3 is positively correlated to gender and age and negatively correlated to education, which was found not to be statistically significant (Wilks Λ = 0.910, F (4, 2.728), p = 0.604). While the new model is not as strong as the previous model (42.9% of original grouped cases correctly classified, (see Table A15 in Appendix C), it provided insights into the probable structural composition of the functions when we do not consider PAD. Gender and education have a high discriminating power while the predicting power of age is negligible.

Discussion and Conclusions
The results confirm the hypothesis that PAD is a better predictor than DD (age, education level, and gender) of negotiation outcomes (whether a water negotiation groups will develop SSI surpassing in and out-group differences and cooperate or they will act unilaterally). The MDA procedure indicate that the strongest predictor of water negotiation outcomes in the Nzoia WeShareIt game is PAD. When assessing the impact on negotiation outcomes in the MDA, a four-structure matrix PAD is the dominant predictor with little or no influence from gender, age and education.
The results also support previous studies indicating that PAD is a stronger predictor of improved decision-making than DD. Based on the reviewed literature, we attribute PAD being the strongest predictor of the water negotiation outcomes to four main factors: increased cognitive ability [45,[60][61][62][63], innovative decision-making [45], unique networks [42], and unique life experiences [64].
Moreover, when we eliminated PAD from the model, gender and education gained more prominence and competed almost equally. Thus, when PAD is negligible, for instance a water negotiation team comprising of only lawyers (functional), with similar knowledge, cognitive skills, capabilities and values, then gender and education diversity will take prominence. Since age and gender are negatively correlated, they jointly have a stronger discriminating power than education.
The discriminating power of gender is the highest because its correlation coefficient is the highest, followed very closely by education. However, when gender and age are combined, they possess more discriminating power than education.
These second result clearly presents a challenge and possibly an opportunity. Based on previous research, it is important to focus more on PAD rather than DD [42]. As Putnam state DD "at least in the short run, seems to bring out the turtle in all of us" [36] (p. 151). However, the results in this paper indicate that when PAH negotiation team is constituted, there is a high likelihood that decisions will be influenced not by PAD (because it no longer exists) but by gender and education level. Moreover, as earlier discussed, the literature on DD is not conclusive. It provides numerous outcomes that could not be easily substantiated since most of the studies on DD were conceptual in nature with no empirical backing. Moreover, DD research indicate that in the short term, DD does not build trust [65], social cohesion [36,42,65], nor does it lead automatically to improved decision-making [66]. Research also indicates that DD is useful for monitoring, evaluation, service delivery, broadening the network, tapping into unique networks and fostering equity [45,54].
Therefore, if the aim of the basin management institution is to improve decision making and enhance cooperation, PAH may be a threat to this aim, thereby presenting a challenge. Nevertheless, in such instances, there are two policy options that water policymakers may decide upon, which may open more opportunities for integrated river basin management. First, diversify their PAH negotiation teams by introducing new members who possess divergent values, skills, capabilities, functional, cognitive abilities and knowledge. Second, introduce SSI to develop bonding and bridging social capital that enables the demographic heterogeneous PAH group to surpass their differences, perceive themselves as one team, and be willing to cooperate.
This empirical study, conducted in Kenya, supports the argument that diversity discussions should move away from whether diversity is good or bad towards understanding how the different diversity attributes contribute to cooperative decision-making, their respective elements and their unique value addition. If the policy aim is improved decision-making in water management, then more focus should be on PAD than on DD. However, if the aim is improved supervision, monitoring, evaluation and service delivery, then DD should be the focus when deciding on group composition. In addition, as argued above, there are different types of DD and PAD, each with different influencing power on specific circumstances and outcomes. However, it should be noted that the conclusion regarding the functions of PAD and DD is inferred from the literature review conducted at the beginning of this paper and not directly assessed in this particular study. Therefore, to get more clarity there is a need for further investigations on the contribution of different diversity attributes to negotiation outcomes in a given river basin.
The research approach faced a few limitations. First, the broad assertion that water resource negotiation teams can be ideally formed with attention to the diversity characteristics presented in the paper may be considered is an assumption. Sometimes, the formation of the team is based on the national laws and regulations or other considerations. Moreover, we acknowledge the limitation that the research is based on outcomes of a simulation game and not an actual negotiation. This is one of the major challenges faced by transition management experiments, whether gaming simulation or otherwise. We acknowledge the limitations of the study and propose caution and further analyses in the form of an actual pilot programme before considering scaling up. Nevertheless, gaming simulation has an effective role to play in research and knowledge diffusion. Gaming simulation can be attributed to real life situations through use of a game as a research tool and ensuring a diffusion of knowledge on real world situations. The Nzoia WeShareIt game was entrenched to the ongoing water policy reforms in Kenya to test the efficacy of proposed diversity related water policies in a game environment before actual application in a real-life setting. Another limitation is that the Nzoia WeShareIt game constructed solely intra-county basin management, whereas most of the water negotiations are conducted at the international basin level. The Nzoia WeShareIt game is inspired by the Nile WeShareIt game, which we developed for the Nile basin. The Nzoia river basin is a sub-basin of the Lake Victoria basin and the Nile basin. Considering the complexity and challenges in this sub-basin; and it being the largest tributary to the Lake Victoria basin in Kenya and second largest at the international level after Kagera basin, and also considering the upstream-downstream tensions within this sub-basin, it seems justifiable to use the term basin. We also note that the conclusions and the use of the games as a learning tool for negotiators would have been made much stronger with a meta-game session involving several or all of the county teams. We hope to implement this in future studies to ensure that the results are validated at the basin level by all the county governments.
Clearly there is need for further studies to understand these complexities and provide more guidance to decision-makers on what mix of diversity attributes to focus on, so as to get the intended outcome. Our study concentrated on water negotiations in the Nzoia river basin in Kenya. Our findings, therefore, are valid only for this particular situation, but the implications might be wider. The findings put to discussion the idea that DD rather than PAD characteristics are the best predictor of successful negotiations. Evidently this is not always the case and we are not the first to make this claim explicit. Therefore, at the river basin level, determining the predictors of negotiation outcomes can guide the water policymakers to focus on strengthening the high predictors to get the best outcome out of a planned water negotiation. This information would support or expedite the process of developing cooperative framework agreements aimed at increasing water access and reducing water scarcity.    Game   Busia_1  1  3  0  1  0  5  Busia_2  1  1  1  1  1  5  Kakamega  1  1  1  2  0  5  Bungoma_1  1  0  1  2  1  5  Bungoma_2  0  1  1  2  1  5  TransNzoia_1  0  3  2  0  0  5  TransNzoia_7  0  2  1  2  0  5   Total  4  11  7  10 3 35   Game   Busia_1  0  0  1  4  0  5  Busia_2  1  2  1  1  0  5  Kakamega  0  1  1  3  0  5  Bungoma_1  0  1  2  2  0  5  Bungoma_2  0  0  1  4  0  5  TransNzoia_1  0  0  0  3  2  5  TransNzoia_2  0  0  1  3  1  5   Total  1  4  7  20  3  35  Table A9. Chi-square tests: Game * Education.

Appendix B : MDA Assumption Testing
We ran some Pearson correlations between the twenty dependent variables to test moderate range correlations. The results indicated strong correlations between the dependent variables. Additionally, the Box's M test was assessed. The Box's M value of 40.776 associated with a p-value of 0.478, is non-significant (i.e., p < 0.05). Thus, we assumed equal covariance matrices between the groups, for purposes of conducting the MDA procedure.
These outliers are not due to data entry errors or data collection errors. Data entry was electronic through the SurveyMonkey website. Therefore, the chances of data entry errors due to lack of attention, negligence, and tiredness were eliminated. Data collection was also done electronically through the participants filing the online questionnaires. Thus, eliminating errors due to human mistakes or equipment malfunction. Also, data transfer from SurveyMonkey to SPSS was not manual. Thus, eliminating any form of data transfer errors. Therefore, the outliers we were dealing with are genuine non-typical and unusual values in the population.

Appendix B: MDA Assumption Testing
We ran some Pearson correlations between the twenty dependent variables to test moderate range correlations. The results indicated strong correlations between the dependent variables. Additionally, the Box's M test was assessed. The Box's M value of 40.776 associated with a p-value of 0.478, is non-significant (i.e., p < 0.05). Thus, we assumed equal covariance matrices between the groups, for purposes of conducting the MDA procedure. These outliers are not due to data entry errors or data collection errors. Data entry was electronic through the SurveyMonkey website. Therefore, the chances of data entry errors due to lack of attention, negligence, and tiredness were eliminated. Data collection was also done electronically through the participants filing the online questionnaires. Thus, eliminating errors due to human mistakes or equipment malfunction. Also, data transfer from SurveyMonkey to SPSS was not manual. Thus, eliminating any form of data transfer errors. Therefore, the outliers we were dealing with are We had two solutions to deal with genuine outliers. The first option was to remove the outlier from the data series. The second option was to keep the outliers in the data series. Based on the graphical representation of all the assessment results of the 20 dependent variables distributed according to the seven game sessions, we realized that removing the outliers would remove all the assessment results of respondent ID number 33 and 27. It would also impact on the results of some respondents, for instance, respondent ID number 1, 11 and 23. Therefore, we decided to keep the outliers.   We had two solutions to deal with genuine outliers. The first option was to remove the outlier from the data series. The second option was to keep the outliers in the data series. Based on the graphical representation of all the assessment results of the 20 dependent variables distributed according to the seven game sessions, we realized that removing the outliers would remove all the assessment results of respondent ID number 33 and 27. It would also impact on the results of some respondents, for instance, respondent ID number 1, 11 and 23. Therefore, we decided to keep the outliers.       Busia_1  0  0  1  1  0  3  0  5  Busia_2  0  3  1  1  0  0  0  5  Kakamega  0  0  2  1  2  0  0  5  Bungoma_1  0  0  1  2  2  0  0  5  Bungoma_2  0  0  1  1  3  0  0  5  TransNzoia_1  0  0  0  0  1  4  0  5  TransNzoia_7  0  0  0  0  2  2  1  5   %   Busia_1  0  0  20  20  0  60  0  100  Busia_2  0  60  20  20  0  0  0  100  Kakamega  0  0  40  20  40  0  0  100  Bungoma_1  0  0  20  40  40  0  0  100  Bungoma_2  0  0  20  20  60  0  0  100  TransNzoia_1  0  0  0  0  20  80  0  100  TransNzoia_7  0  0  0  0  40  40 20 100 a 42.9% of original grouped cases correctly classified.


Social Innovation for Sustainability Challenges

: Social innovation is concerned with social mobilization and impact, and is increasingly seen as an option to address sustainability challenges. Nevertheless, the concept of social innovation is quite open in character and requires empirical accommodation to establish how it di ﬀ ers from other types of innovation in this setting. This article contributes empirically to the concept of social innovation as it reviews categories of success factors of social innovation against those of ﬁve other innovation types (product, service, governmental, organizational, system) in 202 innovation cases that focus on climate action, environment, resource e ﬃ ciency and raw materials. Statistical analysis with contingency tables is applied to examine the distribution of ﬁve kinds of success factors across the innovation types: economic, environmental, political, social, and technological. The results conﬁrm empirically that social innovation is indeed a distinct type of innovation. There are statistically signiﬁcant di ﬀ erences in the distribution of categories of success factors between social innovation on the one hand and product, service and governance innovation on the other. In addition to the prevalence of social success factors, social innovation is characterized by a lesser emphasis on political and technological success factors.

Introduction
Social innovation remains an underdeveloped and to some extent also contested concept [1,2]. Major issues concerning its focus, scope, and transformative potential remain unclear, which is particularly troublesome, as there are pressing needs to develop innovations that reconstruct markets towards sustainability [3][4][5]. Climate change, in particular, poses a threat to the excessive lifestyles prevalent in current societies, and it has been acknowledged that while technological innovations are required, they are insufficient to address challenges in complex societal systems [5,6]. Accordingly, social innovations have been sought in order to achieve the transformation of existing structures towards low-carbon societies [7]. At the same time, it is somewhat unclear how social innovation should be understood and even whether or not it should be considered a distinct concept [8][9][10][11].
Expectations on social innovation transcend those typically attributed to other types of innovation. The impacts, which social innovations target, relate to the social economy, bridging societal divides and strengthening new social practices [6,12,13]. Many social innovations have targeted sustainability challenges such as the prevention and impacts of climate change. Pressing questions relate to concerns on how to reorganize cities, transport systems, and housing to reach dramatic reductions in carbon dioxide emissions, which all constitute sustainability challenges that can be addressed in a variety of ways [14]. These all represent examples of expected impacts for social innovation, which other types of innovation may contribute to, but are usually not expected to address by themselves. Similarly, the kind of economic progress sought for in social innovation differs and, for instance, relates more directly to abolishing poverty rather than seeking economic growth towards the same aim [15].
When compared to other types of innovation, social innovation connects to social values, processes, and impacts. In this respect, the concept of social innovation complements and redefines established innovation typologies, which consider products, services, and processes, amongst others [16]. At times, social innovation has been seen as an alternative to or an extension of technological innovation [9], although this definition is arguably too limited to describe its full and varied scope. Indeed, social innovations are gaining more attention as responses to societal challenges [17,18].
It has been recognized that the concept of social innovation should be developed further in particular through systematic analysis of success factors and empirically demonstrative cases [19,20]. Even though the openness of the concept of social innovation need not necessarily hinder the achievement of accompanying practical aims, systematically developed and tested conceptualizations may contribute to insights, which can be transferred across settings. The empirical analysis may further distinguish social innovation from parallel innovation concepts that perform similar social and critical functions in society. For instance, the concept of disruptive innovation challenges established business practices [21], whereas social innovation is seen to aim at broader structural change and transformation [5,19], and it is only the concept of social innovation, which targets social concerns directly.
This article characterizes social innovation in comparison to other types of innovation based on an analysis of their identified success factors and reviews the results against key conceptualizations of social innovation [9,10]. The applied research design reaches beyond the examination of single or small numbers of case studies and is apt to provide new and comparative knowledge on innovation types at a higher abstraction level. Accordingly, success factors from 202 innovation cases collected in a European database on sustainable innovation are examined against innovation typologies. The Casipedia [22] database includes innovations categorized in the domains of climate action, resource efficiency, raw materials, and the environment. The article theoretically contributes to clarifying the concept of social innovation through empirical accommodation [19], and examines how social innovation is distinct from other types of innovation (e.g., product, service, governance, organizational and systemic innovation). For instance, social innovation cases target climate action more in relation to sustainable lifestyles but less by direct climate change mitigation solutions.
The upcoming section discusses how theoretical insights into social innovation benefit from the empirical analysis of success factors for innovation. Then the methodological approach of comparing the different types of innovation is argued for and the utilized sustainable innovation database is described with case examples. Results from a quantitative case analysis are presented next and discussed from the perspective of the conceptualization of social innovation. The concluding section of the study discusses the idea that social innovation indeed represents a distinct type of innovation, which is beneficial in considering both its conceptualization and the management of innovation activities.

The Conceptualization of Social Innovation
The theoretical conceptualization of social innovation is still evolving [1]. Previously, the concept has been examined mainly through frameworks and case studies [8,9]. It is probably the elusive and broad character of the targeted social impacts of the concept, which has prompted such exploratory approaches. Social innovation appears against this background to constitute a boundary concept, which permits interpretive flexibility, yet is sufficiently robust to allow shared interpretations [23,24]. For instance, Pol and Ville [10] follow such a line of thought in their definition of desirable social innovation: instead of providing a definition, which exactly defines what social innovation is, they focus on the actionability of the innovation in question, and consider desirable social innovation to be founded in new ideas and to target a positive impact on the quality and quantity of life. While conceptual flexibility is certainly acceptable for recognizing the phenomenon and using it in scientific discussions, it has its shortcomings, when attempting to apply it as an analytical term across large numbers of innovations.
Mechanisms that contribute to a change in social institutions occur at the individual and group levels. Changes in the institutionalization of societal behaviour then have the potential to achieve systemic transformation [19,25] and, correspondingly, engagement with social objectives and social development constitute key objectives in social innovation [26]. Furthermore, social innovation is comparable with self-organization that is driven by internal dynamics in the markets, often by common concerns [27]. Cajaiba-Santana [9] similarly elaborates that social innovation contributes to new social practices, institutions and systems, and that its ultimate goal is social change while respective actions often take place at the level of collectives or groups. In addition, self-organization is more of a bottom-up approach to solve market challenges than a top-down response to them [1,27].
Accordingly, social innovations are more than simply non-technical enhancements to products [9,28,29]. Indeed, it is the lack of a clear definition of social innovation [2,26] that provides opportunities to carry out explorative and empirical work towards this aim. There is a wide-ranging array of social innovations as Phills et al. [11] has already pointed out: emissions trading, socially responsible investing, fair trade, community-centred planning, habitat conservation plans, charter schools, individual development accounts, international labour standards, microfinance, and supported employment.
Cajaiba-Santana [9] identifies the key roles of agency, institutions and social systems in the conceptual schematics of social innovation processes. This is somewhat in contrast with Pol and Ville [10] who review competing conceptualizations. Furthermore, they suggest that government intervention is needed to encourage the creation of pure (i.e., non-business) social innovations. There is indeed a knowledge gap between the aims attributed to social innovation and the means for achieving them.
The theoretical focus has been on frameworks and qualitative analysis of limited numbers of case studies [9,11], which perhaps highlights the contextual and situational character of social innovation. The difficulties in comparing social innovation with other types of innovation have contributed to scholarly interest in concept and case studies. Conceptual studies then distinguish the particularities of social innovations and case studies bring empirical accommodation to the concept [6,9,19]. While both types of contributions are crucial when characterizing social innovation, the generalizability of their contributions is by no means a straightforward task. Similarly, both approaches might miss out on something that comes forth through larger surveys.
Systematic overviews of the field of social innovations, use of quantitative datasets and long-term analyses are still missing although this shortcoming has been identified [19,30,31]. Nevertheless, a change in this direction is taking place with the emergence of extensive case mapping activities [20,32]. Indeed, both theoretical conceptualization and more practical innovation management would benefit from quantitative comparative empirical studies, which contribute to results that are more readily generalizable. In particular, analysis of the success factors of social innovations would help to bring about a better understanding, development and management of social innovations.
While there are meta-studies on success factors of single innovation types, which examine service and project innovations [33,34], there are new quantitative studies that review success factors across innovation types [35] or distinguish between types of social innovation [20]. This article contributes to the empirical characterisation of social innovation by reviewing a large number of case studies on social innovation with uniformly reported case studies on other types of innovations in an international, sustainable innovation database 'Casipedia' [22]. Sustainability in this context considers environmental, economic and social dimensions. The case studies originate from Europe although many of them have a global outreach. This article contributes to the theoretical conceptualization of social innovation in that it aims to abstractify the situational context of each case by relying on a statistical method and examining empirically observed data [36]. The objective of the current study is thus to analyse how types of innovation correspond to kinds of success factors based on the empirical material of 202 innovations. The research design thereby differs from the usual case-study approach [37], where conclusions are drawn based on singled-out cases and with a focus on success factors in specific contexts. What the method can produce are results of comparative analysis that are more generalizable than individual case studies. Indeed, the accommodation of a significant number of empirical cases is needed to define a satisfactory and comprehensive conceptualization of social innovation.

Materials and Methods
This study attempts to clarify the concept of social innovation and examine how it differs from other types of innovation. This research design encompasses a review of how different kinds of success factors are associated with different types of sustainable innovations: social, governance, organizational, product, service and systemic innovations. The reviewed success-factor categories are economic, environmental, political, social and technological in character. The upcoming section presents the database of empirical innovation cases and describes the methodology used to examine the association between the types of innovations and kinds of success factors.
Data of the altogether 202 innovation cases from a European database of sustainable innovation, Casipedia [22], is used in the study. These cases all went through a screening review process, in which they were first nominated and briefly described by organizations in 28 European countries. In a succeeding selection stage, two academic organizations joined in to conduct a final selection of cases according to predefined criteria such as sustainability and cross-sectoral linkages, multi-dimensional transformations, degree of public participation and mobilization, deployment and diffusion of innovation as well as a degree of novelty and originality [38]. These cases are evenly distributed across the 28 European countries, as much emphasis was placed on gaining a balanced European coverage. Later, 34 additional and similarly screened cases were added to the database. The database is still open and updatable, but as no screening process is in place any longer, the analysis here is limited to the data that was submitted to the database by December 2015, as all these cases went through the screening process. The database categorizes sustainable innovation cases according to several other criteria, such as key areas, success factors, geographical scope and sectoral relevance.
The database contains seven types of sustainable innovation (social, service, product, governance, organizational, systemic and marketing) relating to the key domains of climate action, resource efficiency, raw materials and the environment. Social innovations form the largest group in the database next to service innovations, thereby providing a comprehensive evidence base against which to carry out a comparative analysis. The definitions used in the database for innovation build on and extend the definition in OECD's widely used Oslo manual [16] (p. 46): An innovation is the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations. Social innovations transcend these innovation types and are understood in the database as novel solutions that meet social needs more effectively than existing solutions. Moreover, such innovations lead to new or improved capabilities and relationships as well as to the better use of assets and resources. Governance innovations, on the other hand, are seen to include novel forms of citizen engagement, new democratic institutions, new public and user participation in service design and delivery, and the use of public boards to govern particular choices. Systemic innovations, in turn, relate to interconnected innovations, which depend on each other [22,39,40].
The examined empirical data includes six types of innovation cases in the database as the number of marketing-innovation cases was too limited for statistical analysis and as the database did not cover cases of process innovation. The categorization of cases in the database adheres to how sustainable innovation priority areas were identified in the societal challenge of "Climate action, environment, resource efficiency and raw materials" as defined in the European Union's Horizon 2020 framework programme for research and innovation [41]. Table 1 describes how the 202 examined sustainable innovation cases in the Casipedia database relate to the types of innovation and priority areas. It shows that sustainable innovation cases in the database are distributed unevenly across the priority areas, giving the first indication that there may exist differences in objectives between the types of innovation. Table 1 shows that social and organizational innovations focus especially on sustainable lifestyles, whereas governance innovations particularly address strategic intelligence and citizens' participation. System innovations target eco-innovation and green economy transitions. Service and product innovations are spread more evenly across priority areas, although it is quite striking that product and system innovations do not target strategic intelligence and citizens' participation. In relation to climate change, social innovations target climate action more through sustainable lifestyles but less by direct climate change mitigation innovations. Hence, as shown by the prevalent differences in Table 1, a closer comparison of the different types of innovation is warranted. In order to examine in greater detail how social innovations differ from other types of innovations, we look at their very foundation, i.e., factors of success. Success factors are here defined as issues that are critical for the initiation, realization or success of innovation cases. The reviewed success factors are categorized in the database to economic, environmental, social, political or technological kinds. The database includes also ethical and spatial success factors, but these are not included in the statistical analysis as their numbers are too small.
In the database, economic success factors refer to efficient use of money and to lower prices as well as to gain savings or profits. These success factors also relate to new business models and increased competitiveness, as well as securing funding in the form of subsidies, grants or investments. Environmental success factors relate to waste and emissions as well as a reduction in resource use (efficiency) and in harmful activities (e.g., transport). Political success factors deal with the involvement of authorities (in particular local authorities), local political will and support as well as securing the access to or the availability of critical resources. Social success factors include the empowerment of citizens and residents as well as social inclusion (employment of or housing for disadvantaged people) accounting for families, youngsters and children. These are mainly associated with social interaction and community involvement (such as cooperation with schools) and social acceptance. Technological success factors are connected with new technical solutions and processes, especially with low-energy and green technologies. These success factors relate to issues of usability and to the creation of sufficient technological base for the innovation. They also refer to knowledge sharing via technological platforms.
Examples of social innovation cases and their success factors derived from the database demonstrate the wide scope of the analysis: • Real Pearl Foundation-Against fuel poverty with biomass briquettes, Hungary [42]. The innovation enables the production of biomass briquettes that are affordable for heating by poor communities. The raw material for the briquettes comes from paper waste and agricultural by-products so it is a low-carbon solution. Local workers also gain social benefits by being employed in the project. Economic success factor: Availability of cheap, low-carbon inputs for production of briquettes (agricultural by-products and paper waste  [45]. Aha! Car is the Eastern Europe International web platform for carpool. A trip is "shared" when a driver provides transportation to one or more passengers without direct financial gains. The company is a community-powered collaborative consumption start-up. Social success factor: Carpooling offers a commuter option to complement other methods of transportation favouring the creation of social networks. • Sharing Torino, Italy [46]. This is a temporary social housing initiative in Turin established to meet needs for temporary rental properties at controlled costs. It is characterized by high energy efficiency solutions with low environmental impacts (e.g., solar panels or recycled materials). It mixes business objectives and social commitment. Technological success factor: New technologies in the management of energy, water and recycling process. The analysis of the success factors of each innovation type is conducted with contingency tables. These are used to examine how observed success factors relate to examined types of innovations. This methodology provides two analytical opportunities as it firstly brings together observed success factors across the examined types of innovations, and secondly offers a statistical procedure for assessing the outcome. This methodology has been applied towards similar aims when surveying urban climate change experiments across 100 cities [47], and when analysing the critical issues of climate governance experiments [35]. The methodology is appropriate when the aim is to compare categorical variables such as types of innovations and kinds of success factors.
In this study, contingency tables are used to analyse if the observed success factors are distributed evenly across the case sample, or if there are statistically significant differences in their distribution. The method examines frequency distributions and is suitable for analysing variables that are qualitatively different. The statistical significance of differences of the frequency distribution of the independent, categorical variables of success factors is analysed with the Pearson's chi-squared (χ 2 ) test, which reviews how the counts in the rows are distributed against the categories in the columns [48][49][50]. The method further provides an expected count for each cell in the contingency table, which provides an outlook of over-or under-representation of that particular variable in comparison to the other kinds of success factors and types of innovations. Further, pairwise analysis of success factors between innovation types is used to provide a more detailed account of differences.

Results
This research has been designed to reveal how the success factors of social innovations related to those of other types of innovations. The results are based in a comparison of a large number of social innovation cases against governance, organizational, product, service and systemic innovation cases [16,51]. A contingency-table analysis of the success factors indeed shows that statistically significant differences can be observed between the distribution of kinds of success factors across different types of innovations. Table 2 presents the results of the contingency analysis of kinds of success factors (in columns) and kinds of innovations (in rows). Each cell in the contingency table shows the observed counts of a success factor for each type of innovation and its expected count. Individual χ 2 values are displayed for each count, with high χ 2 values accounting for great differences between the observed and expected counts. As each innovation can have more than one independent factor of success, there are 553 observed success factors distributed across 202 cases. Statistically significant differences between how the success factors are distributed across the innovation types could be observed at the 0.01 level (χ 2 = 43.556, df = 20, p = 0.002). This indicates that the distribution of success factors across types of innovations does not occur due to random variation, and accordingly, the results merit closer examination. The contingency table analysis shows that differences in the distribution of success factors come forth especially in cases of social, product and governance innovation. Concerning social innovations, reviewing observed against expected counts shows that social success factors were over-represented, whereas political and technological factors were under-represented in the distribution. The emergence of social success factors relate closely to the very definition of social innovation, and hence the low numbers of technological and, in particular, political success factors characterize social innovation better empirically. Product innovations form a counterpart for social innovations as they depict strong under-representation of social and strong over-representation of technological success factors. Governance innovations, in turn, show an over-representation of political success factors. These political success factors were under-represented in cases on social and product innovation. Interestingly, economic or environmental success factors are quite equally represented across all types of innovation.
Accordingly, the overall analysis shows that the success factors of examined cases of social innovation were more social, less political and less technological in relative character. To gain further insights, pairwise analyses of the distribution of success factors across types of innovation were conducted and their results are presented in Table 3. The pairwise analysis shows statistically significant differences between the distribution of success factors across social and product innovation cases at the 0.0001 level. Differences at the 0.05 level could be observed between social and governance cases and social and service innovation cases.
Differences in the distribution of success factors between social and product innovation were statistically significant at the 0.001 level (χ 2 = 26.079, df = 4, p = 0.000). This result further confirms that social innovation should be considered as something very distinct from product innovation particularly concerning the former's over-representation of social and under-representation of technological success factors. Similarly, governance innovation should be considered distinct from social innovation as the former shows statistically significant differences in the distribution at the 0.05 level (χ 2 = 10.127, df = 4, p = 0.038). When compared, social factors were over-and political under-represented in social innovations. Finally, statistically significant differences in success factor distribution were observed at the 0.05 level (χ 2 = 9.737, df = 4, p = 0.045) between social and service innovations. This also supports the previously identified characterisation of social innovations. Over-representation of social success factors was combined with under-representation of technological and political ones, respectively. By contrast, no statistically observed differences in the distribution of success factors could be observed between social and organisational nor between social and systemic innovation, which indicates-albeit does not validate-possible similarities between the types. In conclusion, the results provide insights on how social innovation compares to other types of innovation in terms of success factors. The next section discusses how the results support the influential conceptual model of the social innovation process as proposed by Cajaiba-Santana [9], while they do not correspond well to the conceptualisations discussed by Pol and Ville [10]. The results are also discussed against the definitions of the types of innovation.

Discussion
This article has addressed the conceptualisation of social innovation [9,10], and its research design has relied on an empirical and quantitative comparison of social innovation against other types of innovations. The main contribution of the article is that it empirically accommodates the conceptualisation of social innovation in a way that reaches beyond the examination of small numbers of case studies and thereby produces new knowledge at a more general level.
Contingency-table analysis with data from 202 cases on sustainable innovation targeting climate action, resource efficiency, raw materials and the environment found statistically significant differences in the distribution of success factors across the examined types of innovation. A first finding was that social innovation indeed differs from product, governance and service innovations in terms of success factors. This result empirically confirms the notion that social innovation indeed establishes a distinct form of innovation [1,12], and that its systemic examination may contribute to insights, which can be reapplied and transferred across settings.
Secondly, positioning the results against the identified knowledge gap in the conceptual framing of social innovations provides further empirical accommodation. In fact, it can be determined that the results relate better to the conceptual framework of Cajaiba-Santana [9] than the conceptions brought forth by Pol and Ville [10]. More specifically, as the social innovation process proposed by Cajaiba-Santana [9] is built on agency, institutions and social systems, the results support this conceptualisation as it also accentuates the role social success factors (social systems and agency), while also downplaying the roles of technological and political success factors (as enabling and constraining institutions). In this respect, Cajaiba-Santana's model not only appears to effectively reflect the key features of social innovation processes that could be determined here empirically but also succeeds in leaving out factors with lesser meaning for social innovation.
The conceptions of social innovation as presented by Pol and Ville [10], in turn, consider institutional change, social purposes, the public good and non-market needs. The results of the empirical analysis neither support nor question these conceptions. Impact-oriented conceptualisations would indeed require a different empirical approach and still be at risk for categorising the case sample rather than describing their underlying, unifying relationships. The results challenge the claim that government intervention is required to encourage the creation of pure social innovations because political success factors did not have a prominent role in the examined empirical cases on social innovation. Further research on the issue is nonetheless warranted, as not all examined cases constitute pure social innovations in the sense that Pol and Ville [10] describe, and that governance innovations might in some circumstances address some of the expectations set for social innovations.
The results further support the idea that social innovation is distinct from other types of innovation due to its forms of collaboration [12]. Indeed, and not surprisingly, the role of social success factors is much more prevalent in social innovation than in other examined types of innovation. Similarly, political success factors are particularly relevant in governance innovation [40]. Furthermore, the results are in line with the definition of product innovations (goods or services) in the Oslo innovation manual [16], which emphasises the linkage between product and technological innovation, in that the results show that technological success factors are strongly over-represented in product innovations and over-represented in service innovations when compared to social innovations.
Furthermore, the results do not question the possibility that social innovations could contain similar elements as organisational and systemic innovation [12]. Systemic innovations target systemic change as do social innovations. This is because many of the social innovation cases, to a large degree, target sustainability challenges, which require thorough systemic changes in society to become realised. Organisational innovations, by definition, highlight the role of organising activities between and with people, which is similar to social innovation. Research shows that engaging publics can bring forth novel and disruptive ideas [52] and thus social innovations could well be considered potentially disruptive also in the light of the empirical findings. These findings indeed support the idea that disruptive [21] and social innovations can perhaps be advanced through similar means. Additionally, the prevalence of social innovation cases in a database that focuses specifically on sustainable innovation including climate actions suggests that social innovation for its part also accomplishes sustainability and helps to target climate change [14].
The marketing implications of the results hold that social innovation is related to social impacts more than business activities [10]. This allows making use of alternative or even non-profit marketing models, which rely on social capital. Networks of people acting in new ways may be the very essence of the innovation, whereas the underlying business arrangements are simply a way to ensure the provision of services. Indeed, it is useful to be able to differentiate social innovation from other forms of innovation as well as from business as usual. This is, however, a highly complex task as there is so much variety in what is targeted with innovation. Definitions typically consider novelty and significant improvements but leave out contexts and scopes [10,16]. In the realm of social innovation, this would highlight the quest for new or significantly better ways of achieving social impacts. Such innovation activities would need then not only take into account the factors contributing to social innovation but also be assessed according to the social impacts they contribute to.

Limitations and Future Research
While the empirical considerations for over two hundred innovations provide a grounded empirical accommodation for the concept of social innovation, there are some limitations in the interpretation of the findings. It should be considered that the study has not examined marketing and process innovations, the successes of the examined innovations nor to which degree success factors have contributed to a particular innovation. These limitations derive from the data in the Casipedia database [22]. Assessing successes of innovations would require quite a different research design and development of metrics for the indicators of success. It would also call for reselection of the period of assessment, i.e., the determination of when an innovation stage ends.
A further issue to be recognised is that the study has not examined the success factors qualitatively. Each factor can include quite a variety of more detailed activities. For instance, economic success factors include both subsidies for product development as sales of an innovation, which relate to different stages and modes of business. Hence, the results of this article should be examined at a level of abstraction, which corresponds to the conceptualisation of social innovation.
The examined cases of innovation all relate to the domain of climate change and sustainable development. It is, therefore, an open question to which degree the results can be generalised to other fields. While it can be argued that the economic, environmental and social dimensions of sustainability are relevant to many if not all innovations, further studies are required to establish this empirically.
Finally, cases attributed to a particular innovation typology may include elements from more than one type of innovation [40], whereas the cases in the database were examined according to their main type of innovation. While this is not likely to have affected the key results of the analysis, it can be argued that an examination of only non-overlapping types of innovation cases could contribute to clearer results albeit at the expense of neglecting the full scope of innovations in real-life settings.
Author Contributions: The article is a joint effort of the two authors. P.R. and K.M. together conceived the research idea, designed and performed the research and co-wrote the article. Both authors participated in the finalising and approval of the manuscript. All authors have read and agreed to the published version of the manuscript.