 Okay, so let us start with the first presentation and I'm very happy to have Professor Dr. Martin Schneider here from the University of Paderborn. He will give the first presentation on commitment satisfaction and health among crowd workers, first findings of the Paderborn-Bielefeld survey. And without further ado, the floor is yours and we are very much looking forward to your presentation. Yeah, thank you for your welcome and for inviting us and myself here to Bremen. We're quite excited. Well, I am excited for a particular reason. That's because we have the opportunity to present for the first time first results of the Paderborn-Bielefeld survey on German crowd working. And as you said, I come from Paderborn. That's why I christened the survey Paderborn-Bielefeld survey using my first mover advantage. I hope it will be named like that, named after that forever, but I don't know. So this is a this is a questionnaire survey we've been conducting in the Forschungsschwerpunkt digitale Zukunft, which is a larger research project funded by the Land NRV in order to find out more, among other things, about crowd working. So regulatory questions, but also questions on, in my case, how human resource management works or what are the incentives, what is motivation, what is motivation among crowd workers, health issues, issues on wages. So it's a whole range of issues we are interested in. And today I want to follow, I want to pursue two goals. First of all, I want to give you an idea what the survey is about. And in the second part, I want to give you a first version, if you like, of a study we've been doing. So Paul Hampton and myself, Paul, is a doctor student. And so the second study is focused on commitment on the question, who is committed to crowd working, which is in a way a paradoxical question, isn't it? Flexible work, you're not committed at all, but you can still measure commitment and ask yourself why certain workers are committed and others are not. So that's the study. And in the first part, I will give you just a few ideas about the survey. And here, of course, a number of people have been involved. There are many of them here. So Nicholas here, Zara, Katharina and Julian, they've all been involved in this study and they know the technical report much more, much better than I do. Okay, when we started out studying crowd working, which is, by the way, a German word, although it sounds English. When we started off, we had a discussion, what is crowd working? And we, for our research project, we limited crowd working to a particular group of platforms. It had to be paid work, and it had to be digital work. So not delivering pizzas or something, that is not our interest, digital work. And finally, we were interested in platforms that manage the platform. It's not only a marketplace or something like that, but it has to be a platform that has its own incentive system that manages quality, things like that. Okay, we tried to convince platforms to have a questionnaire survey among workers. And we found four platforms who would be willing to do so. And the survey has just been finished. And you see, the four platforms cover a range of different types. So we have a testing platform, a mobile crowd working platform, so it's micro tasks but conducted for mobile phones, smart phones, a classical micro task platform, and finally a writing platform. And that writing platform was the only one who didn't insist on being, on staying anonymous so we can actually name them in the economic context. So it's content for the DDE, writing platform specialized in writing. Very interesting. And we think this survey is quite special for the German context, not only because it's a fairly large sample, but also there's a whole range of disciplines involved in putting together the survey. So we have information scientists of course, but we also have psychologists, sociologists, and management scholars. So what we try to do is we try to adapt the scales that we use from other types of work contexts. Scales on health, commitment, working conditions, things like that. We try to adapt them to the platform context and thereby try to adapt the theory, the ideas, the concepts that we know from psychology, from sociology, from human resource management to the platform context. Because we slightly, I mean, Lamas is not here yet, but we slightly have the idea that existing work has very, very often focused very practically on socio-democratic variables, but not so much on theoretical constructs. I mean, this may be provocative, but we think this is the case overall if you consider the German context. And we try to make some progress on that. So I want to give you a first impression on the data. So first finding is not very surprising. Workers, crowd workers, are very heterogeneous. So in terms of the highest educational attainment they've got, we have a lot of people with a master's or bachelor degree. So second lesson, a lot of them are highly skilled. Third lesson, there seems to be a systematic difference in terms of, for example, vocational educational attainment between the platforms. So if you look at content, those are the blue bars, we see that in content we have a large share of workers with an academic degree, master's degree 36%, bachelor degree 20%. So content seems to be special in that case, which is quite interesting. And content stands out in other respects as well. When we look at the data, we see that on the writing platform content, 44% of the workers report crowd working to be their main employment. It's professional crowd workers, if you like. And the share is much lower among the other types of platforms. Content also stands out because the workers report the highest number of working hours per week. So almost 16 hours per week on average. So among those, they are full-time employees working on content and perhaps other platforms. So content stands out. And again, you can see there's a whole range of things. There's a whole range of the platforms differ, usually in terms of, for example, work hours and also pay. I mean, pay, we calculated pay by taking the hours per week, those workers report, and the monthly wage they say they've earned through crowd working. So this is probably a very rough estimate. It's not very accurate. It's reported, indirectly reported pay per hour, but still you can see that probably hours and pay per hour also differs. And in one case, not even covering the minimum wage requirements. We have complied with minimum wage requirements in our questionnaire, by the way. Let's make sure of that. Okay. So taking the fact that content, in a way, stands out. So it's a writing platform where people work a lot of hours and many people do writing on crowd working platforms as their main employment. The study I want to look at focuses only on content. So I'm focusing on one case study, content DE, so the writing platform. And the question I want to address is the problem of commitment. So here's the paradox. You have professional work. We are all scientists. We know writing well is difficult, isn't it? And also you have, this is a task where you can actually have some specific skills, customer specific skills. So the customers of this platform may ask for similar texts over and over again. And the same workers, the same crowd workers may take on these jobs time and again. So there's some customer specific human capital, if you like. And if you have this type of work, professional work, customer specific, what you can do, what you should do as an employer is hire these workers, retain them, make them happy, make them committed to you. And that's what they can't do. I mean, this is crowd working. These people are freelancers. They're not employed. They haven't been selected in a proper assessment or something. They just register. And they can stop working the next minute. I mean, this is, if you like, for specific skills and professional work, this is a nightmare. And so the question, but it seems to work. So that's the paradox. So what's the point that makes it work? And we think that content and other similar platforms have found a solution. Namely, they have come up with a certain incentive system, a rating system that can be considered a commitment device, a device that attracts workers, makes them happy, and gives them reasons to stay on and remain engaged. That's the main idea here. And what do these incentive systems look like? I mean, this is text broker. Content has a similar thing. So usually, it's the five star idea. One, two, three, four, five stars. You can actually get promoted in this system when you're a good worker. Customers are happy. You write a lot. Then the platform might say, well, you used to be two star, now you're three star. Or you used to be four star and five star. Or your engagement has not been so perfect the last six months. You're going to come down from three to two. And the point is that workers have an incentive to be four or five rather than one or two. Because first of all, they get a higher rate per word. That's the direct incentive. But also, they get more interesting tasks. So customers may say, this is a five or four star task. And only those workers can actually get access to their task. And in a way, this is a commitment device. So five star workers, maybe five star at content, but not at text broker. And that's maybe a reason why they want to work for content. That's the main idea here. And in this study, and again, it's the first version, first shot of this. In this study, we asked basically two, two and a half questions. So why are workers committed, which is in a way a paradoxical question to that platform? And how does the rating system, the five star system, how does that matter in increasing commitment? And the question I haven't really fully addressed is, who are the committed workers? So actually, we try to find out what among these heterogeneous workers, what are particular types of workers, workers who work full time, and are independent, there may be people with side jobs and students, which are the groups that we can identify, and which are all, which are very heterogeneous, but they all are committed to the platform. So that's the question we want to address. And the key points are, well, we take up literature and management and psychology and adapt the distinction between calculative commitment and effective commitment. It's two types of being engaged or being committed to the platform. Calculative commitment is a rational thing. I stay with the platform because it pays me better than other platforms do, because it would incur a lot of costs to change the platform, things like that. So one of the items that we actually asked workers is, if I left this platform right now, I would have many disadvantages for me. And they may say, yes, that's true or not, not so true. Effective commitment is more of an emotional affair. One item by which we have measured that is I'm extremely happy to have chosen this platform. And we think, or I think, and we actually found out that this type of commitment, emotional effective commitment is triggered by social exchange mechanisms. So I like the platform and it gives me, for example, I feel comfortable in the platform. I get a lot of recognition from the platform. These are social exchange mechanisms. And what we find is that economic exchange drives calculative commitment, whereas social exchange drives effective commitment. And surprisingly, and very good news for the platform is, when people say, well, the rating system is very good, because the pay aspects of that rating system are very good, then this drives both types of commitment. And we measure this by asking them whether they think that when they move up, when they get one additional star, will this increase their income significantly? We also ask them whether they're happy with the pay level of this platform. And when they agree to those, then they both show high calculative commitment and a strong effective commitment. And we think that the empirical model is meaningful, because we can actually see that different groups of workers follow different patterns that lead to commitment. So it's a two-step argument to make here. So our theory is that effective and calculative commitment will depend on certain theoretical mechanisms, motivation and satisfaction with platform aspects. And of course, these mechanisms can then be related systematically to certain individual characteristics. So it's a two-step approach. In the psychology literature, what you usually find is that where you have the dependent variables, effective commitment, calculative commitment, and that depends on a whole range of factors, for example, age. Age may be related to commitment or things like organizational support may be related to commitment. But these are different things. One is individual characteristics, sociodemographic characteristics. The other variable is a perception on the platform. And rather than putting them all in one model, what we suggest is it makes sense to look at mechanisms, identify mechanisms, and then go back to the first step and try to find out whether certain mechanisms are related to personal characteristics. So that's the idea. And we think this is better because we do not know much about crowd workers. And because crowd workers differ so much in their motivation, in their working hours, in their perception. And that's why we think a two-step approach is better. So let's have a look at the variables here, six variables or conditions. So we first ask, are these workers motivated by a lack of job alternatives? And they may agree on a five-point scale or not. Are they motivated by additional income? These two motives, we think, are economic exchange mechanisms. They may also be motivated by just the idea of passing the time. I want to have a bit of fun online. That may be a different motive. This is a social exchange motive, we think. And also, they may be motivated by interesting tasks. Now, these motivations are possible. They're not exclusive. They're not mutually exclusive, of course. And they're not specific to the platform. What is specific to the platform in our model are the two satisfaction variables. So workers may be satisfied with the pay aspects. I've mentioned that. The pay aspects of the rating system, they say, I make a lot of money when I receive another star, things like that. And they may also be satisfied with the status aspects. The status, I termed this status aspects because it had something to do with standing, with recognition, with reputation. So workers were asked, the rating system gives me recognition by the platform. And it also gives me recognition among my coworkers. And this is a social aspect, if you like, status aspect. And we think that this may be related to effective commitment. Okay. That's the theory. What you could do is, I mean, you could run regressions. That's the knee drug method we all use. What we try to do is we try to do something else. We try to do a fuzzy set qualitative comparative analysis. This is a method introduced by Charles Reagan in 2000. And it has become quite popular in political sciences and sociology and also in management. And there are a number of differences. I mean, it does a similar job as a regression analysis in linking an outcome to certain conditions. But other than the regression analysis, it argues that the effects are conjunctural and equifinal. What does that mean? Well, in the regression analysis, you always assume, for example, that satisfaction with pay will increase calculative commitment. Whatever the other variables are like, men or women, motivated by pay or other things. So other things being equal, this will increase calculative commitment. The FSQCA argues, well, causation may be conjunctural. It may be that people, workers who are satisfied with pay, will only show strong calculative commitment if they are motivated by pay and not by other things. It's a conjunctural argument. This and that will lead to something else. And also, equifinality is a possibility. And we were talking about heterogeneous people. So maybe we find people who are motivated by pay and satisfied with pay and, therefore, they are committed. There may be other people who do not have an alternative. And whatever may not be satisfied with pay, but they're still committed because they have to. So there are different routes to the same outcome. That's equifinality. And you see this, actually, in the chart here. When you move from left to right, you see the more people comply with the idea they are satisfied with pay and motivated by the higher the calculative commitment. But if they are not satisfied with pay and not motivated by commitment, they may still have a strong calculative commitment. You see that? Zero here and one over there. So these are these other people. They may be committed for other reasons. And that's where you see equifinality. And you see by the triangle that the type of causation is a sufficiency. So here in this example being satisfied with pay and at the same time being motivated by pay is a sufficient condition for effective commitment. It's not a necessary condition. There are other ways of becoming committed. So that's the idea here. Technically what we do is we're using Boolean logic and set theory. And we compare cases. People are each person, each worker is a case defined by being in the set of various conditions and the outcome. And we compare these cases and by comparison we find out who will be committed or who will not be committed. What we do just to indicate this with the data, we take raw values and have to decide in a set theoretic sense when are people in the set, when are they out of the set. And for example, the calculative commitment, we said well if the raw values are 1, 2 or 3, this is not very committed because you know with questionnaires 3 is really bad. But 4 and 5 we say this is committed. So it's a calibration procedure here and we're actually using the fuzzy set values ranging from 1 to 0 in doing the analysis. How much time do I have? That's perfect. Okay, rather than explaining more of the method which is possible in this short period of time, I will show you the results. So we have done two minimization procedures, one for calculative commitment and one for effective commitment. You see the conditions, so in a regression you would say explanatory or independent variables on the left-hand side and each column here is a solution. So the colored dots mean the condition is present, it's rather 1 than a 0 and the dots with the cross mean the condition is absent, it's more a 0 not a 1. And the solution can be read like this. Well people who are motivated by additional outcome, by an additional income and are not motivated by passing the time will show strong calculative commitment and we find 103 workers who are consistent with this explanation. And then we have another second path, we call them path to commit to calculative commitment, people who are motivated by additional income and who are motivated by interesting tasks will show strong calculative commitment. So these workers they differ in all other aspects, they may be motivated by lack of drop alternatives or not, they may be satisfied with pay aspects or they may not be satisfied with pay. So they are heterogeneous in all these other variables, in all these other conditions, but they all are motivated by additional income and by interesting tasks. Then we have two interesting paths to effective commitment, one is people who are, so this path 4, who are not motivated by a lack of drop alternatives, definitely not and but they are motivated by interesting tasks, they will show a strong effective commitment. And then people, the smallest group here, 31 persons say well I want to have fun working on that platform, passing the time, I mean not having fun necessarily. Sight for time, passing the time. This is a sufficient condition for effective commitment and interestingly, this is very interesting, path 3 and 6 say that being satisfied with the pay aspect of the rating system is a sufficient condition both for calculative commitment and for effective commitment. They're different in all other aspects, so it's not a conjunctural causation, it's a path that says that if only this condition is met, then people will be committed to the platform. That's very interesting. So this is the first interesting finding, satisfaction with pay aspects, in a way we want to evaluate the rating system, this is very important. The other aspect, being satisfied with status aspects, doesn't really make a difference. Yeah, there's no no circle whatever for this this dimension, doesn't matter. So this is the first interesting finding, the second is, apparently there is a difference in the causal mechanisms. You see that the blue dots here, they all refer to economic exchange mechanisms and they are important in explaining calculative commitment overall. There are two exceptions I mentioned, so being satisfied with pay aspects also explains effective commitment and here we have a path that combines a social exchange and an economic exchange mechanism to explain calculative commitment. Overall we think that the theoretical idea of being two types of mechanisms hold out. Good, that's the main part of the study and now each of these paths they include different types of people and what I wanted to finally is have a look at the types of people. Maybe we don't go through all the cases, but the path one says workers are motivated by additional income but not motivated by passing the time and what we did is we took those 103 people who are the workers on that path and compared them to all the other workers and what you see here is that the workers on that path, the blue bars here, they work more hours in crowd working and on the platform than the others and very often they are students, sometimes also employees but predominantly students and they basically those other people who want to generate additional income and rather than go through them all what I would like to show you is this, this is the path three and path six. People who are said is what would pay offered by the rating system, they spend much time in crowd working more than other people and in particular on that particular platform, if you take the first two bars here, the difference between the blue and the red bar is bigger for time on the platform than it is for crowd working overall. So it seems to be that these people are more, I mean they're really committed to that platform and not to others and also this is a group of people including very often women, they more often than in other paths do not hold an academic degree and they often do crowd working as their main employment and of course that's why they're so interested in the pay aspects of the rating system. This is step two and I'm aware this is only preliminary so let me conclude. Effective calculated commitment are affected by different mechanisms, social exchange versus economic exchange mechanisms and then these mechanisms commit different types, different groups of people which is important for the platform to know and the platform's incentive system seems to commit workers in both ways effectively and rationally which is good news for the platform and well what we're going to do next, the first step can also be transferred to an FSQCA type of analysis and we also want to have a look at the other platform perhaps I forgot to mention the smallest group of people just to show you that the type of analysis is interesting and the people are really heterogeneous in this platform, this is the smallest group of workers, 31 persons who say that well I want to I'm motivated for crowd working by looking for passing the time that's sufficient for being committed in an effective way. These are mostly men mostly employees and I mean usually would say in psychology would say you have to take, you have to look after your committed workers okay perhaps that's true but these people they do not work so much it may not pay off to look so much after these workers so they the time they spend on crowd working and on the platform is much lower than for other workers they this type of workers may not be as important as the other ones so and this I think supports the the type of analysis that we are suggesting with the FSQCA okay thank you and I look forward to your questions. So we have a microphone and if you have questions raise your hand and Elisa bring the microphone to you. Okay okay thank you very much it's a really interesting topic you're working on I have a question about the QCA method you used now you basically argued around the novelty of the method which is which is correct I know I know the method I like it a lot in fact but I wonder why is your sample suitable for it because a QCA at least in my understanding applies to a an end somewhere between 15 and 35 or something like that so too big for in-depth cases but too small for regression analysis but your end is much larger so I'm just wondering it's the way QCA is used in business you would say study what 35 aviation firms and then look for configurations there. That's the the should I respond immediately? Okay so the Reagan developed the method originally for exactly the samples you mentioned so countries 20 to 50 countries something like that but today actually people say you should you can use FSQCA if and when you have a configuration of theory when you have the idea that congenital causation matters and different paths different roads lead to Rome then it makes sense to use this method because when you use regressions the variable theory put in they will wash out they will not show any results and it doesn't have anything to do with the sample I could go on about this when you have large samples you have to include a larger number of conditions to get meaningful results but that's another topic. You've just said the configuration theories which are very important to see to what extent do you go into configuration arguments when you have the two mechanisms that are sort of conjunction but really problematize the meaning of what your configurations are what they come from how far do you go down? Yeah that's always the question how specific are you with your with your configurations so what I mean implicitly I haven't I haven't mentioned any hypotheses but implicitly we would argue that conjunction such as being motivated by interesting tasks and being satisfied with status aspects will explain effective commitment but not calculative commitment. Similarly yeah yeah and that would be the type of hypotheses that you would be able to come up with. Thank you very much first of all I have a question on the rating procedure you mentioned if I remembered correctly you mentioned it in a quite a positive way and as an incentive for commitment for the crowd workers and I have read some criticism too about these rating system concerning the crowd workers and according to your opinion would it be a good solution to implement rights for workers to say something against bad ratings for example. I would definitely be in favor of that but Paul is actually the expert on the rating system do they have something like that can workers speak up against the rating? No I mean it's just yeah it's so what you're arguing is there should be some voice mechanism we only have exit they go to tax broker but of course it's a problem I mean if you if you look at the if we assume that this is an an accurate estimate of the pay per hour the writing skills by mostly academic people makes 10 euro 40 per hour on average not so generous is it so in a way you could you could be you could be critical but I didn't want to say it's positive we try to understand we're trying to understand why these platforms do that and we think that's one of the reasons they do that. Thank you very much for your interesting contribution I've actually two questions first on the sample as I understand it very well it's online work so it's not compared to offline work like deliverance one is a sample German-German in the sense that the people you have been well interviewing are asking where they're German in Germany for a German platform or do you also have foreign people working for the platform I assume it's about text so German text or is it broader than that the reason why I ask is that sometimes with online platforms it's very hard to find who is actually doing the job it's it potentially is it can be worldwide which can be for systems like law systems quite problematic to govern. So Nicole was nodding she knows the questionnaire by heart we know whether these are foreign workers or not. No it's German it's all on German workers people who live in Germany and German platforms and the texts are also in German. Well in content of course. In content. The other because they do German texts and German but the other platforms I mean with the other platforms there may possibly be other nationalities represent. The whole study it's only German crowd workers. Oh really. So we limited that to the Germans. Oh dear because okay because of the funding. Because it can have an influence of course on some of the answers are some of the measurements because some of the online platforms they act globally and they try to maybe they don't have that ambition to commit. They don't care maybe here that ambition exists because it's very hard to find enough people and so on and so forth. So that was one and the second question. Do you find some correlations between the economic satisfaction if I can say so and people who are mainly working on the platform as the main occupation and then the ones who do it as a kind in their spare time as a part time as something in addition or people who have a partner having a main income and don't need that money so much that they do it more out of affection if I can say so. Because what we I mean I'm mainly working as a lawyer in the field and what we do miss is a clear picture on who they are. The people working on platforms whether they do that as a main occupation whether it's a kind of side job there are not so many data on it and so we are still a little bit in a kind of black box. We have some averages of income I have seen some studies in UK and US and so on where the average income is is rather low but that can be partially explained because it's a side job they are doing. So what we are very much eager to find out is and here I saw some data that apparently half of them do it as a main occupation but that can be explained of course it's rather well it's a high skilled to some extent a high skilled job. Whether you see some correlations there that there is more economic drive and it's your main occupation and it's more affectional drive and it's a kind of side. This piece of evidence is actually one example what you're getting I think. These are the workers who are satisfied with the pay offered by the platform and this group of people is distinctive for having a larger shelf of workers who say crowd working is my main employment. So in a way you can see that they they are more focused on the pay aspects. But of course you could we could have a look at that more systematically and that's the nice thing about these configurational methods when you say we want to know who these people are. I mean this is not the correct answer to that question because what you say what you say here well they are a little bit more likely to be women than men. But you want to know how many women and combined with other characteristics and that's what the configurational methods and our data could actually have a look at. And to your first question I mean this is an additional additional thing you want to work out and want to find out and Paul is going to talk about this tomorrow which platforms implement this type of rating. And we think it's systematically those platforms who need really skilled professional workers and not the others. So thank you very much for the interesting talk. I'm working mainly on micro-tasking and in micro-tasking workers usually tend to be loyal towards an employer. So this is what you sometimes see on forums like Turkernation. You see that if you are a large employer people know when you submit tasks and they are actually expecting when you're submitting tasks. So this means for from my perspective workers are less committed to the platform but more to a specific employer or to a set of employers which are on a platform. So did you also ask basically the workers or did you check what kind of employers you have on those platforms? Are there a few large ones which regular submit tasks or are they very diverse so that you could really say workers are committed to the platform or not to the set of employers on the platform? I don't think we've got that. We know whether they are dependent employees but we don't have any information about the employers do we? No we don't know that. Well okay yeah I'll get your point yeah but let me say this I mean are you talking about this type of micro task platforms paid work? Like the clinical talk folks. Okay. Maybe you can also drive from the phrasing of your questions. So maybe you can also drive from the phrasing of your question so did you really ask for the platform specific things or for example like the possible income is basically dependent on the employer side? Well we know a lot about this we know family income and income from other sources income from crime work we know this maybe so we really we asked about their commitment to the platform not to the employer. Okay this is this is a different thing we adapted the scales we usually use it for organizational commitment or commitment to your profession to team and now we only ask commitment to the platform not the employer sorry I didn't get your question. I have another question it's kind of like related to that one since you focus on the commitment I mean there are also some downsides of these reputation mechanisms and I'm wondering do you also take these into account because reputation might also generate some kind of enforced dependency so that you that the work is locked in. Yeah well it's actually part part of the scale so I cannot I cannot change I'm committed to the platform because it it takes too many it has too many switching costs so to go away we actually know this it's part of the of the outcome here. I have my own microphone so I can directly ask so one one result that surprised me in your survey was that I mean being an economist I'm very surprised by the fact that you know if you have a higher the degree in your education right I mean you go to this one platform where you have to write the text more often but at the same time at this platform you earn less money so the the you know the conclusion you could draw out of this is that you know education doesn't pay off at all because you you would be free to switch right I mean just go to the testing platform you earn three time as much money as on the other platform so I don't understand why these people are doing the job they are doing if they could go to another job which requires less training and you would earn I mean 300 percent right I mean so I don't I don't see why they are doing that. Nikon has been criticizing this chart perhaps I shouldn't have shown this. Make a chart but the people they're not behaving like. First I'm not not really sure whether this is an inaccurate estimate but if we assume it is well first of all these people in content they they know how to write and they may not know how to do other things they may not know how to design or to test software or not they've been interested so it's a different skill and third answer I mean I don't know you have to have tasks that are available don't you I mean I'm not a crowd worker but I could imagine that lucrative tasks well paying tasks are not so easy to get for 30 hours per week in content you may have that paper per hour may not be so so so high but you are you know you know you know the website you know the you've got the skills you know perhaps the customers the types of texts you have to write and there may be a lot of this of this type of task around so what you're saying is is testing a software testing or could it be another testing like in the in the data we have for instance you could test the new nail polish or whatever okay software testing web testing of web interfaces and similar but we have to know that the paper hour is really low on the content of the e-page but the overall income on this platform on company is the biggest one of all platforms it's so it's a bit confusing in this chart we have you at the same time you have the highest number of hours per week it can be a worker decision but it can also be the fact that enough jobs are around so you may not be interested in maximizing your pay per hour but when you rely on a regular income you may be interested in a monthly wage okay so do we have any more questions if not thank you very much again