PRE2020 4 Group4

Coco, The Computer Companion

Group Description

Members

Name	Student ID	Department	Email address
Eline Ensinck	1333941	Industrial Engineering & Innovation Sciences	e.n.f.ensinck@student.tue.nl
Julie van der Hijde	1251244	Industrial Engineering & Innovation Sciences	j.v.d.hijde@student.tue.nl
Ezra Gerris	1378910	Industrial Engineering & Innovation Sciences	e.gerris@student.tue.nl
Silke Franken	1330284	Industrial Engineering & Innovation Sciences	s.w.franken@student.tue.nl
Kari Luijt	1327119	Industrial Engineering & Innovation Sciences	k.luijt@student.tue.nl

Logbook

See the following page: Logbook Group 4

Subject

We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.

Problem Statement and Objectives

Problem Statement

Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation .

Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality.

Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families. In addition to this, it requires greater concentration for home office workers during communication . Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 .

Objectives

Our objectives are the following:

1. Help with concentration and motivation (study-buddy)
2. Improve physical health
3. Provide social support for the user

USE: User, Society and Enterprise

Target user group

The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions . It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation . Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more . This can affect cardiovascular and metabolic health, but even mental health . In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms . Hence, students and employees should become more physically active to improve their physical (and mental) health.

Secondary users

When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying.

Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks.

Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment

Society

When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.

Enterprise

There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.

Approach

In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.

Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships.

Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users . Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks .

Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students. In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot. Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible.

Method

At the end of the project, we will present our complete concept of the AI companion. This will include its design and functionality, which are based on both literature research and statistical analysis of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the user needs and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a risk assessment will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a Wiki-page and final presentation. A schematic overview of the deliverables can be found in Table 1.

Table 1: Schematic overview of the deliverables

Topic	Deliverable
Functionality	Literature study
	Results questionnaire 1: user needs
Design	Results questionnaire 2: design
	Example companion
Additional	Risk assessment

Milestones

During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.

Table 2: Overview of the milestones

Topic	Milestone
Organization	Complete planning
Functionality	Complete literature study
	Responses questionnaire 1: user needs
	Complete analysis questionnaire 1: user needs
Design	Responses questionnaire 2: design
	Complete analysis questionnaire 2: design
	Design of the companion

Survey

To find out more about the group of people that will be most interested in having a virtual companion, a survey will be conducted. This survey is meant to specify the target group and to get to know their preferences regarding the functionality of the virtual agent. The survey consists of 21 questions about Coco divided into the topics demographics, general computer work, productivity, tasks of a VA, privacy and interest. The link to the survey can be found here. And the analysis of the survey can be found here.

Planning

Research

State of the Art

Productivity agents

As discussed by Grover et al. multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork.

Grover et al. mention how Kimani et al. designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection.

With their research, Grover and colleagues wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration . The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.

Companion agents

When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.

Physical health agents

Cambo, Avrahami, & Lee investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.

Computer assistants

Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.

Related Literature

General

A list of related scientific papers, including short summaries stating their relevance, can be found here.

Design of the Virtual Agent

In week 3 a separate literature study has been conducted, specifically focused on the design aspect of the virtual agent. As a result of this study, it is planned to form a scientifically grounded recommendation for the design of the virtual agent, based on the direction of all papers taken together.

An overview of the collected data regarding the design and appearance of the agent can be found here.

Motivation for virtual agent

This section contains the motivation for the decision to develop a virtual agent instead of a physical robot. Several important aspects of Coco will be discussed to emphasise why we've chosen a virtual agent.

Pros and cons of a physically embodied agent

There are a few very clear pros for an embodied agent. Firstly, a physically embodied agent enhances social telepresence, which is the feeling of face-to-face interaction while a person is not physically present (e.g. using videocalls) (Tanaka, Nakanishi, & Ishiguro 18) . An embodied agent is also able to provide affordance for proper social interaction, meaning that a physical robot has fundamental properties that determine its way of use (Lee, Jung, Kim & Kim) . These two things show that a physically present robot has is better able to provide good social interaction than a virtual agent since a virtual agent does not have these same affordances.

However, these arguments for a physically embodied agent can also be refuted. The social telepresence mentioned before decreases the smoothness of speech of the participant (Tanaka, Nakishi, & Ishiguro) . Besides this, Lee et al. found that if an embodied agent had anthropomorphic-physical embodiment, that humans had very high expectations of a robot. However, if the robot then did not have the ability to respond to touch-input, the high expectations dropped immediately and they became frustrated and disappointed in the robot. This is in general a very negative effect of physical embodiment .

Another con of physically embodied robots is that they are very expensive to develop and to maintain (the many embedded sensors and motors can make sure many technical difficulties arise). A paper on design of social robots from Puehn et al. compares a low-cost social robot ‘Philos’ to commercial social robots. Their design idea Philos has a commercial value of $3,000 and the associated software is free. But the more widely known, higher quality robots, like the animal robot Paro and humanoid robot Nao, cost way more than that, namely $6,000 and $15,000 respectively . This shows that a very low-cost social robot will still be sold for $3,000, which probably won’t attract our user group, students.

Pros and cons of a virtual agent

A virtual agent also has quite some clear pros. Firstly, a virtual agent can process both spoken and written language and therefore it allows easier communication between Coco and its user. Especially as no confusions can arise due to accents or bad pronunciations of words. The communication software, for example language processing software, can very easily be updated. Secondly, a virtual agent can be accessed at any time and it does not have to be repaired, only updated (Milne, Luerssen, Lewis, Leibbrandt & Powers). Besides this, a virtual agent can easily communicate with other applications on a laptop, computer or phone .

Conclusion

The physically embodied robot and virtual agent can be compared on different subjects. The largest pro of a physically embodied agent is that it enhances social interaction and you could say that that is of course favorable, since it has a positive influence on the student’s. However, since the target group of our research are scholars or students we have to take into account their needs. The average student does not have the money to invest in a physically embodied robot such as Paro. Even if low-cost social robots are developed, such as the aforementioned ‘Philos’ it will still cost at least $3000 (even more if it needs repairs) and if it the robot not realistic, peoples’ expectations will drop. And with that, the advantage of a physically embodied agent becomes minimal.

The robot that we want to develop needs to have multiple functionalities. In order to improve the students’ concentration and motivation (objective 1), Coco would benefit from permission to control apps or websites. Imagine, for instance, that someone has his/her social media open when trying to make homework. Incoming messages from the social media platform would cause a lot of distraction, which could be prevented by (temporarily) blocking the platform. Additionally, if Coco is able to monitor activity on your computer, it can provide you with summaries and recommendations on how you can invest your time more wisely. Making these recommendations can performed even better when Coco is able to see your online planning. All of these desired functionalities of Coco are easier to implement in a virtual agent than in a physically embodied agent because of the pro’s that were mentioned earlier. The software for this can be easily updated online and since it’s a virtual agent, it can easily communicate with other applications (such as a Google Agenda) to help you schedule tasks for example. This is not possible with a physical agent. Additionally, a virtual agent can process both spoken and written language, therefore allowing easy communication with the user. And lastly, you can just take a virtual agent with you everywhere, and with a physical robot, this is harder.

Artificial Intelligence applications

One of the important characteristics  of  our computer companion Coco, is the fact that it is not an ordinary chatbot, but an advanced virtual agent. This means that it uses artificial intelligence (AI) in order to  function better  and meet  the user  needs. These  user needs  have been investigated  in  the first survey we sent out.  Hereafter, the main objectives have been set to  (1)  help  with concentration and motivation, and to (2)  provide  social support. 

Natural Language Processing

First of all, Coco will need to use natural language processing in order to analyze the user’s  textual  input and  subsequently provide correct  textual  responses.  What makes Coco an artificial agent is the fact that it does not have all its questions and answers preprogrammed. Instead, it  processes and analyzes the conversation and responds to this.

In the first survey, only 26% of the respondents in our user group (people of <= 25 years) would give permission to access the microphone. For this reason, it has been chosen to only  communicate via textual messages.  Another benefit of this approach is the fact that less confusion will arise since wrong pronunciations or background noise will not interfere. Also,  Coco itself will not  “speak”  to its users,  but only communicate via text-messages and popups. This makes  it easier to use  Coco  in settings where other people are present as well (i.e., public places). 

Sentiment Analysis

Furthermore, Coco will include sentiment analysis. As explained before, we would like Coco to show emotionally intelligent behavior, to be able to provide the correct social support. Sentiment analysis would be an important step in reaching this goal, as it allows Coco to analyze its users' emotions. Often, models categorize sentiments as either being positive or negative . However, more precise distinctions could be made, although this is more difficult. Coco could adapt its responses then to the matching sentiment of the user. For example, an extremely happy response would not be appropriate in case the user is feeling sad. However, it would be if the user just received some positive news (e.g., that he/she passed an exam).

Also, by analyzing the users’ sentiment, it would be able to see whether the user would need social support. Imagine that someone is feeling bored or lonely and is therefore responding negatively. Coco could detect this and ask how he/she is doing. It could also send some suggestions to feel better or provide some motivating messages to support the user. By analyzing the response of the user again, Coco could know whether the person is appreciating its support (e.g., by checking the used emojis). Sentiment analysis should namely be able to analyze different types of input, such as text, emoticons or symbols .

To conclude, sentiment analysis would be important for providing the correct social support, which is one of the main objectives of Coco. However, at this moment, the analysis methods are not yet perfect. Difficulties can arise because of various reasons, such as sarcasm and interpunction, but also the specific context in which a word is used. As an example, the model discussed in the Ted Talk by Andy Kim reached an accuracy of around 60% . However, as technology is constantly developing, the accuracy is expected to increase further.

Preference Learning

Preference learning allows Coco to adapt to its specific users. For instance, if Coco would start making jokes to cheer its user up and he/she reacts in a positive way, it could learn that this specific user likes a joke as motivator. However, if he/she reacts with “stop it”, this user does not appreciate jokes. Another example could be Coco asking if the user would like to take a short break. If a certain person always responds “no” to breaks at 15:00 and “yes” to breaks at 16:00, it could learn that this user would like to have a break around 16:00 instead of 15:00.

As becomes clear from these two examples, it is necessary to save user data in order to create such preference models. However, the first survey showed that only 46% of the examined participants from our user group (people of <= 25 years) would give permission to save their user data. Hence, this last AI-related application of Coco could be added as a voluntary extra feature, which people could select if they feel comfortable with saving their using data.

Analysis Survey 1

This section focuses on the main topics we are interested in. All parts are extensively analyzed using the statistics application Stata. The Stata code for the analysis of survey 1 can be found in Appendix A.

Interest

The first part of the analysis was quite exploratory: a general question was asked about whether people were interested in having a virtual agent as described in the survey. The research question for this first part was formulated in the following way: 'What are the differences in interests and preferences for people with different demographical characteristics?'

When it comes to interest in the VA, almost half of the respondents expressed their doubts. They did not immediately reject the idea, but thought their choice depended on for example the situation and the functionality of the agent. When looking at age, people that were not sure were distributed very evenly. A Wilcoxon rank-sum test however shows a significant difference in age for people who say they would be interested and people who say they are not (p=0.001). It seems younger people are more open to the idea of having a virtual agent.

Figure X: Scatter plot of interest by age

Figure X: Bar chart of interest by age

Looking at gender, both males and females are interested for around 25% of the time. Even though Figure X below suggests that females more often consider the idea of having a virtual agent, this difference is not significant (X^2, p=0.07).

Figure X: Bar chart of interest by gender

From Figure 4 we can also conclude that people who use their laptop around 7-9 hours a day are most open to the idea of a virtual agent. Least interested are people who use their computer less than 2 hours a day, or more than 9 hours. For both groups respondents indicated that they do not need such a virtual agent.

Figure X: Bar chart of interest by working hours

In conclusion, there are four main findings. First of all, around 75% of the respondents would potentially be interested in a virtual productivity agent. Moreover, younger people (age <= 25) more often express their interest than older people (age > 25). Furthermore, the group that would be interested consists of around the same number of males and females. And finally, people who work on their computer for 2-9 hours a day seem to be interested most often.

Corona impact on productivity and health

The second part of the analysis was about the influence of corona on productivity and health. Several research questions have been formulated:

Do people feel that their productivity, including concentration and motivation have decreased because of the corona virus?

Do people feel that their physical health has decreased because of the corona virus?

Do people feel that their mental health has decreased because of the corona virus?

Considering the effect of COVID-19 on people's perception of their work situation, several observations can be made. Generally, mental health, loneliness and productivity are the three areas that have been negatively impacted by the corona crisis the most.

Figure X: Bar chart of breaks

Figure X: Bar chart of concentration

Figure X: Bar chart of loneliness

Figure X: Bar chart of mental health

Figure X: Bar chart of motivation

Figure X: Bar chart of physical health

Figure X: Bar chart of productivity

Since this questionnaire was also made to determine the user group that would need the help of a VA the most, the following matters will also be analyzed based on age. Generally, people older than 25 take less breaks since COVID-19, and younger people tend to take more breaks. Younger people generally think their concentration has decreased since COVID-19 and on the other hand, people older than 25 generally say their concentration has increased. However, both groups state their productivity has decreased. Although both groups state that their mental health, physical health and feeling of loneliness have decreased since COVID-19, the younger user group indicates a greater negative impact.

Figure X: Bar chart of breaks by age

Figure X: Bar chart of concentration by age

Figure X: Bar chart of loneliness by age

Figure X: Bar chart of mental health by age

Figure X: Bar chart of motivation by age

Figure X: Bar chart of physical health by age

Figure X: Bar chart of productivity by age

In Table 1 the means of the two groups are shown to clarify the above made statements. When the mean is smaller than 0, there is on average a decrease and if this impact is bigger than 0, there is on average an increase. There is a significant difference between the two groups if the p-value is smaller than 0.05. All this information suggests that it might be best to focus on people of 25 or younger for this research and the development of the computer companion.

Table 1: The means for several corona impact measurements and the differences between the different age groups.

Aspects	Mean <= 25	Mean > 25	p-value
Taking breaks	0.19	-0.49	0.0001
Concentration	-0.30	2.9	0.0004
Feeling of loneliness	-0.40	-0.36	0.7745
Mental health	-0.61	-0.28	0.0117
Motivation	0.37	0.19	0.1868
Physical health	-0.74	-0.09	0.0001
Productivity	-0.47	-0.31	0.2165

Regarding the open question 'Are there other activities than the ones mentioned above that you often experience to negatively impact your productivity?', around 20 people mentioned that phone calls, applications like Netflix, and message platforms like Microsoft Teams and WhatsApp disturb their productivity. Besides that, 15 participants mentioned that their family and other people in their household negatively impact productivity. Children for example ask for a lot of attention when parents are working at home, and they need to be helped with homeschooling (unfortunately, Coco will not be able to solve this problem). Besides these two main impact factors, some participants also mentioned no breaks, lack of physical contact, the mailman, spontaneous household chores, unstructured schedules, bad sleep quality, a non-stimulating workplace, ambient noise, and the monotony of the day as things that negatively impact productivity.

To summarize, considering all respondents the productivity, physical health and mental health have decreased. Generally, concentration has not been affected by COVID-19. Respondents even indicated an increase in motivation. Once the respondents were grouped by age, a clear difference in responses could be seen. The people of 25 years old and younger indicated a slight decrease in concentration, while people over 25 indicated a slight increase in concentration. Even though both groups indicated a decrease for most other cases, people of 25 years old and younger indicated a higher decrease in mental health, physical health and productivity. For both groups motivation increased, but there was a bigger increase indicated for the younger group.

Helpful tasks for a virtual agent

The third part of the analysis was about the tasks of a virtual agent users would see as useful. The research question for this part is: Which tasks are most important for a computer companion to possess?

Sixteen different variables are used to see what users prefer as tasks for a virtual agent. For every variable, the respondents could choose five options from a Likert scale varying between ‘Not useful at all’ to ‘Very useful’. From the histograms it can be seen that ‘Help avoiding distractions by blocking applications’, ‘Help to split up daunting tasks’, ‘Encourage taking breaks’, ‘Encourage physical activity during breaks’, ‘Help increase productivity with your favorite concentration/focus application’, ‘Reflect on work at the end of the day’, ‘Help schedule tasks in your preferred scheduling application’, ‘Help you to focus on a currently scheduled task by getting you back from distractions’, ‘Providing reminders of your schedule’, ‘Make task switching smoother’ and ‘Help you to work according to your preferred time-scheduling technique’ all seem to lean to the right side, indicating that they are thought to be more useful.

Figure X: Bar chart of interest in blocking applications

Figure X: Bar chart of interest in splitting up tasks

Figure X: Bar chart of interest in encouraging to take breaks

Figure X: Bar chart of interest in encouraging to be physically active

Figure X: Bar chart of interest in providing physical exercises

Figure X: Bar chart of interest in connecting with focus application

Figure X: Bar chart of interest in providing motivation

Figure X: Bar chart of interest in reflection at the end of the day

Figure X: Bar chart of interest in connecting with scheduling application

Figure X: Bar chart of interest in providing small talk

Figure X: Bar chart of interest in sending auditory reminders

Figure X: Bar chart of interest in helping focus on current tasks

Figure X: Bar chart of interest in connecting with applications to define tasks

Figure X: Bar chart of interest in providing reminders

Figure X: Bar chart of interest in making task switching smoother

Figure X: Bar chart of interest in helping work to scheduling technique

These histograms can be supported by skewness values. A skewness value below zero means that the data is more distributed to the right, while a positive value means that the data is more distributed to the left. We try to find a negative skewness here to confirm what is mentioned above. As can be seen in table 2, all the skewness values of above topics are indeed negative, however, some are larger skewed than others and thus more often chosen to be useful. In conclusion, the most useful tasks (based on negative skewness values between –0.57 to –0.80) are, from highest to lowest; ‘Help avoiding distractions by blocking applications’, ‘Help increase productivity with your favorite concentration/focus application’, ‘Help schedule tasks in your preferred scheduling application’. All other variables have skewness values lower than -0.39 and are thus less helpful than these mentioned above.

Table 2: The skewness values for several variables of what could be helpful tasks of a VA.

Variables	Skewness
Help avoiding distractions by blocking applications	-0.7914
Help to split up daunting tasks	-0.3372
Encourage taking breaks	-0.1631
Encourage physical activity during breaks	-0.1469
Help increase productivity with your favorite concentration/focus application	-0.7717
Reflect on work at the end of the day	-0.3852
Help schedule tasks in your preferred scheduling application	-0.5737
Help you to focus on a currently scheduled task by getting you back from distractions	-0.2038
Providing reminders of your schedule	-0.2560
Make task switching smoother	-0.3755
Help you to work according to your preferred time-scheduling technique	-0.3555

To check whether it was a good decision to focus on respondents of 25 years and younger, we have made bar charts of the three most important variables ,grouped by age. The results can be found below and it can be seen that this is indeed a good choice. More younger people have interest in the three most helpful tasks and older people are doubtful or even sure that it will not be helpful.

Figure X: Bar chart of interest in blocking applications by age

Figure X: Bar chart of interest in connecting with focus application by age

Figure X: Bar chart of interest in connecting with scheduling application by age

Furthermore, it can be checked using t-tests (or if not allowed; rank sum tests) whether there is a significant difference between age groups for the variables. We start with performing Shapiro-Wilk and Skewness/Kurtosis tests to see if the data is normally distributed. Both p-values should be larger than 0.05 here to not reject the null hypothesis (‘The data is normally distributed’). If this is the case, we may perform a t-test and otherwise we may only perform a non-parametrical test. We will only look for a significant difference in the three most helpful tasks, according to above. Table 3 shows that all variables have a corresponding p-value smaller than 0.05 and thus there is a significance difference between the two age groups and those helpful tasks.

Table 3: The corresponding significance values for the three main helpful tasks variables.

Variables	Significance value
Help avoiding distractions by blocking applications	0.0287
Help increase productivity with your favorite concentration/focus application	0.0023
Help schedule tasks in your preferred scheduling application	0.0001

Regarding the open question 'Are there any tasks which aren't mentioned in the list above that you would like a virtual agent to be able to do?', around 20 people have given useful suggestions for extra features of Coco or have confirmed that already existing features will be helpful. Quite a lot of people mentioned that Coco should be connected to streaming services for music or meditations. Furthermore, some people mentioned that they would like it if Coco were connected to their coffee machine or alarm. It is also mentioned a lot that Coco should suggest a planning for the day based on tasks and order their email on priority. Moreover, it has also been mentioned a few times that Coco should be able to block certain applications and signal to colleagues whether they are busy or not, some people add that this should happen automatically. These are ideas we already had, and it is thus great to hear that the respondents think the same. In the survey's additional comments, two people gave additional suggestions for functionality. The first mentioned that it would be useful if you can decide to turn of Coco for a while, especially during the weekend this might be useful. Another person worried that Coco should not make them lose their concentration: “The last thing I want is another application that sends me unsolicited and annoying notifications.”

In conclusion, the three most helpful tasks for Coco would be to help avoid distractions by blocking applications, to help increase productivity with users' favorite concentration/focus application, and finally to help schedule tasks in users' preferred scheduling application. Furthermore, there is indeed a difference between the younger age group (<= 25 years) and the older age group (> 25 years) in whether tasks will be seen as helpful or not. For the three main tasks, there is a significance difference for all of them (p < 0.05).

Privacy

The following section will cover possible privacy concerns, related to the use of Coco. It will try to answer the research question “Will there be any computer features for the computer companion which are not possible to implement because of privacy issues?”. If so, it would be important to know this beforehand, since potential users of Coco should be comfortable using Coco.

Subquestion 1: 'Are people generally comfortable with the computer features being used for the computer companion (that require access to personal data)?'

To investigate whether people feel comfortable when Coco would use personal data that it acquired with the help of, for instance the user's camera, microphone, or agenda, several different questions were asked in the survey. People could indicate on a Likert-scale ranging from 1 (not comfortable at all) to 3 (completely comfortable) how they felt about Coco using their agenda, camera and microphone, as well as it monitoring their activity, giving popups, and saving user data. These questions are all encoded as “comfort_agenda", “comfort_camera", “comfort_microphone", “comfort_monitoring", “comfort_popups", and “comfort_userdata". Their means are shown in Table 4.

Table 4: Means of comfort questions.

	Mean (all participants)	Mean (participants, <= 25 years)	Mean (participants, >25 years)
Agenda	2	2.33	1.82
Camera	1.34	1.38	1.33
Microphone	1.51	1.44	1.54
Monitoring	2.05	2.24	1.95
Popups	2.23	2.53	2.06
User data	1.80	1.87	1.77

As can be seen from table 4, people feel most comfortable receiving popups from the computer companion and least comfortable giving access to their camera. A score of 2 would mean that people are “somewhat comfortable”, hence a score higher than 2 could be seen as a minimal to accept the feature. For our chosen user group of people of 25 years or younger, this would mean that the computer companion could feel free to manage the user's agenda, monitor his/her activity, and send popup messages. In general, our user group feels more comfortable with the discussed features than the older user group. The only exception is in giving access to the microphone, although the difference is small.

Subquestion 2: 'Would people give permission for including the different computer features to improve the computer companion's performance?'

The survey also asked people to indicate whether people would actually give permission for the different features mentioned before (requiring access to certain programs and/or personal data). This was asked with the side note that Coco's performance could be improved by allowing these features. People could choose between “no", “yes”, and “I don't know” for Coco using their agenda, camera and microphone, as well as it monitoring their activity, giving popups, and saving user data. These questions are all encoded as “permissions_agenda", “permissions_camera", “permissions_microphone", “permissions_monitoring", “permissions_popups", and “permissions_userdata". The frequencies of people answering “yes” are shown in Table 5.

Table 5: Results of permissions questions.

	Frequency 'yes' (all participants)	Frequency 'yes' (participants, <= 25 years)	Frequency 'yes' (participants, >25 years)
Agenda	58.93%	66.67%	54.95%
Camera	19.64%	15.79%	21.62%
Microphone	32.14%	26.32%	35.14%
Monitoring	60.71%	64.91%	58.56%
Popups	69.05%	78.95%	63.96%
User data	40.48%	45.61%	37.84%

As can be seen in table 5, people are most likely to give permission to popups, and least likely to give permission to use the camera. These results are in line with the ones described in table 4. Hence, it seems likely that people also give more easily permission if they feel comfortable with the feature. This correlation between feeling comfortable and giving permission can also be confirmed by calculating the actual correlation between the two variables “comfort_X” and “permissions_X” for each different pair (agenda, camera, etc.). These values are found to vary between 0.7498 and 0.8312 and are all significant for a p-value of 0.05. When comparing the younger and older user group, the younger user group gives on average more frequently permission. However, the older user group is also this time more positive about the use of the microphone than the younger user group. One other remarkable difference is the fact that the older user group is more likely to give permission to use the camera than the younger user group. However, their difference in being comfortable with using the camera was also extremely small.

To summarize, there can be four main conclusions from the privacy analysis. First of all, people feel comfortable with the use of popups, monitoring, and the agenda. Also, a majority gives permission to use these features. In addition, people do not feel comfortable with the use of the camera, microphone, and user data. Also, only a minority gives permission to use these features. Moreover, there exist significant correlations between feeling comfortable with and giving permission for a certain feature. And finally, the younger age group (<= 25 years) seems to be more positive about the features than the older age group (> 25 years). They express being more comfortable with most of the features and more often give permission to use the features. Exceptions only concern the use of the microphone and the camera, for which older people gave more often permission.

Appearance

The following section will try to answer the research question “Do people have a preference regarding the appearance of the computer companion?". These results will be useful when designing Coco during a later stage of the project.

In the survey, participants had to indicate their preferences for the appearance of Coco. There were three options (human, robot, animal) which they could rank from 1 (most favorable) to 3 (least favorable). The frequency of option 1, 2, or 3 was then encoded for each appearance into three variables (“human_appearance", “robot_appearance", and “animal_appearance"). These results are shown in table 6.

Table 6: Percentages of ranking human, robot, and animal appearance.

	Human	Robot	Animal
Ranking 1	35.71%	34.52%	23.21%
Ranking 2	27.98%	27.38%	32.74%
Ranking 3	29.17%	29.17%	34.52%

As becomes clear from table 6, people like a human or a robot almost exactly the same. An animal, on the other hand, is somewhat less favorable, as it is chosen less often as first option and more often as second or third option. It would be useful to focus more on the preferences between humans and robots in the second survey.

Subquestion: 'Do people from different age groups differ in their preferences regarding the appearance of the computer companion?'

To check for any different preferences between younger (<= 25 years) and older (> 25 years) participants, the histograms shown in Figure X, Figure X, and Figure X are analyzed. Looking at these plots, some differences seem to exist. To check whether these differences are significant, a Chi-square test has been performed between each variable “human_appearance", “robot_appearance", and “animal_appearance" and the variable “high_age", which separates younger (<= 25 years) and older (> 25 years) participants.

The alpha-value has been set at its default-value of 0.05 and the p-values are <0.001 (human), 0.096 (robot), and 0.012 (animal). Hence, indeed a significant difference is found in the preferences for a human or animal appearance between younger and older people. Older people think a human appearance is most favorable, while younger people think the opposite. Also, older people prefer an animal appearance less than younger people. However, the differences in case of a robot appear to be insignificant.

Figure X: Bar chart of human preference by age

Figure X: Bar chart of robot preference by age

Figure X: Bar chart of animal preference by age

Regarding the open question 'Would you rather have a different look for your computer buddy than indicated above?', most people did not have any additional comments about the appearance of Coco, however, some trends could be seen amongst the ones that did gave some input. First of all, five people mentioned that they would like the option to customize the appearance of Coco. They mention, for instance, that they would like to choose a figure on one day, and another one on the next day. One person even mentioned the difference between work and leisure. Next to this, they would like the option to adapt the figures themselves. Of course, this would require more (difficult) options in the software. Furthermore, six people mentioned that Coco did not even need an appearance. Something more simplistic, for example such as Siri (mentioned by a couple), would be sufficient in their opinion. Also, eighteen people gave additional options that could be designed. These ranged from “more natural elements” and “a Disney character” to “a self-chosen picture”. Lastly, two persons suggested to change our proposed appearances. One mentioned to make them “somewhat older", while the other would like to see some more details.

In conclusion, people prefer a human or robot appearance almost the same, and a robot appearance less. Furthermore, when comparing the younger (<= 25 years) and older (> 25 years) age group, a significant difference on three areas in preferences is found, regarding the human or animal appearance. First of all, older people prefer the human appearance the most, whereas younger people prefer the human appearance the least. Moreover, younger people prefer the animal appearance more than older people. And finally, the differences in preferences concerning the robot appearance are insignificant.

Distractions that negatively impact productivity

This section will try to answer the following research questions. The first question is, is there a significant difference in the extent to which the two groups (workers, age > 25 and students age <= 25) experience the negative impact on their productivity? If there is indeed a large difference between the two groups, it might be important to narrow down our user group. And the second research question is what types of distractions have the most influence on productivity? This question is asked so we can prioritize the functions that Coco needs to have.

In the survey, we asked our respondents the following; 'How often do you get distracted from your work by the following activities?'. These activities are the internet, lectures, news, personal mail, scheduled meetings, online shopping, social media, unplanned meetings and work mail. To see what types of distractors have the most influence on productivity, we calculated the mean response for both students and workers. The mean response was based on the different answers the respondents could give to the question, to which they could answer the following: never, less than once a month, a few times a month, a few times a week, daily, 2 or 3 times a day, more than three times a day, in which 1 means never and 7 means more than three times a day. The mean amount of distraction can be seen for both age groups per distraction factor in table 7 below. The significance values are also shown in the table because the effect of the type of distractor can differ significantly per age group. This statistically significant difference is computed with the Wilcoxon rank-sum test.

Table 7: Distraction factors

Distraction factor	Mean for age <= 25	Mean for age > 25	Significance value
Internet	4.97	3.22	p = 0.0000
Lecture	3.6	1.92	p = 0.0000
News	3.71	3.08	p = 0.1617
Personal mail	3.86	3.36	p = 0.6041
Scheduled meetings	3.6	3.46	p = 0.2918
Shopping	3.03	1.82	p = 0.0000
Social media	5.4	2.9	p = 0.0000
Unplanned meetings	2.8	3.58	p = 0.0002
Work mail	4.06	4.64	p = 0.0005

As you can see in table 7, the mean distraction is higher for every sort of distraction type except for work-related mail. This highlights that students’ (people aged <= 25) productivity is more often affected by these distractions than the productivity of working people. To give an example, a mean of 4.97 (internet) for people aged 25 or lower means that they are distracted 'daily' on average. The differences between the two groups are significant for internet, lectures, shopping, social media, unplanned meetings and for work mail. This table also informs us about what types of distractors have the most influence on both groups. For clarity, table 8 below shows the rank order of the amount of distraction per age group.

Table 8: Rank order of distraction factors

Rank	Students	Workers
1.	Social media	Work mail
2.	Internet	Unplanned meetings
3.	Work mail	Scheduled meetings
4.	Personal mail	Personal meetings
5.	News	Internet
6.	Lectures	News
	Scheduled meetings
7.	Shopping	Social media
8.	Unplanned meetings	Lectures
9.		Shopping

The figures below visually show the results we have found above.

Figure X: Frequency of distraction (internet), grouped by age

Figure X: Frequency of distraction (lectures), grouped by age

Figure X: Frequency of distraction (news), grouped by age

Figure X: Frequency of distraction (personal mail), grouped by age

Figure X: Frequency of distraction (scheduled meetings), grouped by age

Figure X: Frequency of distraction (shopping), grouped by age

Figure X: Frequency of distraction (social media), grouped by age

Figure X: Frequency of distraction (unplanned meetings), grouped by age

Figure X: Frequency of distraction (work mail), grouped by age

To summarize, there is indeed a significant difference between the two age groups and the times that they get distracted by different factors, so this again confirms that it is a good idea to split up the groups and investigate them separately. From both the graphs and the statistical analysis you can see that students are more often distracted by multiple factors than workers. For these students, the two largest distractor factors are, by far, social media and the internet. These factors distract students two or three times a day and daily (respectively).

Money

There were quite some different answers to the question what people were willing to pay for Coco. There were three main categories in which people answered the question. One was paying per certain period, for example a monthly or yearly fee. The second category of people wanted Coco to be a one-time buy, and the third category consists of other answers.

Payments per period

Around 17 people said that they would like to pay monthly or yearly, however the amount that they were willing to pay varied widely. Most of these people wanted to pay somewhere between 5 – 20 Euros per month.

One-time buy

The amount of money that people were willing to pay in a one-time buy varied widely again, between 2 Euros to 500 Euro's. However, most people responded with 10 Euro's and 50 Euro's. The average price that people were willing to pay was approximately 68 Euro's.

Other

17 people mentioned that they did not want to pay for the software and there were also some additional ideas posed by the respondents, for example, 9 people mentioned that 'work should pay for this'. Besides that, some people said that they wanted a free tryout first to see the functionalities before they want to pay for it. If results are good, they are willing to pay around 10 Euros per month or around 100 Euros per year. Moreover, a few people commented that it depends on the type of license; a one-time purchase, or pay on monthly base or yearly base? This was an unnecessary dubiety on our side.

General conclusion of analysis 1

As indicated in the second part of the analysis 'Corona impact on productivity and health', it might be best to focus on people of 25 years old or younger. Their productivity, concentration, mental health, and physical health have decreased the most since COVID-19. Coco would be best of help for the users if it could aid them during their work considering these aspects.

Because almost all the frequencies of the distraction types are higher for students than for workers, it can be concluded that Coco would be more useful for students. The two things that distract students the most compared to the other distraction factors are social media, and the internet. Therefore it is of great importance that Coco will be able to prevent the students from getting distracted by at least these two factors.

References

Appendix

Appendix A - Stata code analysis survey 1

Here the Stata code for the analysis of survey 1 can be found.