Measuring Moods from Behaviour

In the previous section it was established with the help of the literature that moods might have an impact on programmers‘ programming tasks and hence on their performance. However, in order to help programmers to improve their performance, there is a need to measure their mood. The objective of this section is to consider the possibility of measuring programmers‘ and computer users‘ mood from their use of a keyboard and a mouse.

As use of keyboard and mouse is a specific kind of behaviour, this section commences with the possibility of measuring moods from programmers‘ and

computer users‘ behaviour. Therefore, in the second part of this section the effect of moods on general behaviour will be discussed, followed by the discussion on the effects moods have on computer related behaviour in the third part. The fourth part will be a discussion on different ways to report moods and to measure moods along with their advantages and disadvantages. What could be used in order to have non- interruptive and non obtrusive measurement of moods in a computer related environment? This concern will be discussed in the last part of this section. In addition, the second hypothesis of the study will also be devised in the last part.

Mood might have an effect on behaviour and there are various studies to support this idea. For example, Armitage, Conner and Norman (1999) showed that positive moods promote risky decision making and heuristic strategies. Similarly, they found that people in negative moods focus more on specific outcomes and other attributes associated with behaviour when making a decision. Bohner et al. (1992) showed that people were uninfluenced by the content as well as context information when in a good mood but used both types of information while in a bad mood. Dow (1992) argued that moods are diffusely related to behaviour.

As reports in the literature show that different moods/emotions might be a reasons for various behaviours, this research is particularly interested in measuring moods based on the behaviour. A considerable amount of research has focused on computer systems that can recognize user's emotions and can adapt them accordingly in order to improve social presence (Nasoz et. al, 2003). There are also continuing attempts to measure moods from behaviour.

There are various methods devised for measuring moods/emotion from behaviour including both self-reporting and detections and measurements by the use of software. Methods like SAM (Self Assessment Manikin) and EPI (Emotion Profile Index) are for manual measurement of moods and are widely used in different researches that require measuring moods and emotions of the subjects. These measurements are also called self-reporting measurements (Swindells, 2006; Poels, 2006).

Poels (2006) divided self-reporting measurement scales into verbal self- reporting scales, visual self-reporting scales and moment-to-moment ratings. Some commonly used examples of verbal self-report scales are Plutchik's emotion profile index (EPI) and Izzard's differential emotion scale. Visual self-report scales include SAM (Self Assessment manikin), PrEMO (Product Emotion Measurement

Instrument). Moment to moment ratings includes warmth monitor which is shown to provide reliable measure of warmth.

Verbal mood measuring methods have various advantages such as being simple and easy to quickly investigate large scale emotional responses. Similarly, these methods also have various limitations such as scales containing a large list of emotion adjectives that might be cumbersome and a cause of fatigue in respondents (Poels, 2006). However, these moods/emotions measuring methods might not be suitable for measuring moods/emotions of the programmers due to their distracting nature and requirement to rate moods constantly (Zimmerman et al., 2003).

Visual reporting or autonomic measures include various techniques like facial expressions, skin conductance and heart rate (Nasoz, 2003; Backs, 2000). Measurements of facial expressions might not be suitable for measuring moods of programmers at work because they rely on the visible facial expression. Measuring moods from facial expression is not always a good choice because affective states are internal and involve cognitive thoughts as well as physical changes (Picard, 1997). Internal cognitive thoughts may not always impact facial expressions.

Physiological measures such as skin conductance, heart rate measurement etc require equipment to be attached to different body parts and this may cause disturbance to a programmers working in a professional environment. (Fahrenberg and Wientjes, 2000; Zimmermann et al., 2003). The mentoring of keyboard and mouse use might reduce these overheads as these are the most basic input devices used by computer users. Computer users are familiar with these devices and there are no risks of external device disruptions such as the loss of concentration due to attached equipment and devices. The use of keyboard and mouse is also cheap as they are available with almost every computer (Zimmerman et al., 2003).

Keyboard and mouse have already been used in various studies such as in personality (Mikkelsen et al., 2007) and usability (Coutaz et al., 1995) domains. For instance Meunier (1996) found that personality and gender had a significant effect on keyboard control. However there is limited literature on the possibility of measurement of moods by the user‘s use of keyboard and mouse. There are some examples where researchers strived for this, like Mahr et al. (2005) used mouse motions to detect emotions with some significant correlations in his thesis. Another example is Zimmerman et al. (2003) who designed experiments to store keyboard keystrokes and mouse movements in log files and later intended to find correlations of

these events with affective state. Zimmerman (2003) results might not be published yet but the study described in this thesis was set up to conduct the empirical examination on the possibility of mood measurement from computer users‘ keyboard and mouse usage.

Although there is some research, which utilized keyboard and mouse as indicated above, the direct use of keyboard and mouse as a mood measurement instrument was simply not found. However research suggests that keyboard and mouse behaviour is influenced by social factors and work environments (Peres et al., 2004). This might lead us to the second hypothesis of the study that indicates that ―Moods can be measured from the computer user‟s interaction with keyboard and

mouse”.