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Frustration in Response to Impairments and Failures in Online Services, and

Resulting Impact on Customer Attitudes

Mark Chignell1, Andrea Jovanovic2, Chelsea de Guzman2, Jie Jiang3, and Leon Zucherman3 Abstract In spite of the development of high-speed networks, the bandwidth intensity and real-time requirements of many online services continue to push the limits of current network implementations. This has resulted in services that may have frequent interruptions (impairments) or where there may be unavailability or loss of service (failures). While the issue of frustration, specifically in response to impairments and failures in online services, has received relatively little attention, there has been considerable research on frustration in general. In practice, every service implementation is a tradeoff between the need for high quality service delivery, and the need for efficient use of resources. This tradeoff is especially relevant for wireless services. In this paper we review the literature on frustration. We discuss the implications of past research findings relevant to understanding user experience with online services. We also discuss the tradeoff that exists between efficiency on the one hand, and quality that is acceptable for users, so that they do not become frustrated, on the other. Introduction Increasingly, people, businesses, and other organizations are relying on the reliable provisioning of online services. But what happens when services become unreliable? How unreliable do services have to become before alternatives are considered? In businesses and other organizations decisions about service selection or discontinuation may be treated as optimization problems based on quantitative variables such as time and costs. However, consumer experiments of service impairments and failures are likely to be more subjective, and based on a psychological and subjective evaluation of the experience rather than an engineer’s more objective view of the service quality. Session failures (e.g., when a video fails to load, or fails to play to the end, or where a video conference is suddenly terminated before completion), while less frequent than impairments (e.g., short interruptions in video playback), have a disproportionate negative impact on customer assessment of experience. It is well known in human psychology that there is a “negativity bias” that affects judged experience (Baumeister et al., 2001; Soderlund, 2003; Kahneman, 2011). As Baumeister et al put it, “bad is stronger than good”. They go on to say that "Bad impressions ….are quicker to form and more resistant to disconfirmation than good ones.” This effect can also be seen in judgments of utility where losses are more heavily weighted than gains (e.g.,

1 Knowledge Media Design Institute, Faculty of Information, University of Toronto 2 Department of Mechanical and Industrial Engineering, University of Toronto 3 TELUS Corporation

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Kahneman and Tversky, 1979, Figure 3). As a result, a single “critical incident” of severe failure may be sufficient to turn a person against a service, while a succession of minor disappointments may end up being tolerable. The disproportionate effect of severe failures happens because emotions are a key part of how people evaluate services and systems (Norman, 2005). Researchers on emotion (e.g., Russell, 1980) have constructed a two-dimensional model of emotion that describes experienced emotions as a combination of a valence dimension and an arousal dimension. Figure 1 shows a schematic view of 28 emotion words embedded in a two dimensional space (positioning of the words is based on a series of figures presented by Russell, 1980). Of particular interest to the current discussion is the position of “frustrated” in the space. It is associated with a strongly negative valence, but the level of activity is only moderate when compared to “Angry”. Based on Russell’s results, “alarmed” represents the most strongly activated negative emotion. Thus the circumplex model depicted in Figure 1describes frustration as a state that has strong negative valence, but only moderate arousal/activation. What can a review of relevant research literature tell us about the properties of frustration, and how it should be detected and managed? In this paper we use a review of relevant research literature to address the following research questions.

What is frustration and what are the factors and circumstances that govern its initiation, growth, and, in some cases, eventual cessation? What is the role of expectation in moderating the experience of frustration?

We are also interested in a further set of questions that address the implications of frustration, and how it is handled, for the management and evaluation of online services. Some relevant questions for this interest are listed below. Note that while not all these questions have been adequately addressed in previous literature, they provide a framework within which to assess the more theoretically based work.

What are the features of frustration that are likely to be most relevant to the design of online services?

When does accumulation of frustration lead to loss of confidence in, or aversion to, a service experience? How does frustration experienced by customers manifest itself in terms of their relationship with the service provider?

In the next section we review the definition of what frustration is and situate it with respect to other emotions. We then present the lifecycle of frustration, from the creation of frustration to accumulation and escalation leading to possible action. We also discuss the role of frustration in consumption of online services.

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A Definition of Frustration According to the Merriam-Webster dictionary, frustration is: “a feeling of anger or annoyance caused by being unable to do something: the state of being frustrated”. Researchers and theorists have characterized frustration as resulting from a blockage of goal attainment. Sigmund Freud defined it in terms of barriers, both external and internal, to goal attainment (Freud, 1958, cited in Ceaparu et al., 2004). Stauss et al. (2005, p.234) note that frustration develops when individuals believe their goals are feasible but those goals are not attained, or when people are promised rewards that are not received. Other authors have also described frustration as a response to interruption, inhibition, or blockage of goal attainment (e.g., Dollard et al., 1939). Freud’s definition of frustration as blocked goal attainment focuses on frustration as an effect, but frustration has also been seen as a causal factor. Amsel and Roussel (1952) described frustration as a motivator for behaviour, with subsequent research on “frustration theory” being summarized by Amsel (1992). For service providers, it is the role of frustration as a causal factor influencing future behaviour that is of particular interest. Frustration as an Emotion Multidimensional scaling analysis of emotion words (after Russell, 1980), shows frustration as being located at a moderate level of arousal (vertical axis, Figure 1) combined with negative valence (the left side of the horizontal axis in Figure 1). This positioning suggests that frustration may not always lead to action unless there is a transition towards anger (e.g., through an accumulation of frustrating events above some threshold). However, there is disagreement in the research literature about whether or not frustration involves a moderate, or high, level of arousal. Stauss et al, 2005 described frustration as “strongly felt dissatisfaction” contrasting it with non-frustrated dissatisfaction, where the level of arousal was assumed to be lower. If frustration is a more activated version of dissatisfaction, then arousal added to dissatisfaction would likely lead to frustration. Further increase in activation might then leading to anger. Behavioural actions would then be more likely in this activated state. While some actions in response to anger might be destructive, others would be designed to reduce the frustration and increase the chances of later goal attainment. Frustration and satisfaction are at almost polar opposites of the circumplex structure of emotion (Figure 1). Thus it is not obvious that they can be represented by a common attribute or contin-uum. Since there is no easy path from the emotion of frustration/dissatisfaction to satisfaction it may not be wise to place satisfaction and dissatisfaction at opposite ends of a bipolar continuum (a single dimension view). It may also be argued that frustration is distinguishable from the more general attitude of dissatisfaction, which tends to accumulate over time. Thus there are grounds for considering frustration and related constructs of negative valence (e.g., dissatisfac-tion) to be a separate dimension from satisfaction. This is consistent with Oliver’s (1993) view that satisfaction and dissatisfaction are separate dimensions.

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Frustration tends to begin as a momentary experience, although it can then accumulate into an attitude of general frustration. An example of frustration as a momentary experience can be seen in the statement “I’m feeling frustrated because I’ve been waiting a while and the video won’t start”. Whereas an example of a more general attitude of frustration would be: “I’m frustrated with my service provider because videos never seem to play properly and Web pages with lots of graphics seem to load really slowly.” Stauss et al. (2005) describe frustration as a form of strongly felt dissatisfaction, that occurs when individuals do not attain expected goals or promised rewards. In their view frustration requires the customer to have goals or rewards in mind before frustration occurs. This is consistent with the view of Freud and others that frustration arises from blocked goals. Dissatisfaction, however, may develop without customers being aware of what their expectations are prior to a problematic event. Frustration also seems to be more sensitive to expectations and prior experience (e.g., customer’s previous experiences and/or promises made to the customer by the company). This relationship between previous experience and expectation does not seem as important for dissatisfaction, which can develop without having a prior expectation of service quality (Strauss et al., 2005). Some marketing theorists have referred to frustration as a synonym for anger, and some appraisal theorists consider frustration to be a milder form of anger (e.g., Berkowtiz and Harmon-Jones, 2004). However, anger and frustration can be distinguished in terms of whether or not blame is attribution to external agents (Clore and Centerbar, 2004) or to situations. Anger and frustration also lead to different forms of coping – anger promotes confrontational coping, whereas frustra-tion tends to lead to support-seeking coping (Gelbrich, 2010).

Figure 1. The Positioning of Frustration and Satisfaction (“satisfied”) within the Circumplex Model of Emotion (redrawn from a Multidimensional Scaling solution presented by Russell,1980). The Physiology and Neuroscience of Frustration and Reward

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Frustration tends to create a pattern of physiological responses that are similar to stress (Scheirer et al., 2002). As noted by Downs (2006), “many of these… are controlled through the autonomic nervous system and are therefore not subject to conscious control or manipulation.” Kreibig (2010) reviewed 134 publications that reported experimental investigations of emotional effects on peripheral physiological responding in healthy individuals. She summarized the results as suggesting that different emotions tend to lead to different bodily (autonomic nervous system) responses: “ANS response specificity in emotion when considering subtypes of distinct emo-tions”, a view that had been earlier proposed by Ekman, Levenson, and Friesen (1983). Frustra-tion was not measured in Kreibig’s review, but of the sets of emotions that were considered, the anger related cluster seemed to be most related. Citing previous research, Kreibig summarized the physiological response to anger as: “adrenergically mediated cardiovascular effects: in-creased HR[heart rate], increased SBP[systolic blood pressure] and DBP[diastolic blood pres-sure], and increased TPR[total peripheral resistance]…”. There is considerable evidence that separate brain locations or circuits handle reward and pun-ishment. Rewards are associated with approach motivation and punishments are associated with avoidance. Gray (1981) posited two main neural mechanisms that he believed to be related not only to specific instances of reward and punishment but also to basic components of personality. The behavioral inhibition system (BIS) was defined as being related to sensitivity to punishment as well as to avoidance motivation. Gray proposed that high activity of the BIS produces a heightened sensitivity to nonreward, punishment, and novel experience. This greater sensitivity was expected to lead to avoidance of situations associated with negative experiences such as fear, anxiety, frustration, and sadness. According to Gray, the physiological mechanism behind the BIS was the septohippocampal system and its monoaminergic afferents from the brainstem In contrast, Gray characterized the behavioural activation system (BAS) as being sensitive to non-punishment and reward. An active BAS was assumed to lead to higher levels of positive emotions such as elation, happiness, and hope. Gray believed that the BAS is related to specific pathways in the brain. Figure 2 shows dopamine pathways in the brain that are involved in re-ward. The mesolimbic pathway is one such pathway that links the ventral tegmental area of the midbrain to the limbic system and the medial prefrontal cortex. It begins in the midbrain (part of the brainstem) and connects to the limbic system as well as to the medial prefrontal cortex (see blue projections in Figure 2). While we are a long way from understanding the detailed neurophysiology of reward and pun-ishment, it seems likely that they involve two different, and relatively independent subsystems within the brain. Research cited by Gray (2001) indicates that approach states lead to greater left hemisphere activation, whereas withdrawal states lead to greater right hemisphere activation. This clear disassociation between approach and avoidance reinforces the notion that satisfaction and frustration will be associated with separate dimensions of psychological experience involv-ing the approach (BAS), and avoidance (BIS), systems respectively.

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Figure 2. Dopamine Pathways in the Brain. Figure downloaded from http://en.wikipedia.org/wiki/File:Dopamineseratonin.png (available in the public domain). With the availability of brain scanning technologies such as fMRI in recent decades there have been a number of studies examining where in the brain different emotions are processed. Table 1 shows a summary of brain areas that have been identified with four emotions (based on material presented by Gazzaniga et al., 2014, p. 464). Of particular interest to the present discussion is the association of anger with the anterior cingulate cortex since this region of the medial frontal lobe of the brain is known to be associated with reward expectation. Since the definition of frustration views it as arising from blocked goals, then the blocking of goals should violate expected re-wards, thus involving the anterior cingulate cortex. Recent research on the role of the Anterior Cingulate Cortex in the experience of frustration seems to support this expectation. For instance Umemoto et al. (2014) discuss disruptions to normal processing in the Anterior Cingulate Cortex and the resulting greater levels of frustration experienced by ADHD patients.

Emotion Brain Area Function

Fear Amygdala Learning, Avoidance

Anger Orbito-frontal cortex, anterior cingulate cor-tex

Indicate Social Viola-tions

Sadness Amygdala, Right Temporal Pole Withdraw

Disgust Anterior Insula, Anterior Cingulate Cortex Avoidance Table 1. Brain Areas Associated with Four Emotions.

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Masten et al (2011) noted that increased activity in the subgenual region of the anterior cingulate cortex has been consistently linked with depression, which may involve maladaptive responses to blocked goals. While evaluation of reward in the anterior cingulated cortex is likely involved in frustration re-sponses, which may also be mediated by individual differences (e.g., people with tendencies to-wards ADHD may be more easily frustrated) the field of neuroscience does not yet offer specific models of frustration and the transition to anger. However, one promising area of research is neuro-economics, which looks at the brain structures and processes associated with economic choice and decision making. Recent work on neuro-economics has been summarized by Graz-iano (2013) and it seems likely that a new understanding of the brain correlates of psychological responses like frustration and anger will emerge from this field in the next few years. Prior to the availability of detailed information about how specific emotions are handled in dif-ferent parts of the brain, it is nevertheless possible to understand the basic properties of frustra-tion and anger in terms of what Wilson-Mendenhall et al. (2013) refer to as “core affect”, i.e., “a person’s fluctuating level of pleasant or unpleasant arousal”. According to Wilson-Mendenhall et al., “subjective ratings of valence (i.e., pleasure/displeasure) and of arousal evoked by various fear, happiness, and sadness experiences correlated with neural activity in … orbitofrontal cortex and amygdala…”. The relationship between rewards and emotional valence in particular is asso-ciated with both the orbitofrontal cortex (OFC) and the anterior cingulated cortex (ACC) and was summarized by Grabenhorst and Rolls (2011) in the following main points:

• Neural activity in the OFC and ACC represents reward value and pleasure on a continu-ous scale.

• The identity and intensity of stimuli are represented at earlier cortical stages that send in-

puts to the OFC and ACC: stimuli and objects are first represented, then their reward and affective value is computed in the OFC.

• Many different rewards are represented close together in the OFC, including taste, olfac-

tory, oral texture, temperature, touch, visual, social, amphetamine-induced and monetary rewards.

• Spatially separate representations of pleasant stimuli (rewards) and unpleasant stimuli

(punishers) exist in the OFC and ACC.

• The value of specific rewards is represented in the OFC: different single neurons respond to different combinations of specific taste, olfactory, fat texture, oral viscosity, visual, and face and vocal expression rewards.

• Both absolute and relative value signals are present in the OFC.

• Top-down cognitive and attentional factors, originating in lateral prefrontal cortex, modu-

late reward value and pleasantness in the OFC and ACC through biased competition and biased activation.

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The Frustration Lifecycle While research on reward and punishment often focuses on responses to specific stimuli and situations, research on online service experience requires a longer-term perspective involving a process that may evolve over many months. In this section we sketch the lifecycle of frustration, beginning with the first stage, i.e., the point at which frustration is created. The lifecycle of frustration as both effect and cause has been characterized in terms of three core elements: the frustration incident, the frustration sensation, and the resulting frustration behaviour (Stauss et al., 2005; Tuzovic, 2010). How Frustration is Created In behavioural psychology of the mid-twentieth century (work summarized by Amsel in his 1992 book) frustration was a presumed motivation created through denial of a reward. For instance, if a rat pressed a bar a required number of times and then failed to be rewarded (as it had been in the past), a reduced amount of later bar pressing was observed, which was attributed to the frus-tration caused by the lack of the expected reward. Similarly, the dwarf goats studied by Gygax et al (2013) showed signs of frustration when their feed bowls were covered. Presumably, for rats, people, and other mammals, frustration either leads to anger or resignation, or else motivates the organism to look for alternative ways to get goals and rewards that are currently blocked. The study of reward and punishment is difficult because of variability in behaviour in response to rewarding or punishing situations: “There is no guarantee that any particular stimulus will produce any particular emotion in any particular individual at any given time.” (Levenson, 2014, p. 107). However, as we look at the cumulative effect of frustration over time, patterns may emerge that are difficult to detect when observing smaller units of behaviour over compressed increments of time. With particular relevance to online interaction, there seems to be quite a bit of uniformity in how people experience frustration when interacting with computer systems. For instance, having to wait longer than expected for the computer to respond is frustrating for most people. Kohrs et al. (2014) found that when system response times were delayed (by 0.5, 1, or 2 seconds), SC (skin conductance) increased and HR (heart rate) decreased. The authors also looked at button press dynamics. They found that participants pressed a button with more force after delayed system response times. However, there is not a single mapping between resulting frustration and the amount of delay in system response times. Ceaparu et al (2004) found that the impact of potentially frustrating fac-tors like response time depend on the context: “the appropriate response time is related to the us-ers’ past experiences, the users’ knowledge level related to technology… and outside time pres-sures”. The causes of frustration most often cited in the study by Ceaparu et al were: “… timed out/dropped/refused connections, freezes, long download time…” Furthermore, when people get frustrated their judgments and perceptions tend to change. For instance, with increasing time de-

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lay users may find the content to be less interesting (Jacko, Sears, and Borella, 2000) and of a lower quality (Ramsay, Barbesi, and Preece, 1998). Responses to Frustration Since frustration is associated with goal attainment, there will typically be a response to each frustrating event in terms of the blocked goal. This response can be adaptive or non-adaptive. Spector (1978) noted the following responses to frustration: withdrawal, aggression, goal aban-donment, and the facilitation or inhibition of task performance. There are also individual differ-ences in levels of tolerance for frustration (Whinghter et al., 2008) that affect how different peo-ple respond to frustrating events. LeDoux (1998) distinguished between a “high road” and a “low road” of emotion processing, which is depicted in Figure 3. In the high road, stimulus activation involves the frontal cortex and may reach conscious awareness. In the low road the cortex is not involved and the activation does not reach conscious awareness. As reported by LeDoux and others, the high and low roads represent separate neural pathways that are being mapped by neuroscientists. The existing of the high road allows rationality to be involved in processing of emotions, although, as we have all observed, emotions are frequently exhibited that are not under rational control. In frustrating situations people differ (both within and between themselves) in terms of whether they use a more rational, or emotional mode to handle the frustration.

Figure 3. The High and Low roads of emotional stimulus Processing (Lottridge et al, 2011, Figure 2). Sekiguchi et al (2013) gave the following example of a frustrating situation. “You are inputting data into your computer. Suddenly, the screen goes black, and soon after, your colleague holds up a plug and says, I’m sorry; I accidently unplugged your PC”. Following Rosenzweig’s frustra-tion theory (e.g., Rosenzweig 1976, Rosenzweig, 1981), Sekiguchi et al. classified possible re-sponses to this situation in terms of two factors, namely the direction, and the type, of aggres-

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sion. Sekiguchi et al. asked people to imagine different responses to the frustrating situation while in an fMRI scanner and then noted which brain regions were more active in the different situations. Based on the resulting evidence they proposed the following organization of re-sponses to frustration events (Table 1).

Direction Goal Self Other No Solution Non-Adaptive Non-Adaptive Solution Adaptive Adaptive Table 2. Adaptive and non-adaptive responses to frustration in terms of the factors of solution orientation and direction (based on the findings of Sekiguchi et al, 2013). In the example of the computer being accidentally unplugged, an adaptive solution directed at the self would be to re-input the data that was lost. A potentially adaptive solution directed at the other might be to request that the person help input the lost data, or to set a policy where all ca-bles are secured to the floor with duct tape so as to prevent the possible that they could be acci-dentally kicked and unplugged. Sekiguchi et al. identified a number of brain regions associated with social responses to frustrat-ing situations: “The neural networks associated with social responses to frustrating situations identified in the present study are consistent with those previously reported in neuroimaging studies investigating social behaviors. These brain areas are part of the brain network that medi-ates social cognition and behavior … such as mentalizing … social interaction … and communi-cative speech production …. Furthermore, they are related to judgments about the adaptiveness of behavior in relation to a social context with regard to considerations such as social norms … and moral judgments.” Frustrating events occur in many situations. Driving situations are of particular interest because of the safety implications of phenomena such as road rage. Even in less extreme situations, frus-tration may cause drivers to drive less safely. In a study conducted in a driving simulator, drivers who experienced high levels of frustration and anger drove at faster speeds (extreme use of the accelerator), and made more use of the brake pedals than those who reported more calmness (Stephens and Groeger, 2006). It is interesting to note the similarity between two very different situations (frustration while driving, and frustration while waiting for a computer process) in that both lead to more forceful motor responses. Evolution of Frustration In the face of a continuing set of frustrating events, how does the overall perception of frustration change? Does frustration escalate to anger, or does the person learn how to adapt and potentially remove or overcome the frustration? In the following sections we will consider these possibili-ties, first examining the topic of escalation of frustration and then considering how people adapt to frustration.

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Escalation of Frustration In a frustrating situation, a person may show changes in behaviour associated with physiological response, e.g., using more force in operating an accelerator pedal or pushing a button. Gelbrich (2010) states that frustration occurs when people attribute blocked goal attainment to situation factors that are beyond their control and anger results when people specifically attribute not attaining their goal to an external source (e.g., a service provider, or to others who are overloading a shared wireless network). Oatley and Duncan (1994) argued that frustration tends to precede anger, and, in fact, may cause it. An example of anger arising from accumulation of frustration is the anger that medical per-sonnel are sometimes exposed to because of long wait times in hospital emergency departments. Research in nursing suggests that nurses are frequently exposed to anger and violence, some of which may be attributable to accumulation of frustration (e.g., Garnham, 2001). However, frustration does not always lead to anger, and this may be partly due to processes of adaptation and internal reasoning. In a self-directed task where there is no obvious external agency to blame, such as threading a needle, difficulty in performing the task may lead to high frustration, but not anger. Susceptibility to anger following frustration is also viewed as being related to differences in personality (e.g., Rosenzweig, 1976, 1981; Sekiguchi et al., 2013) Some people are better at emotion self-regulation (e.g., controlling emotion through reasoning, how-ever, the topic of individual differences in emotion self-regulation is outside the scope of this re-view. Adaptation to Frustration Like other emotions, response to frustrating events is processed along two paths, a rational path-way and an emotional pathway, which may be thought of as an emotional interpretation of events that are otherwise remembered rationally. This is probably related to the high road and low road distinction made by LeDoux and cited above, and is also related to the distinction between Sys-tem 1 and System 2 described by Kahneman (2011). Stauss et al. (2005) outlined a general frustration model, which describes frustration occurring in three stages. First is the frustration incident (refusal of reward, reduction of reward, and post-ponement of reward), which is the blockage of goal attainment that leads to the negative affect defined as frustration. This is followed by the negative emotion (sensation) of frustration with accompanying high arousal and an attempt to attribute a causal responsibility for the frustration. Smith and Ellsworth (1985), in their comparative empirical analysis of 15 different emotions, found that frustration was associated with “a stronger desire to attend to the situation than for any other negative emotion” (Smith and Ellsworth, 1985, p. 833). The third and final stage in the Strauss et al. general frustration model is the frustration behav-iour. This is the action taken following the frustration sensation, with the goal of reducing or eliminate the source of frustration. According to Strauss et al (2005), the three categories of frus-

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tration behaviour (action) are protest, intensification of effort, and avoidance (Stauss et al., 2005). Tuzovic (2010) proposed a “Customer frustration-defection framework” which presents the rela-tionship between different levels of frustration for customers of a service and their corresponding later behavior. Tuzovic proposed a four stage progression as a customer’s frustration increased over time, from “vent but cope with situation”, to “vent and protest”, to “influence and worn oth-ers”, and finally to “saboteur – the real terrorist”.

Figure 4. Draft Model of impact of Frustration on Dissatisfaction and Action Figure 4 shows a model of how frustration is related to dissatisfaction and to action. It focuses on the transition from a neutral state to a disaffected state where actions such as rejection or com-plaint may occur. In keeping with the high-road/low road view of emotional processing, there is a rational process at the top of the figure and an emotional process that occurs in the lower part of the figure. At the emotional level, the impact of a succession of frustration events (FEs) is seen as escalating (with increasing arousal) emotional states, first to a generalized sense of frus-tration (e.g., about a general problem with quality of a service) and then to anger. At the rational level there will first be a general sense of dissatisfaction and this is assumed to transition to ac-tion once a sufficient level of anger has built up. Expectation also affects how people respond to frustration. For instance, if people think that pro-testing won’t help then they are more likely to respond to a frustration incident with avoidance. Stauss et al., 2005 claimed that the intensity of the negative arousal from frustration sensation will be greater if:

• The anticipated reward is larger • The withdrawal of reward happens closer to when the reward is to be achieved – this will

also make the frustration last longer

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Other general rules about reactions to frustration suggested by Strauss et al were:

• Probability of aggressive behaviour increases as arousal intensifies • Efforts towards finding constructive solutions decrease when large rewards are with-

drawn or when the deliberateness/arbitrariness of the reward leads to greater arousal These prescriptions lead to predictions that frustration sensation will be less intense when the withdrawal occurred long before the intended goal was to be achieved. In contrast, the likelihood of protest, and aggressive behaviour will likely be greater if withdrawal is perceived to be delib-erate or arbitrary. The theory of planned behaviour (Ajzen, 1991) is also relevant to the rational level of dealing with the effects of frustration over time. The constructs within the theory of planned assume a rational thought process regarding video quality issues and whether customers will take action. In the case being considered here, the behaviour in question is action (complaining about service, wanting to cancel service altogether). As service declines and there are more video quality is-sues, customers will become dissatisfied with their service and therefore have a positive attitude towards taking action. It is quite common for people to complain about mobile web service, so subjective norms would not stand in the way of people making complaints in this case. Level of assertiveness, perceived self-efficacy (Bandura, 1977), or locus of control (Rotter, 1966) may mediate whether or not people intend to act. Due to a number of psychological factors some peo-ple are more shy/non-confrontational than others, and therefore may not feel comfortable calling to complain or cancel their services. The Technology Acceptance Model (Davis, 1989; Bagozzi et al., 1992) also applies to the ra-tional level of service evaluation. If people think their services are useful and easy to use, they will have a favourable attitude towards those services and will intend to keep using them. How-ever, people do not always make decisions rationally, and emotions often come in to play. The emotional pathway interacts with the rational pathway and influences the intention to stay with their service provider, complain, or cancel service. Video impairments and failures become frus-tration incidents, and they cause frustration sensation. This frustration sensation may lead to a sense of dissatisfaction, but as more incidents arise, the intensity of arousal becomes stronger and eventually the customer will want to try and eliminate the negative feeling by taking some ac-tion. Frustration tolerance may be the mediating factor in determining whether a person handles ex-tremely frustrating situations emotionally or rationally. Someone with a higher frustration toler-ance will be more likely to look objectively at the situation and make rational decisions, whereas someone with a low frustration tolerance will get more easily frustrated and engage in frustra-tion-related behaviour. Most of the research literature has looked at frustration in the context of the transition towards anger and avoidance. But what happens in the case where a person who is otherwise favourable towards a service experiences frustrating events? Reichheld (2003) introduced net-promoter scores as a key predictor of business success (note that “likehihood to recommend” is a synonym for net-promoter scores that has been popularized in places such as Forrester Reports). Net pro-

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moter scores are an attractive criterion for business because they only require one question to be asked of the customer: “How likely is it that you would recommend our company to a friend or colleague?”. Based on responses to a 0 – 10 rating scale (0=”extremely unlikely to recommend”, 10 = “extremely likely to recommend”), respondents are then grouped into three categories (promoters with ratings of 9-10, passively satisfied with ratings of 7-8, and detractors with rat-ings of 0-6). The Net Promoter Score is then calculated as the difference between the percentage of people categorized as promoters and the percentage of people classified as detractors. Keiningham et al. (2007a,b) studied over 8,000 customers of companies in retail banking, mass merchant retail and Internet services for a two-year period. They measured individual customer ratings on common satisfaction and loyalty metrics during the course of the study. They also tracked customer retention, share of spending and recommendation behaviors. Not surprisingly they found that the recommend intention question used by Reichheld (2003) did in fact predict recommendations. However, the best predictors of share of spending, and retention, in their study were past share of spending, and repurchase intention, respectively (not recommend inten-tion). . The relationship between emotions related to quality of experience and satisfaction with a prod-uct of service is still in the early stages of exploration. Martin et al. (2008) argued for including emotions as well as cognitive factors in assessing emotions and used a football match as a setting for evaluating an off-field service in the context of emotions associated with the on-field game. Wu and Lo (2012) studied telecommunications customers’ (in Taiwan) reactions to consecutive service failures. They found that, although customers displayed negative emotional reactions af-ter each service failure, the intensity of the response to the second failure was less than the first. They also found that as long as the service remained in use, that customers tended to have high expectations of it. The high degree of loyalty observed may have been partly due to constraints such as: money deposits, contractual agreements, associated costs, and lack of time. In summary, the available research literature suggests that emotional reactions such as frustration and anger combine with factors of the individual, the service context, and associated rational thinking, to create changes in attitudes and behavioural dispositions relevant to customer loyalty, retention, and abandonment. Measurement of Frustration Frustration is subjectively felt emotion that is accompanied by a physiological response, meaning there are two ways to assess frustration: self-report measures and physiological measures. Self-report measures of frustration One method of collecting self-report data is to put participants in a frustrating situation and ask them to rate their frustration level. Ceaparu et al. (2004) used this technique and required users to log each frustrating experience as it occurred during the test session. Participants reported that most of the frustrating experiences were highly frustrating (74% of the frustrating experiences were rated with 6-9 on the frustration scale). Thus by the time people are ready to subjectively report frustration they tend to be highly frustrated.

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Another measurement method is to present participants with an imaginary situation and ask them to rate how frustrated they would be if the situation occurred. Van Steenburg et al. (2013) devel-oped a paper-based questionnaire that included 33 items aimed at capturing responses to an imagined frustrating retail situation. They also developed a 58-item online questionnaire that asked open-ended questions for qualitative analysis. The online questionnaire included scales that measured attitude toward the company (Goldsmith et al., 2001), repatronage intention (Bol-ton et al., 2000), and frustration tolerance (Harrington, 2005b) as independent variables. Simi-larly, Gelbrich (2010) presented imaginary situations and used the following survey items to measure frustration:

• I would feel frustrated about the situation • I would feel disturbed by the situation • I would feel annoyed at the situation

Frustration has also been measured as an emotional component of mental workload in response to the stress of task demands. The NASA TLX is a popular self report measure of experienced workload that was initially developed to measure workload in military helicopter pilots (Hart and Staveland, 1987) and that has seen been applied in a variety of settings including human-computer interaction. The NASA TLX combines six scales, one of which is frustration: “How irritated, stressed, and annoyed versus content, relaxed, and complacent did you feel during the task?” Harrington’s (2005) Frustration Discomfort Scale (FDS contains components relating to an indi-vidual’s expectation of reward: entitlement; achievement; gratification; fairness. An additional two components are related to discomfort intolerance and emotional intolerance. Soderlund (2003) developed a customer frustration scale and used it in an airline travel setting. His study was concerned with how previous emotions affect future choices. Richins (1997) used a method involving ratings of agreement with seven frustration related adjectives (frustrated, un-comfortable, anxious, stressed, strained, annoyed, and awkward). Guchait and Namasivayam (2012) measured frustration using 14 items adapted from Susskind (2004) and Harrington (2005). Other researchers have looked at positive experiences that may be thought of as the inverse of frustration. Wu and Lo (2012) modified items extracted from the work of earlier researchers to develop ten items aimed at measuring how a telecommunications service is performing relative to customer expectations. In their method, the following items are rated on a seven-point scale of agreement (1 = strongly disagree, 7 = strongly agree):

• The performance of the cell-phone meets my expectations • The performance of the wireless communication company meets my expectations • I think that the communication quality of the company is clear • I think that the communication system prodded by the company is professional • I think that the cell-phone is guaranteed service is reliable • I think that the service provided by the company is complete • I think that the service range is convenient

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• I think that the engineers of the firm have professional skills • I think that the service employees in the firm are polite • When I have trouble with the product, I think that the company will try their best to solve

my problem Although the items listed above do not explicitly mention frustration, we might infer that lower ratings would generally mean higher frustration. Although a number of scales relating to frustration have been developed, there is no consensus on which scales to use and when. The situation is somewhat akin to the confusing number of somewhat related personality inventories and associated personality factors that existed prior to the integrative work that led to the modal “Big Five” theory of personality (Goldberg, 1990) that explained the various existing inventories in terms of the five main factors of openness, consci-entiousness, extraversion, agreeableness, and neuroticism. Similar integrative research is likely needed to assess the factor structure that is common across the wide range of frustration-related scales that have been developed. Patience is a factor that likely affects the way a person responds to frustrating situations. Schnit-ker and Emmons, 2007 developed a Patience Scale-10 (PS-10) to measure people’s self-evaluations of patience, and their attitudes concerning the importance of patience. Examples of items used in the scale are shown below:

• Most people would say I am a patient person • Waiting in lines does not bother me • I believe that when it comes to getting along with others, patience is an important factor

Schnitker (2012) later developed a revised inventory for measuring patience (the 3-Factor Pa-tience Questionnaire), containing 40 items that measure interpersonal patience, life hardship pa-tience, and daily hassles patience; rated on 7-point Likert scale (1 = very much unlike me; 7 = very much like me). Example items in the questionnaire are:

• When someone is having difficulty learning something new, I will be able to help them without getting frustrated or annoyed

• I am able to wait-out tough times • Although they’re annoying, I don’t get too upset when stuck in a traffic jam

Assuming that patience can be measured adequately it might be used to predict individual differ-ences in frustration experienced in various situations. For instance, a “patient” person might be less easily frustrated. If one knew the prevalence of people with different levels of patience in the population, and how people with different levels of patience respond to different service experi-ences over time, then it might be possible to predict, at the population level, the proportions of various emotional responses and behavioural outcomes associated with particular profiles of service experience. Physiological measurements

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In addition to the self-report measures reviewed above, physiological measures can be used to measure frustration, since the feeling of frustration is accompanied by physiological changes. The validity of self-report data is a common concern, which makes physiological measures of frustration a useful alternative or supplement when studying user frustration. Not only is physio-logical measurement potentially less biased, but we can also get more fine-tuned detail which would be helpful when investigating how frustration escalates when customers are prevented with repeated service failure. In an early example of physiological measurement in human-computer interaction, Hazlett (2003) found a link between Corrugator supercilii (facial muscle) activity and frustration or difficulty while using a website. Ceaparu et al. (2004) sought to measure the physiological response associated with frustration by simulating frustration experiences that someone might have when using a computer. Participants were asked to play a game, but at specific, but irregular, intervals, the mouse would fail, leading to frustration. The authors used measures of skin conductivity, blood volume pressure, and mus-cle tension, and found a correlation between these signal patterns and the game events. With this data, they were able to train a computer system to identify user frustration using these physio-logical signals. Cooper et al. (2009) conducting research on a computerized intelligent tutoring system (ITS), and outfitted each student with four sensors: a camera that focused on the student’s face, a skin conductance bracelet, a pressure sensitive mouse, and a chair seat capable of detecting posture. The students used the ITS for multiple days in their class, and the program would regularly ask them to rate how they were feeling. They were presented with questions such as “how [inter-ested/excited/confident/frustrated] do you feel right now?” (Cooper et al., 2009, p. 8) and rated their current state on a scale of 1 to 5, with 3 being neutral. The authors found that the best stand-alone sensor for determining frustration was the mental state camera, but that using features from all the sensors yielded the best prediction of self-reported frustration. Kapoor, Burleson, and Picard (2007) were also able to predict with 79% accuracy when ITS users would become frus-trated, using the same sensors. Research is needed to show how different physiological methods (such as skin conductance and heart rate variability) can be combined to develop more effective measures of frustration. Physio-logical measures can be measured continuously during performance of the task and in principle they make it easier to distinguish between different levels of frustration, as opposed to just a self-report rating on a scale that may only have five or six values.

Service failure and negative service experiences In the context of services, and online services in particular, what is the relationship between service failure and frustration? There has been a lot of interest in how people respond to negative service experiences, particularly in the marketing domain. Based on a review of the literature, Schnitker (2012) listed the following findings:

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• an impatient state will arise and service satisfaction will decrease if a person perceives that the time spent waiting has surpassed expectations for waiting

• customers exhibit even greater impatience and dissatisfaction when a wait is attributed to the service provider

• people differ in how long they believe they should have to wait in a specific situation • several factors (e.g. situation factors, past experiences, basic personality disposition,

socio-cultural norms, etc.) impact this sense of what is a fair wait time • when a person wants an activity to occur quickly, the “fair wait time” shortens, and

patience will most likely decrease Wait time is an important component of service experience and this factor should be relevant to quality of experience in viewing online video since impairments may be conceptualized as creating delays where the user has to wait. Rose et al (2005) cited research findings that excessive download delays are the most irritating aspect of using the internet, and that download delays may have a negative impact on the brand perceived to be responsible for the delay. For many people it is not the objective wait time, but the subjective wait time that they experience, that is critical to observed outcomes (e.g., Baker and Cameron, 1996; Houston et al., 1998). For instance, in an online environment, most users do not have an accurate measure of time delays and thus judgments of delay are based upon their perceptions of the wait. In services such as online video, the timing and duration of impairments is likely to impact the perceived waiting time, and thus likely frustration experienced due to those impairments. Carmon, Shanthikumar, and Carmon (1995) suggested that customers perceive waits differently at different stages of service delivery, further supporting the idea that timing of delays is important. Selvidge, Chaparro, and Bender, 2002 found that user frustration was the same for 30- and 60-s download delays, but was worse in both cases compared to a 1-s delay. Weinberg et al. (2003) found that an ascending order of waits on subsequent web pages reduced perceptions of overall waiting time compared with a descending order. According to Wu and Lo (2012), both historical and questionnaire data indicate that the telecommunication industry has the highest ratio of service failures, followed by the insurance industry. Consumers tend to stay with the same service provider even though they have encountered consecutive service failures in some high-involvement situations:

• when the cost to consumers is high • when contracts are restrictive and cannot be broken

Cognitive assessments of a service failure activate negative emotions such as anger, annoyance, and frustration (McColl-Kennedy and Sparks, 2003). However, it is not necessarily the case that emotions will increase as service failures accumulate. While there will generally be a negative emotional response to service failure, there will also be a lowering of expectations, which will tend to make emotional responses to subsequent failures less intense (Solomon, 1999). However, there may still be strong emotional reactions second or later service failures when there is high customer involvement (Sachdev and Verma, 2002). Nguyen and McColl-Kennedy (2003) examined the issue of customer anger. They proposed a conceptual framework to help better understand customer anger following service failures. They

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also suggested that customer anger at the service provider will be moderated by the customer’s cognitive appraisal in terms of goal relevance, goal incongruence, and ego involvement. Conclusions Frustration is a complex construct that is related to the blocking of goals and the subsequent negative emotion that people experience. Frustration can be a precursor to anger, but it is a mal-leable emotion that is influenced not only expectations about the timing and likelihood of the goal but also by attributions about whom or what caused the goal to be blocked. The higher the expectation of the goal, and the closer that that the person believed she was to achieving it, the more frustration there is likely to be. Frustration tends to be associated with increased arousal and thus may be signaled by an in-creased in physiological measures like skin conductance and heart rate. Frustration is also likely to be associated with the brain systems and circuits that respond to frustration and non-reward. Since these brain circuits are relatively distinct from brain circuits involving reward it seems possible if not likely that frustrating, versus rewarding, experiences will be integrated separately over time. Furthermore evidence provided by Kahmenan, Whittaker and others suggests that negative experiences associated with frustration are likely to be more memorable and to domi-nate positive experiences such as satisfaction when people build attitudes towards a service based on their experience of those services. However there is also evidence to suggest that peo-ple can become somewhat habituated to service failures over time providing that they are not frequent and intense enough to create sufficient anger to create behavioral intentions that is suffi-cient to overcome the combination of loyalty and inertia that typically keeps a person using a service. The research reported in this paper has a number of implications for how services are likely to be perceived in the presence of service failures. In particular patterns of frustrating events occur-ring over time tend to be key determinants of how people perceive online services and how they decide when to continue with, or abandon, services. Given the difficult tradeoffs that face serv-ice providers, and the every increasing bandwidth demands of various applications and online services (e.g., video streaming) it seems that a certain amount of frustration will continue to be inevitable for the foreseeable future. Frustration tolerance is a key individual difference that af-fects how badly a person responds to a frustrating event. In principle, frustration tolerance may be inferred from online behaviour. For instance, a person who gets frustrated while watching on-line video that is impaired or has low technical quality may stop watching videos for a period of time. In contrast a person who continues to watch videos, in spite of impairments, or who carries out related online activities, is likely to be more frustration tolerant. Future research is needed to establish models of frustration experience and accumulation based on parameters that can be in-ferred from readily available network probes.

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