Research Questions and Methodological Rationale

Research Questions

A review of the extant trust and trust repair literatures identified some research gaps which this thesis aimed to address. Primarily these gaps relate to three distinct areas. Firstly, my review of the trust repair literature in Chapter 3 showed that while there were a small number of articles that did consider affect as central in the process of trust repair (five of 42 to be precise, only three of which were empirical), each had problems. For the empirical papers, these problems tended to concern the measurement of affect, trust, or both. Moreover, as discussed in Chapter 4, work into feelings-as-information suggests that mood and emotion can influence how we make social judgements, indicating that they may be salient in the study of trust and trust repair

Secondly, returning to Chapter 2, there is scant research into the effects of individual differences on trust, other than the consideration of propensity to trust. Two studies have considered regulatory focus theory and its relationship with generalised trust, but nothing has been considered in relation to trust repair. Regarding trust repair, I was unable to find a single empirical paper that included any individual difference measures. In addition, the experience principle of feelings-as-information theory posits that emotion-related individual differences will influence whether and how affect influences judgement.

Finally, my chosen conceptualisation of trust is that it is a process consisting of belief, decision, and action. The process perspective of trust has received scant empirical attention. Although my review of the trust repair literature suggested that several papers did implicitly measure all three stages of the trust repair process, this was not explicitly acknowledged. Certainly, no article attempted to determine whether the three stages of the process form an integrated, empirically supported model. Based on these gaps, I developed a suite of studies that aimed to answer the following research questions:

Research question 1: Do emotions and mood predict change in trust after a trust failure, controlling for evaluations of trustworthiness?

Research question 2: Does regulatory focus affect trust or interact with emotions?

Research question 3: Do emotion-related individual differences affect trust or interact with emotions?

Research question 4: Do belief, decision, and action processes of trust form a coherent model?

Research question 5: Are emotions central to an integrated model that predicts distrusting acts?

Three empirical studies were conducted to explore the research questions posed above. All used stimuli from real-world incidents of organization-level trust failures. Table 5.1 compares the three studies, showing the characteristics of each and which key elements were explored, and demonstrating the progression of knowledge generation. Foreshadowing results, Study 1 was an experiment, had a small sample size, and only considered mood and a small number of individual difference measures to first ascertain whether such processes had any influence at all on perceptions of organizational trustworthiness and willingness to trust in an organization given a scenario that would not have been personally salient to participants. Results indicated that these processes may indeed be pertinent in Study 1’s trust repair context. Thus Study 2, also an experiment, was conducted using the same stimuli, a larger sample, and the inclusion of specific emotions and additional individual difference variables (emotional reactivity and private body consciousness (PBC); a proxy for embodied cognition). The rationale for using the same stimuli as in Study 1 was to ascertain whether results would replicate in a different, larger sample. Some did, some did not, but the added element of specific emotions did appear to be influential in the decision to trust. Study 3 used different stimuli, and while one aim was to replicate the results of the previous two studies in relation to emotion and individual differences, the extension involved the measurement of the behavioural element of distrust in a personally relevant situation (car ownership). Study 3 had an experimental manipulation and a survey component.

Table 5.1 - The Key Process Elements and Characteristics of Studies 1, 2, and 3

Note. B = Belief stage of trust process, D = Decision stage of trust process, A = Action stage of trust process. Personal relevance = Whether the scenario was personally relevant to the study’s participants. Mood = Measurement of positive affect and negative affect. Specific Emotions = Measurement of anger, fear, sadness, contempt, joy, and calmness.

In the following section, I provide a rationale as to why I chose to use an experimental design for Studies 1 and 2, and follow them with a cross-sectional survey for Study 3.

Methodological Rationale

Here, I present an overview of the experimental method and discuss its merits and disadvantages, before exploring its suitability for the current research programme.

I follow this with an explanation of the nature of internet-based, crowdsourcing marketplaces that allow researchers to recruit participants to take part in experimental research. I utilise such a marketplace in each of my studies, so I present some of the arguments for and against their use in psychological and sociological research compared to more traditional data collection methods before reviewing their use in previous studies.

The Experimental Method

Kerlinger (1986, in Griffin & Kacmar, 1991: 302) defined a laboratory experiment as “a research study in which the variance of all or nearly all of the

Key Elements Measured Study Characteristics Study Mood Specific

influential independent variables not pertinent to the immediate problem of the investigation is kept to a minimum.”

Although the experimental method has been a staple of psychology since the birth of the discipline, there have always been arguments regarding its merit. Critics cite the lack of generalisability to other situations and difficulty in replicating results, as well as the artificial nature of the setting and the knowledge that participants are almost always aware that they are being observed. Perhaps the most often quoted argument against the experiment relates to doubts over external validity (Epstein, 1979; 1980; Pruitt & Kimmel, 1977). According to Campbell and Stanley (1963: 5),

“external validity asks the question of generalizability: to what populations, settings, treatment settings and measurement settings can this effect be generalized?”

In other words, some scholars have reservations regarding the generalisability of experimental results to “real world” settings, and thus the ability to replicate results outside of the laboratory setting. Epstein (1980: 796) asserts that “there is no more fundamental requirement in science than that the replicability of findings be established”, yet claims “the very nature of the paradigm of the single-session experiment is such that very few findings, no matter what their level of statistical significance, are apt to be replicable” (p. 790). A contentious issue in the social sciences that is related to both replicability and generalisability is the preponderance of the use of undergraduate student participants in experimental research. Critics suggest that research conducted with student samples is not representative of the general population, and is therefore not generalizable to other situations (Bello, Leung, Radenbaugh, Tung & van Witteloostuijn, 2009; Lucas, 2003; Sears, 1986). Sears (1986) argued that the predominance of student sample-based research in the social sciences has led to a bias in “what is known” about human behaviour, as students tend to have higher levels of cognitive ability, more compliant behaviour and less crystallised attitudes than older adults.

On the other hand, there are arguments that this lack of generalisability is not always a concern. An example of this is when the research focus is on basic psychological processes or theory building linked to human behaviour, independent of sample characteristics (Bello et al., 2009; Lucas, 2003; Mook, 1983). Berkowitz and Donnertstein (1982) claimed that the meaning assigned to the situation that participants are in and their behavioural responses to it are of greater import to the

generalisability of an experiment’s outcome than the sample’s representativeness. In addition, there are some cases in which the use of a student sample may be representative in that they represent a population of interest. For instance, business students, in theory, should go on to be leaders or followers of the future in organizational environments. Therefore, they may be appropriate subjects for studies relating to management and leadership (Ahmed, Chung & Eichenseher, 2003;

Kirkpatrick & Locke, 1996; Ng & Burke, 2010).

Regarding generalisation, Levitt and List (2007) developed a theoretical model that illustrates three things that cause pro-social behaviour to differ significantly between experimental and field settings, these are stakes, social norms and scrutiny.

Stakes relate to the monetary (or other credit-based) rewards participants receive for completing a task. In the laboratory, participants “play” with the money they receive, whereas in the field the money is earned in some way or another (Benz & Meier, 2008).

In this respect, entitlement may play some role, as demonstrated in a study by Cherry, Frykblom and Shogren (2002) that showed that it mattered whether money in a dictator game was earned by completing a task or whether it was distributed randomly to participants. Social norms may be triggered differently in an experimental setting than in the field because the laboratory lacks the real-life context that may be required for certain behaviours to occur (Bardsley, 2005). Finally, participants in experiments may alter their behaviours because they think that they are expected to behave a certain way or want to please the experimenter. This is the scrutiny component of the Levitt and List (2007) model. Equally, social desirability bias may be an issue, particularly in experiments that are not anonymous. For example, someone who is not particularly generous in a field setting may exhibit greater displays of generosity in an experiment because he may think that he will be perceived in a more positive light by the experimenter or others involved in the process by doing so.

Although there may be drawbacks to the experimental method, it does have its advantages. A major strength of the experiment is the control it allows the researcher;

extraneous conditions and variables can be controlled and independent variables can be manipulated in a way that is not possible in field research. Furthermore, by randomly assigning which units receive which treatment, and to what extent, the investigator can bypass the “unmeasured variables problem”. According to James (1980 in Colquitt, 2008: 616) this problem concerns unmeasured variables that are

either “correlated with a presumed cause or predictive of the presumed effect”.

Random assignment by the roll of a die, use of a random number generator, or any other such method of randomisation, eliminates the possibility of an unmeasured variable being meaningfully correlated to an independent variable. By its very nature, randomisation ensures that no pattern can emerge, thus minimising the possibility of correlation between an independent variable and an unmeasured variable (Colquitt, 2008). With regards to the second point, by controlling the levels of the independent variable the researcher is able to rule out the possibility that the outcome actually causes the predictor in a given study.

Furthermore, whilst the sheer volume of fieldwork in areas such as leadership, performance appraisal and goal-setting suggests that it is relatively straight-forward to conduct field research in those realms, there are some concepts that, usually due to matters of sensitivity, are very difficult to study in the field. Indeed, organizational trust repair is one such concept, as noted by Gillespie and Dietz (2009).

The previous section of this chapter outlined some of the advantages and disadvantages of the experimental method. Whilst there are some justifiable concerns over the use of this method, it is a good fit for the type of research conducted in this thesis. Hence, the experiment was chosen as a relevant method for Studies 1 and 2 for three primary reasons. Firstly, the first research question that this thesis explores, relating to the role of affect in trust repair, is deliberately broad, and the results of Study 1 inform Study 2. For this, the experiment is preferable to a field study because the independent variables of interest can be controlled and isolated.

Secondly, the very nature of trust repair research makes it difficult to study in the field, hence the preponderance of experimental and case-based studies in the literature, as evidenced by my review in Chapter 3. It is unlikely that organizations would be willing to allow scholars to conduct field research with them in the immediate aftermath of a scandal or transgression due to the possibility of negative feedback from stakeholders and potential reputational damage. Therefore, studying the effects of emotion during a “live” trust repair process will be very difficult in a field setting. To illustrate this issue, although recently Gillespie and colleagues (2014) conducted a case study that focused on trust repair and organisational reintegration, there were several unique aspects to getting access to the field setting. The study involved interviews with employees of a British utilities firm involved in a dispute

with the industry regulator which led to the company being fined over £37 million.

However, the interviews were granted by a board of directors that was not in place during the period in which the transgressions took place; they were “once removed”

from the wrongdoing. Furthermore, over three years had passed between the occurrence of the transgressions and the interviews taking place, during which time the company had already managed to rebuild its reputation and improve performance.

When considering affect, such a case-based method would not be appropriate because individuals would have to attempt to recall what their emotions and moods were like after the event. Johnson, Tolentino, Rodopman and Cho (2010) suggest that attempting to accurately assess one’s state mood, that is, their mood at that moment in time, may be difficult due to the conscious self-awareness and inductive reasoning required. Past emotionally charged events that are salient to those who experienced them are sometimes (Bohannon, 1988; Brown & Kulik, 1977), but not always (McClosky, Wible & Cohen, 1988) recalled more accurately than less significant events. However, some studies have suggested that people tend not to recall previous emotional events accurately (Brewer, 1988; Thomas & Diener, 1990). Current attitudes and appraisals may also play major role in how memories of emotional responses are recalled (Holland & Kensinger, 2010; Levine, 1997; Pattershall, Eidelman & Beike, 2012). Taking this into account, the ability to measure mood and emotion at the time a particular event occurs would be preferable in terms of ascertaining accurate indications of what a person is feeling at the time, which in turn will present us with a greater idea of how affect relates to other variables such as trust and trustworthiness. It is possible to do this with an experiment.

Finally, and related to the previous point, causality can be assessed with an experimental design. In contrast, causal direction is generally difficult to establish and ambiguous in other types of designs such as cross-sectional surveys. However, that is not to say that cross-sectional research lacks merit.

Cross-Sectional Research: Study 3

Studies 1 and 2 were comprised of entirely experimental designs, measured over three time-points. Study 3 used a cross-sectional design in order to explore the correlations between attitudes, affect, and behaviour in a sample personally affected by a particular organizational failure. Cross-sectional research designs have been

criticised because they do not allow us to draw confident causal conclusions and common method bias may inflate the observed correlations between variables (Lindell

& Whitney, 2001; Podsakoff, MacKenzie, Lee & Podsakoff, 2003; Spector, 1994).

However, the cross-sectional approach is still one of the most commonly used designs in organizational behaviour research, and can be useful in helping us understand the intercorrelations between various feelings and perceptions (Spector, 1994). Indeed, cross-sectional research can be very useful as part as a suite of studies, as is the case in this thesis. Undertaking experiments to provide first tests of hypotheses and following them up with the more uncontrolled conditions of the field can help demonstrate the robustness of findings (Rietzchel et al., 2017). As stated in Chapter 3, very few of the trust repair articles reviewed contain both experimental and field data.

That my studies do is a strength of their design. Regarding the potential issue of common method variance in Study 3, although this is discussed in further detail in Chapter 8, statistical tests suggest that it was not of great concern.

Crowdsourcing to Collect Data

All data for Studies 1 and 2, and the bulk of the data for Study 3, were collected using a UK-based crowdsourcing marketplace called Prolific Academic. This source is described on the Prolific Academic website (www.prolific.ac) as “the world's largest crowdsourcing community of people who love science. Researchers post studies and recruit the right participants fast. Participants earn rewards while helping to advance human knowledge.”

As stated previously in this chapter, student samples are often used in experimental research. They are often called “convenience samples”, but recruiting them in a UK research institution is not particularly convenient. In other countries, such as the United States, it is possible for academics to offer course credit to students in return for their participation in a research programme. This practice is not possible in the United Kingdom; hence it can be difficult to recruit students to take part in experiments. A possible solution to the issue of recruiting participants presents itself in the form of utilising online crowdsourcing marketplaces. Such marketplaces consist of a pool of participants willing to take part in tasks for money. A particularly prominent platform is Amazon Mechanical Turk (MTurk), but it is currently not available for use by those outside of the United States. Prolific Academic is a

UK-90

based alternative, which, as of May 2016, had over 33,000 members in its participant pool. At this point, just two papers had been published using samples from the platform (Woods, Michel & Spence, 2016; Woods, Velasco, Levitan, Wan & Spence, 2015), though this may not be surprising given Prolific Academic only commenced operations in 2014.

More generally, crowdsourcing as a means to recruit participants is relatively new, though it is becoming more commonplace. To illustrate this point, entering a search term of “Mechanical Turk” in the Psychology sub-field of Web of Science produces 437 papers. The earliest was published in 2010, a year in which two articles using MTurk data appear. Seven papers were published in 2011, 28 in 2012, 57 in 2013, 108 in 2014, 167 in 2015, and 62 in 2016, as of May. MTurk data have been used in papers that have appeared in top ranking management journals such as Academy of Management Journal, Leadership Quarterly, Personnel Psychology, Management Science, and Organizational Behavior and Human Decision Processes.

Thus, it is evident that the use of crowdsourcing platforms to facilitate academic research is growing and has been accepted as a valid by some of the most prestigious journals in the field of management.

Is Crowdsourcing Reliable?

The advent and subsequent growth of the internet has afforded academics new and varied means to collect data. In the past, researchers were relatively restricted by the logistics of either getting participants to a laboratory or the expense of sending paper surveys overseas. Now, due to the proliferation of web-enabled portable devices, it is possible to reach people from all walks of life via a variety of platforms with a few clicks of a mouse. Online surveys can be sent to the other side of the world for the same price (the subscription to piece of survey-building software) as to someone a mile away. However, concerns have been raised in the academic community regarding the use of the internet to collect data. Most of these doubts relate to uncertainty as to whether these data collection methods yield reliable results compared to traditional

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