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The purpose of this paper is to report the results of a study concerning how fundamental-motivated investors, and their subsequent impact on the path of prices, affect the severity of price bubbles in an experimental laboratory asset market.

In a laboratory experiment, asset markets are manipulated by systematically replacing inexperienced human traders with automated traders programmed to submit bids and asks at fundamental value.

When traders in a market are automated to invest on fundamentals, deviations from fundamental value are initially suppressed, but reappear when automated traders cease to influence prices. A significant reduction in the severity of the resulting bubble may be attributed to the interaction of automated traders and humans through the initial path of prices when controlling for changes in liquidity. This reduction corresponds to reduced autocorrelation in the time series of returns.

This paper represents the first attempt (to the authors’ knowledge) to extend the intervention approach of the seminal paper by Smith et al. (1988) to systematically study the extent to which manipulation of initial path of prices impacts the formation and magnitude of bubbles in the laboratory.

The discrete Fourier transformation (DFT) has been proven to be a successful method for determining whether a discrete time series is seasonal and, if so, for detecting the period. This paper deals exclusively with rare data, in which instances occur periodically at a low frequency.

Data based on real-world situations is simulated for analysis.

Cycle number detection is done with spectral analysis, period detection is completed using DFT coefficients and signal shifts in the time domain are found using the convolution theorem. Additionally, a new method for detecting anomalies in binary, rare data is presented: the sum of distances. Using this method, expected events which have not occurred and unexpected events which have occurred at various sampling frequencies can be detected. Anomalies which are not considered outliers to be found.

Aliasing can contribute to extra frequencies which point to extra periods in the time domain. This can be reduced or removed with techniques such as windowing. In future work, this will be explored.

Applications include determining seasonality and thus investigating the underlying causes of hard drive failure, power outages and other undesired events. This work will also lend itself well to finding patterns among missing desired events, such as a scheduled hard drive backup or an employee's regular login to a server.

This paper has shown how seasonality and anomalies are successfully detected in seasonal, discrete, rare and binary data. Previously, the DFT has only been used for non-rare data.

The aim of this study is to explore the nature of the expectations of operations managers (OMs) and the enacted roles of management accountants (MAs) and to understand how MAs construct roles around these expectations.

A qualitative design draws upon company documentation and 36 semi-structured interviews with MAs and OMs. The study uses role theory as a theoretical lens with its core concepts of role expectations, role conflict and role ambiguity. The design draws from role theory’s original development and testing to pair particular roles of MAs with particular roles of OMs in operational settings.

The findings indicate that there are a number of different forms of OMs’ expectations giving rise to role conflicts and role ambiguity for the roles of MAs. OMs’ expectations were identified as conflicting expectations, ambiguous expectations, overloaded expectations and underloaded expectations. MAs construct roles in different ways around these OMs’ expectations, including prioritising the line function, competence deployment, non-accommodation and communication. Factors moderating OMs’ expectations are also identified, including characteristics of the OM and the role of the finance manager.

The study is based on an in-depth investigation of a small number of roles of MAs paired with OMs, and no assurances can therefore be given regarding generalisability of the findings.

The results provide an understanding of the varied nature of expectations that OMs have of MAs and mechanisms through which MAs can address these expectations. It suggests ways in which both MAs and OMs in operational settings can reduce conflicts and ambiguities.

This paper provides an in-depth analysis of the expectations of OMs in relation to the roles of MAs and contributes to the literature on the roles of MAs using role theory. It shows how different forms of OMs’ expectations have related mechanisms used by MAs to navigate these expectations through role constructions.

This paper aims to investigate the paradox of whether prestigious goods help or inhibit a consumer’s social affinity. The goal of this research is to explore whether pursuit of prestigious goods increases consumers’ social affinity or decreases their social affinity, and, more importantly, to understand the mechanisms that drive this process.

Three laboratory experimental studies and a social network study are conducted to show that consumers hold inconsistent beliefs about the social implication of prestigious goods.

In Study 1, the authors showed that prestigious goods evoked stronger social affinity for the self than for the other. In Study 2, the authors showed that people evaluated themselves high in social affinity when they brought a prestigious wine to a party compared to when they brought a cheaper, generic wine, but evaluated others low in social affinity when they brought the same prestigious wine. In Study 3, the authors showed the mediating effects of social image and boastfulness on social affinity. Study 4 utilizes social network study to further validate previous findings in a field setting.

For high-end retailers, the authors suggest framing their promotional messages to explicitly highlight how owning prestigious goods will benefit them (i.e. social image). It is important that these retail managers (and salespeople alike) make it more salient on how their prestigious goods socially benefit the consumer (the self). Thus, it is important to get consumers to think about how a prestigious item looks on them and not on others. However, marketers must be prudent when constructing these messages, as the link between prestigious consumption and network development is merely perceptual.

The findings demonstrate that consuming prestigious goods increases social affinity via positive social image for the self. When evaluating others, the authors demonstrate that consuming prestigious goods decreases social affinity via boastfulness. In sum, owning prestigious items may seem beneficial socially to the self, but people have negative perceptions (boastfulness) of those who own the same prestigious goods. Hence, there seems to be a discrepancy in how the authors evaluate themselves versus how they evaluate others with the same prestigious goods.

Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis.

This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory.

The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations.

LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning.

LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data.

To the best of the authors’ knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.

Individuals health with intellectual disabilities (ID) experience comorbid physical and mental health needs and have poorer outcomes resulting in early mortality. Currently, many training provisions based on ID exist; however, limited research supports their effectiveness. High-fidelity simulation is an innovative training mechanism with promising preliminary results. This study aims to evaluate the longitudinal impact of simulation training on clinical practice in ID.

A mixed-method approach was used in this study. A one-day simulation course using actors who had ID was delivered to 39 health-care professionals from across London hospitals. Nine semi-structured interviews were conducted 12–18 months post training.

High-fidelity simulation training is an effective training modality, which has a sustainable impact on participants, their clinical practice and patients. Core features of the training including debriefing, the use and type of actors, scenario design and the facilitators are crucial learning mechanisms which impacts learning outcomes and changes to behaviour in clinical practice and settings.

To the best of the authors’ knowledge, this study is the first to longitudinally evaluate high-fidelity simulation training designed to improve the physical and mental health needs of those with ID. The research begins to bridge an important gap in the current literature, with a need for more research.