Search results

This study aims to investigate whether consumers and small businesses can transition from disposable to reusable coffee cups, using a community social marketing intervention, led by a Social Purpose Organisation.

An emergent case study approach using multiple sources of data developed an in-depth, multifaceted, real-world context evaluation of the intervention. The methodology draws on citizen science “messy” data collection involving multiple, fragmented sources.

Moving from single-use cups to reusables requires collective commitment by retailers, consumers and policymakers, despite the many incentives and penalties applied to incentivise behaviour change. Difficult post-COVID economics, austerity and infrastructure gaps are undermining both reusable acceptance and interim solutions to our dependence upon disposables.

Although the non-traditional methodology rendered gaps and omissions in the data, the citizen science was democratising and inclusive for the community.

Our practical contribution evaluates a whole community intervention setting to encourage reusable cups, integrating multiple stakeholders, in a non-controllable, non-experimental environment in contrast to previous research. This paper demonstrates how small community grants can foster impactful collaborative partnerships between an SPO and researchers, facilitate knowledge-exchange beyond the initial remit and provide a catalyst for possible future impact and outcomes.

To assess the impact at both the outcome and the process level of the intervention, we use Pawson and Tilley’s realist evaluation theory – the Context Mechanism Outcome framework. The methodological contribution demonstrates the process of citizen science “messy” data collection, likely to feature more frequently in future social science research addressing climate change and sustainability challenges.

Drawing on conservation of resources (CoR) and speech act theories, the authors tested the relationship between managers’ motivating language (ML) and employee service quality and psychological relatedness and competence as mediating variables between their associations.

Using a convenient sampling technique, the authors collected 366 hotel employees’ opinions in Malaysia and analysed them in partial least squares-structural equation modelling.

Three forms of ML, psychological competence and relatedness correlate with employees’ service quality. Although direction-giving language is correlated with competence, empathetic and meaning-making language are not; thus, competence only mediates the relationship between direction-giving language and service quality. Three types (direction-giving, empathetic and meaning-making) of managers’ communication are correlated with relatedness; thus, relatedness mediates the association between the three types of language and service quality.

Hospitality managers are encouraged to enhance psychological relatedness and competence by practising an appropriate ML. Psychological relatedness and competence are significant mechanisms that enlighten the effects of supervisory communicant on service quality, indicating employees’ need satisfaction should be improved.

Our study contributes to speech act and CoR theories by explaining the relationship between ML, psychological relatedness, competence and service quality.

Young people in rural areas often face barriers when accessing participation opportunities in their municipalities. This affects their voices being heard and their ability to create change. Even though almost half the world’s population lives in rural areas, rural young people’s activism is often overlooked in the literature. In addition, when young people’s activism is explored in empirical research, conceptualisations of activism and methods are often not tailored to rural areas. This chapter, thus, adds to our understanding of young people’s activism in rural municipalities by drawing on a mixed methods case study including thirteen focus groups (FGs; n = 35) and a questionnaire (n = 106) with young people aged 13–17, and semi-structured interviews (n = 11) with teachers from one secondary school in a rural municipality in Germany. Five of the FGs were conducted and analysed by Year 10 students, adding unique insights into participants’ experience of activism. In this chapter, activism is conceptualised as one of the multiple dimensions of citizenship. Activism includes demanding systemic change, individually or collectively, which may include refusing to do things, aiming to prevent laws, raising awareness, and making consumer choices. Rather than being full-time activists, the young people in this study were engaged in only a few forms of activism, often carried out ad-hoc, part-time and in connection with other citizenship activities such as volunteering. Spaces for activism included online, the local municipality, everyday spaces such as the supermarket, and school. Participants experienced multiple barriers when engaging in activism including narratives of non-activist young people, age restrictions, power imbalances and few opportunities for creating change, particularly at participants’ school and in their municipalities.

Utilizing a self-regulatory perspective, the authors examine how narcissism influences perceived negative inequity and the downstream effects on self-enhancement motivation and organizational citizenship behaviors (OCBs) over time.

A total of 689 useable three-wave panel data were obtained via Amazon Mechanical Turk during a three-month period. A latent growth curve modeling approach using EQS 6.4 for Windows was employed to test the hypothesized model.

Results illustrate that individuals with higher levels of narcissism perceive higher levels of perceived negative inequity and then form higher levels of self-enhancement motivation, which prompt more OCB directed toward the organization (OCBO) than OCB directed toward individuals (OCBI). When perceived negative inequity increases over time, narcissists experience a faster increase in self-enhancement motivation, which also leads to a faster increase in OCBO compared to the increase in OCBI.

Theoretically, this study provides theoretical and empirical insights into understanding the process through which narcissists' OCBs are motivated. Practically, this study offers several practical recommendations that help managers manage OCBs effectively in the organization.

Large class sizes are becoming the norm in higher education against concerns of dropping learning qualities. To maintain the standard of learning and add value, one of the common strategies is for the course convenor to proactively monitor student engagement with learning activities against their assessment outcomes and intervene timely. Learning analytics has been increasingly adopted to provide these insights into student engagement and their performance. This case study explores how learning analytics can be used to meet the convenor’s requirements and help reduce administrative workload in a large health science class at the University of New South Wales.

This case-based study adopts an “action learning research approach” in assessing ways of using learning analytics for reducing workload in the educator’s own context and critically reflecting on experiences for improvements. This approach emphasises reflexive methodology, where the educator constantly assesses the context, implements an intervention and reflects on the process for in-time adjustments, improvements and future development.

The results highlighted ease for the teacher towards the early “flagging” of students who may not be active within the learning management system or who have performed poorly on assessment tasks. Coupled with the ability to send emails to the “flagged” students, this has led to a more personal approach while reducing the number of steps normally required. An unanticipated outcome was the potential for additional time saving through improving the scaffolding mechanisms if the learning analytics were customisable for individual courses.

The results provide further benefits for learning analytics to assist the educator in a growing blended learning environment. They also reveal the potential for learning analytics to be an effective adjunct towards promoting personal learning design.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

The purpose of the study is to compare methodologies for mapping a historic building, with image capture by smartphones and drones, using photogrammetric techniques for three-dimensional (3D) modeling of the structure. Processes and products are also analyzed, as well as possibilities for storing and visualizing data for structuring a cadastre of historical and artistic heritage are studied.

For mapping with smartphones, the overlapping of photographs was guaranteed, with data acquisition using three different cameras, on the same date as the aerial survey. The models were made from different combinations of camera use. For storage, a conceptual model based on ISO 19.152:2012 is proposed, which was implemented in the MongoDB, resulting in a database for storage. The visualization was carried out on the Cesium ion platform.

The results indicate that the terrestrial 3D reconstruction using smartphones is an efficient alternative to the historical and artistic cadastre, presenting texture quality superior to the aerial survey in a shorter production time. When dealing with the conceptual model, the LADM (Land Administration Domain Model) standardization guarantees interoperability and facilitates data exchange. In addition, it proved to be flexible for the creation of thematic profiles, supporting their effective storage. The insertion of data in the visualization platform was simple and effective, and it even generated sharing links for visualization of the models.

The study analyses a low-cost method with the use of easily accessible devices, with a combination of methodologies and applied techniques. The data storage and visualization method is also simple and flexible, suitable for application in the cadastre of historical heritage.

This study aims to examine investor attention by exploring the trading behavior of investors in US-based exchange traded funds (ETFs) of countries active in the Federation Internationale de Football Association (FIFA) World Cups.

The present study employs event study methodology to measure abnormal returns and excess trading volume of country-specific ETFs during six FIFA World Cups. The sample of ETFs includes 19 participating countries.

Consistent with investor behavior that might be explained by attention effect, the study finds that country-specific ETFs from participating countries do indeed behave differently during FIFA World Cups events. The authors find significant evidence of abnormal trading volume and, albeit weaker, abnormal returns during cups.

This study contributes to the literature on investor behavior, linking investor attention with salient sports events.

The purpose of this study is to propose an extended reliability method for an industrial motor drive by integrating the physics of failure (PoF).

Industrial motor drive systems (IMDS) are currently expected to perform beyond the desired operating conditions to meet the demand. The PoF of the subsystem affects its reliability under such harsh operating circumstances. It is crucial to estimate reliability by integrating PoF, which helps in understanding its impact and to develop a fault-tolerant design, particularly in such an integrated drive system. An integrated PoF extended reliability method for industrial drive system is proposed to address this issue. In research, the numerical failure rate of each component of industrial drive is obtained first with the help of the MIL-HDBK-217 military handbook. Furthermore, the mathematically deduced proposed approach is modeled in the GoldSim Monte Carlo reliability workbench.

From the results, for a 15% rise in integrated PoF, the reliability and availability of the entire IMDS dropped by 23%, resulting in an impact on mean time to failure (MTTF).

The integrated PoF of the motor and motor controller affects industrial drive reliability, which falls to 0.18 with the least MTTF (2.27 years); whose overall reliability of industrial drive drops to 0.06 if it is additionally integrated with communication protocol.