Search results

The study aims to examine fast fashion consumers' negative in-store experiences focusing on the effect of the two store environment factors, product overload and store ambiance, on their confusion and consequent shopping avoidance behavior.

A research model of fast fashion consumers' confusion and store avoidance behavior is proposed using the Stimulus–Organism–Response framework. A pretest and the main online survey with 281 samples are analyzed, and the structural equation modeling (SEM) is conducted to test the proposed model.

The SEM results support the proposed paths statistically. Consumers' confusion, measured with the two dimensions, inefficiency and helplessness, is significantly influenced by their perceived product overload and negative perception of store ambiance in the fast fashion shopping environment. Subsequently, fast fashion consumers' confusion results in less time spent in the store.

The study sheds light on utilitarian shopping value in the fast fashion shopping environment by focusing on the fast fashion consumers' confusion in association with overloaded information caused by too many products and store ambiance.

The study implies that improving fast fashion stores' inherent issues with too many products and store ambiance might help consumers mitigate their confusion and prevent customer attrition. However, the study includes only two factors. Future studies may include other various fast fashion store factors. Additionally, one of the dimensions of confusion, irritation, did not emerge in this study. More work is needed to investigate fast fashion consumers' confusion, such as using a multigroup analysis by age.

This paper aims to use artificial neural network (ANN) methods to predict stock price crashes in the Chinese equity market.

Three ANN models are developed and compared with the logistic regression model.

Results from this study conclude that the ANN approaches outperform the traditional logistic regression model, with fewer hidden layers in the ANN model having superior performance compared to the ANNs with multiple hidden layers. Results from the ANN approach also reveal that foreign institutional ownership, financial leverage, weekly average return and market-to-book ratio are the important variables when predicting stock price crashes, consistent with results from the traditional logistic model.

First, the ANN framework has been used in this study to forecast the stock price crashes and compared to the traditional logistic model in the world’s largest emerging market China. Second, the receiver operating characteristics curves and the area under the ROC curve have been used to evaluate the forecasting performance between the ANNs and the traditional approaches, in addition to some traditional performance evaluation methods.

The number of university students exhibiting mental health concerns have surged considerably in the past decade. Amongst a number of potential contributing factors, this study aims to assess the role of a broader societal phenomenon; the shift in emphasis in our interactions from the physical to the virtual environments. Specifically, a decrease in nature contact and a contrasting increase in smartphone use are identified as two pathways in which this shift may impact negatively on mental health. Previous research evidences both facets as consistent correlates of depression, although limited research extends these associations to the student population or attempts to establish an interaction between the two.

The current study recruited a sample of 380 first-year undergraduate students, via an online survey, to assess if problematic smartphone use (SAS-SV) and nature contact (NCQ) were significant predictors of depression (PHQ-9).

Nature contact frequency and smartphone use were significant predictors of depression.

This is the first study to concurrently assess the impact of smartphone use and nature contact in a student population.

This study explores insights from key stakeholders into the skills they believe will be necessary for the future of work as we become more reliant on artificial intelligence (AI) and technology. The study also seeks to understand what human resource policies and educational interventions are needed to support and take advantage of these changes.

This is a qualitative study where a sample of highly experienced representatives from a range of small to large Irish organisations, both public and private, provide insights into this important topic.

Findings indicate participants see a continued need for soft and hard skills as we evolve towards a more technologised workplace, with a need for employees to adopt a lifelong learning philosophy. As the knowledge economy in Ireland is well established, experts do not expect mass displacement to occur but differ with respect to the predicted rate of change. Novel HR interventions such as hiring for potential, pooling talent and establishing postgraduate supply contracts are seen as key. Current state interventions were mostly viewed positively but revamping of curricula is needed as well as stronger partnerships with tertiary institutions.

The interpretivist nature of the study limits the generalisability of the findings as they are based on a relatively small sample from one country. Also despite the significant expertise of the sample, it is not possible to predict whether their forecasts will manifest.

This research highlights the need for Irish SMEs to embrace the impacts of automation and AI as many are seen to be slow in reacting to changes in technology. The study also reveals cutting edge talent management interventions for employers to adopt that will insulate them from the challenges technological change presents to recruitment and employee development.

The findings from this paper culminate in the development of a conceptual framework, which encapsulates the responsibilities of all parties so that future skills needs will be met. This highlights the interplay between employers, individuals/employees, the Irish Government and educational institutions, demonstrating how they are interdependent on one another as we move towards a more technologised future.

The job satisfaction of university graduates can serve as an indicator of success in their professional development. At the same time, it can be a measure of higher education systems’ effectiveness. The purpose is to assess the relationship of university graduates’ socio-demographic characteristics, aspects of their degree program, experiences in the labor market and current working conditions and their job satisfaction.

The data were collected from a survey conducted at 11 Chilean universities with 534 graduates. An ordinal logistic regression model was fit to calculate job satisfaction probabilities for different graduate profiles.

The results show that sex, field of study, gross salary and horizontal match are related to graduates’ job satisfaction. Men and graduates in education and humanities are more likely to report being satisfied with their current job. Those graduates receiving higher salaries and those who are horizontally well-matched report higher levels of job satisfaction.

This study contributes to expanding knowledge about the job satisfaction of university graduates. Specifically, based on the results obtained, it introduces the idea of aspiration fulfillment as a possible determinant of job satisfaction in different fields of study. This can serve as a starting point for research that delves into differentiated expectations for graduates from different disciplines.

Implementing sustainable practices in multi-tier supply chains (MTSCs) is a difficult task. This study aims to investigate why such endeavours fail and how MTSC partners can address them.

A single-case study of a global food retail company was used in this study. Semi-structured interviews with the case firm and its first- and second-tier suppliers were used to collect data, which were then qualitatively analysed using thematic analysis.

Major barriers impeding the implementation of sustainability in multi-tier food supply chains were revealed such as the cost of sustainability, knowledge gap, lack of infrastructure and supply chain complexity. Furthermore, the findings reveal five possible solutions such as multi-tier collaboration and partnership, diffusion of innovation along the chain, supply chain mapping, sustainability performance measurement and capacity building, all of which can aid in the improvement of sustainability practices.

Future research should investigate how specific barriers and drivers affect specific aspects of sustainability, pointing practitioners to specific links between the variables that can aid in tailoring sustainability oriented investment.

This research supports managerial comprehension of MTSC sustainability, pointing out ways to improve sustainability performance despite the complex multi-tier system of food supply chains.

The research on MTSC sustainability is still growing, and this research contributes to the debate about how MTSCs can become more sustainable from the perspective of the triple bottom line, particularly food supply chains which face significant sustainability challenges.

The purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the Resource Description Framework (RDF). Alongside Named Graphs, this approach offers opportunities to leverage a meta-level for data modeling and data applications.

In this extended paper, the authors build onto three modeling use cases published in a previous paper: (1) provide provenance information, (2) maintain backwards compatibility for existing models, and (3) reduce the complexity of a data model. The authors present two scenarios where they implement the use of the meta-level to extend a data model with meta-information.

The authors present three abstract patterns for actively using the meta-level in data modeling. The authors showcase the implementation of the meta-level through two scenarios from our research project: (1) the authors introduce a workflow for triple annotation that uses the meta-level to enable users to comment on individual statements, such as for reporting errors or adding supplementary information. (2) The authors demonstrate how adding meta-information to a data model can accommodate highly specialized data while maintaining the simplicity of the underlying model.

Through the formulation of data modeling patterns with RDF-star and the demonstration of their application in two scenarios, the authors advocate for data modelers to embrace the meta-level.

With RDF-star being a very new extension to RDF, to the best of the authors’ knowledge, they are among the first to relate it to other meta-level approaches and demonstrate its application in real-world scenarios.

This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

This article explores how firms' unresponsiveness to Black customer feedback influences Black (vs. White) customers' perceived firm-based discrimination and brand engagement.

Two experimental studies (Study 1(N1) = 254) and Study 1(N2) = 484) are conducted to test the modeled relationships. The data are analyzed using ANOVA, PROCESS Model 4 and PROCESS Model 7.

The findings suggest that though perceived discrimination remains modest in all conditions, Black (vs. White) respondents report higher perceived discrimination when the firm fails to respond to a Black customer's negative or neutral (but not positive) brand-related feedback on social media. The results also indicate that Black (vs. White) customers exhibit lower engagement through perceived discrimination in the case of the firm's unresponsiveness to a Black customer's negative and neutral (but not positive) brand-related feedback regardless of the manager's race.

Prior research on intercultural service encounters and ethnic differences in consumer engagement on social media are combined to examine the relationship between customer race and perceived discrimination based on the firm's unresponsiveness to customers' social media posts.

Manipulations were created based on a fictitious e-tailer. Thus, it is recommend that future researchers examine the extent to which the findings hold for existing (r)etailers. In addition, future studies using secondary data could provide additional evidence for the findings.

Managerial attention is accentuated among customer feedback responsiveness, engagement and perceived firm discrimination. Managers are encouraged to adopt communication strategies that complement the firm's strategy and social media presence.

In this chapter, the author critically examines the deeply entrenched practices and theories within counselor education, revealing their roots in historically dominant, Eurocentric, and often racially oppressive assumptions. This study brings to light the pervasive impact of these traditional approaches, illuminating their role in perpetuating racial oppression and disparities in mental health care. The author presents a compelling argument for adopting Critical Race Theory (CRT) as an effective pedagogical and clinical practice framework in the counseling profession, a step toward its much-needed liberation. CRT's tenets are examined as a robust alternative, promoting socially just outcomes in counseling and psychotherapy. The article highlights CRT's capacity to address the well-established relationship between racism, white supremacy, and minority mental health. It proposes a groundbreaking model for praxis, predicated on CRT, which holds potential not only to challenge and disrupt oppressive structures but also to pave the way for the liberation of both the oppressed and the oppressor. This seminal work prompts a re-envisioning of counselor education, asserting a call for a transformative shift toward a liberation-based, social justice pedagogy.