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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.

More complexity, less freedom, distrust and a lack of information seem to pose threats to the success of partner relationships in accounting firms, as approximately 70% of business partnerships fail globally, undermining SDG 17. The low competitive intensity in this industry seems not to help the current situation. Yet, the existing strategic alliance (SA) literature have been somewhat sluggish in adequately addressing how partnership attributes (PAs) affect partnership success (PS) and how brand competition (BC) impacts this relationship. In response, this conceptual work addresses the impact of PAs on PS in accounting firms. It further explores the BC conditions under which the PAs–PS connection may be intensified.

Incorporating theories and empirics on six distinct topics, this study presents a conceptual model and ten hypotheses that are worth testing in future research.

This research finds that PAs will be favourably linked to PS, and this favourable association will be positively moderated by BC such that the PAs–PS connection will be more pronounced if BC within the accounting industry is high than low.

Further research is needed to empirically test the suppositions made. Also, they could extend the proposed framework to cover other moderators like technological turbulence, market dynamism and government regulation.

Practical lessons for governments, shareholders, chief executive officers, consultants and other industry players, particularly those who are interested in the success of accounting partnership firms, are deliberated.

This study demonstrates how PAs and BC interact to foster PS. It also provides a baseline information for upcoming researchers to investigate other external factors under which the PAs–PS connection may be improved.

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.

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.

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 study aims to investigate the influence of sociodemographic characteristics, perceived risks, health and nutritional motives and taste preference on at-home fish consumption.

Data were collected from a nationally representative sample of people responsible for food purchasing in households, using the CAWI (computer-aided web interviewing) method. The eligible study sample comprised 977 participants in Croatia and 967 in Italy, who reported fishery products consumption in the previous 12 months. A questionnaire was used to examine sociodemographic characteristics, fish consumption frequency and factors affecting fish consumption. Determinants of white and fatty fish consumption were estimated with ordered probit models, along with marginal effects for each factor in the models.

Common positive determinants of white and fatty fish consumption in Croatia and Italy were health and nutritional motives (p < 0.001, p = 0.001, p < 0.001, p = 0.010), taste preference (p < 0.001, p < 0.001, p = 0.001, p = 0.001) and maritime nature of the living region. The common negative determinant of white and fatty fish consumption in Croatia and Italy was financial risk (p < 0.001, p < 0.001, p < 0.001, p = 0.005). The country-specific positive determinant in Croatia was the number of household members (p < 0.001), while negative determinants for white and fatty fish were functional risk (p = 0.004, p = 0.013), number of children (p = 0.030, p = 0.001) and female gender (for fatty fish) (p = 0.028). In Italy, older age negatively affected (p < 0.001) fish consumption, while number of children (p = 0.009) and household income positively affected white fish consumption.

An adequate probabilistic model of national representative samples ensures credibility of results. Policy and marketing activities are proposed that can encourage higher fish consumption.

The psychological foundations of consumers’ reasons for product choices are analyzed in the field of marketing. The purpose of this research is to identify the implicit reasons for white meat consumption in the UK and Turkey.

In the scope of the means-end chain theory, in-depth interviews were conducted with individuals, and the reasons for consumers’ product preferences were revealed by moving from concrete to abstract.

It has been determined that the white meat consumption of Muslims in the UK is primarily shaped by their religious approach. In Turkey, on the contrary, both consumption patterns and reasons for preference are changing. It has been found that white meat consumption is associated with values such as security needs, satisfaction with life, self-fulfillment and happiness.

This research has contributed to the marketing literature by examining consumers’ implicit consumption reasons for white meat in the context of religion and culture.

Marketing strategies should focus on building trust in halal certification, particularly in the UK. Brands should associate their promotion strategies with feelings of security and happiness, which are associated in the minds of consumers.

This study is a new study in terms of revealing the connotations of consumers about consuming chicken and fish and showing the implicit needs that the brands can emotionally associate with.

This paper explores the wages of White, Black, Hispanic, Asian, Native American and “other race” women and men once differences in basic characteristics among these 12 groups are accounted for. The authors aim to extend comparisons beyond those of women and men of the same race or the various races within a given gender.

To undertake the conditional analysis, first, the authors propose a simple re-weighing scheme that allows to build a counterfactual economy in which workers' attributes for all gender–race/ethnicity groups are the same. Second, the authors use a well-known re-weighting scheme that involves logit estimations.

Only Hispanic men, Native American men and Asian women have conditional wages around average. Black men and, especially, White, Black, Hispanic, Native American and “other race” women have conditional wages clearly below average, whereas those of Asian and White men are well above average. The wage differential between a privileged and a deprived group is disentangled into the premium of the former and the penalty of the latter, which brings a new perspective to what has been done in the literature based on pairwise comparisons. In this intersectional framework, the authors document that gender penalizes more than race.

This paper examines intergroup earnings differentials using a methodology that allows to examine 12 gender–race/ethnicity groups jointly, which is this work's distinctive feature. The authors' intersectional framework allows to picture the effect of gender and race/ethnicity more broadly than what the literature has shown thus far.