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Apples have always been considered a healthy product able to provide curative properties to consumers. In Italy, there is a long tradition of apple consumption and production both as a fresh product and as processed food. However, as with many other products, the consumption of fruits and vegetables and, more specifically apples, has been drastically affected by the first lockdown in 2020. In this project, the authors investigate whether the change in consumption habits had long-lasting consequences beyond 2020 and what are the main eating motivations, food-related behavior and socio-demographic affecting the consumption of fruits and vegetables after the pandemic.

The authors ran two online surveys with 1,000 Italian consumers across a year (from October 2021 to December 2022). In the study, participants answered questions about their consumption habits and their eating motives. Out of 1,000 consumers, the authors included in the final analysis only the participants who answered both surveys, leaving a final sample of 651 consumers.

The results show that participants have allocated more budget to fruit and vegetables after the lockdown than before it. Moreover, consumers reported an average increase in the consumption of apples. However, the increase was more pronounced for people aged between 30 and 50 years old and identified as female. After showing the difference across time, a cluster analysis identified three main segments that differ in their eating motives, place of purchase and area of residence.

Overall, the results contribute to a better understanding of how the global pandemic is still affecting people's daily life. Moreover, the findings can be used to guide the marketing and communication strategies of companies in the food sector.

To the best of the authors' knowledge, this is the first study that investigates changes in the consumption of fruits and vegetables, and, more specifically, apples, in Italy more than one year after the beginning of the COVID-19 pandemic. Moreover, the study proposes a classification of consumers based on their habits in a time frame during which the COVID-19 wave was at its bottom which is not currently present in the literature.

This paper aims to explore how music festival organisers negotiate diversity and inclusion in marketing and promotion practices through symbolic and social boundaries.

Based on semi-structured interviews with 18 festival organisers in Rotterdam and participant observation with six festival photographers I show that symbolic and social boundaries are employed in three areas: (1) boundaries in festival format (i.e. [partially] free or ticketed), (2) boundaries in distribution partners and technologies and (3) boundaries in promotional content.

Symbolic and social boundaries are intentionally used by festival organisers to build and delineate festival audiences. Implications are drawn on current understandings of the accessibility of music festival spaces, arguing that festival research should move beyond within-space dynamics to grasp the negotiation of diversity and inclusion at festivals more fully.

While music festivals are often marketed as celebratory spaces that are “welcoming to everyone”, few studies have investigated diversity and inclusion nor marketing and promotion practices at music festivals. This study shows how festival audiences are shaped through marketing and promotion practices.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19).

A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities.

Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed.

The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample.

Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach.

This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation.

This paper delves into cross-border E-business, unraveling its intricate dynamics and forecasting its future trajectory.

This paper projects the prospective market size of cross-border E-business in China for the year 2023 using the GM (1,1) gray forecasting model. Furthermore, to enhance the analysis, the paper attempts to simulate and forecast the size of China’s cross-border E-business sector using the GM (1,3) gray model. This extended model considers not only the historical trends of cross-border E-business but also the growth patterns of GDP and the digital economy.

The forecast indicates a market size of 18,760 to 18,934 billion RMB in 2023, aligning with the consistent growth observed in previous years. This suggests a sustained positive trajectory for cross-border E-business.

Cross-border e-commerce critically shapes China’s global integration and traditional industry development. The research in this paper provides insights beyond statistical trends, contributing to a nuanced understanding of the pivotal role played by cross-border e-commerce in shaping China’s economic future.

As after-hours technology-mediated work (ATW) becomes common in organizations, the increased workload and interference to life caused by ATW has induced employee turnover. This research develops a mediated moderation model to explain how employees' intrinsic and extrinsic motivations for ATW affect their turnover intention through work–life conflict.

A survey was conducted to collect data of 484 employees from Chinese companies. Partial Least Square was used to perform data analysis.

The results show that intrinsic motivation for ATW has an indirect negative impact on turnover intention via work–life conflict, whereas extrinsic motivation for ATW has both a positive direct impact and a positive indirect impact (via work–life conflict) on turnover intention. This study also helps find that time spent on ATW can strengthen the positive impact of extrinsic motivation for ATW on turnover intention but has no moderation effect on the impact of intrinsic motivation for ATW. Furthermore, this study reveals that the interaction effect of time spent on ATW and extrinsic motivation on turnover intention is mediated by employees' perceived work–life conflict.

By discovering the distinct impact of employees' intrinsic and extrinsic motivations for ATW on turnover intention, this research provides a contingent view regarding the impact of ATW and offers guidance to managers regarding how to mitigate ATW-induced turnover intention through fostering different motivations.

This paper examines how key actors in the UK food system (FS) understand the role of the local food sector in relation to FS resilience.

Discourse analysis was used to assess and compare the framings of the UK FS in 36 publications released during Covid-19 from alternative food networks (AFNs) actors and from other more mainstream FS actors, including the UK government.

The analysis shows that AFNs actors perceive the UK FS as not resilient and identify local FSs as a route towards greater resilience (“systemic” framing). In contrast, other food actors perceive the UK FS as already resilient, with the role of local food limited to specific functions within the existing system (“add-on” framing). The two groups converge on the importance of dynamic public procurement and local abattoir provision, but this convergence does not undermine the fundamental divergence in the understanding of the role of “the local” in resilient UK FSs. The local food sector’s messages appear to have gone largely unheard in mainstream policy.

The paper presents an analysis of public sector reports focused on the UK FS released during the Covid-19 pandemic years 2020–2021. The corpus inclusion criteria mean that publications during this period which focus on other food sector issues, such social injustices, climate change and health, were not included in the analysis, although they may have touched upon local food issues. The authors further recognise that Covid-19 had a longer lasting effect on FSs than the years 2020–2021, and that many other publications on FSs have been published since. The time span chosen targets the time at which FSs were most disrupted and therefore aims to capture emerging issues and solutions for the UK FS. The authors’ insights should be further validated through a more complete review of both public reports and academic papers covering a wider base of food-related issues and sectors as well as a broader timespan.

A comparison of how different FS actors understand the importance of local food, especially in relation to resilience, has not been undertaken to date. The findings raise important questions about the disconnect between AFN actors and other actors in the framing of resilience. Considering the need to ensure resilience of the UK FS, this study's findings raise important insights for UK food policy about the “local food blindspot” and for food movement actors wishing to progress their vision of transformative change.

This study aims to investigate the impact of consumer ethnocentrism on consumers' evaluation of blockchain-based traceability information. It also examined how the use of quick response (QR) codes for traceability affects consumers' evaluation of traceable food products.

An online choice experiment was conducted to determine consumers' evaluation of the blockchain-based traceability of Feta cheese with a quota sample of 715 Greek consumers. Pearson bivariate correlation and mean comparison were used to examine the relationship between consumer ethnocentrism and QR use behaviour. Random parameter logit models were employed to examine consumers’ valuation of the examined attributes and interaction terms.

The results show that ethnocentric consumers are willing to pay more for blockchain-based traceability information. Ethnocentric consumers tend to scan QR codes with traceability information. Spending more time reading traceability information embedded in QR codes does not lead to a higher willingness-to-pay (WTP) for traceable food products.

The findings suggest that patriotic marketing messages can draw consumers' attention to blockchain-based traceability information. The modest WTP for and low familiarity with blockchain-based traceability systems raise the need for educating consumers regarding the benefits of blockchain in traceability systems.

This is the first study to provide timely empirical evidence of a positive WTP for blockchain-based traceability information for a processed dairy product. This study is the first to attempt to distinguish the effects of the intention to scan QR codes and reading information embedded in QR codes on consumers’ valuation of food attributes.

This study reviews scholarly work in sustainable green logistics and remanufacturing (SGLR) and their subdisciplines, in combination with bibliometric, thematic and content analyses that provide a viewpoint on categorization and a future research agenda. This paper provides insight into current research trends in the subjects of interest by examining the most essential and most referenced articles promoting sustainability and climate-neutral logistics.

For the literature review, the authors extracted and sifted 2180 research and review papers for the period 2008–2023 from the Scopus database. The authors performed bibliometric and content analyses using multiple software programs such as Gephi, VOSviewer and R programming.

The SGLR papers can be grouped into seven clusters: (1) The circular economy facets; (2) Decarbonization of operations to nurture a climate-neutral business; (3) Green sustainable supply chain management; (4) Drivers and barriers of reverse logistics and the circular economy; (5) Business models for sustainable logistics and the circular economy; (6) Transportation problems in sustainable green logistics and (7) Digitalization of logistics and supply chain management.

In this review, fundamental ideas are established, research gaps are identified and multiple future research subjects are proposed. These propositions are categorized into three main research streams, i.e. (1) Digitalization of SGLR, (2) Enhancing scopes, sectors and industries in the context of SGLR and (3) Developing more efficient and effective climate-neutral and climate change-related solutions and promoting more environmental-related and sustainability research concerning SGLR. In addition, two conceptual models concerning SGLR and climate-neutral strategies are developed and presented for managers and practitioners to consider when adopting green and sustainability principles in supply chains. This review also highlights the need for academics to go beyond frameworks and build new techniques and instruments for monitoring SGLR performance in the real world.

This study provides an overview of the evolution of SGLR; it also clarifies concepts, environmental concerns and climate change practices, particularly those directed to supply chain management.

This study delves into the psychological processes underlying sport fans' post-purchase innovativeness behaviour. This exploratory research aims to establish a theoretical framework that elucidates the formation of sport fans' word-of-mouth (WOM) behaviours, particularly emphasising the structural relationship between motivated consumer innovativeness and satisfaction in using AR live-streaming services.

Utilising an online survey and convenience sampling, the study garnered a total of 243 usable responses from three online baseball fan communities in South Korea. Confirmatory factor analysis was employed to assess the psychometric properties of the constructs. Subsequently, a structural equation model was used to probe the influence of motivated consumer innovativeness on WOM, with a particular focus on the mediating role of satisfaction.

Three dimensions of motivated sport fans innovativeness – functional, hedonic and cognitive – showed a positive association with WOM, partly mediated by satisfaction. In contrast, socially motivated sport fans innovativeness did not directly lead to WOM but influenced it solely through satisfaction. The full mediating role of satisfaction in the relationship between socially motivated fans innovativeness and WOM was found.

This research stands out as one of the scant studies exploring motivated sport fans innovativeness in the context of AR live sport streaming. The findings not only corroborate but also augment the extant literature by empirically confirming that three dimensions of motivated fans innovativeness, coupled with satisfaction, are pivotal antecedents to WOM intention.