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This study aims to identify the economic impact of a potential implementation of a Climatic Reserve for the Swiss predominant white grape variety (Chasselas) vinified in the AOC (controlled designation of origin) category. The Climatic Reserve would imply the possibility of harvesting an additional quantity of grapes whose commercialization in wine would be delayed until it is approved by the relevant authority.

The impact of a potential implementation of this wine supply management tool is simulated through an innovative method that combines the vector autoregressive (VAR) model to estimate the influence of the previous consumptions and productions on the current consumption and linear regression [ordinary least square (OLS) method] to estimate the price elasticity to measure the evolution of the price depending on the simulated consumption. The VAR model is based on state-level data about production, stocks, and consumption (all the channels of distribution combined), while the OLS regression for estimating price elasticity uses the retail market data (Nielsen Panel). With the sales and price variables on a monthly frequency design, the latter represents about 40% of the wine market in Switzerland.

According to simulations carried out at the level of a region from the canton of Vaud in Switzerland (2000–2018), the increase in turnover linked to the release of the Climatic Reserve would be +3.1% for the indigenous white grape variety Chasselas.

The Climatic Reserve is a wine supply management tool that could complement the existing yield restriction, which does not significantly influence the quantities sold, according to previous studies. Our paper contributes to the literature by demonstrating the economic advantage of this supply management tool to deal with the increasingly frequent climatic hazards in wine production and market. The methodology could be applied to other wine regions (contexts) or other agricultural sectors.

The research provides a systematic literature review on the environmental sustainability of the globe artichoke [Cynara cardunculus L. var. scolymus (L.) Fiori], in the cultivated cardoon [Cynara cardunculus L. var. altilis DC.] and in the wild cardoon [Cynara cardunculus L. var. sylvestris (Lamk)] supply chains, to fill in the literature data gaps and to identify new research directions.

In the light of the PRISMA standard guidelines, the research provides a systematic literature review according to six research strings on Scopus and Web of Science. 45 scientific articles have been selected out of 407 contributions. Data have been synthesized according to a co-word analysis through the VOSviewer software, to provide insights into the structure of the research network, to offer a multidimensional scaling and clustering into research groups and to discuss the results.

The research identifies five main research trends: (1) biomass-to-bioenergy or biomass-to-biocomposite materials; (2) waste-to-bioenergy or waste-to-bioproducts; (3) crop management to increase yield productivity; (4) environmental impacts assessment; (5) irrigation water management. Besides, the research highlights momentous challenges and adopted strategies to tackle climate change and to reduce natural resources consumption, as well as the nexus between circular economy and environmental impact assessment.

The novelty of this study relies on the fact that it analyses the environmental sustainability of the Cynara cardunculus L. spp. Supply chain in a systematic way, giving the opportunity to identify future research directions regarding the environmental impacts associated with agricultural production and industrial transformation.

This paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.

The authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.

Fifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.

The authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.

Earlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

This study aims to adopt a holistic approach to understand cruise revenue management (RM) practices that cover ticket and onboard revenues, through a cross-disciplinary literature review and practitioner interviews. An integrated cruise RM framework was developed and served as a blueprint for future cruise studies and practices.

A multi-stage approach was adopted, including a systematic literature review, two-waves of interviews with 26 cruise industry practitioners and the development of a holistic RM framework.

This study clarifies cruise RM functions across product planning, delivery stages and identifies ticket and onboard RM components. These are incorporated into the integrated framework, with weather and itinerary/ route attractiveness as additional considerations. Interviews revealed that there is no difference in the RM cycle before and during the COVID-19 pandemic, although strategies and tactics may vary in response to the market situation.

Suggestions are made regarding product and service bundling and ways for ticket and onboard revenue teams to work together to optimize total revenue. Future research directions are also provided under the categories of RM applications and concepts, ticket core activities, onboard core activities and overall issues.

To the best of the authors’ knowledge, this is the first paper to conduct a cross-disciplinary systematic literature review of cruise RM without imposing publication dates or specific databases and the first to develop an integrated cruise “total” RM framework that includes ticket and onboard revenues.

The construction industry continues to struggle to deliver a right first time culture, seeking a panacea for improvement whilst maintaining project milestones. Complex construction projects demand stringent programmes, however, (un)foreseen changes, political influences and human behaviours all have significant impacts on delivering schemes without error. Previous studies have questioned the ability of the construction industry to successfully learn from errors. A major barrier has been the sharing of sensitive data from failed outcomes. Hence, this paper investigates non-conformance on an existing scheme and suggests avenues for improvement.

A mixed-method approach was adopted whereby 1,260 non-conformance reports (NCRs) from a highways megaproject were interrogated using root cause analysis (RCA) techniques to uncover the most frequent and costly areas. This was followed by a survey to industry professionals within a tier 1 principal contractor to gain insight into their perceptions of non-conformance and rework on construction projects.

Using Pareto analysis, the authors find that materials management, workmanship (poor quality execution) and supervision issues are the most frequently occurring and costly root causes of non-conformance on a major highways scheme. Furthermore, the authors link corresponding viewpoints of two project professional groups to the posed, achieving a high degree of consensus for the areas requiring development. Lastly, the authors suggest avenues for improvement via lessons learnt. These include greater emphasis on quality culture via a strong leadership mandate, enhanced vetting of workforce competence and improving the way materials are managed by embracing technology to drive efficiency.

This paper interrogates a current highways scheme using a uniquely rich, sensitive dataset to determine how the construction sector may improve efforts to achieving right first time outcomes.

This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.

A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.

The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.

This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.

The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.

Pricing studies have largely focused on sellers' pricing strategies and price determinants. To expand earlier work on sellers' pricing decisions, this study considers time as a major factor driving sellers' ticket prices in the secondary market. Specifically, because most secondary market transactions occur in the last moments before a game, this study considers how resellers adjust ticket prices within a few days prior to a game day including an actual game day.

To examine the impact of time on secondary market ticket prices for Major League Baseball (MLB), ticket prices were collected from StubHub (one of the largest secondary ticket markets) four times per game: from 3 days to 1 day prior to a game day and on the actual game day. Additionally, 10 control variables were obtained from previous research on price determinants (N = 19,155). A multiple regression model was created based on the extant literature regarding secondary market ticket prices.

Results indicate the number of days before a game negatively influenced ticket prices: resellers decreased ticket prices consistently during the last few days prior to a game's first inning. Specifically, secondary market ticket prices decreased relatively dramatically on an actual game day. Time had no significant effects on ticket prices 2 days prior to a game day. In addition to the role of time, league affiliation and the number of all-star players were identified as key price determinants in the secondary market. Moreover, changes in weather forecasts and the home team starting pitcher's ERA played significant roles in price changes.

Despite containing a relatively high number of data observations compared with prior pricing studies, this study's findings were limited to certain teams. Additionally, as only MLB secondary market ticket pricing was considered, different outcomes and implications may apply in other major sport ticket markets (e.g. NBA, NFL, NHL and MLS) featuring distinct league structures, policies and demand.

This study offers practical guidance for sellers' pricing decisions. Most secondary ticket market sellers lowered their ticket prices relatively dramatically on an actual game day. Reducing ticket prices prior to a game day can lead to greater chances to avoid unsold tickets that compromise revenue management. This study's results also afford professional sport organizations and secondary ticket market consumers a clearer understanding of the factors resellers consider when setting ticket prices.

Although previous studies have uncovered essential elements influencing ticket prices and consumer demand in the secondary ticket market, little work has examined how time affects sellers' pricing decisions within a few days prior to a game day. Little is known about the elements that significantly influence sellers’ decisions to adjust (i.e. increase or decrease) ticket prices in the secondary market as well. This topic deserves ongoing attention, as new outcomes can supplement previous studies' findings due to changing market environments.

This paper finds the returns from soil conservation practices and examines whether the welfare implications of adopting the conservation practices are heterogeneous across the farming groups in Indian agriculture.

The study uses an endogenous switching regression (ESR) method on the data collected from the 77th round of National Sample Survey (2019–21) to quantify the returns from adopting soil conservation practices.

It finds that farmers adopting soil health conservation practices would have reduced their crop yield by 13% if they did not implement them. Similarly, smallholders who have not adopted soil health management practices would have increased crop yield by 16% if they had adopted the practices. The authors also observed that the returns from adopting soil health management practices vary across farming groups, where marginal and large farms tend to gain higher yields. Finally, the authors find that regardless of farm size, smallholders who did not adopt soil health management practices would benefit from adopting these with increased crop yields of 29%–31%.

More data could have been better for drawing policy implications, since the number of soil card users are relatively less.

This research work uses nationally representative data, which is first in nature on this very aspect.

The purpose of this paper is to identify the root cause of low yield problems in the semiconductor manufacturing process using sensor data continuously collected from manufacturing equipment and describe the process environment in the equipment.

This paper proposes a sensor data mining process based on the sequential modeling of random forests for low yield diagnosis. The process consists of sequential steps: problem definition, data preparation, excursion time and critical sensor identification, data visualization and root cause identification.

A case study is conducted using real-world data collected from a semiconductor manufacturer in South Korea to demonstrate the effectiveness of the diagnosis process. The proposed model successfully identified the excursion time and critical sensors previously identified by domain engineers using costly manual examination.

The proposed procedure helps domain engineers narrow down the excursion time and critical sensors from the massive sensor data. The procedure's outcome is highly interpretable, informative and easy to visualize.

Scientific publishing has recently faced challenges in dealing with questionable (predatory and hijacked) journals. The presence of questionable journals in any field, including management science, will yield junk science. Although there are studies about questionable journals in other fields, these journals have not yet been examined in the field of business and management. This study aims to identify facilitators and barriers to dealing with questionable journals in management science.

A Delphi research method consisting of three rounds was used in this study. Data were collected from 12 experts in the first two rounds, and ten experts in the final round.

The present study shows that management science is vulnerable to questionable journals. A total of 18 barriers and eight facilitators to dealing with questionable journals in management science were found. The present study also identifies some new barriers and facilitators for avoiding questionable journals, which are specific to management science and have not been identified in previous research. Most of these barriers and facilitators were identified as “important” or “very important”. Publishers and scientific databases, government, the research community and universities and research centers were identified as critical players in overcoming challenges posed by questionable journals.

The number of articles that investigate predatory journals in management science is limited, and there is no research focused specifically on hijacked journals in this field. This study identifies facilitators and obstacles to dealing with predatory and hijacked journals in the field of management, by gathering opinions from experts. Thus it is the first study to examine hijacked journals in the field of management science. It is also one of the few studies that examine predatory and hijacked journals by conducting exploratory research rather than with a descriptive/conceptual approach.