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The purpose of this study was to determine the incidence and concentrations of aflatoxin M1 (AFM1) in raw buffalo milk samples obtained from four different seasons: winter, spring, summer and autumn in Sohag City, upper Egypt, and compared the determined findings against the several regulations regarding AFM1 that have been legislated by the European Union, US Food and Drug Administration and Egyptian Regulations.

Simultaneous determination of aflatoxins in raw buffalo milk samples trailed in different seasons, in Sohag City, Egypt. The aflatoxin that has been included in this survey is AFM1. Milk samples were obtained from February to October 2021. The enzyme-linked immunosorbent assay was used as a methodology technique.

The results of AFM1 presented the highest frequency of occurrence, with a detected incidence of 85.5% in winter samples, 64.2% in spring samples, 78.5% in summer samples and 78.5% in autumn samples. The positive samples showed concentration range levels of AFM1 between 0.0 and 0.9 µg/kg (12 samples) in winter samples, 0.03 and 1.2 µg/kg in spring milk samples (9 samples), 0.06 and 0.8 µg/kg in summer milk samples (11 samples) and lasted with 0.028 and 0.9 µg/kg in autumn milk samples (11 samples). The percentage of AFM1samples exceeded the maximum residues limit of Egyptian Standard Regulation 2010/7136 last updated, 78%, 57%, 100% and 64% in the four examined seasons, respectively.

The residue levels of AFM1 obtained in the investigated samples represented a serious concern about the health risk of consumers. Milk is introduced to the diet plan all over the people units especially the offspring ones as it is a complete food that contains a lot of important nutrients. So it is worth to set a regular schedule for monitoring and inspection of dairy products for aflatoxin residues.

The purpose of this paper is to describe and analyze the impacts that the recent proliferation of private and overlapping standards is having in the trade of agricultural products from developing countries.

In a first stage industry experts in the Chilean fresh fruit trading industry were interviewed to understand the perceived impact that private standards are imposing in the industry. These interviews allowed to identify the market case study, table grapes, the landscape of private standards and their prevalence in different countries. In a second stage, a gravity trade model for trade in table grapes was estimated, with a focus on the more stringent countries identified by experts in the first stage.

We show evidence that the proliferation of private standards required by large European retailers has diverted trade away from more stringent countries that require more certifications (and into less stringent European markets). We also show that the costs of these additional certifications have been shared by trading partners, via an increase in direct sales, as opposed to consignment (the traditional marketing mode), which is associated with higher prices.

The impacts of the recent proliferation of private and overlapping standards in international trade needs to be better understood both by the legal and economic literature. While the use of private standards has been growing since the 1990s, there is a recent trend of large European retailers imposing their own and overlapping standards that needs to be better understood to inform policy.

While there is a thin literature on the impact of private standards on trade, most of this has studied the effects of the now de facto mandatory GlobalGAP certification. However, there is a recent trend by large European retailers of demanding their own private certifications, together with other already existing overlapping private standards. This study describes and analyzes the impacts of this rather new trend.

When a large number of project proposals are evaluated to allocate available funds, grouping them based on their similarities is beneficial. Current approaches to group proposals are primarily based on manual matching of similar topics, discipline areas and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and requires excessive time. This paper aims to demonstrate how to effectively use the rich information in the titles and abstracts of Turkish project proposals to group them automatically.

This study proposes a model that effectively groups Turkish project proposals by combining word embedding, clustering and classification techniques. The proposed model uses FastText, BERT and term frequency/inverse document frequency (TF/IDF) word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish. The extracted terms were grouped using both the clustering and classification techniques. Natural groups contained within the corpus were discovered using k-means, k-means++, k-medoids and agglomerative clustering algorithms. Additionally, this study employs classification approaches to predict the target class for each document in the corpus. To classify project proposals, various classifiers, including k-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), classification and regression trees (CART) and random forest (RF), are used. Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.

The results show that the generated word embeddings can effectively represent proposal texts as vectors, and can be used as inputs for clustering or classification algorithms. Using clustering algorithms, the document corpus is divided into five groups. In addition, the results demonstrate that the proposals can easily be categorized into predefined categories using classification algorithms. SVM-Linear achieved the highest prediction accuracy (89.2%) with the FastText word embedding method. A comparison of manual grouping with automatic classification and clustering results revealed that both classification and clustering techniques have a high success rate.

The proposed model automatically benefits from the rich information in project proposals and significantly reduces numerous time-consuming tasks that managers must perform manually. Thus, it eliminates the drawbacks of the current manual methods and yields significantly more accurate results. In the future, additional experiments should be conducted to validate the proposed method using data from other funding organizations.

This study presents the application of word embedding methods to effectively use the rich information in the titles and abstracts of Turkish project proposals. Existing research studies focus on the automatic grouping of proposals; traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals. Unlike previous research, this study employs two outperforming neural network-based textual feature extraction techniques to obtain terms representing the proposals: BERT as a contextual word embedding method and FastText as a static word embedding method. Moreover, to the best of our knowledge, there has been no research conducted on the grouping of project proposals in Turkish.

This article investigates the impact of teleworking, especially concerning work intensity, during the Covid-19 pandemic, on the routine of women university professors in Brazil.

The authors collected data through a web-based survey disseminated through social media and sent e-mails to professors (addresses obtained from educational institutions’ public information), reaching 1,471 responses which were analyzed by correlation and multinomial logistic regression (MLR).

The authors find evidence to confirm all hypotheses tested at different levels. The professors who noticed increased workload during the pandemic are mostly from private higher education institutions (HEIs). The authors also demonstrate the impact of professional and family contexts and find a higher number of negative feelings and workplace correlates that influence the perception of working more.

This work problematizes the condition of women in Brazilian society, revealing the overload of work in the intersection between family, work, self-care, and other tasks. This study contributes to the literature exploring the home-office/telework in extreme periods, as is the case of the Covid-19 pandemic period.

This research aims to extract Industry 4.0 technological building blocks (TBBs) capable of value generation in collaborative consumption (CC) and the sharing economy (SE). Furthermore, by employing a mixed methodology, this research strives to analyse the relationship amongst TBBs and classify them based on their impact on CC.

Due to the importance of technology for the survival of collaborative consumption in the future, this study suggests a classification of the auxiliary and fundamental Industry 4.0 technologies and their current upgrades, such as the metaverse or non-fungible tokens (NFT). First, by applying a systematic literature review and thematic analysis (SLR-TA), the authors extracted the TBBs that impact on collaborative consumption and SE. Then, using the Bayesian best-worst method (BBWM), TBBs are weighted and classified using experts’ opinions. Eventually, a score function is proposed to measure organisations’ readiness level to adopt Industry 4.0 technologies.

The findings illustrated that virtual reality (VR) plays a vital role in CC and SE. Of the 11 TBBs identified in the CC and SE, VR was selected as the most determinant TBB and metaverse was recognised as the least important. Furthermore, digital twins, big data and VR were labelled as “fundamental”, and metaverse, augmented reality (AR), and additive manufacturing were stamped as “discretional”. Moreover, cyber-physical systems (CPSs) and artificial intelligence (AI) were classified as “auxiliary” technologies.

With an in-depth investigation, this research identifies TBBs of Industry 4.0 with the capability of value generation in CC and SE. To the authors’ knowledge, this is the first research that identifies and examines the TBBs of Industry 4.0 in the CC and SE sectors and examines them. Furthermore, a novel mixed method has identified, weighted and classified pertinent technologies. The score function that measures the readiness level of each company to adopt TBBs in CC and SE is a unique contribution.

The aim of this article was to present a retrospective assessment of the intellectual structure of private agricultural and food standards research in global trade. This study was motivated by the increasing role of standards and certifications in governing global agricultural and food trade.

The current investigation was carried out with bibliometric methods using VOSviewer software. Techniques such as citation, co-citation, keyword co-occurrence, keyword evolution and co-authorship analyses were performed to tackle the research questions. Articles were extracted from Scopus database for the period 1998–2022 (30th August 2022) with selected keywords (“Private food standard*” OR “food standard*” OR “agri-food standard*” AND “agri*” OR “agro*” OR “farm*” OR “food*” AND “international trade” OR “global trade” OR “international business”) along certain filters (subject – Economics and Business management: language – English: Document – article and review articles and source – journals).

The results show that the intellectual structure of private agricultural and food standards research in global trade has evolved around five clusters, namely: (1) the political economy of food standards, (2) food standards and their challenges in global trade, (3) food standards and integration into value chains, (4) food standards and market access and (5) food standards and exports from developing countries. However, the authors found the research gaps in each of the thematic clusters.

The main limitation of this study is that the authors focused their attention on certain aspects of bibliometric review, such as the intellectual structure of the field, the citation analysis and the collaboration network. Future research could attempt to explore new field development through bibliographic coupling and deepening of conceptual structure using content analysis by incorporating the research methods used in the respective studies.

The emerging research areas in private agricultural and food standards in global trade are related to topics on food quality, sustainable development, genetically modified organisms, World Trade Organization, tariff structure, trade agreements, food industry and European Union. However, there is less research and little collaboration between Africa and developed countries. For example, Africa's total publications were (15), while the US had (46), China (15), Belgium (23), Germany (27), Italy (32) and the UK (24).

There are limited studies that have conducted a retrospective evaluation of the intellectual structure of private agricultural and food standards research in the global trade using bibliometric analysis. The present investigation is novel in identifying the thematic research clusters, emerging issues and future research directions. This is more important to developing countries as their agricultural produce face challenges to access markets of the developed world.

The purpose of this paper is to analyse the influence of economic growth, digitalisation, eco-innovation, energy consumption and patents on environmental technologies on the volume of greenhouse gas emissions (GHG) recorded in European countries for a period of nine years (2010–2018).

Two empirical methods were integrated into the theoretical approach developed based on the analysis of the current scientific framework. Multiple linear regression, an extended version of the OLS model, and a non-causal analysis as a robustness method, Dumitrescu–Hurlin, were used to achieve the proposed research objective.

Digitalisation described by the number of individual Internet users and patents on environmental technologies determines the amount of GHG in Europe, and economic growth continues to have a significant effect on the amount of emissions, as well as the consumption of renewable energy. European countries are not framed in well-established patterns, but the economic growth, digitalisation, eco-innovation and renewable energy have an impact on the amount of GHG in one way or another. In many European countries, the amount of GHGs is decreasing as a result of economic growth, changes in the energy field and digitalisation. The positive influence of economic growth on climate neutrality depends on its degree of sustainability, while patents have the same conditional effect of their translation into environmentally efficient technologies.

This study has a number of limitations which derive, first of all, from the lack of digitalisation indicators. The missing data restricted the inclusion in the analysis of variables relevant to the description of the European digitalisation process, also obtaining conclusive results on the effects of digitalisation on GHG emissions.

A similar analysis of the relationship among the amount of greenhouse gas emissions and economic growth, digitalisation, eco-innovation and renewable energy is less common in the literature. Also, the results can be inspirational in the sphere of macroeconomic policy.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Using perspectives from the technology affordance and social capital theories, this study aims to unpack the process through which platform-enabled co-development unfolds in supply chain contexts. Specifically, it explores how innovation outcomes can be fostered through platform affordances and supply chain relationship (SCR) capital.

The paper integrates literature on digital platforms, SCRs and co-development to produce an integrative framework, developing propositions on the relationships among digital platforms, SCR capital and innovation outcomes.

The authors identify affordances for distinctive strategic use of platforms: value co-creation, relationship building and strategic learning. The authors discuss ways in which each affordance contributes to the advances in SCR capital, thus altogether enabling focal firms to orchestrate and integrate internal and external resources to attain incremental and radical innovation.

Based on the proposed research framework, further empirical studies can use quantitative data to measure the relationship between affordances and SCR capital and use longitudinal case studies to explore how affordances and SCR capital evolve to provide more fine-grained and contextualised information in different research settings.

This paper sheds light on how the relation between the adoption of digital platforms and SCR capital shapes digitally enabled service co-development. The authors provide an alternative explanation of resource integration in platform-mediated supply chain contexts and enrich the related literature on how digital platforms can maximise value from introducing ambidextrous innovation by leveraging internal and external resources.

After completion of this case study, students will be able to apply the triple bottom line concept to a spice manufacturing and export company (RBT 3); to examine the options before Value Ingredients Private Limited (VIPL), namely, to cultivate spices in the traditional way versus adopting integrated pest management (IPM) to cater to international markets (RBT 4); to analyse the returns for an IPM farmer vis-à-vis a conventional farmer, and to compare the returns therein (RBT 4); and to evaluate the ways and means of engaging farmers to change their way of cultivation (RBT 5)

The COVID-19 pandemic heightened awareness about the benefits of spices and buoyed its demand worldwide, which presented an opportunity to VIPL, a spice manufacturing company based in Chennai, to expand its business. However, the export markets demanded residue-free spices grown with little or no use of pesticides. Traditional farmers supplying spices to VIPL were accustomed to spraying pesticides whenever there was a pest attack. This case study discussed the options that the protagonist Mr Sijil Karim, managing director and CEO of VIPL, had, who wanted to onboard farmers for pesticide-free cultivation. The options before him were either to continue traditional farming or adopt IPM. This case study discussed the merits, demerits and challenges of each of these options.

The triple bottom line concept discussed three Ps – people, planet and prosperity – for this case as follows: The farmers and the consumers constituted the people in the spice supply chain. The farmers supplying organic, export-worthy spices under the guidance of VIPL gained 30% more than regular spice farmers, which were accrued through cost savings and better prices. The consumers benefitted from the pesticide-free, organic spices through accrued health gains. The manufacture of organic, pesticide-free spices helped the planet, as the process did not release hazardous chemicals into the atmosphere. VIPL manufactured pesticide-free spice with a focus on prosperity.

The case study can be introduced in a course on sustainability while discussing the triple bottom line concept. This case study showed how a for-profit company grew without losing sight of the planet or its focus on people. This case is best suited for students who have preliminary knowledge of supply chain management, operations and sustainability. Therefore, it is suited for sophomore-year students of MBA.

Teaching notes are available for educators only.

CSS 11: Strategy.