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Professionals from the dairy sector commonly believe that the results of Global Dairy Trade (GDT) auctions are a good leading indicator for prices of dairy commodities. The purpose of this paper is to test that hypothesis for prices of key dairy commodities (skimmed milk powder (SMP), whole milk powder (WMP), butter and cheddar) in the main dairy markets (the US, EU and Oceania).

The leading properties of the GDT auctions are investigated using vector error correction models (VECM).

The results show that prices at GDT auctions may be treated as a benchmark for global prices of WMP and SMP as they affect prices in all considered markets. However, in case of EU market the relationship with the GDT is bidirectional. GDT prices reveal some leading properties also in cheddar market, however price relationships in this market are much more complex. In case of butter market, GDT can be regarded as a benchmark only for Oceania.

The results of this paper improve knowledge on price transmission in dairy markets, show the role of the GDT auctions in the price setting process, and thus may help professionals from the dairy sector to formulate their price expectations more precisely.

Despite the fact that many professionals from the dairy sector treat GDT auctions as a benchmark, so far their leading properties have not been scientifically proven.

The authors propose a dairy bundle, integrating strategies to jointly maximise producer revenue and consumer utility according to the latter's preferences.

An algorithm based on a nested logit model identifies the bundle maximising producer revenue based on factors affecting consumer purchase behaviour. The data are drawn from a mall-intercept survey administered in Iran, with consumers stating a hypothetical choice among a comprehensive set of dairy products.

Demographic characteristics and marketing mix elements significantly affect consumers' preferences. An algorithm based on the estimated dissimilarity parameter determines the best bundle of dairy products, simultaneously obtaining the highest utility and the highest expected revenue.

Consumer preference and maximum producer or retail seller income are considered simultaneously. The bundling promotion strategy is widely used for food offerings and fresh foods and can be extended to other products.

我們擬根據消費者偏好，提出一個整合了多個策略的捆綁包，以使生產製作者得到最高的收入和最佳的消費者效用。

研究人員根據巢式Logit 模型的演算法確認了一個捆綁包，以使生產製作者能得到最高的收入，而這均建基於會影響消費者購買行為的各個因素。有關的數據取自於伊朗的商場內進行的攔截調查，而回應的消費者須假想他們從一整套乳製品中選擇他們會購買的產品。

研究結果顯示，人口特徵和市場營銷組合元素均會顯著地影響消費者的偏好，一個基於估算的相異性參數而建立的演算法可確認最佳的乳製品捆綁包，這演算法同時也可取得最佳的裨益和最高的預期收入。

於本研究中，研究人員同時考慮消費者的偏好和生產製作者或零售賣家的最高收入。捆綁式的促銷策略在食物供品和新鮮食品方面被廣泛使用，這策略可擴展至其他產品。

乳製品捆綁包、消費者偏好、最佳化演算法、巢式Logit 模型.

Output from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.

Combining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.

Application of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.

The results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.

To the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.

The purpose of this study is to investigate what consumers think about food waste, best before date (BBD) and appreciation of food in the context of a model project in the food-retailing sector. The focus was on the following key questions: How is the issue of food waste itself perceived by consumers? What understanding of the BBD was present in the sample and what do consumers imagine under the term appreciation in the context of food? The study also included an evaluation of the acceptance of the model project by customers. In this project, food no longer suitable for sale was distributed free of charge to visitors of a supermarket via a freely accessible refrigerator.

The research design was based on a mixed methods approach in an explorative sequential design. First a qualitative survey was conducted via interviews (n = 8) with customers, and the results were used for a subsequent quantitative survey (n = 88) in the supermarket.

The majority of those questioned were sensitized to the topics of food waste, BBD and appreciation of food. The results of the interviews and the questionnaires revealed a consistently positive opinion about the model project. These results indicate potential for reducing food losses among consumers and in food retailing and for improving appreciation.

This was the first study conducted as part of a model project in the retail sector in the context of food waste. The study also investigated within in the project what people think about the BBD, food losses and appreciation. At the same time, the acceptance of the project was assessed.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

Customer experience research predominantly anchors the customer journey on a specific offering, implying an inherently firm-centric perspective. Attending calls for a more customer-centric approach, this study aims to develop a goal-oriented view of customer journeys.

This study interprets the results of a phenomenological study of a transformative journey toward a sober life with the self-regulation model of behavior to advance understanding of customer journeys.

The consumer's journey toward a higher-order goal encompasses various customer journeys toward subordinate goals, through which consumers engage in iterative cognitive and behavioral processes to adjust or maintain their experienced situation vis-à-vis the goal. Experiences drive behavior toward the goal. It follows that negative experiences may contribute to goal attainment.

This study highlights the importance of looking at the consumers' higher-order goals to obtain a more holistic understanding of the customer journey.

Companies and organizations should extend their view beyond the immediate goals of their customers to identify relevant touchpoints and other customer journeys that affect the customer experience.

This study proposes conceptualization of the customer journey, comprising goal-oriented processes at different hierarchical levels, and it demonstrates how positive and negative customer experiences spur behaviors toward the higher-order consumer goal. This conceptualization enables a more customer-centric perspective on journeys.

The purpose of this paper is to identify the potential and the challenges for different firms in adopting an AI-first strategy. The study attempts to discern if any company can prioritize AI at the forefront of their strategic plans.

Drawing from illustrative examples from well-known AI-leaders like Netflix and Spotify, as well as from upcoming AI startups and industry incumbents, the paper explores the strategic role of AI in core business processes and customer value creation. It also discusses the advent and implications of generative AI tools since late 2022 to firms’ business strategies.

The authors identify three types of AI-first strategies, depending on firms’ starting points: digital tycoon, niche carver and asset augmenter. The authors discuss how each strategy can aim to achieve data, algorithmic and execution advantages, and what the strategic bottlenecks and risks are within each strategy.

To the best of the authors’ knowledge, this paper is the first to systematically describe how companies can form “AI-first” strategies from different starting points. This study includes actionable examples from known industry players to more emerging startups and industrial incumbents.

This paper aims to investigate three promotional publications produced by the Postum Cereal Company – A Trip Through Postumville (1920), How I Make Postum (1924) and The Wonderful Lunch Boxes (1925) – with the aim of understanding how language and other semiotic resources are used to promote its products as good and healthy choices.

The three publications were collected from the HathiTrust Digital Library and University of South Florida Tampa Special Collections. They were subjected to multimodal critical discourse analysis to tease out their subtle characteristics and how a combination of language, image, colour, typography and composition are used to represent certain ideas and values related to health and well-being.

The publications subscribe to three distinct genres – “inside the factory”, “friendly spokesperson” and “fictional world” – each of which are aimed at different target audiences. The first seeks to promote Postum as an open and transparent company; the second to promote Postum as a company that cares about its consumers; and the third to promote the health benefits of Postum in a fun and accessible manner. Nonetheless, they are united in their overall objective to link the regular consumption of Postum as essential for good health.

To date, few studies have been conducted on the Postum Cereal Company, while the limited research conducted on promotional publications has tended to overlook discourses of health and well-being. The three genres outlined in this study, thus, have the potential to foster a reappraisal of promotional publications and showcase their ability to offer new understandings on historical approaches to marketing, particularly the link with health and science.

This study aims to analyse the components of sustainable business models (SBMs) in the dairy industry, in relation to firm-relevant organisational features (size, ownership structure and production process) and through the lenses of the business model framework and the sustainable value exchange matrix (SVEM). This contribution proposes a taxonomy of emerging SBMs and sustainable value creation in the dairy industry.

This research makes use of a multiple case study approach, with cases selected in collaboration with industry experts. The selected firms are highly committed to sustainability transition. Results are drawn from qualitative data obtained from in-depth interviews and secondary sources. The interpretation phases, initially based on open coding, have been enriched by applying the components of business models (BMs) frameworks and the SVEM, and the analyses have been enhanced through an additional interpretative workshop with experts.

The authors related the BMs characteristics of some typical dairy firms transitioning to sustainability, using SBM components and taxonomies emerging in the literature, based on the formalisation of sustainability practices, the scope of operations, and the degree of integration of the three dimensions of sustainable value. These findings led to the discovery of three types of SBM in this dairy industry, referred to as “Milky Ways”.

This paper contributes to the scant literature on sustainability in dairy firms, highlighting the different paths followed by small and medium-sized enterprises (SMEs), cooperatives and large companies in remoulding their business models towards sustainability and thus achieving sustainable value creation.