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Using a generic cheese as an anchor product, in this study consumers' preferences for different EU quality schemes have been investigated. Specifically, the study aims to understand whether “Protected Designation of Origin” (PDO), “Organic” and “Mountain Product” labels are independent or if there are some synergies existing between them, questioning – at the same time – whether this alleged exchange of value plays a positive or negative role in terms of consumers' willingness to pay.

A discrete choice experiment (DCE) was conducted on 600 Italian consumers performing a random parameter logit model. The respondents were representative of the Italian population in terms of age, gender and geographical distribution. Consumers' preferences for the presence of “Organic” and “Mountain product” labels were assessed in the DCE, together with the effect of price, for both PDO and generic cheeses.

Consumers are willing to pay a premium in price for “Organic” and “Mountain Product” per se, for cheese with and without the PDO denomination. Considering the interaction effects, results showed that the combined use of “Organic” and “Mountain Product” labels do not decrease consumers' intention to buy. However, when applied on a PDO product, these attributes generate a lower consumers' willingness to pay in comparison with the generic ones, highlighting a possible overlapping between them.

Despite the abundant literature on EU quality schemes in many food categories, this study represents one of the first attempts to measure the interaction effect between different EU quality schemes.

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.

Packaging and labelling have become essential to how food manufacturers generate and deliver value to customers. The information displayed on the packaging can be used to communicate to customers the properties and unique characteristics of a food product (e.g. nutrients, calories and country of origin). To achieve communication goals effectively, manufacturers need to understand how consumers evaluate products based on their attributes. In particular, companies should be aware of which specific product attributes affect consumer buying behaviour and which product attributes are more critical during food assessment. So, the paper aims to investigate consumer's behaviuor linked to typical product attributes indicated on the packaging.

The present study examines consumer willingness to pay (WTP) for a cherry jam with different attributes (brand, type of production method and price) on a sample of 2,166 Italian respondents through a choice experiment using a random parameter logit-error component model.

The results showed that WTP for jams can be affected by attributes such as brand, price and production methods; precisely, they indicated that the level of naturalness in the production process constitutes the main element for the consumer’s choice; however, the considerable weight that price and brand have in influencing the purchasing behaviour of the food consumer was still confirmed: in fact, a p-value of less than 0.05 was found in all cases.

To the best of the authors’ knowledge, this is the first study that assesses the effect of different types of production on WTP for food products. In addition, this study also reflects on the importance of the level of education for consumer choice.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

Although the Net Promoter Score (NPS) index is simple, NPS has weaknesses that make NPS's interpretation misleading. The main criticism is that identical index values can correspond to different levels of customer loyalty. This makes difficult to determine whether the company is improving/deteriorating in two different years. The authors describe the application of statistical tools to establish whether identical values may/may not be considered similar under statistical hypotheses.

Equal NPSs with a “similar” component composition should have a two-way table satisfying marginal homogeneity hypothesis. The authors compare the marginals using a cumulative marginal logit model that assumes a proportional odds structure: the model has the same effect for each logit. Marginal homogeneity corresponds to null effect. If the marginal homogeneity hypothesis is rejected, the cumulative odds ratio becomes a tool for measuring the proportionality between the odds.

The authors propose an algorithm that helps managers in their decision-making process. The authors' methodology provides a statistical tool to recognize customer base compositions. The authors suggest a statistical test of the marginal distribution homogeneity of the table representing the index compositions at two times. Through the calculation of cumulative odds ratios, the authors discriminate against the hypothesis of equality of the NPS.

The authors' contribution provides a statistical alternative that can be easily implemented by business operators to fill the known shortcomings of the index in the customer satisfaction's context. This paper confirms that although a single number summarizes and communicates a complex situation very quickly, the number is ambiguous and unreliable if not accompanied by other tools.

The purpose of this study is to understand the profit implications of analytics-driven centralized discriminatory pricing at the headquarter level compared with sales force price delegation in the purchase of an aftermarket good through an indirect retail channel with symmetric information.

Using individual-level loan application and approval data from a North American financial institution and segment-level customer risk as the price discrimination criterion for the firm, the authors develop a three-stage model that accounts for the salesperson’s price decision within the limits of the latitude provided by the firm; the firm’s decision to approve or not approve a sales application; and the customer’s decision to accept or reject a sales offer conditional on the firm’s approval. Next, the authors compare the profitability of this sales force price delegation model to that of a segment-level centralized pricing model where agent incentives and consumer prices are simultaneously optimized using a quasi-Newton nonlinear optimization algorithm (i.e. Broyden–Fletcher–Goldfarb–Shanno algorithm).

The results suggest that implementation of analytics-driven centralized discriminatory pricing and optimal sales force incentives leads to double-digit lifts in firm profits. Moreover, the authors find that the high-risk customer segment is less price-sensitive and firms, upon leveraging this segment’s willingness to pay, not only improve their bottom-line but also allow these marginalized customers with traditionally low approval rates access to loans. This points out the important customer welfare implications of the findings.

Substantively, to the best of the authors’ knowledge, this paper is the first to empirically investigate the profitability of analytics-driven segment-level (i.e. discriminatory) centralized pricing compared with sales force price delegation in indirect retail channels (i.e. where agents are external to the firm and have access to competitor products), taking into account the decisions of the three key stakeholders of the process, namely, the consumer, the salesperson and the firm and simultaneously optimizing sales commission and centralized consumer price.

This study investigates the optimum size for manufacturing firms and the impact of subcontracting on firms' likelihood of achieving their optimal scale in Vietnam.

Using data from the enterprise census in 2017 and 2021, the paper first estimates the production function to identify the optimum firm size for manufacturing firms and then applies the logit model to investigate factors associated with the optimal firm size.

The study reveals that medium-sized firms exhibit the highest level of productivity. Nevertheless, a consistent trend emerges, indicating that nearly 90% of manufacturing firms in Vietnam operated below their optimal scale in both 2017 and 2021. An analysis of the impact of subcontracting on firms' likelihood to achieve their optimal scale emphasizes its crucial role, especially for foreign firms, exerting an influence nearly five times greater than that of the judiciary system.

The paper's findings offer crucial policy implications, suggesting that initiatives aimed at enhancing the overall productivity of the manufacturing sector should prioritise facilitating contract arrangements to encourage firms to reach their optimal size. These insights are also valuable for other countries with comparable firm size distributions.

This paper provides the first empirical evidence on the relationship between firm size and productivity as well as the role of subcontracting in firms' ability to reach their optimal scale in a country with a right-skewed distribution of firm sizes.

This paper aims to determine whether policy uncertainty caused by institutional decay in countries with populist rulers influences the internationalization decision of emerging market firms (EMFs).

The study used binary logit analysis on firms from Latin American countries undertaking cross-border greenfield investment projects.

The results suggest that internationalization decision is demotivated by policy uncertainty generated by populist chief executives and promoted by that of political parties.

This study uses populist rhetoric to describe policy uncertainty due to chief executives and ruling parties, which influences internationalization decision by increasing anticipated transaction costs. This inquiry identifies populism as a variable that influences EMFs to internationalize, while empirically testing the claim of theoretical scholarship that populism reconfigured the sociopolitical and institutional forces that shape the world’s business. This study further advances institutional theory by offering a fresh perspective on the influence of home instead of host-country institutions on the internationalization motivation of firms due to institutional decay caused by populist regimes.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

The literature about the dynamics of Muslims' awareness and perception of waqf has been generally scarce,especially in Nigeria. As renewed efforts are emerging to develop a new waqf regime in the country, this study examines this aspect in Gombe metropolis, Nigeria, with a view to identifying the key factors shaping people's perception and awareness towards waqf.

To achieve the objective of this study, primary data were collected through a survey among 494 business owners in the Gombe metropolis. The data were then analysed using probit and Tobit regression models.

The study found that altruistic behaviour, religiosity level, educational level and young age are among the factors that determine people's perception and awareness towards donating to waqf among business owners in Gombe. Altruism and young age embolden businesspersons to recognise the effectiveness of waqf institutions in solving socioeconomic challenges.

The findings of this study imply that waqf has huge potential in the study area and that with well-structured, organized waqf education programmes within the business community using diverse avenues, a robust waqf sector can be developed.

As far as the study area and the entire Nigerian Islamic economics and finance landscape is concerned, the study has explored a novel research area. Given the infant stage of empirical studies on waqf in Nigeria, there are virtually no previous attempts to examine the awareness and perception of businesspersons towards waqf; variables that are key to the development of an effective sector.