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The purpose of this study is to investigate empirically how the determinant attributes of coffee quality, service, food and beverage, and extra benefits influenced customer‐perceived value in the coffee outlet industry.

A self‐administrated questionnaire was distributed to 834 respondents from chain and independent coffee outlets for the study. Multiple regression analysis was used to identify which factors of determinant attributes of service quality influenced customer‐perceived value.

The study found that factors of determinant attributes of service quality significantly influenced functional and symbolic dimensions of perceived value with the former being related with coffee quality, service, and food and beverage, whereas the latter is positively related with coffee quality, food and beverage, and extra benefits.

The paper bears significant theoretical and practical results. Theoretically, determinant attributes of service quality can enhance not only functional value but also symbolic value, ignored in previous value‐based research. It is believed that perceived value should be considered as a base in designing determinant attributes of service quality. Practically, it is imperative for marketers to gain a thorough understanding of the consumption experience of their customers by enhancing value perceptions in terms of determinant attributes of service quality.

The study challenges the existing literature by identifying how customers perceived function and symbolic values based on determinant attributes of service quality offered in the coffee outlet industry.

In the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.

A novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.

Throughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.

A novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.