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Psychographic segmentation is popular within the tourism literature. It is useful in describing a prototypical customer, however psychological attributes are hard to detect at the individual level and by front-line staff. This paper tests the viability of prior visits (first-time vs. repeat visits) as a segmentation strategy, given this information is readily available to tourism operators. We test an interaction effect between prior visits, service quality, and perceived value using the ECOSERV model, a well-established model of ecotourism customer satisfaction. Using a sample of ecolodge guests, we demonstrate that a prior visit attenuates the relationship between perceived value and customer satisfaction. Among repeat guests, perceived value has less impact upon customer satisfaction and intentions to revisit or recommend an ecolodge. Conversely, service quality continues to predict satisfaction for both first-time and repeat guests. The data suggest attracting first-time guests requires appeals to the setting, features and price of an offering. Meanwhile, strategies to maximize repeat guests should emphasize non-monetary qualities of the experience.

The transition of a business to a circular business model (CBM) calls for significant and ongoing shifts in different business management models and strategies. However, there is a lack of research focused on the technological, financial, societal, and institutional influences on the CBM transition in small and/or medium-sized enterprises (SMEs). To address this gap, our study develops a theoretical framework for the transition towards CBM. We conducted a systematic literature review with the objective of determining the relationships among technological, financial, societal, and institutional influences for CBMs. Following this, we then established a conceptual framework that comprises these four key influences for a transition plan in the context of an innovative business model with a focus on the value proposition, value creation, and value delivery. An illustrative case example of the manufacturing industry for the transition plan to CBM was presented as well. The proposed framework is designed to lead the shift towards circular economy-oriented business models that aim to promote sustainability in business. In addition, we uncovered several potential avenues for further investigation. We expect the framework towards both contribute to the expansion of the existing body of research in the field and provide business practitioners with guidelines on the CBMs’ transition for SMEs.

PurposeThis paper proposes optimisation models to evaluate and examine the selling of extended warranty policies in terms of improved profits in producing/marketing remanufactured products. These models are numerically solved using a quadratic programming solution approach and implemented in the decision support system (DSS).Design/methodology/approachThe purpose of this paper is to develop the optimisation models for a DSS and evaluate different warranty policies for buyers.FindingsThis study has demonstrated the flexibility and usefulness of a model-driven DSS for the quality and warranty management, which is applied to examine and evaluate different configurations (i.e. component reuse, rebuild and recycle) for remanufactured products and propose the selling of extended warranty policies for buyers.Research limitations/implicationsThe developed model-driven DSS can assist manufacturers to select and increase the number of components, e.g. to be reused, rebuilt, and recycled for producing a remanufactured product and propose suitable warranty policies for buyers. However, this study focusses only on the evaluation of warranty policies for specific remanufactured products in a DSS, i.e. types of air compressors for production operations in manufacturing industry.Originality/valueThis study developed optimisation models to be used in a DSS for proposing the selling of extended warranty of a remanufactured product to improve customer satisfaction and maximise the gained profits for manufacturers.

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